TECHNOLOGY CHALLENGES FOR CONTEXT AWAREMULTIMEDIA SERVICES

by

Suneth Namal Karunarathna

A thesis submitted in partial fulfillment of the requirements for thedegree of Master of Engineering in

Information and Communication Technologies

Examination Committee: Dr. R.M.A.P. Rajatheva (Chairman)Associate Prof. Tapio J. ErkeDr. Matthew N. Dailey

Nationality: Sri Lankan

Previous Degree: Bachelor of Science in Computer EngineeringUniversity of PeradeniyaPeradeniya, Sri Lanka

Scholarship Donor: The Government of Finland

Asian Institute of TechnologySchool of Engineering and Technologies

ThailandMay 2010

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ACKNOWLEDGEMENT

First of all it is my pleasure to express sincere regards to my thesis advisor, Dr.

R.M.A.P. Rajatheva for the guidance, encouragement and tremendous help throughout

the whole two years of my study. His encouragement and guidance made me to start my

research work beginning of the inter-semester while setting more chances to familiarize

with the work. His sound knowledge and the experience in the field was the fact behind

motivation during my study.

My special thanks must go to examination committee member Associate Professor

Tapio J. Erke for the guidance and the corrections of my proposal. Further, I am thank-

ful to the Assistant Professor in the Computer Science and Information Management

program Dr. Matthew N. Dailey for being in my examination committee.

I would make this an opportunity to thank my scholarship donors, Government of

Finland and Institute Telecommunication for supporting me financially. It brought me

success in academic life investigating my capabilities and creativity during past two

academic years.

I am grateful to Dr. Gyu Myoung Lee being my advisor at Institute Telecom-

munication, France. Further, my special thanks must goes to Prof. Noel Crespi the

program director of Master of Science in communication network. His timely decision

to select me to the dual degree program brought me lot of opportunities to explore real

industry environment and the new technologies.

Many thanks to Mr. Keeth Saliya, Mr. Shashika Manosha, Mr. Madushan Thilina

and Mr. Sanjeewa Herath for the support in both academic and day today life during

the stay in AIT. And I value the friendly discussions we had together regarding aca-

demic matters and the contributions to each others work. All the academic staff and

administrative personnel, specially senior lab supervisor , Mr. Rajesh Kumar Dehury,

TC secretaries, Mrs. Nantawan Nakasen, Miss. Chutikarn Kridsadavisakesak and Mr.

Jaruk Noonkhao are thankfully admired for immense help in all many ways during my

stay.

Finally, I will make this an opportunity to avail the gratitude to my beloved

parents for everything they have done for me. And I dedicate this thesis dissertation

to my parents.

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ABSTRACT

This work presents an effort on quality improvement in the existing multimedia

services. It is identified that the multimedia services are more sensitive on certain

parameters which significantly degrade the service quality in the target domain. The

issues related to the multimedia services are discussed and ways presented to maximize

the end-user satisfaction in a focused environment. The effects of context reasoning in

the evolutionary multimedia applications are observed and a novel context reasoning

scheme based on AHP is proposed to utilize the processing power. Interestingly, the

nature of AHP is based on the pare-wise attribute comparison in the proposed home

environment. In addition to that, it is found that the service response time and the

blocking probability have notable effects on the user experience. Hence, two evalua-

tion models are developed to observe the performance level in terms of all the above

parameters at the target scenario. Moreover, two hierarchical proxy architectures are

defined and the results are compared with an ideal case where no proxy servers are

deployed in the network. The evaluation process in terms of the service continuity gives

rise to the investigation of the blocking probability. Thus, the demand for the system

resources is presented with a general birth-death process in the proposed analytical

model. In addition to that the importance of reliable and accurate context informa-

tion is identified and a novel scheme for anomaly detection is presented based on the

rate and power anomaly detection. Moreover, the detection probability is measured

for Gauss-Markov mobility model and compared with an ideal simulation model based

on the Random-Waypoint. Overall, the study contributes to maximize the quality of

service in a multimedia service environment.

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Table of Contents

Chapter Title Page

Title Page iAcknowledgement iiAbstract iiiTable of Contents ivList of Abbreviations viList of Tables viiList of Figures viiiList of Symbols xi

1 Introduction 1

1.1 Context Awareness and Context Reasoning 11.2 Technology Evolution in Multimedia Applications 11.3 Hierarchical Analytical Process 21.4 Intrusion Detection in Sensor Networks 21.5 Objective of Study 31.6 Scope and Limitations of Study 41.7 Organization of the Thesis 4

2 Technology Challengers for Multimedia Services 5

2.1 User-Driven Ubiquitous Networking Environment 52.2 Requirement for Seamless Multimedia Services 62.3 Challenging Technologies for Ubiquitous Multimedia Services 8

2.3.1 Service Continuity 92.3.2 Context Awareness in Multimedia Applications 102.3.3 Content Delivery in Ubiquitous Environment 132.3.4 Cross-Layer Adaptation for Multimedia Applications 14

2.4 Mobile IPTV Service in Ubiquitous Networking Environment 172.5 Threats Over Context Collectors 18

2.5.1 Limited Resource Constrain of Sensors 182.5.2 Communication Unavailability 202.5.3 Unattended Operation 20

2.6 Security Concern of Sensors 202.7 Attacks on Wireless Sensor Networks 21

2.7.1 Attacks on Data Link Layer 212.7.2 Attacks on Network Layer 222.7.3 Log-distance Path Loss Model 24

2.8 Mobility Models for Sensor Nodes 24

3 Context Reasoning in context aware Multimedia Services 27

3.1 Introduction 273.2 Technical challenges in Context Reasoning 273.3 Analytic Hierarchical Process in Context Reasoning 30

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3.4 Proposed Model for Context Reasoning 313.5 Conclusion 37

4 Performance improvement in multimedia services 38

4.1 Introduction 384.2 Proxy Architecture and Cache Arrangement 384.3 Mathematical Analysis of Response Time 40

4.3.1 Proposed Proxy Architecture 404.3.2 Existing Models in Performance Evaluation 414.3.3 Proposed Model for Response Time Measurement 424.3.4 Simulation Results Obtained with Proposed Model 46

4.4 Seamless service Continuity in Multimedia Service 534.4.1 Analytical Model for Proxy Handover 534.4.2 Analytical Results Obtained with Defined Model 56

4.5 Conclusion 61

5 Secure Context Gathering for Context Reasoning 62

5.1 Introduction 625.2 Baseline Approach to Anomaly Detection 62

5.2.1 System Model for Baseline Approach 625.2.2 Modified Detection Algorithm 635.2.3 Sensing Model and the Detection Algorithm 665.2.4 Simulation Results of the Proposed Model 72

5.3 Conclusion 77

6 Conclusion and Recommendations 78

6.1 Conclusion 786.2 Recommendations 79

References 80

APPENDIX A 84

APPENDIX B 92

v

List of Abbreviations

BS Base Station

CDN Content Delivery Network

CDS Content Delivery System

GRAB Gradient Broadcast Routing

GEAR Geographical and Energy Aware Routing

GAF Geographic Adaptive Fidelity

GPSR Greedy Perimeter Stateless Routing

IDS Intruder Detection System

LEACH Low Energy Adaptive Clustering Hierarchy

MANET Mobile Ad-hoc Networks

MS Mobile Station

OSI Open Standard Interconnection

PDA Personal Digital Assistance

RFID Radio Frequency Identification

RTS Request To Send

SPINE Secure Positioning for Sensor Networks

SMIL Synchronized Multimedia Integration Language

TTDD Two-Tier Data Dissemination

WSNs Wireless Sensor Networks

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List of Tables

Table Title Page

3.1 Calculation of attribute comparison matrix 33

3.2 Calculation of priority vector 34

3.3 Example for calculating attribute comparison matrix 35

3.4 Example for calculating option comparison matrix 35

3.5 Calculation of composite matrix using attribute and option com-parison matrix 35

4.1 List of parameters bring used in the simulation 46

4.2 Parameters used in the analytical model for evaluating call blockingprobability 54

5.1 IDS parameter list used in mathematical simulation 65

5.2 Parameters being used for the simulation purpose 72

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List of Figures

Figure Title Page

2.1 Ubiquitous networking service environment ensures the service con-tinuity regardless of mobility pattern or user behavior 6

2.2 Multimedia content delivery system in ubiquitous networking envi-ronment for seamless service availability 8

2.3 Seamless service continuity in user-driven ubiquitous networkingenvironment guarantees service access irrespective of user context 9

2.4 Context aware applications are responsible for self determinationof communication technique and the suitable terminal with propermedia format and codec 11

2.5 Ontology based context reasoning scheme (Wei and Chan, 2010) 12

2.6 IPTV content delivery in ad-hoc service environment providingplatform independent service environment 14

2.7 Multimedia adaptation middle-ware platform which controls themedia characteristics to format the content to fit the end user requirements 15

2.8 Service scenario in home environment for seamless service accessi-bility over different end user terminals 17

2.9 Mobility pattern of a sensor node when routing is simulated withRandom-Waypoint model 26

2.10 Mobility pattern of a sensor node when routing is simulated withGauss Markov model 26

3.1 User context management in ubiquitous home environment whichsupport seamless service availability. This figure shows the exis-tence of different devices inside home and how they are connectedto home gateway or the set-top-box for content divergence 28

3.2 Unicast and multicast traffic in IPTV services. Liner TV streamalways broadcast reserving fixed bandwidth for each channel. Onthe contrary, VoD and time shift TV assign an individual streamfor each connected user 29

3.3 This figure explains decision-making process in context reasoner ata home environment. Context information is gathered by the sen-sors around user and sent to the context reasoner which is locatedin home network or operator network 31

3.4 Expanded partial tree for device selection. This partial tree in-cludes preference for each device and score assigned to each property 32

4.1 Service request procedure. Transmission path is divided in to twocore network path and access network path. Proxy server supportsto achieve CBR type of transmission over core network 39

4.2 Hierarchical proxy architecture. Considered architecture has a hi-erarchical caching system where contents are stored in different tiers 40

4.3 Target proxy architectures for response time measurement 41

4.4 Model defined to measure the service response time (Nikolov, 2009) 42

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4.5 Model for performance evaluation of hierarchical proxy architec-ture. For this model, content is cached to different tiers with re-spect to their popularity, size and many other factors. (The dottedline shows the requests missed by proxy server) 43

4.6 Response time vs. arrival rate for architecture 1, architecture 2 andno proxy architecture. Here we consider multimedia type of trafficwhile limiting hierarchy to single tier (n=1) 47

4.7 Response time vs. arrival rate for architecture 1, architecture 2 andno proxy architecture. Here we consider multimedia type of trafficwhile limiting hierarchy to two tiers (n=2) 48

4.8 Response time vs. arrival rate for architecture 1, architecture 2 andno proxy architecture. Here we consider multimedia type of trafficwhile limiting hierarchy to three tiers (n=3) 49

4.9 A comparison of lowest Response time towards Arrival rate fordifferent n. Architecture 2 gives the best response time in all casesfor multimedia based traffic 50

4.10 Response time vs. arrival rate for architecture 1, architecture 2 andno proxy architecture. Here we consider web based traffic whilelimiting hierarchy to single proxy level (n=1) 50

4.11 Response time vs. arrival rate for architecture 1, architecture 2 andno proxy architecture. Here we consider web based traffic whilelimiting hierarchy to two tiers (n=2) 51

4.12 Response time vs. arrival rate for architecture 1, architecture 2 andno proxy architecture. Here we consider web based traffic whilelimiting hierarchy to two proxy tier (n=3) 51

4.13 A comparison of least response time towards arrival rate for differ-ent n. Architecture 2 gives the best response time in all cases forweb based traffic 52

4.14 Target environment for service handover for mobile multimedia ser-vice users 53

4.15 Call arrival model 55

4.16 Queuing model for proposed system 56

4.17 Blocking probability Vs number of total channels in proxy server 57

4.18 Blocking probability Vs number of total channels in proxy serverfor different λt 58

4.19 Blocking probability Vs number of total channels in proxy serverfor changing number of reserved channels in server side for handover calls 59

4.20 Blocking probability Vs λh for changing number of exclusively re-served channels in the server 59

4.21 Probability of not having a slot in the queue Vs λh for changingnumber of exclusively reserved channels in the server 60

5.1 Block diagram of power anomaly detection module which detectsnodes transmit above defined threshold 64

5.2 Block diagram of network unavailability detection module whichmeasures transmission attempts of legitimate nodes 65

5.3 Effect of intruders in a wireless sensor network 67

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5.4 Power anomaly detection procedure in proposed detection architecture 68

5.5 Rate anomaly detection procedure in proposed detection architecture 69

5.6 Detection probability against transmit power level for two definedmobility models Random-Waypoint and Gauss-Markov keeping othervariables constant 73

5.7 Detection probability against transmit power level for Random-Waypoint mobility model for changing speed, 1ms−1,5ms−1,10ms−1 74

5.8 Detection probability against node speed for Random-Waypointmobility model for changing transmit power levels, 5dBm, 10dBm,15dBm 74

5.9 Detection probability against transmit power level for Gauss-Markovmobility model for changing speed, 1ms−1,5ms−1,10ms−1 75

5.10 Detection probability against node speed for Gauss-Markov mobil-ity model for changing transmit power levels, 5dBm, 10dBm, 15dBm 75

5.11 Detection probability against transmit power level for changing sen-sitivity parameter while keeping all other variables constant forRandom-Waypoint mobility model 76

5.12 Detection probability against transmit power level for changing sen-sitivity parameter while keeping all other variables constant forGauss-Markov mobility model 76

A.1 Power anomaly detection probability towards transmit power levelfor changing frame sizes 84

A.2 Power anomaly detection probability towards transmit power levelfor nodal speed 85

A.3 Average detection time towards transmit power level for changingframe sizes 86

A.4 Congestion probability towards transmit power level for changingradio range 87

A.5 Congestion probability towards transmit power level for changingnodal speed 88

A.6 Block diagram for response time measurement 89

A.7 Block diagram for blocking probability measurement 90

A.8 Analytic model to evaluate blocking probability 91

x

List of Symbols

Pi Property i

Oi Option i

Si Score assigned to property i

Di Percentage on device preference

pij property i relative to j

Pij Normalized pij

Ptransmit Transmit power

Preceived Received power

σ Standard deviation

pb Blocking probability

K Buffer of capacity K

Vt Velocity at time t

θt Angular velocity at time t

A Total coverage area by sensors

Rij Packet rate at node i compared to node j

λa Arrival rate on proxy server

Pa Hit rate of the proxy server

Ip Lookup time for proxy server

Is Lookup time for proxy server

F Average file size

BWp Client network bandwidth

BWs Operator network bandwidth

Bs Server buffer

Bp Proxy buffer

ts Static time for head-end

tp Static time for Proxy

λt Total call rate in a cell

λh Handover call rate

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λs call rate generated by stationary users

λc Carried call rate

λm New call rate by mobile users

Nh Exclusively reserved channels for handover users

Ploss Probability of not having a position free for handover call

PfhA Handover failure even with free positions in queue

Pdrop Probability of call drop for mobile and stationary users

Phv Call in progress will required a handover for vehicular user

PBA Call Blocking probability for stationary and mobile users

µc Call completion rate

µv Handover departure rate for vehicular calls

µq Rate at which being in an overlapping area

T c Average call duration for any user

T v Mean sojourn time for vehicular users

T q Average time duration MS reside in an overlapping area

γ1 Probability that served call due to first stream

γ2 Probability that served call due to second stream

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CHAPTER 1

INTRODUCTION

1.1 Context Awareness and Context Reasoning

The evolution of the mobile technology facilitates the humanity with fascinating con-text aware applications in to hand-held devices like PDAs, Ipods and laptops. Reactaccording to the context being experienced by the users is found to be the key featureof the context aware application. In other words, services are changing their signifi-cant parameters according to the user context, to adapt to the current environment(Khedo, 2006). These contexts can be emotions, physical activities or environmentalfactors such as location, time, temperature, pressure or humidity (Pavel and Trossen,2006). In order to make such systems effective, sensors have to capture the data whichis accurate and reflects real-time situations and events (Godbole and Smari, 2006). Insome cases, prototypes are required for collecting context data which are not readilyavailable (Kerpez et al., 2006). Thus, the presence of inaccurate context data causeslow performances and inefficiency of the system. It is noteworthy to point out that, theultimate goal of context aware applications is achieved by adapting to the environmentwith proper user interfaces and services considering the user context profile.

As an instance, we can consider a situation where a user is going to the supermarket with a mobile. It can be used as a device which helps him to select the goodswhich he is intending to bring back to the home. Further, it can suggest some otheritems which he may prefer to purchase. Moreover, he will receive a detailed descriptionof the products to be purchased and options or substitutions for missing items. Finally,at the end of the day it will display the shortest route to return home. This scenariosimply explains the context awareness.

The received context awareness information through the sensors, adapt the appli-cations to match the current environment settings (Wang, 2004). But, there shouldbe an intermediate module which evaluates these information and makes decisions.These modules are introduced as the context reasoner (CR) in literature (Pavel andTrossen, 2006; Godbole and Smari, 2006; Wang, 2004). The context reasoner obtainsthe inputs from sensors and handovers it to the evaluation scheme, which is probablya rule based evaluation module (Docter et al., 2007). According to the results of thecontext reasoner, the service or the device itself adjusts to fit the user context withoutany human interaction (Wang, 2004).

1.2 Technology Evolution in Multimedia Applications

Multimedia applications are quite sensitive to delay, jitter and packet loss situations.Thus these factors significantly degrade the quality of the services (Menai et al., 2009).Therefore, it is a challenging issue to reduce the service delay or the response time(Docter et al., 2007). More significantly, present multimedia applications are capableof service adaptation depending on the user context for delivering a better experienceto the end users (Hsiao et al., 2008).

But, the whole process of context evaluation depends on sensors which collect and

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aggregate contextual information. As we already mentioned above, delay is the mostcritical issue related to the multimedia services. To overcome this problem, operatorsuse proxy servers in their networks. The operators further reduce the response timeby means of distributed content management techniques. Multimedia applications likeIPTV, VoD and time shift TV integrate many other services together and presented ina single package to the subscribers (Menai et al., 2009). In terms of content delivery,researches have taken a great effort to reduce the bandwidth usage by introducingcompressed media formats. There is always a trade-off between channel bandwidthand the quality of the service (Bellinzona and Vitali, 2008). Therefore, maintainingthe service while reducing per user bandwidth is a challenge towards the operators.Novel video formats are capable of performing this task much better (Hsiao et al.,2008). But, the costly bandwidth still demands capacity optimization techniques inthe networks to utilize the bandwidth and other resources.

1.3 Hierarchical Analytical Process

Analytic hierarchical process (AHP) is a mathematical model which simplifies the ob-jectives and selects the most suitable alternatives in the real environment (Zahedi,1986). AHP contributes in decision making, to find the best option standing on theirunderstanding level of the problem (Pavel and Trossen, 2006). In many engineeringapplications we can use AHP as a powerful tool for object comparison (Pawar et al.,2008). By introducing the pair-wise matrix comparison, the AHP gives the relative im-portance of the options or alternatives compared to others. Irrespective of the field, thistechnique can be used in any area like industry, health care and business environment(Loke, 2006).

