Locality and Interest Awareness for Wireless Sensor Networks(LIASensor)∗

Bruno Andrade, Lúıs Veiga and Paulo Ferreira

INESC-ID, Instituto Superior Técnico, Lisbon, Portugalbruno.jesus.andrade@ist.utl.pt, luis.veiga@inesc-id.pt, paulo.ferreira@inesc-id.pt

Abstract. Wireless Sensor Networks (WSN) consist in spatially distributed set of autonomoussensors along an area to be monitored. Such WSNs can be used both in military and civilianindustry. Whereas in the military field, WSNs are used to monitor areas, that otherwise couldnot be possible to monitor, in civilian industry their usage covers various fields, such as health,farming, and environmental monitoring. WSNs can monitor vast extensions; however, there arelimitations regarding their usage, being that energy consumption is the biggest problem.Power consumption is directly related with communication distance and message’s length. Thiswork presents Location and Interest Awareness for WSNs (LIASensor), a WSN architec-ture for environmental monitoring. LIASensor reduces the network traffic through interest andlocality awareness techniques thereby increasing the whole network lifetime. LIASensor wasimplemented and simulated in Castalia Simulator with promising results.

Keywords. Wireless Sensor Network, Interest Management, Locality-Awareness, Interest-Awareness

1 Introduction

Nowadays, sensor nodes are used worldwide in alarge range of applications. Due to technology ad-vances [3, 4] sensor nodes have become powerful,cheap, small and consequently disposable, whichallowed the emergence of Wireless Sensor Net-works (WSN). WSNs are composed by a largenumber of scattered sensor nodes that must beable to self-organize and forward information col-lected back to the end-user.

The biggest problem regarding WSNs is thescarce power source, typically a limited battery,and it is well known that sensors consume moreenergy when they are transmitting informationthan when they are collecting and processing data[11]. Furthermore, power consumption increasesas the message’s length, and the communicationdistance increases. With regard to power con-sumption any extra bit or extra meter counts sincethe death of few nodes may create blind spots, orin the worst case scenario, the death of the en-tire network. So, at this moment, developments

and new techniques capable of reducing both thenumber and size of messages are necessary.

The main goal of this work is to reduce the num-ber of messages within a wireless sensor network,and thus increase the lifetime of the entire net-work. This work focuses in environmental moni-toring by foot, in scenarios on which the evolu-tion of the situation is important. For instance, involcanic or seismic region where sensors must re-port more information than in other kinds of en-vironmental monitoring, since these regions canchange dramatically fast. In this type of moni-toring, a user patrols an area with a portable de-vice that transmits queries requesting informationfrom sensors.

This work proposes an improvement for thesescenarios: LIASensor is able to reduce bothmessages’ size and number; through Interest-awareness LIASensor reduces the number ofmessages, and through Locality-awareness themessages’ size.

This document is organized as follows. In sec-tion 2 we describe the state of art regarding Wire-

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less Sensor Networks, and methods that are ableto reduce messages in Ad-hoc networks. Then, insection 3, and 4 we present respectively the archi-tecture and implementation of LIASensor, at last,in section 5, we evaluate and analyze the resultsof the system prototype

2 Related Work

WSNs may vary a lot; there are many factors in-fluencing and changing these networks. Over theyears, many protocols and algorithms have beenproposed, making these networks more complexor simpler.

This section describes the state of the art ofWireless Sensor Networks. Firstly, it is presentedsome fundamental concepts, followed by WSNclassification and wireless sensor protocol stack.Secondly, it is presented a brief section about en-ergy consumption. Then, it is introduced somecluster-based routing algorithms. And at last, itis presented methods that reduce the number ofmessages in ad-hoc networks.

2.1 Fundamental Concepts

A sensor is a converter that measures a physicalquantity and converts it into a signal which canbe read by an observer or by an instrument. Theoutput can be immediately read or be transmittedelectronically over a network for reading or furtherprocessing. Sensors are used in everyday objectssuch as touchscreens displays (tactile sensor) andlamps which dim or brighten by touching the base.

