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Seminar Report on
A Unified Analysis of Routing Protocols used in MANETs
Submitted in partial fulfilment of the
requirement for the award of the degree
of
BACHELOR OF TECHNOLOGY
in
ELECTRONICS & COMMUNICATION ENGINEERING
Under the Guidance of: Submitted By:
Astt.Prof. Trivendra Joshi 060070102154
Department of Electronics & Communication Engineering
Dehradun Institute of Technology, Dehradun
2012-13(ODD SEMESTER)
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DEHRADUN INSTITUTE OF TECHNOLOGY DEHRADUN
CANDIDATES DECLARATION
I hereby certify that the analysis work on research paper which is being presented in the seminar
report of PEC-754 entitled A Unified analysis of routing protocols used in manets in
partial fulfilment of the requirements for the award of the Degree of Bachelor of Technology in
Electronics and Communication and submitted in the Department of Electronics and
Communication Engineering. Dehradun Institute of Technology, Dehradun, UttarakhandTechnical University Uttarakhand, is an authentic evidence of my own analysis work under the
guidance of Astt.Prof. Mr.,Department of Electronics and Communication Engineering,
Dehradun Institute of Technology, Dehradun.
TRIVENDRA JOSHI
This is to certify that the above statement made by the candidate is correct to the best of our
knowledge.
DATE:29/11/2012
Guide name with destination Seminar Coordinators Head of Department
Mr. Kuldeep Chaudhary Mr. Dhruva Chaudhary Prof. (Dr.) Sandip Vijay
(Astt. Prof) Mr. Udit Gupta
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TABLE OF CONTENTS Page No.
1. List of figures 4
2. Abbriviations used 4
3. Introduction about topic 5-6
4. Theory 7-12
5. Analysis and Methodology used 13-14
6. Result discussion 15-21
7. Area of application and present research status 22-25
8. Conclusion 25
9. References 26-28
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1.List Of Figures
Fig. no. Title Page no.
1 A multi-hop data transmission scenario in wireless sensor network 8
2 Simulation Parameter 17
3 Comparision on transmission cost when prouting = 25% 18
4 Comparision on transmission cost when prouting = 50% 18
5 Comparission on transmission cost when prouting = 75% 19
6 Comparision on end-to-end packet delay 20
7 Comparision on energy consumption per packet 20
8 Basic modle of a two way relay system 22
9 Example of a multi hop relay system 23
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2.Abbreviation used
1. AODV Access On Demand Vector Routing Protocol
2. MANET Mobile Ad-Hoc Networks
3. MAC Medium Access Control
4. ARQ Automatic Repeat Request
5. COMAC Cooperative Medium Access Control
6. CBF Cluster Based Forwarding
7. MIMO Multiple Input Multiple Output
8. PRR Packet Reception Rate
9. SNR Signal to Noise Ratio
10.CSMA Code Sense Multiple Access11.SIFS Short Inter Frame Signal
12.DIFS Distributed Inter Frame Signal
13.NAV Network Allocation Vector
14.MANET Mobile Ahoc Network
15.RDSTC Randomized Distributed Space Time Code
16.QOS Quality of Service
17.EZW Embeded Zerotee Wavelet
18.ROI Region of interest
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3. Introduction to Topic:
Mobility brings fundamental challenges to the design of routing protocols in mobile adhoc networks (MANETs). The mobility of nodes implies that the routing protocols of
MANETs have to cope with frequent topology changes while attempting to produce correct
routing tables. To accomplish this, two types of routing protocols have been proposedproactive routing and reactive (or on-demand) routing.
Proactive routing protocols provide fast response to topol- ogy changes by continuously
monitoring topology changes and disseminating the related information as needed over the
network. However, the price paid for this rapid response to topology changes is the increasein signaling overhead, and this can lead to smaller packet-delivery ratios and longer delays
when topology changes increase. In the worst case, broadcast-storms" [1] can result in
congesting the entire network. Reactive routing protocols operate on a need to have basis, andcan, in principle, reduce the signaling overhead. However, the long setup time in route
discovery and slow response to route changes can offset the benefits derived from on-demand
signaling and lead to inferior performance.