AHP models the target scenario in a hierarchy which decomposes the main probleminto sub-problems. In a hierarchical architecture, the complete solution can be obtainedby solving each sub problem separately. After building the hierarchy, evaluation couldbe done systematically in order to compare elements. Because of the ability to convertpractical problems in to numerical models, AHP could be used in context evaluation(Balasubramaniam and Indulska, 2004). Further, it allows to weight over the significantelements depending on the preference (Loke, 2006).

1.4 Intrusion Detection in Sensor Networks

Wireless sensor networks (WSNs) are susceptible to many form of attacks due to theresource constraints and the unsecured environment in which they have been deployed(Onat and Miri, 2005). The broadcasting nature of the WSNs makes it more vul-nerable to attacks. Most of WSNs are application oriented. That means they havespecific characteristics to perform a predefined set of tasks (Pires Jr et al., 2004). Thisrestricts developing a general platform for intrusion detection. All security solutionsfor the sensor networks or ad-hoc networks could be divided in to two classes namely;prevention techniques and detection techniques (Silva et al., 2005). Limited resourcesand low computation capability in sensor networks pose many challenges in terms ofnetwork security.

WSNs can have one or more central nodes called as base stations (BS). They

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perform the gateway operation while providing the storage and data processing func-tionality on behalf of other nodes. Due to powerful data processing and aggregationcapabilities, the base stations operate as a human interface to perform administrativefunctions (Ilker and Ali, 2005). If the BS fails due to some reason, data aggregationand detection capabilities could be reduced or stopped (Liu et al., 2004). From anadversary’s point of view it is quite easy to find the location of the BS by lookingat the traffic pattern and hence to physically damage it. This might result to reducethroughput or eliminate the entire communication with the BS. Denial of service (DoS)attack is one of the most common attacks over WSNs which makes the resources un-available to intended users. Therefore, it is important to come up with an IntrusionDetection Scheme (IDS) which efficiently utilizes resources since WSNs have scarceresources (Hamid et al., 2006). This fact encourages the researchers to develop powerbased connectivity concept which reduces the average power consumption of the sensornetworks.

Collection and organization of context information is an important considerationin context aware services (Elias et al., 2007). The performance of a context evaluationscheme depends on the reliability of gathered information. Sensors are widely used tocapture the context. They are autonomous devices having low processing power withless energy consumption. Because of this nature they are more reluctant to differentforms of attacks (Chen et al., 2009). Rather, it is difficult to implement heavy securitymechanisms over these devices due to low computation capabilities. This demands forlight weight effective security schemes to be implemented over sensors.

1.5 Objective of Study

This thesis analyzes technology challenges in context aware multimedia services. Theobjectives are as given below.

1. Conduct a survey on challenging technologies for multimedia services in user-driven ubiquitous networking environment.

2. Developing a light weight context reasoning scheme at home environment formultimedia services based on AHP.

3. Developing a performance evaluation scheme for multimedia services.

(a) Measure the performance in terms of the response time with a defined ar-chitectures and compare it with no proxy architecture.

(b) Measure the performance in terms of call blocking probability for a multi-media subscriber under dynamic system conditions.

4. Developing a scheme for secure context gathering

(a) Developing a scheme based on power and rate anomaly detection

(b) Measure the performance in terms of detection probability for different mo-bility models.

i. Measure detection probability for Random-Waypoint mobility model.

ii. Measure detection probability for Gauss-Markov mobility model.

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iii. Compare detection probability of the proposed scheme with an idealcase.

1.6 Scope and Limitations of Study

1. All the analysis are applicable for multimedia type of applications.

2. The clients are assumed to be knowledgeable enough to decide their preferencecompared to each other.

3. Consider a single server with a buffer of length K for blocking probability mea-surement.

4. User devices are capable of deciding the basic context.

1.7 Organization of the Thesis

The rest of the thesis is organized as follows. Chapter 2 provides a general litera-ture review of the research area exploiting the technical challenges of context awaremultimedia services.

Chapter 3 discusses context reasoning in context aware multimedia services athome environment. The section 3.4 presents the proposed context reasoning model.The challenges in context evaluation and use of AHP in context reasoning are addressedin sections 3.2 and 3.3. The chapter 4 presents detailed performance evaluation inmultimedia service in terms of response time and blocking probability. Section 4.3proposes an analytic model for response time measurement. The model defined tomeasure blocking probability is presented in section 4.4.

In chapter 5 we discuss secure context gathering with sensors. The section 5.2.2presents a baseline approach for intrusion detection. The proposed anomaly detectionscheme is presented in section 5.2.3 The conclusion and the recommendation presentedin Chapter 6.

Finally, in the appendix A we include some graphs obtained for the model discussedin section 5.2.2.

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CHAPTER 2

TECHNOLOGY CHALLENGERS FOR MULTIMEDIA SERVICES

2.1 User-Driven Ubiquitous Networking Environment

Over the past two decades context awareness has greatly evolved from its locationawareness roots to include properties such as situation, emotions, user and environ-mental properties, and so on (Pavel and Trossen, 2006). Technologies like sensors,machine learning algorithm, data mining tools evolves to generate contextual datasuch that application developers can use them more intelligent and effective manner.The improvement of technology brings many challenges in the field of communication.As a result applications are getting more and more complicated day by day. The intro-duction of mobility in to communication makes it more vulnerable in terms of qualityassurance. But, later on services were developed to dynamically adapt the user context.

We can consider such context change as a change in user location, his/her emotions,environmental conditions or physical fact. Adaptability of services and applications arerecognized as one of the most important characteristics for future systems (Godboleand Smari, 2006). Therefore, the developing ubiquitous context aware systems usingcontext data is a challenging task in future applications. Managing the user contextin more intelligent and effective manner is the biggest challenging issue for applicationdevelopers. However, this brings a good opportunity for both researchers and applica-tion developers to pay their attention on context adaptation. Combination of contextadaptation with content delivery in mobile environment tremendously increases the us-ability of services and devices. In context aware system design, sensors are the primarymeans for data acquisition.

Increasing number of the mobile devises collaborate with the context awarenessimprove the usability of newly developed systems while accomplishing every day activi-ties which are carried out on move (Khedo, 2006). In mobile and ubiquitous computingenvironment anticipation of reaction to the users expectation highly depends on thecontext of use, events, as well as prior experiences. We could say any system to becontext aware if it capture, interpret and manage the context data to adjust the sys-tem functionalities to suit the existing context. Still there are open issues in managingcomplexity like gathering, processing and representing context data.

Ubiquitous networking suggests back-end model of processing information fromobjects and human activities daily encounter in their life. The term ubiquitous impliesfact that connectivity or the technology is everywhere and that could be accessedirrespective of location or the time. Basically, ubiquitous networking has two behaviorslike fixed to mobile and mobile to mobile. In either model, devices are mesh networkedallowing seamless access to different services and information. In ubiquitous networkingenvironment (see Fig. 2.1), all devices are connected to distributed servers which aresomehow connected to each other and make decisions according to input information.

Usage of micro devices placed over public and private places communicates eachother and gather context information from the service environment via the sensorsdeployed in the field. Further, it enables connection of all devices together in to asingle network where they can communicate each other seamlessly. Advancement incommunication and computing together with new technologies does many changes in

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Figure 2.1: Ubiquitous networking service environment ensures the service continuityregardless of mobility pattern or user behavior

living environment. Basically, the concept of ubiquitous networking facilitates humanlife changing vision of the environment. However, ubiquitous networking significantlydiffers from any other kind of traditional networks due to their seamless availability.In terms of home networking system, users suppose to have a good interaction andbetter understanding of each other to sophisticate their fundamental needs. Further,individuals can use different devices, technologies and adaptation techniques in variousenvironments to access the services in ubiquitous environment. These devices could beused to access service information as user preference.

In ubiquitous environment user satisfaction is realized with smooth real time infor-mation access regardless of user context or the access content. Ubiquitous networkinglimits the user performances due to computational and processing power of the con-nected terminals, which we call the heterogeneity of the network. Since, these deviceshave different capabilities the requirement of common platform arises. This is one ofthe biggest challenges in existing ubiquitous networks.

Serving individual terminals with different capabilities request specific protocolsto occupy in the networks to make it efficient and robust to compatible with differentterminals. Generally in sensor and home networks we find lot of devices which operateson heterogeneous environment. With the increment of number of heterogeneous termi-nals in the ubiquitous environment needs more service adaptation, media adaptationand robustness to provide better service over the ubiquitous environment.

2.2 Requirement for Seamless Multimedia Services

The advancement of technology results the development of many services over the tra-ditional communication network. Services like IPTV, Mobile TV, High Definition -TV intergraded with many other services makes subscribers to access and shift be-tween different services. Improvement in technologies like media adaptation, channel

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adaptation, cross-layer adaptation assures seamless accessibility. IPTV is one of theinteresting services provided over the traditional communication network which is inte-grated together with many other value added services. The latter development fallowstransferring media content from television to mobile devices like PDAs, mobile phonesor laptops. This introduces the concept of Content fallowing you while enabling seam-less service accessibility.

Service continuity is another important quality constrain in modern communica-tion. This guarantees users will have the seamless access to service and the contents.For an example, we consider a person who just finishes his work at office. During histravels on the bus or the train he can access the work that he was doing at the officeand finish it along the way. Once he gets down, he will go to the nearest supermarketand download the item list over the Internet to be taken to home. And he will pay theitems on-line over the internal Wi-Fi network with his PDA inside the supermarket.This scenario provides a good example for seamless service continuity.

Service continuity assures the service will not get obstruct by the user localizationor the access mechanism. Basic idea of service continuity implies few important facts.This relies on the seamless service coverage regardless of platform or content type. Thisrapidly growing momentum behind multimedia content delivery merged to the contentadaptability or the scalability. Delivering compatible contents to heterogeneous devicesis a challenging issue for service providers. Generally, context awareness is an abstractmodel behind the application which makes decisions to deliver the best content withthe best quality to subscribers.

For an example, when a person entering to his home the songs we was listening inhis iPod will immediately transferred to home theater and the iPod will automaticallygo in to sleep mode. Meantime, the home theater will itself adjust the tempo andequalizer settings to feel him the best experience. Context aware devices or the appli-cations always keep track on context of use. And whenever they request service thisinformation is sent attached with the request. Then the source will query the contentsfrom the best possible content providers considering received information. Next, theselected content required to deliver towards the destination.

Content delivery network (CDN) or content delivery system (CDS) does this onbehalf of the subscribers or the user. Generally this delivery is done over transmissioncontrol protocol (TCP) or user datagram protocol (UDP) connections. However, theperformances in TCP session can hardly affected with packet loss or the delay. Thestream control transmission protocol (SCTP) shares features of both of TCP and UDP.SCTP transmit data over chunk with a header. Then these chunks are bundled toSCTP packets and handover to Internet Protocol.

Packet loss and delay hardly affect to the user experience. To minimize this effectsources or the media content servers are placed closer to edges. Distributed contentservers can also improve the delivery performances. We can find there are three dom-inant content delivery systems as World Wide Web (WWW), network providers net-works and Peer-to-Peer sharing systems. Even though they are being used for the samepurpose still the system architectures are quite different from each other. Fig. 2.2,shows us how the media content is delivered over the access network. Normally, mostof the multimedia contents are transported as broadcast traffic. However, most of lat-est researches targets on introducing unicast traffic model to replace broadcast traffic.This is found to be more complex and need more processing power and resources.

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Figure 2.2: Multimedia content delivery system in ubiquitous networking environmentfor seamless service availability

Maintaining sustainable end-to-end quality over network throughout whole du-ration of transmission is a challenging concern for network providers. The solutionrequires interaction between different elements and layers. Even though, the initialadaptation technologies are limited into single layer, now researches are more concernin finding global optimal solution for cross layer adaptation.

It increases the interaction among different layers to maximize the quality of ex-perience and minimize the service deployment cost in service providers perspective.Compared to wireless networks wire networks offer many opportunities providing bet-ter quality of experience (QoE) and exploding variety of services. Considering thefallowing facts we find the requirement for cross layer adaptation. Perfect networkstatus measurement involves observation of different layer parameters and merge themtogether in to one model.

In the context of services or content adaptation, cross layer adaptation can play akey role to handle enormous dependencies arise due to heterogeneity. Characteristics ofwireless channels like higher packet loss ratio, signal-to-noise ratio (SNR), bit error rate(BER) implies the requirement for better adaptation between different communicationlayers to minimize the loss. Further, cross layer optimization can help to providesmooth transmission over best effort infrastructure like WWW.

2.3 Challenging Technologies for Ubiquitous Multimedia Services

The technology used in multimedia services has vastly changed during the past decade.Delivering continuous and smooth media stream over wireless link is a challenging taskdue to continuous change in channel properties over the time. The unpredictable behav-ior of wireless communication resulted in developing the concept of adaptation. Ubiq-

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uitous computing has become a major concern in many of the scientific researches dueto advancement in communication architectures like next generation network (NGN),universal mobile telecommunications system (UMTS), IP multimedia subsystem (IMS)and long term evolution (LTE).

Moreover, the introduction of digital TV replaces the traditional analog TV andintegrates many other services together in to one box. Ubiquitous nature guaranteesthe services are available anywhere at any time regardless of the access technology orthe access terminal. Compared to other service architectures ubiquitous architecturehas its own model for multimedia services where services are provisioned in order torealize a business model interconnected with software and hardware. In terms of pro-viding pleasant multimedia service over ubiquitous network require proper monitoringto assure all functional models and elements performs properly and enables reachabilityto different domains of the network.

2.3.1 Service Continuity

Provisioning mobile multimedia content is still a challenging issue in intergraded serviceenvironment due to the requirements in quality assurance, latency, jitter and packetloss. Providing uninterrupted smooth flow of transmission require more sophisticatedtechnologies and more investment on implementation. The Fig. 2.3 shows examplescenario for service continuity.

Figure 2.3: Seamless service continuity in user-driven ubiquitous networking environ-ment guarantees service access irrespective of user context

Avoiding interruption in mobile environment tackles with proactive decision mak-ing about user context. Moreover, the devices equipped with multiple access technolo-gies improves accessibility and guarantees smooth delivery of content. Developmentof cost effective electronic devices in communication poses individuals to use morehand-held devices connected to each other in their day-to-day life. Further, servicecontinuity has become a crucial issue due to heterogeneous network elements in the

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local networks. Specially, delivering multimedia contents over local networks or WWWhas become a demanded issue for content providers.

Broadcast or the cable television is another form of competitive technique usedby many service provides to deliver real time content to subscribers. However, ser-vice integration over IPTV made it more popular among many other technologies.IPTV provides guaranteed delivery over the local network to the customer premises,at least one video stream together with an audio stream. In IPTV service environ-ment service provider fix the performance metrics. In order to manage the quality ofservice, content providers develop video quality metrics (VQM) where all performancemeasures merged together and assigned to a common scale (Kerpez et al., 2006). Au-thentications, authorization, accounting (AAA), capacity planning, error correctionand subscriber management are some of the important aspects in multimedia serviceassurance.

Unlike in many other traditional streaming technologies IPTV uses different au-thentication, authorization mechanisms to reduce the latency. In commercial perspec-tive subscriber management is an important task in service management. Normally,they provide bulk of services as a package with few value added services. Subscriberscan choose the preferred packages on their interest. Generally, IPTV service providesuse security measures for authentication, authorization and accounting. Video on de-mand (VoD) is another value added service over IPTV. Even though, generated trafficis smaller comparable to IPTV traffic, multiple simultaneous VoD traffic flow over samechannel can increase the load considerably. The content protection is achieved withdigital right management (DRM) which performs similar to AAA (Kerpez et al., 2006).And it manages the subscribers in terms of controlling access to authorized media con-tents. Bandwidth and delay are the key factors which affect the content delivery inIPTV environment and it hardly affect the quality of service.

Moreover, the development in broadband technologies facilitate access IPTV overmobile devices like PDA, mobile phones over the technologies like enhanced datarates for GSM evolution (EDGE), 3G, hi-speed downlink packet access (HSPDA) andWiMax. NGN platform is developed to meet the requirement of IP base environmentfacilitating different media contents like audio, video, text, graphics and data overthe network. Continuity feature in NGN enables service providers to deliver contentwithout any significant change to the existing infrastructure. The immense IP sup-port in NGN enables exploding many services easily over NGN while ensuring servicecontinuity over the network.

2.3.2 Context Awareness in Multimedia Applications

Widespread mobile technology and portable devices tremendously increase the devel-opment and usage of context aware applications in hand-held devices (Wang, 2004).Once user context (see Fig. 2.4) is clearly determined by the system or the applica-tion, it will select the best fit content, end terminal and an appropriate media codecfrom available resources with subscribers. When a subscriber initially makes a request,context aware application grab the context information received with service request.And that information is used to select the best content. Particularly, this informationis provided by the context provider.

Further, it temporarily stores this information in a context server for further usage

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Figure 2.4: Context aware applications are responsible for self determination of com-munication technique and the suitable terminal with proper media formatand codec

in decision making. This context information results execution of automated commandsor fetching some specific information with respect to user context. Evolutionary de-velopment of all-IP networks connects various devices together over local network andInternet. This ensures the seamless accessibility. Context awareness fundamentallybelieves three basic steps like context capturing, context analysis and content delivery.

In a business model network provider gathers user context and update the contextprovider enabling delivery of appropriate content to context user (Docter et al., 2007),selecting terminal device, choosing among codec and adaptation technology. Respon-sibility of context provider always lay on delivering right context at the right timeto the content provider. Context aware applications or services always interpret thereceived context information and process data to choose among the correct contents.Context aware service providers are capable of delivering the best fit content accordingto received context information. This intelligence is realized by training the systemsand analyzing different context information.

In (Wei and Chan, 2010) propose a scheme for context reasoning based on ontology.They have developed a layered architecture which promotes a hierarchical design, witheach layer assigned a well-defined role. This architecture is divided as program layer,decision layer and knowledge layer. In program layer there are two main functionsservices and tasks. Further, it divides the context knowledge in to three componentsas; service ontology, context ontology and Tasklet ontology.

• Context ontologies model: This module various the context entities to share thecontextual information in a dynamic service environment.

• Tasklet ontologies; This describes the properties of tasklets and the requirementsfor conditions.

• Service ontologies; This describes context-aware service properties the require-ments for tasklets.

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Figure 2.5: Ontology based context reasoning scheme (Wei and Chan, 2010)

But this implementation involves many functional modules while increasing the com-plexity of the application.