According to [13], a WSN typically consists ofa large number of low-cost, low-power, and mul-tifunctional sensor nodes that are deployed in aregion of interest. These sensor nodes are small insize, but are equipped with sensors, embedded mi-croprocessors, and radio transceivers, and there-fore have not only sensing capability, but also dataprocessing and communicating capabilities. Theycommunicate over a short distance via a wirelessmedium and collaborate to accomplish a commontask, for example, environment monitoring, bat-tlefield surveillance, and industrial process con-trol.

2.2 Classification of WSN and ProtocolStack

Wireless Sensor Networks are applications spe-cific; usually, they are deployed for particularapplications, so different networks have differ-ent characteristics. According to different criteria,WSNs can be classified into different categories[13]. For instance, WSNs may be static, mobile,deterministic, nondeterministic, single-hop, multi-hop, etc.

Like regular networks, WSNs also have a fivelayer protocol stack [1]: physical layer, data linklayer, network layer, transport layer, and applica-tion layer.

In addition to the five layers, the protocol stackcan be also divided into three groups of manage-ment planes across each layer, including power,connection, and task management planes.

2.3 Energy Consumption Models

Accurate and low-cost sensor localization is a crit-ical requirement for the development of wirelesssensor networks in a wide variety of applications.There are some techniques suited to locate nodes;however, all of them have pros and cons. [10] de-scribed measurement-based statistical models use-ful to describe many of these methods.

Among the location techniques, these are themost used:

– Time of Arrival (TOA) - Calculates thedistance through time and signal propagationvelocity. It is the travel time of a radio signalfrom a single transmitter to a remote singlereceiver. Since TOA relies on the differencebetween the time of arrival and time of depar-ture, all receivers and transmitters must besynchronized so there is no error in the differ-ence due to clock offsets. This may prove tobe a problem, especially considering the highspeed at which the signals travel. Also, as withany time sensitive systems, there is also thepossibility of significant hardware delays thatmust be accounted for to calculate the correctdistances.

– Angle of Arrival (AOA) - Calculates thedistance by getting the signal direction sendby the adjacent node through the combinationof array antenna and multiple receivers;

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– Received Signal Strength Indication(RSSI) - Calculates the distance through thestrength of the received signal.

A global positioning system (GPS) receiver oneach device is a good solution to the future, butat this moment it still monetary and energy pro-hibitive for many applications.

2.4 Clustering algorithms

Routing protocols are one of the most importantcomponents of WSNs; these protocols are respon-sible for identify, select and decide which pathsare the most energy-inexpensive, direct and reli-able; without these protocols WSNs would not befeasible. According to Al-karaki [2], routing proto-cols in WSNs can be divided into three categories:flat-based routing, hierarchical-based rout-ing and location-based routing depending tonetwork structure.

Among these three types of protocols,hierarchical-based or clustered-based is themost energy-inexpensive. Moreover, the uti-lization of clustering algorithms provides 1)scalability enhancement, 2) communicationefficiency and 3) possibility of data aggregation.

In cluster-based routing protocols, sensors areorganized in regions known as clusters. Each clus-ter has a single cluster-head (CH), and manycluster-members. Cluster-members (just calledsensor nodes) send their information to cluster-heads, which forward the information collected tothe sink or base-station. Each node assigns itselfto one cluster-head. Depending of which algorithmis being executed; the number of clusters mightbe very different, yet zero cluster-heads or 100%of cluster-heads is the same as direct communica-tion. The number of cluster-heads can be assigneddirectly before the network being deployed or dy-namically during the routing algorithm execution.

Cluster algorithms communications are madethrough the following two modes:

– Intra-cluster communication (forwardingto cluster-head): each sensor node sends thesensed data to the elected cluster-head;

– Inter-cluster communication (forwardingto base-station): each cluster-head sends dataeither to neighboring cluster-heads or directlyto the sink whether it is near.

2.5 Message Reduce Techniques inAd-Hoc networks

Besides enhancing the network lifetime throughthe utilization of efficient routing protocols, it isabsolutely necessary to reduce message’s lengthand number too. To achieve this, some methodsand techniques have been used in WSNs. Datafusion [6] is currently used in WSNs; Vector FieldConsistency [12] is a more recent model, but withgreat prospects.