Given these striking differences between proactive and on- demand routing in MANETs, a
basic question to ask is whether one routing protocol always performs better than the other.
Extensive simulations have already given the answer: both of routing algorithms exhibit
advantageous and inferior performance to the other one depending on different network
configurations, particularly with respect to different node mobility, node density and traffic
load.
Given that proactive and reactive routing schemes for MANETs have relative advantages
and disadvantages, com- paring the two is important. Significant work (e.g., [2][5]) has been
conducted to evaluate and compare these protocols under network profiles of various mobility
and traffic configurations. Such performance comparisons have been mostly conducted viadiscrete-event simulations. Simulation-based studies of routing schemes are a powerful tool to
gain insight on their performance for specific choices of network parameters. How- ever, it is
difficult to draw conclusions involving multidimen- sional parameter spaces, because running
several simulation experiments for many combinations of network parameters is impractical.
Few if any analytical studies have been pursued on this topic, and prior analytical work has been
mostly restricted to the analysis or comparison of routing control on routing metric overhead
[6][8], information theoretic aspects [9], [10] or multipath routing protocols [11], [12].
Furthermore, these works do not evaluate the effects of signalling overhead on unicast capacity
at nodes, and none reveals the underlying connection between protocol performance and
network parameters. Hence, the following two questions have remained open:
Does there exist a unified analytical framework, simpli-fied but able to provide the
characterization of essential behaviors of routing protocols?
If there is one such framework, can it also provide network scalability?
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This paper provides a unified analytical framework that parameterizes and quantitatively
evaluates the performance of routing protocols from a joint characterization of the routing
logic and MAC layer. The solution emerges from a combina- torial model that synthesizes the
evaluation of both the routing logic and the operation of the MAC layer, with each of them
being parameterized individually.
The MAC layer at nodes is as well as simplified as a two-customer queuing model, where
the packet loss probability and delay at nodes can be effectively computed. When an- alyzing
the performance of the MAC layer, we consider the cases of a scheduled MAC (TDMA) and
a contention-based MAC (802.11 DCF MAC). In the combinatorial model, the computed
metrics are synthesized along with the routing logic to produce quantitative measures of the
routing protocols in terms of end-to-end packet loss probability and delay.
It is important to note that this work does not attempt
to model or compare between specific proactive or reactive routing protocols. Rather, the
intent is to capture the essential behaviour and scalability limits in the network size of both
classes of protocols by quantifying their performance within a unified framework. Thesimulation results in this paper are not intended to provide an exact match with our analysis;
they are provided only as a supporting evidence for the conclusions regarding to protocol
behaviors observed in literature. With the aid of simulations, we show that the analytic results
agree with the simulation findings.
We model each wireless node as a two-customer queue
without priority, where the two kinds of customers are unicast and broadcast packets. By
analyzing the service time and waiting time in queue for any unicast packet, we derive the
transmission delay time for any link; by summing up the transmission delay time for each link
of any routing path, we derive the end-to-end delivery delay.The paper is organized as follows. Section II presents
related work in the modeling of routing protocols. Section III presents the mobility model,traffic model and simplified mod- els of routing algorithms used in the analysis in Section IV.Section V characterizes the performance of the MAC layer and provides performancemetrics in evaluating protocol per- formance. Section VI compares our analytical resultsagainst Qualnet simulations based on scenarios on various traffic loads, mobility and nodedensity configurations. The results illustrate the correctness of our analytical framework,which captures the essential behaviors of protocols and is capable of pinpointing the effect ofvarious parameters analytically. Section VII concludes this paper.