Pervasive systems are effort of in-cooperating devices to build up common com-puting paradigm to establish context aware system in day today human life (Zaslavsky,2004). Pervasive systems monitor the user context and react according to the intel-ligent. Basically, the applications discover the context and adapt accordingly to fitthe existing environment. Even though, it is invisible from our day-to-day life ubiqui-tous computing fulfill many human needs and wants embedded in our daily life style.Nano-technology and tremendous improvement in wireless communication assure hu-man activities somehow related with computers and software which are carefully tunedto offer automated human assistance. This nature facilitates context aware applica-tions to perform better in existing communication environment. Specially, contextaware mobile applications provide a good artifact for self support context awareness.If a person is traveling on a train or a bus, his mobile can suggest him to listen some mu-sic, watching video or do on-line shopping until he reach his destination. This providesa good example for self support applications. Basically, any context aware applicationgathers context information from the sensors round it and process accordingly to servethe best quality of experience (Menai et al., 2009). With the improvement of contextawareness attached to devices, many applications or services was developed to retrievethe context information and dynamically change application nature to deliver the bestuser experience. The contextual information received by infrastructure is forwarded tothe application and appropriate action is taken by the single or multiple devices usingthe application.

RFID is a leading technique of gathering context information. Especially, they areused in detection of location information. RFID enabled mobile supports auto config-uration appropriately to the context. For an example, if you enter in to a shoppingmole your PDA will suggest you items to purchase while you walk through different

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sections. If you enter in to a class room or cinema theater, your mobile will automati-cally configure in to silent mode or reduce the level of ringing volume. RFID providesgood tracking mechanism providing location base information to context aware appli-cations. The context aware middle-ware infrastructure transforms the physical spaceto computational model. This middle-ware facilitates gathering environmental condi-tions and transforming them in to context information. Basically, it operates as a dataacquisition mechanism like in sensor networks. Mobile learning is another interestingcontext aware application. And it senses the mobile environment and adapts contentfavorable to user context. Dynamic change in environment exploits the challenge indeveloping systems which can be trained for learning sequences obtained in differentuser context.

Heterogeneity of the network introduces another challenge in accessing contextservice. However, in service providers perspective they are responsible for providingservice for all heterogeneous devices in the network. Context awareness requests changein the context to dynamically adapt the content. This is the challenging issue in futurecontext aware applications.

2.3.3 Content Delivery in Ubiquitous Environment

Improvement in wireless communication integrates many value added services intohand-held mobile devices. This came more popular among mobile subscribers due tothe concept of Content following user. Mobile TV is such a service where the televisioncontent could be carried with the subscriber. Actually, we are in the evolving ageof mobile TV. But providing non disrupted continuous video stream is a challengingissue in mobile environment. Mobile TV service allows seamless service accessibility inmobile range while providing continuous streaming over the wireless medium. Deliv-ering traditional TV media content to mobile devices clams reproduction of content into compatible formats and delivering over noisy channel. This clams error detectionand correction mechanisms in customer side. Still it is tolerable since dropping fewpackets does not harm the experience severely. In mobile TV transmission they usedigital video broadcasting hand-held (DVB-H) or 3G scheme. Newly developed IPbased video transmission (see Fig. 2.6) provides more advance video streaming overfixed line for delivering contents. The service integration among VoD, IPTV and In-ternet browsing together in a single package makes subscribers to interchange amongservices over a single access session. Basically this technology introduce personalizetelevision concept.

So, the subscribers will get the access to common media content over live broadcasttelevision but at the same time they will have the access to control the content in theirpreference. IPTV provide immense control over the contents to subscribers. Apartfrom standard definition high definition television came in to the arena due to higherexpectation of digital subscribers. But, this introduces a trade-off between video qualityand the bandwidth. Standard Definition television (SDTV) uses 1-4Mbps while highdefinition television ranges from 4-13Mbps.

This restricts the maximum number of HD channels to 10-20 due to high band-width consumption. Since, quality video streaming is highly affected by the packetloss or delayed transmission. Quality assurance is a quite important phenomenon inIPTV or VoD environment. Due to limited bandwidth and the geographical disper-

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Figure 2.6: IPTV content delivery in ad-hoc service environment providing platformindependent service environment

sion of subscribers delivering content under acceptable quality measures is a criticalproblem for operators. So, that they came up with a new architecture where contentis geographically spread to increase the accessibility to overcome this issue.

CCDN concept is becoming more popular since it is very well supported in IPTVand many other real-time services. CDN consist of three basic operation models likeCDN controller, cluster controller and content delivery or the media servers (Menaiet al., 2009). CDN controller manages the client requests initiate the user session.Further, it can identify the user localization, network load and redirect the requestto closest or the desirable cluster controller or another CDN controller. Basically,cluster controller is responsible for handling or redirecting user request media serversin the same geographical areas. This mechanism distributes the network load amonggeographical clusters. The content storage is known as content delivery function (CDF).Basically, this architecture proves that load balancing could be easily achieved in IPTVenvironment. Generally, we consider the live media content delivery to be broadcasttype of transmission. Later it was narrow down to unicast transmission where only therequested user is provided the media content. This dramatically reduces the networktraffic in the mobile and IPTV environment. Especially, with mobile TV environmentthis is more significant.

2.3.4 Cross-Layer Adaptation for Multimedia Applications

Content adaptation is a complicated process which consumes more systems resourceslike processing power and memory. So that, there should be a better interaction be-tween different operational layers which utilize the resources over this process. Thisimplies the requirement for cross layer adaptation as shown in Fig. 2.7. Designers aremore concern in resource optimization while cross layer designing. However, this is stillan unsolved problem among the researches since no solution could optimize resources

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Figure 2.7: Multimedia adaptation middle-ware platform which controls the mediacharacteristics to format the content to fit the end user requirements

in different layers at the same time. So, they suggest iterative optimization or decisiontree approach to solve this problem (Schaar and Shankar, 2005). Basically, the solu-tion suggests optimizing few strategies but not all. This involves grouping strategies,identifying parameters, layers or sub layers to be optimized. Real-time streaming overwireless network is a challenging task. Since, channel properties could be changed overthe localization, time or the environmental conditions. Traditional stream expect tohave long buffers for error correction and adjusting channel parameter. In other words,long buffers perform poorly in video transmission. Since, it can introduce more delayover the channel. The nature of transmission over wireless media is not similar to wiredtransmission. This requests for separate protocol architectures like modified automaticrepeat request (ARQ) and error correction.

Robustness of video implies the fact that transmission can be managed to adjustsuch immediate drop in quality. Scalability of multimedia content refers to the numberof users simultaneously access the media. Due to the evolvement in Internet the basicneeds for services immensely changed. It results to reduce the complexity of accessingservices and same time brings down the cost of subscribing to new services. Still it is agood technique in exploding many services where quality of service (QoS) is not a hardconcern. We find the most critical issue in network scalability as heterogeneity. Due toheterogeneous devices connected to the network, scaling the media content to fit deviceproperties is a challenging task. Because, this implies the requirement of exchangingscaling parameters, selecting best coding model, transfer rate and tolerating channelnoise makes this to be a complex process.

Transcoding is a technique used in reducing object size in the content to be de-livered. The process of transcoding systems are divided in to three classes as clientbased, server based and proxy based. But, client base transcoding is found to be dif-ficult due to the low bandwidth and client processing power. In server based modelsuggest centralized approach where server do the scaling and transmit to the client.But, this introduces a problem in understand the client requirement for transcoding.This results transcoding process assigned in to the proxy. Proxy has two options like

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merely transcoding the input media or consider user context in transcoding process(Hsiao et al., 2008). Even though, this process lowers the media quality still it couldbe presented in a satisfactable level. Further, it is noticed that multimedia contentmanagement is another important concern in content or the service providers perspec-tive. It is more complicated to deal with media contents of different size and formats.And at the same time it requires large capacity to store the content. Not only that theyfind this complexity in service provisioning and billing (Bellinzona and Vitali, 2008).

The work proposed in (Elias et al., 2007) suggests a proxy based framework forcontent adaptation. This approach relies on building up an efficient tree with selectedset of services optimizing resources. This acyclic graph or the tree is constructedconsidering client, end terminal, multimedia content and the network profile. Contentadaptation is generally classified in to two classes as dynamic adaptation and staticadaptation (Elias et al., 2007). Rather than accessing already created content (static)by the provider, dynamic adaptation gathers information like network, client, anddevices to recreate the content accordingly. Proxy based adaptation involves enteringa third party entity making decisions between the content servers and clients. Thesuggested framework deals with matching destination profile and the source profiles.

Client proxy integrate the client profile with the device properties extracted fromthe client request and merge it to the source profile in order to decide the best adap-tation model. Different video coding standards like moving picture experts group(MPEG)-2, Video/H-261, H-263 and MPEG-4 provides scalable options to certainextend (Aggoun et al., 2008). Content providers perspective DRM or the contentprotection is another important security concern in media delivery. This implies therequirement of DRM in multimedia delivery. The growing technology in high speedtransmission over Internet provides increased media experience for customer. And atthe same time it introduce a new era in personalized high quality media services.

In multimedia communication cross layer design has become a good research areasince media content delivery for triple play devices is a hot topic among the researchers.Service convergence is another important aspect in content adaptation. Scalability ofmedia content allows heterogeneous devices to connect to the network regardless ofits localization. This raises the requirement for cross layer adaptation to suit thecontext of use. There are many cross layer adaptation mechanisms which allows adaptthe service environment to user context. Even though, terminals like mobile phones,PDA are capable of receiving media content at anytime from anywhere the problem ofadapting the content is still an unsolved problem.

The challenge of meeting the terminal capabilities, delivery constrains and man-aging quality of service is a quite complicated process in multimedia services. Thisimplies the requirement of maximizing the cross-layer utility to improve the QoE. Thisraises the problem of What is the context of use and what the best adaptation model?But the answer for this question entirely depends on the information received fromthe end terminal. There are many cross model adaptation techniques being suggested.The model suggested in (Prangl et al., 2006) introduces decision making process con-sidering user context, terminal capabilities and resource limitation on server, networkand client side. This work suggests four basic processes in adaptation like parametermapping, utility model configuration (UM), adaptation decision taking engine (ADTE)and adaptation engine (AE).

Further, it implements and observes the audio/video stream variation to maximize

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user experience under given resource constrains. And they suggest four algorithms find-ing optimal audio/video variation in cross model multimedia adaptation (Prangl et al.,2006). The latter improvements proposed with distributed contents reducing the delayin accessing the media. Normally, such applications are applicable for commercial,health care, emergency and tourism. Such systems we name as pervasive systemswhere multimedia content is distributed and content is adapted on user preference,device properties and network capabilities (Berhe et al., 2005). M-learning is anotherfast moving research area where learning process is done over the hand-held or fixeddevices. This technology lay down the basic content accessibility making intelligent de-cisions over the accessible devices. The complexity in M-learning comes when adaptingthe media content and selecting the proper device to receive.

For an example, assume a scenario where a person is watching a movie on hisway back to home over his mobile phone. Once he reaches to home, he necessarilydoes not have to use the same device. But now the content is delivered to a HighDefinition-TV at home with improved audio/video impacts. In that sense, scaling themedia content is an important concern in content delivery. Scalable video coding (SVC)provides solutions to overcome this problem. SVC supports the backward compatibil-ity for traditional media contents like video, speech while assuring network/terminalcompatibility (Hewage et al., 2007).

2.4 Mobile IPTV Service in Ubiquitous Networking Environment

In Fig. 2.8, we see an example scenario in the home environment. Bob is watching amovie in the TV set at home. When he requests the movie, the service platform wedefined here automatically detects all possible rendering devices around access radius.For an example, context-aware system will recognize different types of devices such asthe high-definition television, mobile phone and PDA. Then it will decide which device

Figure 2.8: Service scenario in home environment for seamless service accessibility overdifferent end user terminals

to deliver different media contents like visual, audio affects and subtitles. For an

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example, the visuals are delivered to high definition television while audio is deliveredto hi-fi setup and subtitles to his PDA. Further, he will gain total control over videopresentation over his mobile phone. Meantime, his friend comes to see him and both ofthem watch the movie together. Ultimately, Bob is getting a call from his wife to pick upher from the supermarket. But Bob is so interesting watching the movie. So, he decidesto takes the session with his PDA. We term this capability as The content following

user. His PDA is having an inbuilt content guide implemented using open source -synchronized multimedia integration language (SMIL) player. Likewise, he can gettremendous control over video presentation while moving outside the room. Moreover,the same video might play in wide screen in his room. The above example presentsservice continuity and service integration of ubiquitous computing environment.

In service providers perspective, there are many undergoing processes for deliv-ering quality content to their subscribers. First, incoming user is authenticated andauthorized. Attached to the example scenario, Bob request the same content over hisPDA, service provider let him download the stream and automatically start from theplace he stopped. Then, service will find the best rendering technology which minimizethe noise and interferences. Basically, this platform provides dynamic content deliveryand rendering over the selected content in a user friendly manner while adapting tothe user context.

2.5 Threats Over Context Collectors

2.5.1 Limited Resource Constrain of Sensors

Context aware services use sensors in order to capture context information. Withthe improvement of micro-controllers and wireless communication technologies sensornetworks plays a big role day today human applications. Though, it is originallyintroduced for military purposes, now it is being used for many industrial applicationslike environmental information gathering, health care, home automation, traffic control,navigation and many other civilian applications.

Gathering context information is a challenging issue in adaptive service environ-ment. The incorrect and erroneous information can mislead the applications and de-grade the user satisfaction. Therefore, gathering necessary information in trustfulmanner helps to provide a quality service. Normally, sensors are used for gatheringcontext information. But, sensors can be compromised or mislead easily to produce in-correct data or disturb the communication among them. This implies the requirementfor a secured channel and a protected environment which guarantees an uninterruptedcommunication.

Any sensor is equipped with micro controller with limited computational capacitybattery power and a radio transceiver. Always the battery life time is a critical factorin most of sensor networks since they need the remote operation. Sensing nodes aremade to be cheaper with the use of latest technology helping large deployment andgathering more precise data. We can find the sensor network applications operating ondifferent areas like tracking, monitoring and controlling. Special applications for WSNsinclude habitat monitoring, object tracking, nuclear reactor control, fire detection, andtraffic monitoring. There are many application of WSNs used in detection of naturaldisasters, sensor nodes can sense and detect the environment to forecast disasters before

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they occur.In biomedical applications, surgical implants of sensors can help monitor a patients

health. For seismic sensing, ad hoc deployment of sensors along the volcanic area candetect the development of earthquakes and eruptions. WSNs are deployed over aregion where some phenomenon is to be monitored. Specially, in battle fields thesenodes are deployed to detect enemies movement to track their activities by detectingthe desired parameters (heat, pressure, sound, light, electro-magnetic field, vibration,etc). These information need to report to the base station, which analyze the data andmake decisions over the received information. Unlike any other traditional wirelessnetworks, WSN has specific design and resource constrains. Resource constraints insensor network includes limited amount of energy, short communication range, lowbandwidth, and limited processing and storage capacity in nodes. This feature enablesWSNs are more prone to attacks and threats. Basically, sensors could be divided in totwo as generic sensor nodes and gateway sensor nodes.

Generic sensors are equipped with sensing elements which are capable of measuringphysical environmental factors like light, temperature, humidity, barometric pressure,velocity, acceleration, acoustics, magnetic field, etc. The task of gateway nodes isto gather data from generic sensors and relay them to the base station. Gate-waynodes have higher processing capability than generic nodes. Basic sensor networkmodel generally assumed to be static. However, some recent applications of sensor-nets make use of mobile sensor nodes, which poses some unique challenges to sensor-netsystems researchers. Some applications like detecting land mine in battle field needsremote operation their own. Mobile sensor networks are used where remote operationis required for gathering information. Mobile sensor network have distributed nodesaround the target area, each of which has sensing, processing, communication andlocomotion capabilities.

Sensor nodes have very limited storage capacity and Memory. Due to the restric-tion in code space the algorithms suppose to be more optimized in code space andthere functionality. Further coding supposes to minimize the storage for variables,arrays and other resource consuming modules. The properties like capacity, power,topology, mobility and routing of the wireless sensor networks have interdependentcharacteristics (Karlof and Wangner, 2003). Especially in the security aspect develop-ers are worried about resource utilization (power management, bandwidth utilizationand mobility management) and secured data transmission. Therefore it is important tounderstand the relationships between different aspects of wireless sensor network op-eration to guarantee secure communication among sensor nodes (Walters et al., 2007).

Normally, nodes are equipped with non rechargeable batteries where as some couldbe charged after the usage. But batteries cannot replace easily due to higher operationalcost. Since battery charge decides the life time of the nodes it is necessary to controlthe transmit power. In the implementation of protocols they are more concern aboutthe energy consumption. Sensor nodes are developed to operate under least powerconsumption due to limitedness of source power. This restricts the radio range of thenodes.

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2.5.2 Communication Unavailability

This implies the condition where data get lost due to channel problems like channelnoise or network congestions. This results data loss, damaged packets or incompletedata transfer among the nodes. A channel error in communication medium implies thenecessity of error correction mechanisms in sensor networks. This could result failuresin data transmission in WSNs. If the lost data consist of some necessary informationlike security keys, then the whole transmission will get corrupted (Du et al., 2004).Even though the channel does not have any problem still communication could bedestructed by any other node who is trying to retransmit data. This happens due tothe broadcast nature of the wireless communication. When the node density is higherthere is a higher probability to get congested. Therefore in the designing stage it ismore important to consider the radio range and node density of the network (Tameret al., 2000).

2.5.3 Unattended Operation

Depending on the nature of the application attacks model differs. That could bedue to natural facts or due to human activities. When node exposed to the physicalenvironment it could harm with natural occasions like wind, rain or humidity. Else,it could be physically damaged by human (adversary) with the interaction to disturbthe transmission. Therefore the deployment is expected to be in a secured location toprotect them from such physical attacks during the operation. In the case of remotemanagement of WSNs are more critical in military applications. In such cases, thenodes need unattended or remote operation to capture reliable data. So that the nodeitself expected to be organize and utilize the existing resources to operate in the bestperformance level. Specially, in mobile sensor networks we allow nodes to move in thetarget field in gathering data and transmitting them to the base station. This improvesviability of sensor network. Therefore the design of the sensor network more importantto achieve proper operation.

2.6 Security Concern of Sensors

Sensor networks share many commonalities with wireless networks. Therefore, wecan say that WSNs claims both unique and common requirements claimed by wirelesscomputer networks. Data confidentiality is another security issue in computer networksas well as sensor networks. Confidentiality refers to the extent to which data couldbe trusted. Depending on the nature of the application the level of confidentiality isdifferent.

To protect data from intruders the transmissions are encrypted with secure keys(Chan et al., 2003). But, in this case all kind of keys and sensor identities need tobe encrypted since these data are very much sensitive in communication among thenodes. To achieve data confidentiality encryption methodologies are use. Though, dataconfidentiality is guaranteed in a network still we cannot ensure security since intrudercan hanged existing data. This means the data is still not protected. For example,an intermediate intruder might modify or add some tracking data in to the normalIP packet resulting a threat to the network. Then the packet is sent to the original

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destination as usual. With data integrity we can ensure the data is not modifiedor added during the transmission. Further, we must make sure freshness of data.Older messages need to be dropped since they can stay in the network and reach thedestination being delay. So that, destination might think it is the most recent dataand act accordingly misleading the nodes. Therefore, the freshness of data needs toguarantee in the network for secure communication.