Interest Management Interest Management(IM) is a set of techniques able to filter and dis-pose relevant information to entities interested init. This method can be abstracted using publish-subscribe models [5]. In such models, Publishersare objects that produce events and Subscribersare objects that consume events, whereas an ob-ject can be both a publisher and a subscriber.Without Interest Management, a receiving entitywould receive messages from every producing en-tity; so, the receiving entity would be responsiblefor sorting through and discarding useless mes-sages. The concept of Interest Management wasdeveloped to address this problem by reducing thearriving messages to a smaller and relevant set.Under Interest Management, an entity expressesits data interests in terms of location and otherapplication-specific attributes. According to Mor-gan [9], other agents in the simulation infrastruc-ture, interest managers (IM), accept entities’ in-terest expressions (IEs) and use them to filter mes-sages to sets (or reduce supersets) which meets theentities’ needs. An Interest Expression (IEs) is aspecification of the data one entity needs to re-ceive from other entities in order to interact withthem correctly. IEs may refer to several attributesof the simulation entities or to the radius of inter-est, i.e. an entity may express interest about allentity within a 50 meters radius around itself.

Vector Field Consistency Vector Field Con-sistency (VFC) [12] is a client-server architec-ture based on interest and locality awareness tech-niques. Furthermore, VFC is an optimistic consis-tency model allowing bounded divergence of ob-ject replicas.

In VFC, the server is the responsible for keep-ing the actual state of the world, and for regu-larly update clients. Each client registers within

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the server the objects, also called pivots, that willbe shared, as well as, their consistency parame-ters, which consist in a 3-dimmensional vector.

A pivot generates a consistency field determin-ing the consistency of each object as a functionof the distance between the object and the pivot.The pivot’s surroundings are updated accordingwith its consistency degree.

Consistency degrees are defined through 3-dimmensional vector, which specifies: 1) the maxi-mum time a replica can be without being refreshedwith its latest value, 2) the maximum number oflost updates replicas, i.e., updates that were notapplied to a replica, and 3) the maximum rela-tive difference between replica contents or againsta constant.

Data fusion Data fusion, also called, data ag-gregation is the process of integration of multipledata and knowledge representing the same real-world object into a consistent, accurate, and use-ful representation. The expectation is that fuseddata is more informative and synthetic than orig-inal inputs.

Applying data fusion to ad-hoc networks,specifically WSNs is very beneficial to the wholenetwork due the decreasing of redundant informa-tion. Sensor nodes are usually deployed in large oreven huge number in systems or areas of interest.Because of dense pattern of sensor deployment,neighboring sensor nodes may sense similar dataon specific phenomenon. Since sensor nodes arerun by battery power, it is critical to perform ev-ery operation in an energy-efficient manner. Forthis purpose, it is desirable for a sensor node toremove the redundancy in the data received fromits neighboring nodes before transmitting the fi-nal data to the sink. Data aggregation is an effec-tive technique for removing data redundancy andimproving energy efficiency in WSNs. The basicidea is to combine the data received from differ-ent sources so that the redundancy in the data isminimized and the energy consumption for trans-mitting the data is reduced. The data-centric na-ture of WSNs makes data aggregation a crucialtask [7]. Different authors consider different fusionmethods. Li [8] considers five representative fusionmethods. However, Zheng [13] considers there areonly three major data aggregation techniques.

Over the years many solutions have been pro-posed to increase WSNs lifetime; however none

of them really tried to reduce the number and thesize of messages within WSNs. The majority of so-lutions aimed data routing optimization, neglect-ing the number and the size of their messages.Other methods, such as VFC are well suited formessages reduction, but the adaptation to WSNsis not straightforward. Data fusion is already usedin WSN; however it is useless in environmentswhere sensor nodes are far away from each other.

3 Architecture

This work proposes a new approach in WirelessSensor Networks; LIASensor reduces the numberand size of messages in networks through Localityand Interest awareness techniques.

LIASensor architecture was been designed forconstant changing environmental monitoring, andits main properties are locality and interest aware-ness. Considering sensor networks protocol stack(subsection 2.2), LIASensor is an application layertechnique. As a result, LIASensor uses bottomlayers API, which permits it to be executed ontop of any routing or mac protocols. The rest ofthis section presents in more detail both LIASen-sor architecture and operation mode.