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4. Theory:This paper presents a mathematical framework for the evaluation of the performance of
proactive and reactive routing protocols in mobile ad hoc networks (MANETs). This unified
framework provides a parametric view of protocol performance, which in turn provides adeeper insight into protocol operations and reveals the compounding and interacting effects of
protocol logic and network parameters. The parametric model comes from a combinatorialmodel, where the routing logic is synthesized along with the characterization of MAC
performance. Each wireless node is seen independently as a two-customer queue withoutpriority, where the two types of customers are unicast and broadcast packets. The model capturesthe essential behavior and scalability limits in network size of both classes of routing
protocols, and provides valuable guidance on the performance of reactive or proactive routing
protocols under various network configurations and mobility conditions. The analytical results
obtained with the proposed model are in close agreement with simulation results obtainedfrom discrete- event Qualnetsimulations.
Parameterizing and comparing the performance of routing protocols analytically is a very
complex problem. Conse- quently, the characterization and comparison of routing pro- tocolshas been limited to simulation-based approaches [2] [5], [13][16], under various
configurations. The performance evaluation metrics used in these simulations or experimental-
based approaches include packet delivery ratio, delay and throughput. Network configurations
vary on traffic pattern, mobility and network density. for proactive and reactive protocols to
evaluate the individual routing control overhead. Zhou and Abouzeid [7] presented an
analytical view of routing overhead for reactive protocols, assuming a static Manhattan-grid
network and studied the scalability of reactive protocols. These studies concentrate on the
impact of traffic patterns and they also provide [8] a mathematical and simulation-based
framework for quantifying the overhead of reactive routing protocols.
Zhou and Abouzeid extended their analytical studies to information theoretic techniques toderive analytic expressions for specific metrics, such as control message routing overhead and
memory size requirement [9] where entropy is utilized to derive bounds for these metrics.
They have made funda- mental contributions [10] toward a rigorous modeling, design and
performance comparisons of protocols by deriving lower bounds on the routing protocol.
Tsirigos et al. [11], [12] studied multipath routing protocols with the aim of increasing packet
delivery ratio in inherently unreliable networks and developed an analytical framework for
evaluating them. Their work took mobility and topology changes into considerations.
Our work has been inspired by research work in [17] [20] which analyzes and
evaluates the performance of routing protocols by constructing mathematical models of the
routing operations.
Yang et al. [17] set up a mathematical model that imitates the operation of proactive routingand is utilized to optimize the operation of routing protocols in order to strike a balance
between protocol overhead and accuracy. However, this work is specific for proactive protocols
operating under certain conditions.
Lebedev [18] showed an analytical tool for both proactive and reactive protocols and
proposed a model to study the operation of two classic reactive (Ad-hoc reactive Distance
Vector Routing (AODV) [21]) and proactive (Optimized Link State Routing (OLSR) [22])
protocols in the presence offaulty links. However, this work does not cover other aspects of
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network parameters on the performance of routing protocols. Nogales [19] models routing
protocols with the consider- ation of the behavior of connections at the link layer and
MAC protocol. However, that model is greatly simplified when modeling the packet routing and
scheduling process and does not take into consideration traffic load and the topology of thenetwork.
Jacquet and Laouiti [20] compare proactive and reactive routing protocols performance byproposing probability-based models. However, their models focus on calculating specific
performance metric values, rather than evaluating the general operations of these routing
protocols. Their study is based on random graph models that are simplified and idealized,
such that mac and link layer failure effects are not taken into consideration.
4.1. NETWORK MODEL
4.1A. Mobility Model
Nodes are mobile and initially they are distributed equally over the network. The movement
of each node is independent and unrestricted, i.e., the trajectories of nodes can lead to
anywhere in the network. For nodei
V=
{1,
2, . . .
,N
}, let {Ti(t), t
0}be the randomprocess representing its trajectory and take values in Y, where Y denotes the domain
across which the given node moves. To simplify the model, we make the following
assumption on the trajectory processes. Assumption 1: [Stationarity] Each of the trajectory
pro-cesses (Ti(t)) is stationary, i.e., the spatial node distributionreaches its steady-state distribution irrespective of the initial location. The N trajectoryprocesses arejointly stationary, i.e.,
the whole network eventually reaches the same steady state from any initial node placements,
within which the statistical spatial nodes distribution of the network remains the same over
time.The above assumption is quite fundamental in the sense
that it lays the foundation for the modeling of node move- ment. Most existing models, (e.g.,random direction mobility models [23], random waypoint mobility models [24], [25] and
random trip mobility model [26]) clearly satisfy our assumption. In other words, our
assumption ensures that, on the long run, the network converges to its steady state and the
stationary spatial nodes distribution can be used in the performance analysis of the network.