2.7 Attacks on Wireless Sensor Networks

2.7.1 Attacks on Data Link Layer

Since, all sensor nodes in the network have same rights for accessing the communicationmedium data link protocols has been more vulnerable to attacks. Any adversary gotrights to access network can access the channel randomly and transmit or eavesdrop.This could be more serious, when nodes inject and alter data being transmitted. Suchattack models can be subdivide in to three categories as below (Xiao et al., 2005):

1. Data integration attack

2. Collision attack

3. Exhaustion attack

To pretend against these attacks link layer suppose to have three basic security con-ditions. Those are presented in the IDS architecture proposed in (Xiao et al., 2005)which consists of three basic modules in detection architecture:

1. Collision detection system

2. Power anomaly check

3. Data integration check

Nodes compute the rate of collision (per second), and the ratio of collision. Moreover,nodes records waiting time after sending the RTS packets, packets stay in the queueand the packet drop during the communication. In case of a collision, these parame-ters will remarkably change. And the nodes will detect the presence of intrusion andgenerate an alarm to notify others. The presence of power check module guaranteeslong term battery usage while protecting system from adversaries those who try todepreciate the battery life of reluctant nodes (Lazos et al., 2005). If the nodal powerdepreciates rapidly, we can expect that an adversary is attempting to send some packetsrepeatedly. The collusion detection module can detection of such behaviors immedi-ately and generates an alarm. The module defined for data integration keep checkingthe messages to assure no adversary modify the message in between during the trans-mission. If the data received not similar to the original data send by the legitimatenode detection module will generate an alarm (Xiao et al., 2005). In case of collisionsand power alarms, the communications is temporarily stopped and force the nodes tomove into sleep mode for a few seconds. This can mislead an advisory forcing to stopcommunication with the reluctant node. In the presence of an integrity alarm, thetransmitting node will drop a message requesting the source node to retransmit sanemassage back to the generated node (Xiao et al., 2005). With the received message wecan make sure existence of data integration.

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2.7.2 Attacks on Network Layer

Initially, the nodes suppose to organize themselves and when they receive HELLOpackets. So that, protocol assumes those nodes reside in the given radio range. Nodesshould be authenticated such that messages are not proofed or eavesdropped by anadversary. Periodically, nodes suppose to store the neighbor information in the memorythen this information is sent to the base station frequently. So, the network topologycould be easily mapped at the base station. Since, nodal power depreciates with thecommunication turns nodes get vanish sometime after deployment. Moreover, themobility of the nodes results to timely change the network topology demanding tomonitor the nodes continuously. Therefore, the topology derived at the base stationwill valid only for a given instant. When the sensor node is activated the detectionmodule must automatically start to detect any intrusion in the network. Whenever,the module detects the existence of an attack the countermeasures are taken. Thesecountermeasures could be defined as,

1. Checking artificial links

2. Checking neighborhood information

There are many routing protocols used by existing sensor networks (Marti et al., 2000).These protocols could be fit in to different categories as shown below (Sattar, 2004):

1. Flooding - SPIN (SPIN-1 and SPIN-2)

2. Gradient - Directed Diffusion, GRAB, GEAR

3. Clustering - LEACH, TTDD, GEAR, GAF

4. Geographic - GPSR, GAF GEAR

The routing protocols used in sensors networks developed with least complexity duethe low processing power and less capacity. So, developers pay more attention onsimplicity of the protocols with effective routing. Because of this, sensor nodes aremore susceptible to different form of attacks (Ngai et al., 2006). Almost all routingprotocols fail at least against single attack model shown below.

1. Selective forwarding

2. HELLO flood attack

3. Sinkhole attack

4. Sybil attack

5. Wormhole attack

6. Acknowledgment spooling

7. Altered replayed or spoofed routing information

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Malicious nodes might simply drop the packets that are forwarded in the network.In selective forward attack malicious node refuses to forward the received message.Simply it forms black hole for certain source packets or to the whole incoming packets.We can believe that a malicious node intended in selective forwarding might followthe path with least disturbance. Many existing protocols in sensor networks requireHELLO packets to advertise themselves and to get to know the presence of other nodes.HELLO flood can be formed by advertising a very high quality route to neighbors.This will force other nodes in network to choose the same path (Hamid et al., 2006).A wireless sensor network consists of autonomous devices which keeps track on theirneighbors and exchange information in between. And the captured information istransferred to a sink node or base station for decision making process (Lazos et al.,2005). Many-to-one communication model is a highly vulnerable to sinkhole attack.Flooding unfaithful routing information intruder easily execute attacks on neighboringnodes. This form of attack could be more risky since whole communication systemcould be misled.

Node impersonation and resource depletion attacks are two types of attack mod-els which can disrupt both sensor and ah-hoc networks. Node impersonation means toestablish as legitimate node in the network by using an identity of some other node. Sothat it can operate as normal node in the network and extract all necessary informa-tion in the network. Resource depletion attacks more focused on consuming networkresources such that it will disrupt the normal operation (Sang et al., 2006). For anexample, an attacker might create large volume of data to a single node. It will resultto occupy all resources alone the path unusable for other nodes. This will rapidly wastethe battery and reduces the life time of the nodes.

Being exposed to external environment and mobility in nodes makes WSNs morevulnerable to attacks. Through, it is difficult to protect against physical attacks, thereare many other techniques to protect against technical attacks. In the other hand,prevention based techniques like data encryption and authentication consumes moreresources compared to detection based techniques. (Eschenauer and Gligor, 2002)presents key management scheme which satisfy both operational and security require-ments of WSNs. Since they need cryptographic protection in information exchangekey management protocols performs quite important job. But, it is a challenging is-sue that WSNs still use traditional key exchange and distribution techniques wheretrusted third party involves in communication. (Pavel and Trossen, 2006) introducenovel detection based security scheme for WSNs assuming stable neighborhood infor-mation. It allows detecting network anomalies and transceiver behaviors consideringsignal strength and data rate. Initially, malicious node tries to establish as a legitimatenode in the network before it becomes a threat to network security. Sensor nodes aremade to be capable of isolating such intruders with dynamic statistical models whichdetects anomalies.

Time synchronization is an important concern for any wireless network in hostileenvironment. But, most of the existing time synchronization techniques are not de-signed with security concern. This makes the WSNs more vulnerable to attacks. (Songet al., 2005) presents a novel time synchronization scheme which resist to delay attackwhere malicious attackers intentionally delay the transmission of time synchronizationframe with the intension to magnify the synchronization offset which makes attackerget in easily. Topology of sensor networks change frequently with time and the broad-

23

cast nature of communication allow malicious nodes to enter the network with lesseffort. Therefore, it is important to block such nodes entering to network before theycompromise legitimate nodes.(Kerpez et al., 2006) propose scheme for tracking multipleco-dependently maneuvering targets using radio frequency identifiers (RFIDs). Appar-ently, using RFIDs is a common technique since it involves less cost compared to otherlocation based services. Generally, intrusion detection schemes can be categorized into three as stand-alone, distributed, cooperative and hierarchical (Wang, 2004). Ourproposal carries attributes of both hierarchical and distributed architectures.

2.7.3 Log-distance Path Loss Model

Most of the actual propagation models could be simulated using analytical or empiricalmodels. Empirical approach involves recreation of observed data with derived equationsor curves. For the calculation of the link budget, empirical method uses all knownand unknown parameters resulting to reliable evaluation. There are some classicalmodels developed to predict large scale mobile communication system designing withmathematical models.

By observing both theoretical and practical parameters, it was found that thereceived power decreases logarithmically with the distance both indoor and outdoorenvironments. Below we present the power depreciation with the transmitter receiverseparation (Onat and Miri, 2005).

P dpathloss ∝ (d/d0)

n, (2.1)

Ppathloss[dB] = P d0pathloss + 10nlog(d/d0), (2.2)

where d is the transmitter receiver separation, d0 is the close-in reference distanceand n is the path loss component. This implies the fact that path loss is logarithmicallyproportional to the transmitter receiver separation. In this model, path loss exponentdepends on the propagation environment. Normally, in urban areas it is assumed tobe 2.7 - 3.5. And it is found that the path loss at a given point is random and lognormally distributed. So, we modify the equation adding Gaussian random variableXσ with standard variation σ. Therefore, we modify (2.2) as (2.3).

P totpathloss = P d0

pathloss + 10nlog(d + speed

d0

) + Xσ (2.3)

This is known as log-normal shadowing. Basically, this implies the fact that signallevel at a defined location has Gaussian distribution.

2.8 Mobility Models for Sensor Nodes

In a mobile environment nodes move gathering and exchanging information. Therefore,the distance between nodes always changes with time. We use Random-Waypointand Gauss-Markov mobility model for the evaluation of proposed architecture. In ourstudy we propose a intrusion detection architecture where nodes forward information tobase stations. Base stations are responsible for the detection architecture and makingdecisions. Nodes dynamically connect with the base stations according to the received

24

signal strength. There are instances where one node is connected to more than singlecluster head. Specially, the instances like handover.

We organize whole network in to three hierarchical layers as sensor nodes, clusterheads and base stations. And they are assigned distinct responsibilities. Sensor nodesalways gather information and forward to cluster heads where cluster heads analyze andexchange information among peers in the same hierarchy. Moreover, cluster heads runthe detection algorithm for power and rate anomaly detection. BS gathers informationfrom cluster heads and stores them for future reference. In the manufacturing itselfcluster heads are developed with more capacity and processing power.

When nodes move, they experience different channel conditions and more impor-tantly they adjust the transmit power level to maintain expected signal to noise ratiodepending on the channel condition. In the defined model transmission is assumed tobe connectionless while routing decision is taken based on packet based. Moreover,sensor nodes have isotropic antennas and they are aware of the locations. We assumenodes are randomly deployed and mobility is modeled with both Random-Waypointand Gauss-Markov model. Firstly, we observe the detection probability for differenttransmission power levels. We simulate the Random-Waypoint with (Camp et al.,2002),

xnew = xold ± speed (2.4)

ynew = yold ± speed (2.5)

In Random-Waypoint mobility model nodes move randomly without any restriction.In other words, nodes have no memory. The direction and speed is randomly cho-sen independent of the movement of neighboring nodes. Such mobility models arecommonly used to simulate mobility patterns of mobile ad-hoc and sensor networks.Random-Waypoint is firstly proposed by (Zhang et al., 2000). Then it became acommon benchmark for evaluating routing scheme in ad-hoc and sensor networks.Gauss-Markov mobility constrained with laws of acceleration, velocity and the changeof direction. It implies the fact that current velocity and the direction depends on theprevious parameters. Thus Gauss-Markov model believes nodes have temporal depen-dency of velocity and direction. This model was firstly introduced by (Mehedi et al.,2008), where velocity and the direction is timely corrected. We present the stochasticprocess Gauss-Markov with following equations (Camp et al., 2002).

Vt = αVt−1 + (1 − α)Vmean + σ√

1 − α2 Wt−1 (2.6)

θt = αθt−1 + (1 − α)θmean + σ√

1 − β2 Wt−1 (2.7)

xnew = xold ± Vt cos θ (2.8)

ynew = yold ± Vt sin θ (2.9)

The degree of dependency in this model is determined by the parameter α and β

which express the randomness or the level of memory. This model becomes memoryless when both α and β equivalent to 0. Wt−1 is a random Gaussian process with0 mean and σ standard deviation. When nodes move out from the boundary themobility model manage to hold them within the target area. This is done via changingthe angular velocity by 1800 degrees.

When α and β equivalent to 1, Gauss-Markov model have the strongest memoryor the least randomness while illuminating some terms from the equations.

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0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

700

800

900

1000

X − Coordinate

Y −

Coo

rdin

ate

Figure 2.9: Mobility pattern of a sensor node when routing is simulated with Ran-dom-Waypoint model

0 100 200 300 400 500 600 700 800 900100

200

300

400

500

600

700

800

900

X − Coordinate

Y −

Coo

rdin

ate

Figure 2.10: Mobility pattern of a sensor node when routing is simulated with GaussMarkov model

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CHAPTER 3

CONTEXT REASONING IN CONTEXT AWARE MULTIMEDIA

SERVICES

3.1 Introduction

In this chapter, a context reasoning scheme is introduced for home environment basedon the AHP. This model is capable of handling multiple objectives and sub-criteria si-multaneously. Further, user-driven ubiquitous networking environment provides seam-less integration of services and content from different local/global resources (Loke,2006). Moreover, media adaptation is a major consideration when same content isdelivered to multiple devices simultaneously (Pavel and Trossen, 2006).

In addition, a use case is provided for seamless IPTV services in home environ-ment and AHP is introduced in context reasoning. The proposed scheme for contextreasoning over the hierarchical context information tree provides service adaptationby early identification of the user context. This scheme could be used to interpretand enhance explicit user inputs to deliver accurate and precise predictions based ongathered information. The modern context reasoning considers emotions and feelingsto produce more user friendly decisions (Pavel and Trossen, 2006). The process ofevaluating the context information is known as the context reasoning (Zahedi, 1986).In (Pawar et al., 2008), a scheme of vertical handover is proposed which supports forthe multi-homed nomadic mobile service. Hence, it is found to be one of the mostchallenging and important issues in future communication systems (Loke, 2006).

AHP based context reasoning is found to be comparatively easy and cheaper com-pared to ontology based context reasoning schemes (Gu et al., 2004; Wei and Chan,2010).

In this chapter technical challenges are discussed in the section 3.2 for contextreasoning. The usage of AHP in context reasoning is presented in the section 3.3. Thesection 3.4 discusses the proposed model for context reasoning. Finally, the section 3.5presents the conclusion.

3.2 Technical challenges in Context Reasoning

Context awareness provides good back-end support over existing IPTV service. Itmakes the services more user-friendly and flexible. Here we focus IPTV services inhome environment which is subjected to frequent change in context. In addition tothat, context aware IPTV service in home environment is supported by the location,time, device capabilities, network characteristics, etc.

Location-based information can be classified into two categories as indoor andoutdoor. Indoor environment is an extension to the smart home concept. In such en-vironment, context information is forwarded to the local context manager at homenetwork or distributed context managers in the operator network. Moreover, the localcontext manager is not always smart enough to take crucial decisions relate to flowcontrol like; quality of service management, minimizing delay and utilization of re-source. Strategically, the network operators locate global context managers closed to

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access networks to minimize the response time.The concept of smart home provides a good support over context aware services in

the indoor environment. In home environment, context information is gathered withthe sensors which are capable of detecting voice, motion and environmental factorslike temperature, humidity and brightness. RFID is another common technique useto capture the context information due to compactness and low manufacturing cost.In addition to that, time shift TV is one step ahead IPTV which supports trick modeoperations like forward, backward, pause and play functionalities over broadcast TV.In other words, time shift TV service offers subscriber freedom in time domain byfacilitating them to watch preferable media content which is already broadcasted overlinear TV. Basically, this service allows users to customize the normal broadcast TVservice according to their preferences.

For example assume a person who is watching normal broadcast TV in his livingroom wants to go to the dining room and continue to watch the same content from theplace he stopped over another device. The Fig. 3.1 shows several supported devicesin the home environment related to context awareness. The concept of doublecast-

Figure 3.1: User context management in ubiquitous home environment which supportseamless service availability. This figure shows the existence of differentdevices inside home and how they are connected to home gateway or theset-top-box for content divergence

ing, introduced in (Balasubramaniam and Indulska, 2004), proposes solution for realtime seamless service continuity. In this scenario the service continuity is preservedby delivering the same content over two channels simultaneously during the time ofhandover. In the other hand, there are instances where same content is received overseveral channels. Thus, during the time of handover devices have to synchronize thestreams. Handover is defined in two generic ways; device to device with or without

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a location change and location to location with or without a change in device. Ina heterogeneous networking environment, different devices have different capabilitiesin processing, storing, codec supportability and so on. Hence, in context aware ser-vice environment, local context manager identifies all supported devices in the targetenvironment and exchanges information with them.

When handover takes place in a home environment from one device to another,local context manager at home sends necessary information to the remote server toadapt media content in terms of format, resolution, volume level, etc. In the abovediscussed scenario, the local context manager is responsible for making the local deci-sions like handover within home environment. When, user comes out from the homeenvironment context information is hand-over to the context managers in operatornetwork.

In multimedia services proper utilization of the network resources and managingof QoS parameters are very much important. Hence, compared to broadcast traffic,unicast traffic consumes more resources in a network. Unicast traffic can significantlyincrease the network utilization since each subscriber consumes individual stream fromcontent server or proxy server. Thus time shift TV and VoD brings more weight in tothe network than liner TV since users are served with dedicated streams as presentedin Fig. 3.2. Meantime, proxy servers are used to reduce the traffic load in the corenetwork with buffering the contents requested by the subscribers.

Figure 3.2: Unicast and multicast traffic in IPTV services. Liner TV stream alwaysbroadcast reserving fixed bandwidth for each channel. On the contrary,VoD and time shift TV assign an individual stream for each connecteduser

Delivery of an uninterrupted stream requires good understanding of the communi-cation channel and proper error correction mechanisms (Menai et al., 2009). InternetEngineering Task Force (IETF) has developed a protocol suite to support content deliv-ery in multimedia service environment. Further, the standards defined by audio-videotransport working group of IETF has two protocols as real time protocol (RTP) andreal time control protocol (RTCP) for content delivery in real time service environment.In an actual implementation couple of functional models belongs to both network anduser device are defined (Pawar et al., 2008). In addition to communication tools, de-signers are now endowing everyday objects with context-aware capabilities. Even, toysare developed with inbuilt context aware systems. There are dolls which are capableof recognizing events like hitting, touching, lifting etc. Further, they can emit musicor sounds according to the situation and the way it is handled.

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3.3 Analytic Hierarchical Process in Context Reasoning

Compared to operator networks, home networks are small and provides limited func-tionalities. Home network provides a good environment to experience context aware-ness since it limits to a defined boundary. In a home environment, subscribers arereluctant to frequent handover from device to device and location to location sincethere is a greater possibility that user keeps moving here and there and changes thedevices being used by the time (Wang, 2004). Operators introduce sensors integrated todevices for gathering context information like user context, environmental and systemconditions (Mehedi et al., 2008). Seamless service continuity demands proper handlingof gathered information and isolating useful information for context reasoning.

However, too much context information increases the complexity of context rea-soner or decision support system since it involves more unnecessary computations andanalysis (Loke, 2006). The AHP breaks down decision support process into severalparts. Decision-making is achieved by developing a pair-wise comparison over all levelsin hierarchical context tree.

It deals with relative rating instead of absolute rating. Therefore the final re-sults are more reliable and realistic compared to the results obtained with absoluterating since proposed methodology does overall grading. The proposed scheme per-forms better when there are several options available for the final decision to be made.This nature of AHP propose a mathematical model upon which many problems canbe modeled in its own domain. AHP approach can be further used for predictinglikely outcomes, project planning and decision-making, group-wises decision-making,resource allocation process, and cost/benefit comparisons (Randall et al., 2004). Themost important consideration with AHP is that, it forces prioritizing over differentfactors which significantly affect the final decision. Further, it allows revisiting avail-able information periodically to determine and recalculate for the dynamic changes incriterion or intensities. In addition, at the same time, some factors could be ignoredin the process due to insufficient information or low weight on final decision.