Fig. 1. Conceptual consistency zones.

3.1 Client-Server Architecture

The model of communication used by LIASensoris a client-server architecture. In This kind of ar-chitecture servers provide functions or services.On the other hand, clients are responsible for ini-tiating communications with servers, requestingservices or functions. In LIASensor every node

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plays a single role; the sensor nodes act like serverswhereas the user-ends acts like clients.

Further, LIASensor only considers two relation-ships in this architecture: many to one, andmany to many. This means that in any pointof time, it is possible the existence of one or moreend-users requesting data from the environment.On the other hand, sensor nodes must always existin larger numbers.

3.2 Locality-awareness

To accomplish Locality-awareness the end-usergenerates radius of interest around him, which areformed through the device’s signal strength. Theuser transmits his query directly to nodes, with-out any forwarding; nodes that do not receive anyquery are considered outside of the radius of in-terest, and so, these nodes do not respond. If theend-user wants to know any information outsidehis radius of interest, he must increase his devicesignal, or move through another direction.

LIASensor, by analogy with the electric ~E andthe gravitational ~G fields generates consistencyfields determining the consistency of each sensornode as a function of the distance between thesensor and the pivot. Thus, pivots generate con-sistency zones, iso-surfaces, ring shaped, concen-tric areas around them, such that the objects po-sitioned within the same consistency zone are en-forced the same consistency degree, For examplein Figure 1 a user P is in the center of four consis-tency zones labeled zi, where 0 ≤ i ≤ 4. Objects o2and o3 are enforced the same consistency degreesince they are in z3.

Sensors in pivot’s surroundings respond accord-ing with their consistency degree, a node closerhas a higher consistency degree than a node fur-ther, and therefore its response message is moredetailed and complete. Sensors know on whichconsistency degrees they are by calculating thedistance between them and the user.

3.3 Interest-awareness

WSNs contain many sensor nodes, and these sen-sors may have different sensing abilities. For in-stance, they might be skilled to sense temper-ature, humidity, pressure, seismic activity, pres-ence of humans or animals, among others environ-mental attributes. Therefore, the end-user mustbe able to request only appropriate information.

This is accomplished before query transmissions.The end-user’s device is able to transmit specificsrequests throughout the network. Upon receivingthe query, it is up to each sensor to decide eitherif the received query matches its sensing functionsor not. Only sensors capable to respond coherentlyreply their response back.

3.4 Finite State Machine

Fig. 2. State Machine Representing the sensor nodes.

LIASensor works as a finite state machine. Eachsensor is at one state at any given time. The listof states contains four different states (sleeping,assessment, sensing and transmitting states), andthe list of inputs contains six elements (queryreceived, unanswerable query, answerable query,no requests, another request and environmentsensed).

Considering the FSM (figure 2), every sensorbegins at the sleeping state (the initial state).Upon the occurrence of the event (the query ar-rival) the sensor switches from the sleeping stateto the assessment state. At the assessment state,the sensor checks whether the query is addressedto it or not. The assessment is based in both in-terest and locality awareness techniques. If the as-sessment returns a positive value, i.e. the sensorcan answer the query, the sensor transits to thesensing state. Otherwise, the sensor gets back tothe sleeping state.

At the sensing state the sensor senses and storesenvironmental data. When the sensing task is

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completed, the sensor switches from its sensingstate to the transmitting state. At the transmit-ting state the sensor constructs a packet com-pound by the actual sensed data and its previousrecords, and then sends the information to theend-user. When it finishes the transmissions task,the sensor has two different paths regarding thetriggering event, If a new query event is triggeredthe sensor get back to the sensing state, other-wise the sensor finishes the cycle and returns tothe initial state again (the sleeping state). Table1 summarizes the FSM operation.

Table 1. LIASensor state transition table.

4 Implementation

LIASensor is implemented over Castalia1, a re-alistic wireless and radio modeling simulatorfor Wireless Sensor Networks. Castalia is basedin OMNeT++2 platform and develop in C++.LIASensor algorithm is an application layer pro-tocol also developed in C++.

The rest of this section describes the solution’senforcement.