4.1.B. Traffic Model
We consider a new traffic flow or simply a new session as one that is associated by the
arrival of a new application-levelsession request at a node i with some destinationj, j = i,in the network. Traffic flows are randomly generated with uniformly distributed sources and
destinations. Long-lived traffic flows are assumed in order to investigate protocol performance
under the steady state of nodes mobility and traf- fic distributions. Short-lived traffic flows,
reflecting transient behaviors, are beyond the scope of this paper. Furthermore, well-
connected networks are assumed, i.e., if an existing path for any traffic session is broken, there
is always an alternative path (with high probability) available to support continuing operations
of the traffic flow. 1
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4.1.C. Neighbor Sensing
Neighbor sensing protocols, such as periodic broadcasts of HELLO messages, are effective
approaches used in routing protocols (both proactive and reactive) to detect the availability of
links between neighbor nodes. New links are detected when HELLO messages are received
from nodes not included in the neighbor list. Existing links are declared as failed if none of
HELLO messages from the neighbor node is received during a certain amount of timewindow.
4.1.D. Routing Protocols Model
We provide descriptions of generic proactive and reactive routing protocols, which we
believe capture the essential behavior of many designs and implementations of existing routing
protocols. However, this analysis, and hence the generic protocols below, does not consider any
protocol- specific techniques, such as multi-point relay, local repairs and route caching
mechanisms.
Note that the alternative path is not necessarily disjoint with the former broken path.
1) Proactive Routing Protocol: In proactive routing proto- cols, every node maintains a list ofdestinations and updates its routes to them by analyzing periodic topology broadcasts from
other nodes. When a packet arrives, the node checks its routing table and forwards the packet
accordingly.
Every node monitors its neighboring links and every change in its neighbors results in a
topology broadcast packet. That is flooded over the entire network. Other nodes update their
routing tables accordingly upon receiving the update packet. In a well-connected network, the
same topology broadcast packet could reach nodes multiple times and therefore enjoy a good
packet reception probability. In the paper, we assume that every node reliably receives
topology packets from other nodes.
2) Reactive Routing Protocol: In reactive routing protocols, nodes maintain their routingtables on a needed basis. This implies that when a new traffic session arrives, nodes have to
set up the paths between sources and destinations before starting to deliver data packets.
The process of path setup is called route discovery. Complementarily, another process called
route maintenance is necessary to find an alternative path if a former path was broken. More
specifically:
a) Route Discovery: a mechanism initiated by a node iupon the arrival of a new traffic session in order to discover a new path to a nodej. Nodei floods the whole networkwith route request (RREQ) packets. Upon receiving the RREQ packet, nodej sends out a routereply packet (RREP) along the reverse path to i. As a result, node i usually gets a shortest pathto nodej.
b) Route Maintenance: a mechanism by which a node iis notified that a link along an active path has broken, such that it can no longer reach thedestination nodej through thatroute. Upon reception of a notification of route failure, node i can initiate a route discoveryagain to find a new route for the remaining packets destined toj.
In reactive routing protocols, each node does not maintain routing tables before a routing task
is triggered. They only find a route on demand by flooding the network with RREQs, i.e.,
before sending data packets sender broadcasts router request and initiates a route discovery
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process. If a link breakage is detected during packets delivery, a new RREQ is generated.
The main disadvantages of such algorithms are high latency time in finding routes and
excessive flooding when traffic load is high.Assume that a successful delivery between source node Sand destination node Utakes Khops, that at the first step a route discovery is initiated at Sand that after time 0 source nodeSreceives a RREP and starts sending data packets. Assume that a link breakage occurs at arelay node Fwith probability pi and then a new RREQ is generated for Fand
that it takes time i to restart delivery. Let us suppose that the transmission delay for any
linki is Ti. Then the end-to- end delivery delay Dp between S and Ucan be formulatedasDp = 0 +
K(pii+ Ti).