We assume an example scenario where the subscriber is watching a normal broad-cast TV (i.e., linear TV) in his/her bedroom. After some time he/she moves to anotherroom and starts watching the same content on another device from where he stopped.In this scenario we can figure out some important events in operators point of view.This involves service provisioning in operators perspective. Those subscribers havemore than one profile to access different applications. Initially he/she was a liner TVsubscriber but after some time, he/she becomes a time shift TV subscriber. This makesthings more complicated in operator side since it demands to handle complicated sub-scriber profiles (Kerpez et al., 2006). In addition, service provider must make sure thatsubscriber will not get disturbed when they switch to different user profiles.

Generally, context information is categorized into two, Static and Dynamic. Staticinformation is quite stable with devices or prefixed by the subscriber, operator or themanufacture. On the other hand, dynamic information can change over time, location,system condition, network traffic, etc. Moreover, the predictions on dynamic infor-mation are difficult since it depends on many other external factors. And it demandsto study previous information sets to predict on the expected behavior or the results.Herewith, we propose a new context reasoning scheme for handling contextual infor-mation and decision-making system combined with AHP. The AHP is a multi-criteriadecision-making tool which derives the ratio scale from pair vise comparison. This

30

ratio scale is derived using the principles of Eigen vectors.

3.4 Proposed Model for Context Reasoning

AHP is an excellent tool which could be used in decision making to support contextaware applications. In terms of multimedia services, this process could be used todevelop the context reasoner.

Figure 3.3: This figure explains decision-making process in context reasoner at a homeenvironment. Context information is gathered by the sensors around userand sent to the context reasoner which is located in home network oroperator network

Target Environment for Proposed Context Reasoning Scheme

• The proposed context reasoning scheme targets a home environment.

• Home environment may have different multimedia devices like High Definitiontelevision, laptop, PDA, iPod and etc.

• Inside home there could be different networks like WiFi, Bluetooth or LAN.

• Service continuity is achieved in the home environment. Content may be deliveredto more than one device simultaneously to guarantee continuity.

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The Fig. 3.3 presents a brief description of a suggested scenario. And it presentsfew context changes like change in device, change in location, subscriber actions, net-work failures and new service requests coming in to system. There are situations wheresubscribers are responsible for the events. In some case, it can happen due to changesin environment and social interactions. For an example, someone who is watching avideo content can carry the content over his/her mobile device while he/she movesout from the home or work place. Context aware services are always responsible fordelivering continuous uninterrupted stream to the subscribers. Concept of smart homeand content following user supports in such scenarios. However, implementation willalways demand reliable context information to the context reasoner which will leadto make right decisions. The concept of content following user supports situationswhere subscriber changes his location frequently. Location change can demand contextreasoner to find the best suit network to reach expected QoE or handover betweennetworks to reach the subscribers.

The network supportability is also an important fact of delivering content withminimum delay, jitter and packet loss. When we bring this scenario in to mathematicalmodel, we consider that all context information could be put in to a single hierarchicaltree. For example, the scenario we explained in Fig. 3.3 consists five context changes.Basically, that defines five branches at a certain level of the tree. Fig. 3.4 exaggeratesone scenario explained in Fig. 3.3.

Figure 3.4: Expanded partial tree for device selection. This partial tree includes pref-erence for each device and score assigned to each property

The Fig. 3.4 shows a partial tree with change in criteria relate to multimediadevices. The goal is to select the best supported device to deliver the content. In Fig.3.4 level 1 represents different devices with user preferences D1,D2 and D3 Where,∑3

j=1 Dj = 100% Each device has its own property list which describes attributes ofindividual devices. However the impacts of the device properties change from one toanother. Next, we calculate the comparison matrix which exhibits the impact on eachdevice compared to the other devices for all attributes of the devices. Next, we presentthe calculation process. Device property list has scores assigned to each attribute withrespect to the goal (i.e., changing device). There can be many other goals which are tobe minimized or maximized. In the hierarchy we consider a single objective at a time.

• Step 1: We calculate the comparison matrix for the attributes in level 2 aspresented in Fig. 3.4. This can be found by the expression, pij = Si/Sj . Where,pij is the ratio of relative scores assigned to attribute i compared to attribute

32

j. Table 3.1 represents the comparison matrix where impact of each property ispresented compared to other properties. This simple scenario helps to isolatefacts with higher impact on goal and reject others minimizing the complexity ofcontext reasoning.

Table 3.1: Calculation of attribute comparison matrix

P1 P2 P3 P4

P1 S1/S1 S1/S2 S1/S3 S1/S4

P2 S2/S1 S2/S2 S2/S3 S2/S4

P3 S3/S1 S3/S2 S3/S3 S3/S4

P4 S4/S1 S4/S2 S4/S3 S4/S4

• Step 2: Calculate the normalized relative weight for each element in matrixwhere Pij = pij/

∑nj=1 pij ,

∑nj=1 Pij = 1 and pij = 1/pji.In the table 3.1, shows

the attribute comparison matrix. Further, it is used to normalize the attributeimpact. In addition to that it is further described in the given steps.

• Step 3: The Table. 3.2 is obtained to normalize the priority vector by averagingrelative index over a single row. Ni = (1/n)

∑nj=1 Pij. This vector can be used to

predict up to what extent each attribute have an impact on the final decision.

• Step 4: Now we have a vector which defines the relative impact of differentattributes. N is the impact for each attribute obtained by the normalized Eigenvector, where

∑ni=1 Ni = 100%. Next we follow the same steps over options in

level 1 relative to function goal.

• Step 5: Now we have two matrix, property priority matrix P and option prioritymatrix O. We introduce P, O in following example. Finally, we find the compo-sition matrix C with the operation (P.O). This operation will compare differentoptions in the level 1 with respect to final Goal. We define this algorithm asfollows.

Rule: Hierarchical trees exceeding more than two levels could be solved partially bring-ing down the problem in to sub problems. The previous algorithm can be applied atany level of hierarchy to choose best options or attributes. The same operation canbe repeated to choose options after prioritizing the attributes. Partial minimizationof tree helps to reduce the overall complexity of hierarchy and reduce the number ofexpected calculation towards final goal.

The context reasoning algorithm is shown below which describes the proposedcontext reasoning scheme in step wise. In this case, the algorithm is executed to reacheach of the goals in each level in hierarchy. Then the above mentioned steps are fol-lowed to calculate the composite matrix. When the number of levels in the hierarchyis more than 1, the sub-criteria are associated to the goal.

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Table 3.2: Calculation of priority vector

P1 P2 P3 P4 priority vector

P1 P11 P12 P13 P14 N1

P2 P21 P22 P23 P24 N2

P3 P31 p32 P33 P34 N3

P4 P41 P42 P43 P44 N4

Context reasoning algorithm

1. For each Goal2. For each level in hierarchy;

3. If n=1;4. for level n and n+1;

5. find Option comparison matrix level n with respect to Goal;6. find Property comparison matrix level n+1 with respect to Goal;

7. ignore less impact attributes or properties;8. Calculate normalized matrix [

∑ni=1 Pij = 1];

9. Calculate priority Vector [Ni = (1/n)∑n

j=1 Pij ] ;

10. Calculate composite matrix;11. end

12. end13. If n>1;

14. associate sub-criteria to Goal;15. Calculate composite matrix;

16. ignore less impact attributes/properties;17. end18. end

19. end

This approach proposes a matrix calculation over context information which leadsto reduce the complexity of processing. This process is handled by context reasonerwhich is responsible for handling context information. Next, we present an examplescenario for the scheme proposed here. The proposed scheme assigns scores in therange from 1 to 5. Options and properties or attributes are assigned scores from 1 to5 depending on their impact on final goal. Below, we illustrate a numerical examplefor context evaluation. The Table. 3.3 shows a numerical example of the normalizedcomparison matrix. Further, the weighted percentage over each property is presentedin the last column for each property. In addition to that, Table. 3.4 presents thecalculation of weighted percentage over each option. At last the composite matrixis obtained by multiplying the priority matrix and the options matrix. The result isshown in Table. 3.5. Matrix (P) is obtained with respect to the goal or the root inthe defined context tree. Next, we find the comparison matrix compared to different

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Table 3.3: Example for calculating attribute comparison matrix

P =

P1 P2 P3 P4 sum percentage

P1 3/8 3/8 3/8 3/8 12/8 37.5 %

P2 1/8 1/8 1/8 1/8 4/8 12.5 %

P3 1/8 1/8 1/8 1/8 4/8 12.5 %

P4 3/8 3/8 3/8 3/8 12/8 37.5 %

properties or attributes separately. Here, we do it for property-1 (P1). Then, find thecomparison matrix of options with respect to all properties and goals. Assignment ofscores supposes to be done by human users those who have a good understanding ofthe system and final goal.

Table 3.4: Example for calculating option comparison matrix

O =

P1 O1 O2 O3 sum percentage

O1 5/11 5/11 5/11 15/11 45.45 %

O2 3/11 3/11 3/11 9/11 27.27 %

O3 3/11 3/11 3/11 9/11 27.27 %

Here, the matrix (O) represents options against their property P1. Next, we willfind the composite matrix which expresses the best suit option/ property or attributerespect to the goal. Finally, find the O1 : O2 : O3 ratio as 1.82: 1.71: 1. Thisexpresses impact of option 1 is 1.82 times beneficial than option 3 relative to property 1.This methodology allows making decision on context information easily with minimumnumber of calculations. Moreover, it simplifies the hierarchical tree.

Table 3.5: Calculation of composite matrix using attribute and option comparison matrix

P1 P2 P3 P4 composite weight

W 37.50 % 12.50 % 12.50 % 37.50 %

O1 45.45 % 21.10 % 33.30 % 43.60 % 40.19 %

O2 27.27 % 52.30 % 35.60 % 44.10 % 37.76 %

O3 27.27 % 26.60 % 31.10 % 12.30 % 22.05 %

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Our proposed scheme supports Context Reasoner by suggesting a technique forcontext evaluation when decision gets more complicated with bulk of information.When we compare this scheme with ontology based scheme, we find that proposedscheme is simpler. Further, it brings complex problem in to small sub problems andsolves them individually to obtain the final result. This scheme performs with both sta-tic and dynamic data since it involves mathematical evaluation on context informationfrequently in the context reasoner.

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3.5 Conclusion

A context reasoning scheme for home environment is presented in this chapter whenmultiple options are available at the same time. The motivation of developing thismodel is based on AHP where relative rating is used instead of actual rating to makethe final decision. Thus, unlike in ontology based context reasoning, a simple matrixcalculation over the context information is used to reduce the load on the contextreasoner.

In addition to that, in this system model a user preference criteria is presented byweighting their level of preference. Moreover, the proposed algorithm can be appliedover the entire domain and remove less impact criteria by simplifying the context treeto reduce the complexity. Hence the processing delay can be minimized. Further,problems are scaled in to small sub-problems to simplify the complex problems.

The scheme introduced here, can be used in context aware applications with atrend that has a positive influence on adoption of operator services in the nearby future.Delivering privacy information or user preferences over common network is preserveddue to the nature of AHP used in context reasoner. This makes communication moresecure in this approach.

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CHAPTER 4

PERFORMANCE IMPROVEMENT IN MULTIMEDIA SERVICES

4.1 Introduction

In this chapter two analytic models are developed to measure the response time andthe blocking probability in multimedia service environment. Service delay is consideredto be an important criterion for the performance evaluation in multimedia services.Minimizing the delay is a challenging issue when networks are spread in large scale.Thus, the response time in both web and multimedia services could be reduced bymeans of proxy servers. Therefore, in this study hierarchical proxy architecture isproposed for multimedia services. This model could be used to analyze the impact ofthe proxy deployment in networks.

The response time is an important parameter in multimedia services. The qualityof service (QoS) in multimedia services is significantly affected by the delay. In thismanner, the response time could be reduced while introducing proxy servers in thecore network for minimizing the bandwidth requirements (Slothouber, 1996). Theutilization of the total bandwidth in the backbone is considered to be a challengingissue in designing content delivery systems related to the real time video streaming(Zhang et al., 2000). Multimedia services consume high bandwidth and are sensitiveto response time and jitter (Bose and Cheng, 2000).

Further, staging is a technique proposed to better exploit temporal locality of re-quests made by clients (Cheuk and Lun, 2004). The existing architectures minimize thebandwidth consumption by locating the proxy servers at the boundary of the core net-work(Slothouber, 1996). Hence, caching and streaming of continuous media contentswith proxy servers generates new research interests due to high degree of interactivityexpected by the clients and the real time constraints imposed by continuous mediatraffic (Reisslein et al., 2002).

A new model is proposed to observe the blocking probability for changing parame-ters in the network. The blocking probability increases with the number of active usersin the system (Khamis, 2004). Thus, the blocking probability could be reduced byadding more resources to the system. However, the challenge is to decide the adequateamount of resources to fulfill the requirements.

The section 4.2 presents proxy architecture and the cache arrangement. The modelto measure the response time is presented in the section 4.3. The Seamless servicecontinuity over multimedia services is presented in the section 4.4. The proposedproxy architecture, existing models, the proposed model and the simulation results arediscussed under section 4.3. An analytic model for proxy handover and the analyticalresults obtained with the model are presented in the section 4.4 . Finally, the conclusionis presented in the section 4.5.

4.2 Proxy Architecture and Cache Arrangement

Let us consider a situation where a client requests some content. In a proxy basednetwork, service request reaches proxy server before it reaches the head end. Proxy

38

server searches the requested content within itself and delivers to the client if it isavailable. Otherwise service request is forwarded to the head end. Proxy cache thecontent before it is delivered to client. When another client request the same content,proxy server locally replies with the previous cache (Bose and Cheng, 2000).

The path between server and client is divided in to two sections as server-proxyand proxy-client path. In multimedia services, proxy servers cache a portion or wholecontent to reduce initial response time. Then client downloads only the remainingcontent from head end. Idea behind this is to use the inexpensive bandwidth of localnetwork while delivering remaining content under constant bit rate (CBR) over thecore network (server-proxy path) with the assistance of proxy servers.

Since there are two data sources head end and proxy server, streams must besynchronized either at proxy server or client side. The buffer at client or proxy serversmoothen the stream. Mostly, proxy server synchronizes the both streams together.Basically, proxy server is a temporary staging place and it allows clients to adjust theirbandwidth and buffer requirements to fit the proxy server and network parameters.However, this consumes more resources in proxy server and it results to reduce numberof simultaneous the clients (Begen et al., 2006). Now, we will analyze how stagingis done over a single video content. In real time content delivery CBR is always setbelow the network bandwidth. Fig. 4.1 shows the client request procedure. There

Figure 4.1: Service request procedure. Transmission path is divided in to two corenetwork path and access network path. Proxy server supports to achieveCBR type of transmission over core network

are couples of algorithms used to organize frames between head end and proxy server.The staging technique decides which frame to be stored in proxy server. In client sidebuffers must be allocated not to overflow or underflow, since it can result delay or jitterduring playback (Ma and Du, 2002). There are few important concerns in staging likefacilitating the given CBR, client buffer size and delivering content. This decision canbe differ according to the media format, number of frames, average frame size andrate. Caching few initial frames with proxy server is a good approach to reduce initialresponse time. Proxy server temporarily stag frames from head end and smoothen itsince it has more capacity in buffer than clients (Almeroth and Ammar, 1996). In

39

addition, there are techniques like on-line transcoding to make the content favorableto clients. In our discussion we consider hierarchical proxy architecture where contentis cached in different tiers of hierarchy.

Proxy servers could be deployed randomly or hierarchical manner. Operatorsestablish proxy servers where more clients are connected to the network. This canreduce the traffic load in core network by fetching some contents from the proxy servers(Inoue1 and Hasegawa, 2004). Proxy server always keeps a copy of content which isdelivered to clients. When another client requests same content, proxy server replieswith the content in cache.

Larger the storage in proxy server can store more content and it improves the hitrate. However, it is a trade-off between cost and capacity. Because, more capacityadded to the system will cost more in deployment resulting longer recovery time inoperators point of view. The staging algorithm considers two important facts, videocharacteristics and video access profile (Slothouber, 1996). It is a crucial fact to decidethe amount of data to be stored in proxy server. This is a problem of minimizing totalbandwidth requirement in backbone. It is said multiple video staging design problem.Fig. 4.2 shows hierarchical proxy architecture where proxy servers are arranged in todistinct tiers.

Figure 4.2: Hierarchical proxy architecture. Considered architecture has a hierarchicalcaching system where contents are stored in different tiers

When client requests the content, application will try to find the content from thenearest proxy server to minimize the response time.

4.3 Mathematical Analysis of Response Time

4.3.1 Proposed Proxy Architecture

Our study focuses on hierarchical proxy architecture where content is stored in a hi-erarchical cache system. Proxy servers are organized in tiers. Service requests areinitially reached to the lowest tier in hierarchy. If the content is not found there, it isforwarded to the next tier. This manner it will forward until head end if the contentwas not stored with proxy servers (Berczes and Sztrik, 2006). Our analysis targets ontwo different proxy architectures.

• Firstly, we consider a situation where proxy servers aware of the contents cachedwith peers.

40

• Then, we consider situation where proxy servers aware of the contents cachedwith others in hierarchy.

• Finally, we consider the localization of proxy to measure the service responsetime. The transmission delay is considered for response time measurement.

Figure 4.3: Target proxy architectures for response time measurement

The Fig. 4.3 presents the target proxy architectures for evaluation. We analyzeinitial response time in both cases and compare with a scenario where no proxy serversdeployed in the network. When the hit rate is higher clients have a good chance offinding desired content in the proxy server. Adding more storage will increase proxyhit rate. Designers use larger buffers or compressed media formats to improve the hitrate. We analyze performance while fetching content from different tiers in hierarchy.When proxy servers are established close to client, they experience less response timewhile clients away from proxies experience more response time. On the other hand,this gives rise to a trade-off between network cost and the service quality.

4.3.2 Existing Models in Performance Evaluation

The proposed architecture by (Berczes and Sztrik, 2006) introduces a novel scheme forproxy performance evaluation in multimedia services. This scheme is developed for asystem with single proxy server established in the network. Response is the time takento receive a response from the time the request was made. This is a combination ofsystem delays alone the request flow path. Basically, this was introduced as combina-tion of three elements.

• Time taken to establish a TCP connection with proxy server

• Time taken to deliver the content if it was found with the proxy

41

• Time taken to deliver the content if the content was not found with the proxy

Meantime this considers the proxy hit rate and miss rate in order to measure meanresponse time. Therefore the mean response time,

• Mean response time = Connection establishment time + (hit rate* time to delivercontent from proxy)+(miss rate * time to deliver content from head end)

Moreover, (Nikolov, 2009) propose another scheme for performance evaluation for webcache proxy servers. The Fig. 4.4 presents the proposed scheme by (Nikolov, 2009).Here, λ is the request arrival rate, pxc is the blocking probability at proxy, Pb is the

Figure 4.4: Model defined to measure the service response time (Nikolov, 2009)

blocking probability at server and Λ is the external arrival rate at head end. In thismodel there are four queues and two delay elements. If the requested document isfound with the cache λ1 = λ ∗ hit else λ1 = λ ∗miss. If the file size exceeds the serverbuffer, then process it again and retransmit with a probability Pxc which is equivalentto (File size)/(proxy output buffer). The parameter q is the transmission probabilityof the head end where, q= (File size)/(head end output buffer). With this model theymeasure the user coherence on user preserved performances due to proxy server.