Compared to the local recovery time in proactive routing protocols, the route discovery time in
reactive routing proto- cols is much larger.
5. Analysis and Methodology used
5.A. Models
Previous studies [2] [9] have pointed out that the simple radio models (e.g., the ideal binary
model assumes that there are perfect links within a given communication range, beyond which
there is no link) is unlikely to be valid in any realistic environment. Actually, the reliability of a
link is influenced by a lot of factors, such as the transmitter-receiver distance, the emitting power
and the interference noise. To model the reliability, we choose to use the one derived in [2] and
[19], which is based on the log-normal path loss model. The packet reception rate (PRR) p for a
transmitter-receiver distance dis given by
p(d)={1-exp[-(d)BN/2R]/2}8f (6)
whereBNis the noise bandwidth,R is data rate in bits,is the encoding ratio, andfis the frame
length in byte. The signal to noise ratio (SNR) at the receiver can be given as
(d)= Pt-PL(d0)- 10log10(d/d0)+N(0,)-Pn (7)
where Ptis the output power, PL(d0) is the power decay for the reference distance d0, is the
path loss exponent (rate at which signal decays with respect to distance), N(0, ) is a
Gaussian random variable with mean 0 and variance 2 (due to multi-path effects), and Pnis the
noise floor. In this model, low-power wireless links are classified based on transmitter-receiver
distance in distinct reception regions: connected (p1), transitional (0 < p < 1), and
disconnected (p 0). Then, the communication rangeRccan be defined as
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Rc=max{d|P(d)>p threspthres>0 ptres0} (8)
We model a sensor network by an undirected graph G = (V, E), where V is the set of sensor
nodes and the set of wireless communication links,E, is defined as
E={(u,v)V2|uvdu,vRc} (9)
The neighborhood setH(u) of a node u is defined as
H(u)={vV|vu(u,v)E} (10)
5.B. Assumptions
A wireless sensor network composed of a large number of resource-constrained sensor nodes and
several more powerful sinks in an area is considered in this paper. Sensor nodes are
randomly distributed in the area and remain stationary after deployment, and all of them have
similar capabilities and equal significance. Each sensor node sends its data packets to sink nodes
through multi-hop wireless links. All nodes in the network are supposed to implement a CSMA-
like MAC (with ARQ) to share the physical medium. Due to link unreliability, the network is
assumed to employ a more optimal metric [19] [20] instead of the conventional shortest-path-
first policy for setting up an optimum routing path (referred to as intended path) between
source and sink. Each node knows and keeps record of the PRR between itself and any
neighboring node.
6. Result Discussion
6.1 PERFORMANCE EVALUATION: ANALYTICAL RESULTS
Based on reasonable assumptions, we compare the energy efficiency of our EECC and the
traditional methods, i.e., pure retransmission-based and FEC-based. According to previous
researches [23], wireless communication is responsible for the greatest weight in the energy
budget when compared to sensing and data processing. Assume Msensor nodes are randomly
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deployed in an area of size S. LetEtandErbe the energy consumed by a single transmission and
reception respectively.
Theorem 1. Let the per hop energy consumption for FEC, pure retransmission and node
cooperation are denoted byEFEC, Eretrans, andEEECCrespectively, we haveEFECEretrans>
EEECC.
Proof: The energy consumed for each transmission attempted
by a sensor node is
Esignal=Et+(MR2c/S-1)Er (11)
Ideally, we assume that a node retransmits for infinite times. Thus, in the retransmission-based
mechanism, the expected energy consumption for a successful delivery from s to ris
Eretrans
= ps,r
Esingle
+......+nps,r
(1-ps,r
)n-1
Esingle
=nk=1kps,r(1-ps,r)
k-1Esingle (12)
then (12) can be rewritten as
In the FEC mechanism [4], it is required that
Now we analyze the energy consumption of EECC in three different cases of cooperation
scenario. From (13), it is more energy efficient to relay packet on link ( t, u) than (r, u). Thus,
to simplify the analysis, we only consider the energy consumed for data packet transmission in
one hop range, i.e., from s to its next hop relay. Let the cardinality ofTand Q is
denoted by |T| and |Q| respectively.
where
Therefore,we have
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where
and lQ is thenode that relays the packet as the cooperator. Becausepl,r>ps,r, we have
Summarizing the above analytical results in (11) ~ (19), the following inequality holds: EFEC
Eretrans>EEECC.
Now we give the performance bound of EECC
Lemma 1. In idealistic cases with perfect links (ps,r = 1), the data forwarding in one hop range
succeeds at the first time. Thus, the lower bound of per hop energy consumption is Esingle.
Lemma 2. In realistic cases with imperfect links, the packet can be received by the receiver and
overheard by the cooperator candidates. The overall reception rate provided by these nodes is
Hence,transmission cooperation is more likely to be realized when the node density is relatively
high. In sparsely deployed scenarios, we can know that the per hop energy consumption is upper
bounded byEretranswhen there are few cooperation opportunities.
6.2 PERFORMANCE EVALUATION: SIMULATION RESULTS
The performance of EECC was evaluated by conducting simulations via the ns-2 simulator.
Here, we adopted a model which is widely used in previous researches to calculate energy
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consumption for transmission. To receive l bits at the receiver with a distance of d from the
transmitter, energy consumption for a single transmission and reception is given by
where Eelec is the electronics energy, fsd2and mpd
4 are the amplifier energy in the free space
and in the multi-path fading environment respectively, and d0 is a distance threshold that
depends on the environment. In our simulation experiments, the wireless link model is the same
with that in [2], and other parameters are set as shown in Table I.
Fig: 2
The first set of simulation experiments is performed to verify our analysis above in analytical
result. Thus, we manually deployed two sensor nodes and a single sink to create a simple2-hop
data transmission topology as that has been introduced in analytical result. Different amount of
sensor nodes are randomly deployed as the neighbours of the intended data source node to create
different cooperation scenarios, i.e., (|T| > 0, |Q| = 0), (|Q| > 0, |T| = 0), and (|T| > 0, |Q| > 0). To
make the results more pronounced, for each specific transmission scenario the experiment is
carried out independently for multiple times by network redeployment. The performance metricselected is called transmission cost, which is defined as the ratio of the total number of data
transmissions by the total number of non redundant packets sent out by the data source. This
metric reflects both communication overhead and energy efficiency. To fully investigate on the
performance effect by EECC, experiments are performed under different routing reliability
conditions (That is, the PRR metric for becoming an intended receiver, prouting, is set as low
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(25%), middle (50%), and high (75%) respectively). The simulation results for this experiment
set are shown in Fig. 3 to Fig. 5.
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We have several observations concerning these results. First, EECC can actually help to reduce
transmission cost to some extent, as compared to the pure retransmission mechanism (i.e., (|T| =
0, |Q| = 0)). Such cost reduction is even more obvious and stable when proutingof the intended
receiver is relatively low (e.g., prouting = 25%) and when the total amount of cooperator
candidates is relatively high (e.g., |T| = 3 or |Q|= 3). On the other hand, node cooperation
contributes little to the cost reduction when |T| = 1 and ps,t is extremely low (e.g., Experiment
Index = 12, 16, 20 in Fig. 5(a)) or when |Q| = 1 and pq,r is slightly greater than ps,r (e.g.,
Experiment Index = 9, 10 in Fig. 3(b)). This observation helps to validate our analysis on EECCperformance in analytical result.
Second, relaying by the receivers cooperator (tT) is more efficient than by the senders
cooperator (qQ) in general. The reason is that the receivers cooperator is able to reach the next
hop of the receiver directly, while the senders cooperator just substitutes for the original sender
to retransmit the packet to the receiver with a relatively better link. We could even notice that it
becomes less effective to invoke the cooperation by the senders cooperator when prouting
becomes relatively higher, as shown in Fig. 3(b), Fig. 4(b) and Fig. 5(b). Therefore, EECC can
be an optional choice if the sender has a good transmission link to the receiver while there are no
receivers cooperators.