4.3.3 Proposed Model for Response Time Measurement

We have modeled the system architecture as shown in Fig. 4.5. And the hierarchicalproxy architecture is represented with a loop towards proxy server. This represents asituation where content is searched until it finds in proxy hierarchy. We define λa to beinternal arrival rate of proxy server. And ηa be the external arrival rate of head end.The requests to proxy server are served with a probability of pa and it is forwarded tonext tier with a probability (1-pa) if the content is not found in the previous tier. Andpb is the blocking probability of head end. This model exhibits the features of,

• Change in network bandwidth is taken in to consideration in the simulation (corenetwork to client networks)

• Localization of proxy servers are considered to evaluate the mean response time

42

• Architecture 1 is defined such that proxy knows the contents stored with peerproxies

• Architecture 2 is defined such that proxy knows the contents stored with allnetwork proxies

Figure 4.5: Model for performance evaluation of hierarchical proxy architecture. Forthis model, content is cached to different tiers with respect to their pop-ularity, size and many other factors. (The dotted line shows the requestsmissed by proxy server)

The Fig. 4.5 introduces another set of notations used in analysis. The associatedblock diagram is shown in Fig. A.7. We assume that arrival follows a Poisson processwith a rate λa. And hit rate is defined as pa. System model represents the proxy cachewith a M/M/1 queuing system.

λa = λf + λa,6 (4.1)

λa,1 = (1 − pa)n−1.pa.λa (4.2)

λa,2 = (1 − pa.(1 − pa)n−1)λa (4.3)

λa,3 is the total arrival rate where, λa,3 = λa,2 + ηa and ηa is the external arrivalrate. The parameter n is the tier index where highest index assigned to lowest tier.The traffic λa,3 experience a channel establishment delay. This is the time requiredto establish a TCP connection with the head end which is denoted by Is (Slothouber,1996; Bose and Cheng, 2000).

43

Performance of the head end depends on its buffer size Bs, static server time tsand dynamic server time Rs (Cheuk and Lun, 2004). We can find the service demandto head end as Bs/(F ∗(ts +Bs/Rs)). Where, F is the file size of the requested content.pb is the blocking probability of head end. Head end represents with a M/M/1/Kqueue of single server and a buffer of capacity K. Referring to queuing theories we findblocking probability in terms of ρ and K where ρ = λa,3/(Servicedemand) .

pb = (1 − ρ) ∗ ρk/(1 − ρk+1) (4.4)

If we going to apply drop at the server we can introduce the parameter λe4. Where

λa,4 = (1 − pb).λa,3 (4.5)

while λa,3 = λa,2 + ηa. When the requested content is large, it cannot be delivered at asingle transfer. So that it will repeatedly transfer the remaining content till end of thefile. Depending on the value of q, proxy server decides whether to go on transmissionloop or not. Where q is defined such that q = min(1, Bs/F ) and λa,4 = q ∗ λa,5.

Architecture 2 is defined assuming proxy servers know the contents stored with allother proxies in the network. Architecture 2 comparatively takes more time than thearchitecture 1 during the search since it has records of all proxy servers in the network.However, architecture 2 will find the location of requested content at the first search.Here we define the hitting probability to be n ∗ pa. So, λa,1 and λa,2 are defined as

λa,max = min

{

1

I p,1

I s,

qBsRs

F (tsRs + Bs),

qBpRp

F (tpRp + Bp)

}

, (4.6)

Fmax = min

{

qBsRs

λa(tsRs + Bs),

qBpRp

λa(tpRp + Bp)

}

, (4.7)

where λa,6 is defined such that λa,6 = (1 − pb) ∗ λa,2. We measure the responsetime of architecture 1 with (4.8).

∆1 =

1Bp

F∗(tp+Bp

Rp)− λa,1

+F

BWp

+D

n

(4.8)

∆2 =

11Is− λa,3

+1

Bs

F∗(ts+BsRs

)− λa,4

q

+1

BWs

+1

Bp

F∗(tp+Bp

Rp)− λa,6

+F

BWp

+ D

(4.9)

Tp =n

1Ip− λa

+ pa(1 − pa)(n−1) ∗ ∆1 + (1 − pa(1 − pa)

n−1) ∗ ∆2 (4.10)

This 4.10 consists of three terms where first term gives the search time of theproxy server with two parameters; static time of the proxy and the arrival rate. Secondterm represents the response time if requested content is found in proxy server with aprobability (1−pa)

(n−1)∗pa. The first term of second term defines waiting time at proxyserver while next term defines the time to deliver content over client network. The last

44

term of the second term represents delivery time of content over operator network. Andthird term represents response time if the content is not found in proxy servers. Thenthe request will reach head end and download the content from there. First term of thethird term defines the search time of head end and next term represents waiting time.The third term of this term is the time to deliver the content over service network andnext term is the waiting time for dropped requests at the head end.

Tp =n

1Ip− λa

+ npa∆1 + (1 − npa)∆2 (4.11)

In architecture 2, we slightly modify 4.10 and come up with 4.11. Since, proxy serversaware of the content cached with other proxies there are no redundant searches likein architecture 1. This reduces the complexity of the searching algorithm. In otherhand, this architecture demands all proxy servers in the network to multicast theircontent list periodically or in an occurrence of an event. Because of this, large networkwith many servers adds considerable amount of traffic in to network as a result of thisconversation.

45

4.3.4 Simulation Results Obtained with Proposed Model

In this section, we analyze response time for two defined architectures and comparewith an ideal case where no proxies established in the network. We analyze performancein terms of response time for both web and multimedia traffic. Firstly, we evaluate theresponse time for multimedia type of data. Normally, multimedia traffic is produced bythe applications like VoD, time shift TV, IPTV, distance learning and online gaming.Multimedia traffic has a bursty nature where data is received in bulk. In the next step,we evaluate performances for web-based (e.g., http) traffic. Table.4.1 shows a list ofparameters used for numerical analysis.

Table 4.1: List of parameters bring used in the simulation

Notation Value Description

λa 10-110 Arrival rate on tier n proxy server

pa 0.2 Hit rate of the proxy server

Ip 0.004 Lookup time for proxy server (in second)

Is 0.004 Lookup time for head end (in second)

F 5000/2000 Average requested file size (in byte)

BWs 1544 ∗ 103 Operator network bandwidth (in bit/second)

BWp 128 ∗ 103 Client network bandwidth (in bit/second)

Rs 1250 ∗ 106 Dynamic server time of server (in byte/second)

Rp 1250 ∗ 106 Dynamic server time of client (in byte/second)

Bs 2000 Buffer size of head end (in byte)

Bp 2000 Buffer size of proxy server (in byte)

ts 0.000016 Static service time for head end (in seconds)

tp 0.000016 Static service time for proxy server (in seconds)

K 100 Buffer size of head end (in requests)

D 0.001 Time taken to reach head end/transmission delay (in seconds)

In Fig. 4.6, we analyze the response time with arrival rate for no proxy archi-tecture and single tier architecture. In (Berczes and Sztrik, 2006) present the sameanalytic explanation for no proxy architecture which we use to compare the proposedarchitectures. We notice the single tire architecture indicates the least response timecompared to no proxy architecture. When arrival rate is higher, no proxy architec-ture increases the response time exponentially. But, the other architectures dont show

46

significant increment in response time with arrival rate. On the other hand, all archi-tectures exhibit nearly the same response times for low arrival rates. When there islarge number of clients, graph indicates significant change in response time betweenno proxy architecture and single tier architecture. The result of architecture 1 and 2lie on one another since it doesnt show any architectural change for n=1. This impliesthe fact that adding proxy server will not significantly help to reduce response timewhen arrival rate is low.

10 20 30 40 50 60 70 80 90 100 1100.335

0.34

0.345

0.35

0.355

0.36

0.365

Arrival Rate (/S)

Res

pons

e T

ime

(S)

Architecture 1,(n=1)Architecture 2,(n=1)No Proxy

Figure 4.6: Response time vs. arrival rate for architecture 1, architecture 2 and noproxy architecture. Here we consider multimedia type of traffic while lim-iting hierarchy to single tier (n=1)

The Fig. 4.7 shows network performance when n=2. It presents four differentcases; no proxy architecture, architecture 1 and architecture 2 while fetching from tiersone and two. Our analysis shows architecture 2 indicates the best response time whilefetching content from tier one. Both graphs of architecture 2 lie quite close to eachother. Apparently, architecture 1 takes more time to respond than architecture 2.For no proxy architecture, response time is always higher than the other architectures.When we increase the length of the hierarchy, proxy servers locate more close to clients.This can minimize the response time.

In the Fig. 4.8, we add another tier in to the proxy hierarchy. Now the proxyservers are located more close to clients. Therefore, clients receive responses quickerthan the previously discussed scenarios. Architecture 2 performs comparatively bet-ter compared to other architectures. Unlike architecture 1, distributed content list inarchitecture 2 mitigates jumping between tiers finding requested content. We analyzeresponse times for no proxy architecture, architecture 1 and architecture 2 while fetch-ing the content from first, second and third tier in hierarchy. In hierarchical proxy

47

architecture, architecture 2 always performs better with distributed content list eventhough it consumes more bandwidth for multicasting the content lists over the network.

10 20 30 40 50 60 70 80 90 100 1100.33

0.335

0.34

0.345

0.35

0.355

0.36

Arrival Rate (/S)

Res

pons

e T

ime

(S)

Architecture 2 − tier

1,(n=2)

Architecture 2 − tier2,(n=2)

Architecture 1,(n=2)No proxy

Figure 4.7: Response time vs. arrival rate for architecture 1, architecture 2 and noproxy architecture. Here we consider multimedia type of traffic while lim-iting hierarchy to two tiers (n=2)

The Fig. 4.9 presents best response time for all n (1 ≤ n ≤ 3). It shows thebehavior of architecture 2 when content is fetched from first, second and third tier.For multimedia traffic, we observe the architecture 2 corresponds to best response timefor all n. In other words, it implies the fact that locating proxy servers close to clientsimproves the service quality in multimedia services like IPTV, VoD and time shift TV.

Unlike multimedia frames, http frames are smaller in size. Next, we observe howdefined architectures behave with web based traffic. The graphs in Fig. 4.10 indicatethat no proxy architectures perform better than other architectures when arrival rate isnearly less than 55. Compared to multimedia services, our results indicate low responsetime for web services. This explicates that web services have better response time evenwithout a proxy server when λa is low. In Fig. 4.10, architecture 1 and 2 coincide eachother since there is no any architectural difference for n=1.

The graphs in Fig. 4.11 have almost similar pattern like in Fig. 4.7. Since webframes are smaller compared to multimedia frames, response time for web traffic alwaysbetter than multimedia services. Architecture 2 gives the best response time for n=2.It gives the least response time when content is found in tier 1.

Fig. 4.12 presents response time for n=3. In the hierarchy, the bottom most tier(tier 3) is the one which is closest to the clients. Therefore, when the content foundin tier 1 it gives the least response time and guarantees the best quality of experience(QoE).

48

10 20 30 40 50 60 70 80 90 100 1100.32

0.325

0.33

0.335

0.34

0.345

0.35

0.355

0.36

Arrival Rate (/S)

Res

pons

e T

ime

(S)

Architecture 2 − tier

1,(n=3)

Architecture 2 − tier2,(n=3)

Architecture 2 − tier3,(n=3)

Architectire 1,(n=3)No Proxy

Figure 4.8: Response time vs. arrival rate for architecture 1, architecture 2 and noproxy architecture. Here we consider multimedia type of traffic while lim-iting hierarchy to three tiers (n=3)

Finally, in Fig. 4.13 we compare least response time of web traffic for differentproxy architectures and different n. Here we find that architecture 2 with (n=3) per-forms the best. If we further increase the number of tiers in hierarchy, response timecould be further improved. Since, operators suppose to optimize their cost; they haveto decide the architecture which satisfies the expected QoS.

49

10 20 30 40 50 60 70 80 90 100 1100.32

0.325

0.33

0.335

0.34

0.345

0.35

0.355

0.36

Arrival Rate (/S)

Res

pons

e T

ime

(S)

Architecture 2 − tier

1,(n=1)

Architecture 2 − tier2,(n=2)

Architecture 2 − tier3,(n=3)

No Proxy

Figure 4.9: A comparison of lowest Response time towards Arrival rate for differentn. Architecture 2 gives the best response time in all cases for multimediabased traffic

10 20 30 40 50 60 70 80 90 100 1100.14

0.142

0.144

0.146

0.148

0.15

0.152

0.154

0.156

0.158

0.16

Arrival Rate (/S)

Res

pons

e T

ime

(S)

Architecture 1,(n=1)Architecture 2,(n=1)No Proxy

Figure 4.10: Response time vs. arrival rate for architecture 1, architecture 2 and noproxy architecture. Here we consider web based traffic while limitinghierarchy to single proxy level (n=1)

50

10 20 30 40 50 60 70 80 90 100 1100.135

0.14

0.145

0.15

0.155

0.16

Arrival Rate (/S)

Res

pons

e T

ime

(S)

Architecture 2 − tier

2,(n=2)

Architecture 2 − tier1,(n=2)

Architecture 1,(n=2)No Proxy

Figure 4.11: Response time vs. arrival rate for architecture 1, architecture 2 and noproxy architecture. Here we consider web based traffic while limitinghierarchy to two tiers (n=2)

10 20 30 40 50 60 70 80 90 100 1100.13

0.135

0.14

0.145

0.15

0.155

0.16

Arrival Rate (S)

Res

pons

e T

ime

(/S

)

Architecture 2 − tier

1,(n=3)

Architecture 2 − tier2,(n=3)

Architecture 2 − tier3,(n=3)

Architecture 1,(n=3)No Proxy

Figure 4.12: Response time vs. arrival rate for architecture 1, architecture 2 and noproxy architecture. Here we consider web based traffic while limitinghierarchy to two proxy tier (n=3)

51

10 20 30 40 50 60 70 80 90 100 1100.13

0.135

0.14

0.145

0.15

0.155

0.16

Arrival rate (/S)

Res

pons

e T

ime

(S)

Architecture 2 − tier

1,(n=1)

Architecture 2 − tier2,(n=2)

Architecture 2 − tier3,(n=3)

No Proxy

Figure 4.13: A comparison of least response time towards arrival rate for different n.Architecture 2 gives the best response time in all cases for web basedtraffic

52

4.4 Seamless service Continuity in Multimedia Service

In terms of service continuity call handover is an important consideration. The studyin (Khamis, 2004) presents analytic model for hierarchical networks to evaluate serverperformances in terms of call blocking and handover failure. In this section, we proposean analytic scheme to measure call blocking. In this analytic evaluation we use set ofparameters like; proxy server capacity, arrival call rate, handover call rate etc. TheFig. 4.14 explains the environment we consider (Khamis, 2004). For the evaluationpurpose we define a new mathematical model relates to call blocking probability.

Figure 4.14: Target environment for service handover for mobile multimedia serviceusers

4.4.1 Analytical Model for Proxy Handover

With the parameters given in the Table.4.2, evaluation of blocking probability wasdone for the defined model. In the probability calculation we have assumed

• Parameter selection was done for multimedia services like IPTV and VoD,

• Calls are generated from both stationary and mobile users,

• Crossing cell boundary result to call handover from one cell to another.

When user moves from one coverage area to another, he will release from the connectedserver and connect to another server which is more close to the subscriber. Thistransition could be done only if the new server has the capacity to employ anotheruser. Otherwise, he may get disconnected from the network. In terms of multimediaservice, call dropping significantly degrades the service quality and the user satisfaction.

This work measures blocking probability against total number of channels in theproxy server. Further, analysis includes the behavior of changing blocking probabilitytowards total mean call arrival rate and the number of exclusively reserved channelsin server. Assuming there are N channels in the server, we observe new incoming callsget blocked when N − Nh channels are already occupied. But, still the handover callscan reach the server since another Nh exclusively reserved channels available with theserver. If more calls reach the server, they will get dropped since no channel existswith server to employ them. But, if the server has a buffer of length L, then totalN + L users could be occupied in the system. In this model we assume that queue

53

Table 4.2: Parameters used in the analytical model for evaluating call blocking prob-ability

Notation Value Description

λs 100 Mean new call arrival rate by stationary users

λm 10 Mean new call arrival rate by mobile users

λh 10 Mean handover call rate

N 5 Total number of channels in proxy server

Nh 0.1N Exclusively reserved channels for handover

µc 5 Call completion rate

µv 1 Handover departure rate for vehicular calls

µq 1 Rate at which being in an overlapping area

L 5 Finite queue length

is dedicated for handover users only. This analytic model follows general birth-deathprocess (Khamis, 2004).

The in the target environment we assume three kind of users; stationary users,mobile users and vehicular user. The generated new call rate by each user is shown inFig. 4.15.

The mean total call arrival rate is a combination of three components, mean callrate generated by stationary users (λs), mean call rate generated by mobile users (λm)who reside in the cell and the mean handover call rate (λh). In the analytic modelstationary and mobile users have average call completion time 1/µc and vehicular usershave a average call completion time of 1/µv.

λt = λs + λm + λh (4.12)

The parameter λt is the total mean call arrival rate.

Pdrop = Ploss + (1 − Ploss)PfhA (4.13)

We introduce new parameters Ploss, PfhA and Pdrop (Khamis, 2004). Parameter Ploss

presents the probability of not having a free position in the queue when a handover callarrives. Whereas PfhA is the handover request failure given that handover attemptsfind space in the queue. Therefore, the probability of the handover request failure canbe measured with 4.13. Parameter T c is the average call duration for any user.

T c =1

µc

(4.14)

54

Figure 4.15: Call arrival model

PfhA =L

∑

k=0

Pj+k[1 −NµH

NµH + µq

k∏

i=1

1 − (1

2)i µq

NµH + µq

] (4.15)

T v =1

µv

(4.16)

Let T v be the mean sojourn time where a vehicular user resides in a serving cell. Itdepends on the speed of the vehicle. The parameter l considered to be the length ofperimeter of the cell while Vv is the speed of vehicular user and S is the call area.

µv =lVv

πS(4.17)

The parameter µv is the handover departure rate per vehicular call and Tq is the averagetime duration MS resides in an overlap area.

T q =1

µq

(4.18)

In the proposed model we assume two traffic streams where one stream is from 0-(N −Nh) and the other stream (N −Nh + 1) to N . Hence, the probability that servedcall is due to the first stream (γ1) is found with (4.19). In addition to that γ2 gives theprobability a call is served due to second stream. λc is the carried call rate to server.

γ1 =λs(1 − PBA)

λc

(4.19)

γ2 =[λm(1 − PBA) + λh(1 − Pdrop)]

λc

(4.20)

2∑

i=1

γi = 1 (4.21)

Let TH be the channel holding time for a randomly chosen call served by BS. Thus,the selected user can be a stationary user, mobile user or a vehicular user. Therefore,

55

the rate at which call departs from the BS is introduced as µH .

TH =1

µH

=γ1

µc

+γ2

µc + µv

(4.22)

The blocking probability of stationary and mobile users denoted as PBA. Further, Phv

represents the probability that a call in progress due to a vehicular user, will require ahandover before completion. In addition to that Ploss is defined as the probability ofarriving the handover requests while no free spaces in the queue.

λc = (λs + λm)(1 − PBA) + λh(1 − Pdrop) (4.23)

λh = λs

{

(1 − PBA)Phv

1 − (1 − Pdrop)Phv

}

(4.24)

PBA =N+L∑

j=N−Nh

Pj (4.25)

Ploss = Pj=N+L (4.26)

Figure 4.16: Queuing model for proposed system

The probability of being j users in the cell is denoted by Pj (in service and waiting).The block diagram for the model is shown in Fig. A.7.

P (j) =

λjt

j!µjH

P0 , 1 <= j <= (N − Nh)

λ(N−Nh)t λ

j−(N−Nh)

h

j!µjH

P0 , N − Nh + 1 <= j <= N

λ(N−Nh)t λ

Nhh

λ(j−N)h

N !µNH

Πj−N

k=1 (NµH+kµq)P0 , N + 1 <= j <= N + L

(4.27)

The Fig. 4.16 presents the call generation procedure assuming general birth deathprocess. Further, the evaluation model is shown in the Fig. A.8.

4.4.2 Analytical Results Obtained with Defined Model

When number of channels increase in the server, the resources could be further allocatedto new users resulting to accept more incoming calls. Subscribers who initiate callsand move from one cell to another resulting to accept or reject their requests dependson the queue length and the channel capacity in the server. The Fig. 4.17 presents

56

reduction of blocking probability due to increase in total number of channels availablein the server.

5 10 15 20 25 30 35 4010

−3

10−2

10−1

100

The number of total channels in a proxy server

Blo

ckin

g pr

obab

ility

of n

ew c

hann

els

Figure 4.17: Blocking probability Vs number of total channels in proxy server

The Fig. 4.18 shows the behavior of blocking probability towards total numberof channels in the server for different λt. Here, we notice the general behavior where,high mean total call rate results to increase the blocking probability.

Next, we investigate the effect of reserved channels for handover calls against theblocking probability and the total number of channels in the server. When number ofreserved channels for handover users increase, there is a higher probability that usersmoving from once cell to another can be served. The Fig. 4.19 shows this behavior forchanging Nh as a scaling factor of N . Further, we notice that higher the scaling factorresults to increase the blocking probability of handover calls since it results to reducethe number of channels available for new calls simultaneously.

Next we observe the effect of blocking probability towards λh(handover call rate).Higher the handover call rate demand more exclusively reserved channels for handoverchannels in the server. But, when the system resources are fixed, increased handoverrate results to block the subscribers at the server. The Fig. 4.20 shows this behavior.Moreover, we observe that increase in number of exclusively reserved handover channelsresult to reduce the blocking probability even in high handover call rates.

At last we present the probability of not having a slot in the queue for new calls.In other words, this is the case where all available positions (N + L) in server areoccupied. The Fig. 4.21 presents the probability of not having an empty positionin the queue at the arrival of new calls. We observation high number of exclusively

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5 10 15 20 25 30 35 4010

−3

10−2

10−1

100

The number of total channels in a proxy server

Blo

ckin

g pr

obab

ility

of n

ew c

hann

els

λt = 120

λt = 125

λt = 130

λt = 135

λt = 140

Figure 4.18: Blocking probability Vs number of total channels in proxy server fordifferent λt

reserved channels for handover calls reduce the probability of not having a slot in thequeue for new calls.

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5 10 15 20 25 30 35 4010

−5

10−4

10−3

10−2

10−1

100

The number of total channels in proxy server

Blo

ckin

g pr

obab

ility

of n

ew c

hann

els

Nh = 0.0N

Nh = 0.1N

Nh = 0.2N

Nh = 0.3N

Nh = 0.4N

Figure 4.19: Blocking probability Vs number of total channels in proxy server forchanging number of reserved channels in server side for handover calls

20 40 60 80 100 120 140

10−0.3

10−0.2

10−0.1

Handover call rate (/S)

Blo

ckin

g pr

obab

ility

of n

ew c

hann

els

Nh = 0.0N

Nh = 0.1N

Nh = 0.2N

Nh = 0.3N

Nh = 0.4N

Figure 4.20: Blocking probability Vs λh for changing number of exclusively reservedchannels in the server

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60 70 80 90 100 110 120 130 140 15010

−7

10−6

10−5

10−4

10−3

Handover call rate−λh (/S)

Pro

babi

lity

of n

o po

sitio

n in

que

ue

N

h = 0

Nh = 0.1N

Nh = 0.2N

Nh = 0.3N

Nh = 0.4N

Figure 4.21: Probability of not having a slot in the queue Vs λh for changing numberof exclusively reserved channels in the server

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4.5 Conclusion

In this chapter, analytic models for evaluating the blocking probability and the responsetime are developed. It was found that the response time of user requests could bereduced with proper localization of proxy servers in the network.

The response time for both multimedia and web based traffic is compared withthe proposed model. It was found that, locating proxies close to the clients reducesthe response time significantly. This study could be used by the operators to choosea proxy architecture which satisfies the expected response time or the channel zippingtime. Here the performances of two proxy architectures are measured by means of theresponse time. By comparing two architectures, the architecture 2 performs better,when the proxy knows the content stored in other proxies. Besides that in architecture2, the modified content list will be distributed among all other proxies in each event.Due to this reason, an additional bandwidth is consumed in the network. It was foundthat the response time is shorter when the tier index (n) is higher. It is noteworthy topoint out that, the response time for the data traffic was found quite low compared tothe multimedia traffic even without proxies in-between the head end and the client.

The network performance in terms of blocking probability is presented. Hence, theprobability of fully occupying the queue for changing number of exclusively reservedchannels for handover is measured with the developed analytic model. The incrementof total call arrival rate results to increase the blocking probability. Thus, the incrementin exclusively reserved number of channels increases the blocking probability since itlimits the number of available channels for new call arrivals.

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CHAPTER 5

SECURE CONTEXT GATHERING FOR CONTEXT REASONING

5.1 Introduction

This chapter proposes a scheme to protect sensor nodes from the abnormal behaviorsof malicious nodes. The scheme proposed here is based on power and rate anomalydetection. The detection probability is compared with respect to the Gauss-Markovmobility model and the Random-Waypoint mobility model. The results obtained withthe Random-Waypoint are considered to be the ideal cases.

The intrusion detection algorithm for power and rate anomaly detection is basedon log normal shadowing and the Poisson process packet generation (Ilker and Ali,2005). The scheme proposed in (Junior et al., 2004), relies on anomaly detection viasignal strength and geographic information. The expected signal strength could becalculated with geographical information and the predefined transceiver specifications.Malicious nodes which disseminate information could be detected with this method.The watchdog approach is another interesting model proposed in (Ioannis et al., 2007)where nodes keep track of the events happening around and generate an alarm whenthey detect an intrusion.

The proposed algorithm supports dynamic power adaptation due to environmen-tal uncertainties. Implementation of both prevention and detection mechanisms aredone in node level and base stations level. The effect of mobility pattern over theproposed anomaly detection scheme is analyzed next. The nodes which transmit atan abnormally high power level or high data rate could be detected with the proposedarchitecture.

In the section 5.2, an extension to the study in (Ilker and Ali, 2005) is presentedby addressing some issues in that model . The section 2.8 presents improvement overthe previous model for power and rate anomaly detection and the simulation resultsfor the proposed scheme. Finally, the conclusion is given in section 5.3.

5.2 Baseline Approach to Anomaly Detection

5.2.1 System Model for Baseline Approach

Compared to other networks, wireless sensor networks have many constrains in termsof communication due to channel conditions and nodal operations. Due to this reason,we can’t use general security mechanisms to protect the network against intrudersand other threats. Therefore, it is a demanding research area to investigate suitablesecurity mechanisms to protect the sensor networks from the anomalies in an unsecuredenvironment.

Unlike most of the other communication devices, wireless sensor nodes have lowmemory and low computational power due to the optimized manufacturing cost. There-fore, they are more reluctant to the attacks and general threats. This implies theimportance of intrusion detection in WSNs. And at the same time intrusion detec-tion algorithms must be light weighted due to low processing power of the sensor

62

nodes. There are many intrusion detection algorithms introduced for WSNs, but stillresearchers are trying to develop more powerful and light weight algorithms to supportfusion detection in WSNs.

The study in (Onat and Miri, 2005) suggests an algorithm for intrusion detectionwhich relates to sliding window protocol. The intruder intentionally transmits at ahigher power level disturbing the nodal communication even beyond the defined trans-mission range. The algorithm suggested by (Onat and Miri, 2005), for power anomalydetection triggers an intrusion alarm when it detects K number of consecutive packettransfer above the threshold power level. If the intruder sends miscellaneous pack-ets randomly, but not consecutively, then it can’t be detected as an intrusion withthe algorithm proposed by (Onat and Miri, 2005). Moreover, this approach uses twobuffers as intrusion buffer and packet buffer. This demands to use more memory inthe operation. In our work we do,

1. Developing new power anomaly detection mechanism addressing failure of thescheme proposed by (Onat and Miri, 2005).

2. Reducing the memory usage in detection mechanism.

3. Introducing a co-operative detection mechanism for power anomaly.

4. Reduce the complexity of existing algorithms.

5. Developing a scheme for detecting network jamming.

Sensor networks are reluctant to many forms of attack models. Still, there is no singlealgorithm which detects all form of attacks over WSNs. That demands the requirementof intrusion detection in wireless sensor networks. In the proposed approach we considerpower anomaly detection and resource unavailability due to malicious nodes. Followingassumptions are considered for the simulation.

1. Assume geographic routing in the network

2. The nodes are assigned with a unique identification (manufacture ID).

3. Maximum received power threshold is fixed.

4. New nodes can be deployed anywhere within the defined boundary.

5. All nodes are expected to transmit at a defined power level.

6. All nodes are synchronized each other.

7. Base stations have both up-link and down-link communication with the neigh-boring nodes.

5.2.2 Modified Detection Algorithm

All the packets received to the sensor nodes are copied to the packet buffer and for-warded according to the routing algorithm. We define K such that it buffers packetsuntil longest delay of transmission. That is the round trip time to the most far endnode located in the considered area. When the buffer is filled with packets, detection

63

rules are applied to figure out power anomalies. If more than K anomalies are foundin the buffer, it is considered as an anomaly. And, it is immediately informed to thebase station to take necessary actions to exclude the malicious nodes who transmit ata abnormal high power level.

The suggested approach is presented in Fig. 5.1 has a single packet buffer. Nodesmonitor received power level. And any transmission above the threshold level is de-tected as an intrusion and reported as a minor alarm. When it detects such N1 alarmsit triggers a major alarm. Then, that alarm is advertised to the neighbors with origi-nated index.

Figure 5.1: Block diagram of power anomaly detection module which detects nodestransmit above defined threshold

Disadvantages:

1. Improper selection of the training data can mislead the detection algorithm.

Advantages:

1. Algorithm does co-operative decision making for anomaly detection.

2. Usage of single buffer ensures minimum storage usage.

3. Intruders who randomly transmit at high power levels with the intension to jamthe network could be detected.

4. Low complexity in algorithm result to save node energy.

When intruder occupies the medium continuously for long after certain numberof transmission attempts, the algorithm detects the existence of an attack due to theresource unavailability in the network. As shown in Fig. 5.2 presents the detectionmodule which monitors the resource occupancy by nodes. If node finds the wirelessmedium is occupied, it will wait for some random time and try to transmit again withtriggering the minor alarm. When it finds certain number of such minor alarms, ittriggers the major alarm and advertises it to the neighbors with its identification. Andthese alarms are reset at fixed time intervals.

64

Figure 5.2: Block diagram of network unavailability detection module which measurestransmission attempts of legitimate nodes

Disadvantages :

1. Algorithm may get complex when the number of nodes increase in the network.

Advantages:

1. Low complexity.

2. Performs better in medium and small scale sensor networks.

The simulation parameters for the detection probability evaluation is presented in theTable. 5.1.

Table 5.1: IDS parameter list used in mathematical simulation

Simulation Parameter Value

Maximum Power Threshold -90 dBm

Transmission Power 5-15 dBm

Directional Movement (S) 1 ms−1

Number of Nodes 1000

Initial Separation (d) 25 m

Close-in Relative Distance (d0) 1 m

Path-Loss Exponent (α) 2.5

In the Appendix A, we have presented some numerical results obtained with pro-posed detection module. For the next step we develop the same concept of power and

65

rate anomaly detection advancing some concepts in this architecture. The next sec-tion presents an extension to this scheme and evaluates detection probability towardsdifferent mobility models Random-Waypoint and Gauss-Markov.

5.2.3 Sensing Model and the Detection Algorithm

Sensing model is quite important concern in the proposed architecture. Power anomalydetection is one of the first line detection approaches in WSN security. Our modelproposes a log normal shadowing effect for power depreciation due to node separation.Here we measure the received power level assuming instantaneous channel properties.Received power is assumed to be independent from packet to packet. The nodes areconsidered to have connectionless transmission where packet routing is instantaneous.Since the received power is inversely proportional to the node separation, a unit powerat a distance d is proportional to (1/d)γ , where γ is the path loss exponent. Wesimulate the channel variation by introducing a zero mean random variable Xσ (indBm) with a standard deviation of σ (also in dB). We define our power loss model asin (5.1)

Ploss(d)[dB] = Ploss(d/d0) + 10 α log(d/d0) + Xσ, (5.1)

Preceived(d)[dBm] = Ptransmit(d/d0)[dBm] − Ploss(d)[dB], (5.2)

where, Ploss(d) is the path loss at a distance d, which is calculated with respectto close-in reference distance d0 (Onat and Miri, 2005). The variable γ is called thepath loss exponent whereas σ is called the shadowing deviation. The received signalstrength of a transmitter at a distance d is calculated with (5.1). It is assumed thatnodes have minimum power threshold to receive a packet correctly. This constrainis used to stop isolated nodes from the network while they move in the field. Inthe defined model nodes have instantaneously changing speed and direction due torandomness. We assume that nodes have a transmission range of rbase. The wholeregion is partitioned into two, the covered region and the uncovered region. If a locationis in radio range we say that location is in the covered region otherwise it is in theuncovered region (Liu et al., 2004). We decide the number of nodes deployed in thearea A with N = A/(πr2

base) where N is the node density. Implementation of WSNsposes many fundamental challenges due to resource constraints and environmentaluncertainties.

The presence of intruders in the network and the effect on other nodes are shownin the Fig. 5.3. It is noteworthy to point out, even when few intruders are present inthe network they can disturb the communication of many other nodes.

The proposed power anomaly detection scheme is shown in Fig. 5.4. It is furtherdescribed below. When the ratio between received power and the average receivedpower (Pij/Paj) exceeds certain threshold t1 intrusion prevention scheme pays specialattention on them assuming that could be a threat for network security. Where Pij

is the received power to node i from the node j and Pai((∑n

j=1 Pij)/n) is the averagereceived power on node i. Then, the intrusion prevention system takes necessary actionsto protect nodes against possible attacks. Here, we refer this process as local detection.The (5.4) express the operation in node i.

[P11/Paj P12/Paj ....... ....... P1(n−1)/Paj P1n/Paj] (5.3)

66

Figure 5.3: Effect of intruders in a wireless sensor network

Nodes periodically send this information to the connected cluster head which is respon-sible for global detection. Cluster heads or base station execute the detection schemeon received data and figure out the malicious nodes.

Once an intrusion is detected, it is immediately informed to the nearby clusterhead or base station. Then, both node and the base station take necessary actionsto excluded intruders from the network. The ability of the proposed architecture toidentify the risk on the network through monitoring relative power level for definedrisk threshold protects network against attacks. The detection algorithm evaluates thepower comparison matrix which measures the signal strength of each connected nodecompared to average power level in the deployed region. Which means the detectionscheme can support dynamic power adaptation due to environment changes. Next, wewill see how the algorithm works. Here, we define elements in the comparison matrix

Cji =∑n

j=1

{

Pij

Pai

}

where Pai =∑n

j=1Pij

nand 1 <= i, j <= n. The Fig. 5.4 presents

the power anomaly detection procedure in the base station.

P11/Pa1 P12/Pa1 .... P1n/Pa1

P21/Pa2 P22/Pa2 .... P2n/Pa2

......

. . ....

Pn1/Pan Pn2/Pan .... Pnn/Pan

• If the summation over a column exceeds the detection threshold, it implies thefact that node j may be an intruder who always transmits at a high data rate.

• If the summation over a row exceeds the prevention threshold, it implies the factthat node i may be targeted by an intruder.

Always nodes communicate with base station uploading the data collected by thesensor nodes. Advantage of this scheme is the flexibility to change the threshold value

67

Figure 5.4: Power anomaly detection procedure in proposed detection architecture

according to the nature of environment nodes being deployed. Especially, the chan-nel property, the environmental condition and the noise level will result to adjust thesensitivity parameter to suit with the deployed background. The records stored withnodes are periodically sent to base station for evaluation. The algorithm shown belowexplains the power anomaly detection procedure in the base station. Initially, the re-ceived power from the neighboring nodes are measured and normalized with the powerreceived from neighboring nodes. Further, the BS simplifies the calculation by convert-ing the power comparison matrix in to a Boolean matrix. The algorithm is executedat the nodes which is required for local detection.

Power Anomaly Detection/Prevention Architecture

1. Foreach node do

2. While End of Buffer = true;3. For j = 1 : all connected nodes;4. Fori = 1 : all connected nodes;5. Cij = Pij/Paj;6. σlocal = Cij;7. if(σlocal > power local threshold) then;8. Cij = 1;9. else;10. Cij = 0;11. end;12. end;13. end;14. end;15 end;16. For i = 1 : all connected nodes;

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17. For j = 1 : all connected nodes;18. if(affected node index > power risk threshold);19. take prevention actions;20. end;21. end;22. end;23. For j = 1 : all connected nodes;24. For i = 1 : all connected nodes;25. if(detection index > power detection threshold);26. multicast resultant node IDs;27. exclude them from network;28. end;29. end;30. end;

Similar to power anomaly detection we can use the same algorithm to detect rateanomalies. But, now the sensitivity parameter must be changed to fit the requirementof rate anomaly detection. Further, in terms of implementation we notice that samearchitecture supports both power and rate anomaly detection. Therefore, manufac-turing of hardware elements could be utilized for minimizing the overall cost budget.