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The above two observations imply that a proper network density can provide EECC with more
and better cooperation opportunities to minimize excessive transmission costs caused by
unreliable links and packet losses. We argue that it is not so difficult to satisfy this condition
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since many applications have already required high density deployment of sensor nodes. In the
second set of experiments, EECC is evaluated in the multi-hop transmission scenarios, by
measuring end to- end packet delay and energy consumption per packet. The network is densely
deployed so that for each intended node, |T| 3 and |Q| 3. The routing metric is the same as
that introduced in [19]. The results are averaged from multiple simulation runs. As shown in Fig.
6, EECC significantly reduces end-to-end packet delay as compared to the pure retransmission
method. An examination of ns-2 simulation traces reveals that this is attributed to a considerable
decrease in the number of packet retransmissions: without EECC, the intended sender will time-
out and resend the lost packet. The delay caused by such time-outs is far longer than that
introduced by the new NAV setting in EECC. On the other hand, EECC is able to decrease such
time-outs by node cooperation on data transmission, and thus effectively reducing the end-to-end
delay. Meanwhile, the decrease in retransmissions also results in the energy saving for packet
transmission, as shown in Fig. 7. These results justify the application of EECC in large-scale
sensor networks with energy constrained sensor nodes and unreliable wireless links.
7. Area of applications and present research status
The research in the area of cooperative communications dates back to the pioneering work in the
1970s, where the capacity of relay channels was studied for the problem of information
transmission over three terminals. Then the channel capacity of relay networks over non-faded
channel was examined. Because of its advantage over conventional one-way communications,
the relaying concept has gained great attention in recent research for an extension to fading
channels .Many important aspects of relay networks have been extensively studied. For example,
the capacity of relay networks over Rayleigh fading channels has been investigated .The
diversity-multiplexing tradeoff of DF and AF relays has been investigated . User cooperation
which is the generalization of relay networks to multiple sources has been investigated
.Relaying/cooperation has been shown to offer a performance enhancement in terms of the
capacity .Although the problems of relaying and cooperations have been studied for years in
many aspects such as communications, signal processing, networking, and information theory,
they still attract the research community as a new paradigm for wireless and mobile networks.
Recently, the relaying method has been introduced in the WiMAX standard and is expected to
spread into many other commercial standards .
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Multi-hop Relay NetworksThe multi-hop communication in relay networks is a very promising approach to improve the
transmission coverage of cellular and ad hoc networks
Fig.8 Basic modle of a two way relay system
In multi-hop relay networks, a transmission between a source node and destination node is
composed of multiple hops as illustrated in Fig. 8. Multihop transmission can significantly
reduce the transmit power compared to direct communications. In addition, because of transmit
power constraints, multi-hop transmission also leads to remarkable coverage extensions by
dividing a total end-to-end transmission into a group of shorter paths. On the other hand, due to
the rigorous effect of fading and shadowing, the lack of line-of-sight can severely degrade the
system performance. Using multi-hop transmission can overcome this dead-end spots problem
and thus another advantage of multi-hop relaying has been pointed out, especially for rural areas
with low traffic density and sparse population. In these areas, there is no economic benefit in
building the whole cellular networks but multi-hop relay networks. Hence, the multi-hop
technique is a promising candidate to meet the desirable goal of contemporary wireless systems
with respect to providing seamless communications.
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Fig.9 Example of a multi hop relay system
Mobile Multimedia over Cooperative Communications
Future mobile communication systems are expected to satisfy the increasing demand for services
with stringent constraints on quality of service (QoS) such as mobile multimedia, mobile video
applications, and mobile streaming on-demand. Due to the source encoding that is usually
imposed to multimedia applications, e.g., speech, audio, image, and video, some parts of the
produced multimedia format have a higher priority for the signal reconstruction than others.
Specifically, errors to headers and markers of such formats that may be induced during
transmission over the radio channel can severely degrade the multimedia quality regardless ofthe reception of the remaining parts. A coded image sequence based on the wavelet transform,
so-called embedded zerotree wavelet (EZW), is divided into two priorities. The higher priority
contains the most significant information of image pixels consisting of the wavelet coefficients
of dominant pass and subordinate pass extracted from several early scanning steps. The lower
priority conveys less important information. Using cooperative communications, two pre-
assigned relays cooperate to form a distributed-Alamouti code which help the source to transmit
the low priority sequence of the image over error-prone wireless channels. Among the whole
relay nodes in the networks, two relay nodes are selected which provide the maximum signal-to-
noise ratio to convey the higher priority. With this strategy, unequal error protection is implicitly
achieved for image transmission without controlling power or varying modulation and coding at
the transmitter at the expense of increasing networks overhead (i.e. feedback information). The
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selection of relays needs limited feedback, hence, making the proposed scheme feasible for
practical applications.
Apart from prioritized data structures caused by source encoding techniques, the actual content
of a multimedia application may result in some parts being more important to the end-user than
others. In particular, as far as context or content of images is concerned, the region of interest
(ROI) prioritizes those areas that attract more attention over the background (BG) content.
Viewers will commonly pay attention to ROI rather than BG. Accordingly, it appears to be more
annoying to observe distortions in the ROI of an image although the BG has high quality. Hence,
it is beneficial from a perceptual quality point of view to rather improve the quality of an ROI
than spending too much resources on the BG. Clearly, this is particularly helpful for wireless
imaging and mobile video services where bandwidth is scarce and expensive. For image
transmission, for example, a more efficient use of available
radio resources such as bandwidth and power may be unequally allocated between ROI and BG
in the way that the former receives a higher portion. In other words, resources could be
controlled to increase the quality of the portion of an image that would be of most interest to the
viewer. For instance, unequal error protection for ROI coded images over fading channels
has been proposed .A novel ROI-based image transmission scheme for wireless fading channels
to improve the quality of ROI using cooperative diversity is proposed. Specifically, image
communications with three terminals, i.e., source, relay, and destination, is considered. In the
first-hop transmission, the source broadcasts the ROI frame to both the relay and the destination.
In the second-hop transmission, the source sends the BG frame to the destination while the relay
assists to enhance the quality of the ROI at the destination by forwarding the ROI received from
the first-hop transmission. With this transmission scheme, the relay does not consume the power
used to transmit BG as in the conventional communications. Therefore, to satisfy the total power
constraint, the relay can allocate more power for the ROI-based image transmission by using the
remaining power reserved for the BG. At the destination, the ROI frames from the first-hop and
second-hop transmission are combined using the MRC technique to enhance the quality of ROI.
It has been shown that the proposed scheme significantly improves the ROI and the quality of the
image compared to conventional approaches.
Using relays to transmit videos has recently also gained an increased interest in the research
community . Specifically, a video multicast strategy for a two-hop cooperative transmission
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scheme has been proposed in .A H.264/SVC encoded video with two layers, namely, base and
enhancement layers is considered so that receptions of more layers lead to better video quality.
The randomized distributed space-time codes (RDSTC) are used to provide users different video
quality based on their channel conditions. The performance of the proposed relaying scheme has
been shown to outperform a conventional multicast systems.
8. Conclusion:
We presented an analytical framework to evaluate the behavior of generic reactive and proactive
protocols. In the model, the operation of the routing protocol is synthesized with the analysis ofthe MAC protocol to produce a parametric characterization of protocol performance. The
effectiveness and correctness of the model are corroborated with extensive simulations. The
model enables in-depth understanding of routing protocol performance, and points out the needto design routing protocols that are capable of confining signaling overhead to those portions of
the network where the routing information is needed, in order to operate efficiently under
different types of mobility and traffic patterns. A similar conclusion can be derived by looking atthe capacity of ad hoc-networks under different types of information dissemination [39]. In our
future work, we plan to incorporate realistic physical layer effect into our modeling framework
to make it more practical and comprehensive.
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