Figure 5.5: Rate anomaly detection procedure in proposed detection architecture

Here, we define elements in comparison matrix Cij =∑n

j=1

{

Rij

Rai

}

where Rai =∑n

j=1Rij

nand 1 <= i, j <= n. The detection scheme consists of two modules in the

nodes and the base stations for local and global detection. Thus, evaluation procedureis shown in the Fig. 5.5. The introduction of data rate matrix helps to isolate thenodes with abnormal high data rates. When nodes receive the packets from neighboringnodes it will keep track on the data rate at which last N packet received. In the matrix,elements alone the row give the ratio between received power level from node j to node

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i over normalized power on that particular node. And in the next step this ratio iscompared with sensitivity parameter. If it is above a certain value, it will generateanother Boolean matrix simplifying the previous data set. Now we have two matrixesincluding the last one which is derived from the first matrix to simplify the scenario.

• If the summation over a column exceeds the detection threshold, it implies thefact that node j may be an intruder who always communicates at a high datarate.

• If the summation over a row exceeds the prevention threshold, it implies the factthat node i may be targeted to an attack by an intruder.

The (5.4) shows the nodal level operation.

[R11/Raj R12/Raj ....... ....... R1(n−1)/Raj R1n/Raj] (5.4)

R11/Ra1 R12/Ra1 .... R1n/Ra1

R21/Ra2 R22/Ra2 .... R2n/Ra2

......

. . ....

Rn1/Ran Rn2/Ran .... Rnn/Ran

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Rate Anomaly Detection/Prevention Architecture

1. Foreach node do

2. While End of Buffer = true;3. For j = 1 : all connected nodes;4. Fori = 1 : all connected nodes;5. Cij = Rij/Raj;6. σlocal = Cij;7. if(σlocal > rate local threshold) then;8. Cij = 1;9. else;10. Cij = 0;11. end;12. end;13. end;14. end;15 end;16. For i = 1 : all connected nodes;17. For j = 1 : all connected nodes;18. if(affected node index > rate risk threshold);19. take prevention actions;20. end;21. end;22. end;23. For j = 1 : all connected nodes;24. For i = 1 : all connected nodes;25. if(detection index > rate detection threshold);26. multicast resultant node IDs;27. exclude them from network;28. end;29. end;30. end;

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5.2.4 Simulation Results of the Proposed Model

The given parameters in Table. 5.2 is used in the simulation model which evaluatedetection probability

Table 5.2: Parameters being used for the simulation purpose

Notation Value Description

N 850 Number of nodes used in the simulation model

A 1km ∗ 1km Area considered for simulation purpose

Pmin −90dBm minimum power threshold

ptransmit 5 − 20dBm Transmit power level

V 1 − 10ms−1 Speed of the mobile nodes

θ 30o Speed of change in angle

α 0.5 Degree of freedom for motion

β 0.5 Degree of freedom for changing angle

r 20m Sensing radius of a node

σ 5dB Standard deviation

d0 1m Close in reference distance

The Fig. 5.6 presents detection probability towards two mobility models on pro-posed detection architecture. We simulate the mobility models; Random-Waypoint andGauss-Markov. Both models perform equally in terms of intrusion detection. Gauss-Markov mobility model is almost similar to the natural random movement of a sensornode. The Fig. 2.10 presents this mobility model which is more close to real the sce-nario. It is well noticed that the detection probability improves when nodes increasethe transmit power level. As a conclusion, the proposed detection architecture equallyperforms for both Random-Waypoint and Gauss-Markov mobility models.

The Fig. 5.7 is generated for Random-Waypoint mobility model for increasingtransmit power level for different speeds of the node. Here, we notice the detectionprobability gradually increase for the higher transmit power levels. Further, we observethat node speed does not hardly affect on detection probability. In the simulation wehave three graphs generated for 1ms−1,5ms−1 and 10ms−1 consequently. Comparedto all three graphs we do not notice a significant change in detection probability fordistinct nodal speeds. But, we notice for higher transmit power levels (18-20)dBm,graphs lies more close to each other implying the fact that, at higher transmit powerlevels detection probability does not significantly changed by the speed. In other ward,irrespective of the speed detection module will detect the anomalies at higher transmitpower level. In the Fig. 5.8 we can closely observe the effect on detection probability.Fig. 5.8 exaggerate the discussed scenario and prove that the speed have some effect

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5 10 15 20

10−0.05

10−0.04

10−0.03

10−0.02

10−0.01

Transmit power level (dBm)

De

tect

ion

Pro

ba

bili

ty

Gauss−Markov mobility modelRandom−Waypoint mobility model

Figure 5.6: Detection probability against transmit power level for two defined mobil-ity models Random-Waypoint and Gauss-Markov keeping other variablesconstant

on detection probability under proposed detection architecture for Random-Waypointmobility model. Further, we notice the increase in nodal speed improve the detectionprobability at any transmit power level.

With Random-Waypoint mobility model we notice that the graphs have slight de-viation from average. But with Gauss-Markov mobility model, we observe the graphsare quite smooth and easy to predict. In Fig. 5.9 we can see that nodes with speed5ms−1 have the best detection probability. Further, graph for speed 10ms−1 lie inbetween other two graphs. Beyond certain speed the detection probability decreases inGauss-Markov mobility model for proposed architecture. This is an impressive obser-vation we notice relate to Gauss-Markov mobility model against detection probability.Moreover, we observe that the speed does not change the detection probability at anytransmit power level for the proposed architecture. This can be observed in Fig. 5.10.Here, we can ignore slight deviations in detection probability towards speed for differentpower levels since they are not significant.

Sensitivity parameter is an important index used for detection procedure in theproposed architecture. Moreover, graphs in Fig. 5.11 and Fig5.12 have the same pat-tern. But, we clearly notice the increase in the value of sensitivity parameter reducesthe detection probability. Not only that, the obtained curves for detection probabil-ity for Gauss-Markov is smoother than the graphs obtained with Random-Waypoint.When we compare the two mobility models we notice the proposed architecture per-forms much better for Gauss-Markov model than the Random-Waypoint. Moreover,tuning sensitivity parameter is required by the detection scheme for better performancein the given proposal.

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5 10 15 20

10−0.05

10−0.04

10−0.03

10−0.02

10−0.01

Transmit Power Level (dBm)

De

tect

ion

Pro

ba

bili

ty

Speed = 1m/sSpeed = 5m/sSpeed = 10m/s

Figure 5.7: Detection probability against transmit power level for Random-Waypointmobility model for changing speed, 1ms−1,5ms−1,10ms−1

5 10 15 20 25 30 35 40 450.88

0.9

0.92

0.94

0.96

0.98

1

Speed (m/s)

De

tect

ion

Pro

ba

bili

ty

Powertx

= 5dB

Powertx

= 10dB

Powertx

= 15dB

Figure 5.8: Detection probability against node speed for Random-Waypoint mobilitymodel for changing transmit power levels, 5dBm, 10dBm, 15dBm

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5 10 15 20

10−0.05

10−0.04

10−0.03

10−0.02

10−0.01

Transmit Power Level (dBm)

De

tect

ion

Pro

ba

bili

ty

Speed = 1 m/sSpeed = 5 m/sSpeed = 10 m/s

Figure 5.9: Detection probability against transmit power level for Gauss-Markov mo-bility model for changing speed, 1ms−1,5ms−1,10ms−1

5 10 15 20 25 30 35 40 450.87

0.88

0.89

0.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

Speed (m/s)

De

tect

ion

Pro

ba

bili

ty

Powertx

= 5dB

Powertx

= 10dB

Powertx

= 15dB

Figure 5.10: Detection probability against node speed for Gauss-Markov mobilitymodel for changing transmit power levels, 5dBm, 10dBm, 15dBm

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6 8 10 12 14 16 180.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

Transmit power level (dBm)

De

tect

ion

Pro

ba

bili

ty

Sensitivity parameter = 1.095Sensitivity parameter = 1.100Sensitivity parameter = 1.105

Figure 5.11: Detection probability against transmit power level for changing sensitiv-ity parameter while keeping all other variables constant for Random-Way-point mobility model

6 8 10 12 14 16 180.86

0.88

0.9

0.92

0.94

0.96

0.98

1

Transmit power level (dBm)

De

tect

ion

Pro

ba

bili

ty

Sensitivity parameter = 1.095Sensitivity parameter = 1.100Sensitivity parameter = 1.105

Figure 5.12: Detection probability against transmit power level for changing sensitiv-ity parameter while keeping all other variables constant for Gauss-Markovmobility model

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5.3 Conclusion

Intrusion detection and prevention in wireless mobile sensor networks based on powerand rate anomaly detection is proposed in this chapter. Quick evaluation of anomaliesdetection in sensor networks can be obtained with the proposed model.

The advantage of this scheme is found as the ability to tune the detection scheme tomatch with the network requirements. Basically, changing the sensitivity parameter wecan increase or decrease the level of sensibility of the detection algorithm. Depending onthe environmental conditions, algorithm could be tuned to improve the performances.Since, the evaluation is based on relative measurements, the results are more reliableand accurate.

According to the proposed scheme, when the detection index reaches the preven-tion threshold it is considered as a risk to the network. Then, necessary preventiontechniques are taken to protect the context sources. Once, it exceeds the detectionthreshold it is considered as an intrusion. In terms of intrusion detection, nodes willgo through two threshold levels following both prevention and detection techniques.

The flexibility over dynamic power adaptation could be considered as anothermajor advantage in the proposed architecture. Performance of the proposed schemefor ideal case and Gauss-Markov mobility model is analyzed to observe how does thedetection probability is changed with the mobility model.

It is observed, that the detection probability in the proposed architecture doesn’tchange with the Gauss-Markov mobility model for different speeds. The major ad-vantage of this scheme is found to be the stability of the detection probability fordifferent nodal speeds. Finally, it is observed the detection probability is higher in theGauss-Markov mobility model than the Random-Waypoint model.

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CHAPTER 6

CONCLUSION AND RECOMMENDATIONS

6.1 Conclusion

In this thesis, technical challenges in context aware multimedia services are considered.Hence, some identified issues are addressed to improve the quality of service in thecontext aware multimedia applications.

Initially, a model is developed to measure the response time of multimedia services.Due to the nature of the AHP, the proposed scheme is more dynamic to the rapidchanging user preferences. The ability to exclude non significant context information isfound as an important feature available with this scheme. The AHP allows prioritizingthe context information according to the user preference. Further, sub-criteria andmultiple objectives can be supported with the proposed scheme. It is identified that,certain information are having less impact than others, therefore AHP illuminates thoseinformation by means of a mathematical evaluation. This scheme can be used withany other context aware applications. Moreover, unlike in semantic or ontology basedcontext reasoning methods, information privacy is certified with the proposed scheme.

The performances of multimedia services are then analyzed in terms of the responsetime and the call blocking probability. An analytical scheme is developed to measurethe response time and the obtained results are compared with an ideal case where noproxy servers established in the network. Hence, by increasing the number of tiers inthe hierarchy, we can reduce the response time significantly. In addition to that, wedefine two proxy architectures to measure the response time and compare it with anideal case. It is noteworthy to point out here that, when there are three tiers in thehierarchy, the architecture 1 reduces the response time by 12ms compared to the idealcase whereas the architecture 2 reduces the response time by 17ms for the multimediatraffic compared to the ideal case. However, for the data traffic the architecture 1reduces the response time by 5ms whereas architecture 2 reduces the response timeby 8ms. Here, we notice that the impact on the multimedia traffic is higher in botharchitectures.

The blocking probability is important in terms of measuring the quality in anykind of network. In addition to that, the blocking probability can be reduced byadding more resources to the network. In this manner, the analytical scheme we havedeveloped, presents the blocking probability for different available system resources.The proposed model can be used to figure out the required resources to control theblocking probability.

Finally, the secure context gathering approach is presented to increase the reliabil-ity of the final decision. The proposed scheme is based on both prevention and detectiontechniques. Prevention is done at the sensor level whereas detection is done by the basestations or the cluster heads. The ability to support dynamic power adaptation makesthis scheme more favorable for the mobile sensor networks. The performance of thedetection scheme is measured by considering the Random-Waypoint as an ideal mo-bility model and it is compared with the Gauss-Markov model. It was observed inthe Gauss-Markov model, the detection probability is almost stable irrespective of thenodal speed and it performs better than the ideal case. At 6dBm transmit power level;

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the detection probability of the Gauss-Markov is around 0.875 whereas the ideal caseis around 0.868. Thus, by changing the sensitivity parameter from 1.105 to 1.095, wenoticed that the detection probability can be improved by 0.064 for the Gauss-Markovand by 0.051 for the ideal case.

Based on these facts, it is obvious that when the sensitivity parameter tends to1, the detection probability can be further improved. Thus, the proposed approachfacilitates to tune the scheme according to the nature of the deployed environment.

6.2 Recommendations

This thesis can be further improved in following areas related to the multimedia ser-vices,

• In terms of performance evaluation in multimedia services, content managementis an important issue in the proxy servers. Change in the hit rate and the missrate of proxy according to the popularity of the media contents, we will able toobserve the performance for different content management schemes.

• The cost of information exchange between proxies in the network has an effecton the performances. We will able to observe this, by considering the networkutilization due to information exchange.

• We can measure the performance of secure context gathering architecture, interms of detection time by adding a packet generation process in the scheme.

• The proposed model for blocking probability evaluation could be improved withthe implementation of more reliable channel assignment.

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APPENDIX A

SOME RESULTS OBTAINED FOR THE MODELPROPOSED IN SECTION 5.2

Power and rate anomaly detections are two major approaches for intrusion detectionin wireless sensor networks. (Onat and Miri, 2005) presents such a scheme whereanomaly detection being used in detection scheme. We have developed a new schemefor intrusion detection with a modification to the concept proposed in (Onat and Miri,2005). This approach more relates to sliding window mechanism defining fixed windowsize and activated by filling the window size.

Implementation was carried out with log normal shadowing and the random fluc-tuation around average power level was simulated introducing a zero mean Gaussianrandom variable Xσ. The Fig. A, presents detection probability of power anomaliestowards transmit power level for different frame sizes. In the graph, we find higherdetection probability when frame size is bigger. Further, we notice that graphs getsaturated quickly at low power levels for big frame sizes whereas, small frame sizesresults to saturate the graphs only at the high power levels.

5 6 7 8 9 10 11 12 13 14 150

0.1

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De

tect

ion

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Figure A.1: Power anomaly detection probability towards transmit power level forchanging frame sizes

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Next, we try to find the effect of nodal speed over the detection probability. Ac-tually, the simulation model consider the relative movement of the mobile nodes in thefield. And we assume they move in a random manner. In this simulation, we modeldeployed area in to grids where communication range fit in to a square of twice thesize of transmission range(r). According to the Fig. A.2 we notice that detection prob-ability is higher when the speed is low, but at higher speeds detection probability islow even for high power levels. This implies the fact that speed affect on the detectionprobability in the proposed architecture.

5 6 7 8 9 10 11 12 13 14 150

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Speed − 1 m/sSpeed − 2 m/sSpeed − 3 m/sSpeed − 4 m/s

Figure A.2: Power anomaly detection probability towards transmit power level fornodal speed

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Performances of an intruder detection system depends on how quick it can detectthe anomalies. The simulation results in Fig. A.3 shows the average detection time to-wards the transmit power level for different frame sizes. We find following infromationsin the graphs,

1. Detection is quickly done at high transmission power levels

2. The larger frame sizes detect intruders more quickly than for small frame sizes

3. At high transmit power levels detection time is almost independent of the framesize

8 9 10 11 12 13 14 156

7

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Power level /(dBm)

Ave

rag

e d

ete

ctio

n t

ime

/ (

in p

ack

et

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)

Frame size − 15Frame size − 20Frame size − 25Frame size − 30

Figure A.3: Average detection time towards transmit power level for changing framesizes

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The Fig. A.4 presents congestion probability towards transmit power level forchanging transmission rages in sensor nodes. Simulation randomly select a particularnode and detects the simultaneous transmitting sensor nodes in the same radio range.

We notice that congestion probability is very high when nodes transmit at highpower levels which result to long radio range. Smaller the radio range will reduce thecongestion probability. But, meantime it will result to deploy more sensor nodes withhigher density to cover the target area. Therefore, this evaluation helps to decide theradio range of the nodes to comply the maximum congestion threshold in the network.

5 6 7 8 9 10 11 12 13 1410

−4

10−3

10−2

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Co

ng

est

ion

pro

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Radio range − 20mRadio range − 25mRadio range − 30mRadio range − 35m

Figure A.4: Congestion probability towards transmit power level for changing radiorange

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Operation in wireless sensor networks are more critical when the nodes start tomove. Behavior of communication channel varies vastly when nodes move at higherspeeds. Therefore, it is important to design the hardware with minimum requiredprocessing power according to the expected movements of sensor nodes. In Fig. A.5 wepresents the congestion probability of nodes moving at different speeds and we observethat the relationship between congestion probability and the transmit power level tobe linear during short range of power variations. Moreover, we find that congestionprobability is higher when nodes move at lower speeds than the higher speeds as shownin Fig. A.5.

5 6 7 8 9 10 11 12 13 14

10−3.4

10−3.3

10−3.2

10−3.1

Transmit power level / (dBm)

Co

ng

est

ion

pro

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Speed − 1 m/sSpeed − 3 m/sSpeed − 5 m/sSpeed − 7 m/s

Figure A.5: Congestion probability towards transmit power level for changing nodalspeed

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Block Diagram of the System Model to Measure Response Time

The block diagram for response time measurement could be presented as below. Con-sidering both proxy system and server system as functional we find the request arrivalrate at proxy hierarchy as λa, outgoing rate to clients λa,1 and outgoing rate to serverλa,2.

Figure A.6: Block diagram for response time measurement

At the server end incoming rate to server is considered as miss rate from proxies andthe external arrival rate ηa. Then λa,3.Pb is considered to be the blocking probabilityat proxy while totally η1

a + λa,6 is considered to be the total output rate at server.

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Block Diagram of the System Model to Measure Blocking Probability

The proposed model partly presents M/M/N/K queuing system. The server consistsof two streams where one stream serves all kind of call arrivals and other stream servesonly handover calls. Here, we notice the probability of call blocking for stationary ofmobile users, probability of not having a space in the queue(Ploss) and the probabilityof having a space in the queue but dropping the call(PfhA).

Figure A.7: Block diagram for blocking probability measurement

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Blocking Probability Evaluation in Section 4.4

Figure A.8: Analytic model to evaluate blocking probability

Assigning initial the values for PBA and P0, we start the evaluation. This iterationis done to saturate the condition to obtain the final answer.

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APPENDIX B

PUBLICATIONS

Conference Publications

• Karunarathna S.N., Lee G.M., Crespi N. and Rajatheva N., ”Context ReasoningScheme for Seamless IPTV Services in a Home Environment ”, Submitted forpublication IPTComm 2010, Germany.

• Karunarathna S.N., Lee G.M., Crespi N. and Rajatheva N., ”Performance Eval-uation of Hierarchical Proxy Servers for Multimedia Services”, Submitted forpublication IEEE Globecom 2010, Miami.

Journal Publications

• Karunarathna S.N., Lee G.M., and Crespi N., ” A survey of challenging technolo-gies for multimedia services in user-driven ubiquitous networking environment”,Accepted on MASAUM Journal Of Reviews and Surveys, Volume 1 Issue 3, No-

vember 2009.

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