4.1 Locality and Interest AwarenessEnforcement

As subsection 3.2 presented, locality-awarenessuses radius of interests as its main element. Theanalysis of radius of interest encloses sensorswithin a consistency degree, which states the re-sponses size.

Every query has a RSSI associated to it, whichis used to calculate the distance between the userand the sensor. When a query is transmitted, thenodes that sensed the query read the querys RSSIand compare it with their consistency view, a ta-ble that associates RSSI values into consistency

1 http://castalia.research.nicta.com.au/index.php/en2 http://www.omnetpp.org

views. Consistency views are detailed in subsec-tion 4.2. If the RSSI matches one of the tablesrows, the response is constructed and sent withthe correct amount of information; otherwise thequery is ignored and dropped.

The enforcement of interest-awareness is easilyaccomplished. The strict comparison between therequest type and the sensor skill is sufficient. Ev-ery sensor node is skilled to perform some taskand every user request has a field that identifiesthe information on which the user is interested.So, when a query arrives, the sensor node cap-tures the packet, read the type field and comparesit with its function. If the type field matches itsfunction a new response is sent, otherwise the re-quest is ignored and dropped.

4.2 Consistency View

Sensor nodes have a consistency view, which canbe described as a table that contains the inter-val of RSSI values mapped into consistency de-grees. Each consistency view row is composed by:a) a RSSI interval; b) a consistency degree; and c)the amount of information a sensor must forwardwhen a query arrives. The amount of informationforwarded should increase as the consistency de-gree increases too. In other words, higher consis-tency degrees mean more information.

Values within the consistency table might bereplicated without affecting the way consistencyviews work. When a query arrives, sensors searchfor matches in this table view through the com-parison between the queries RSSI, and the RSSIinterval in the consistency view. The first matchedrow is returned, and all the others discarded. Aresponse packet is created according with the re-turned row and forward back to the WSN.

Consistency views are a core resource inLIASensor, since it is the only resource that mapsa query request to the responses size. By default,sensor nodes are deployed with a general consis-tency view. The default consistency view is not ed-itable or erasable; however, the end-user can cre-ate and use, as he needs, new consistency views.

5 Evaluation

In order to evaluate and analyze the system pro-totype, we simulate an environment with 1 (one)

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sensor per m2 (square meter). The simulated envi-ronment has 1Km2 (one square kilometer) of totalarea. Thus, we simulate an environment with 1000(one thousand) sensor nodes. The simulation wasexecuted with one single end-user and the sen-sor nodes were deployed uniformly and randomly.We used elementary MAC and routing protocols,and the simulation was performed during a periodequivalent to 10 hours of environmental monitor-ing which allow obtain promising results.

The results of the simulations were obtainedthrough the comparison of three metrics: a) thenumber of transmitted messages, b) the size ofthe messages, and c) the lifetime of the entire net-work. These three criteria, allow understand theimpact of LIASensor in a WSN. For each crite-rion, we made two simulations. In the first simula-tion we execute an elementary application proto-col, whereas in the second simulation we executea simulation with LIASensor as the applicationlayer protocol. Both protocols used radius of in-terest made by one-hop transmission, i.e. only sen-sors that sensed the query were considered insideof the radius of interest. In addition, the end-userrequested information every five minutes, makinga 400 total requests.

– Number of messages with LocalityAwareness

In order to analyze locality awareness, we sim-ulate a network without interest awareness. Thus,the graph in figure 3 represents the results ofa simulation executed with locality awarenessturned on, and interest awareness turned off. Inthis graph we can confirm that locality awarenessis capable of reducing the number of messages. Wedetect in average a decrease of 19% in messagesnumber. This result can be justified with the con-sistency degrees that were formed throughout thenetwork. As have been previously told, sensors po-sitioned behind the last consistency degree do notrespond, which results in a minor number of mes-sages within the network.

– Number of messages with InterestAwareness

In the second simulation, we only test the im-pact of interest awareness over the network. Fig-ure 4 shows the results of this simulation. So, weexecute LIASensor with locality awareness turnedoff and interest awareness turned on. As expected,

Fig. 3. Number of messages received in LIASensorwith locality awareness.

the number of messages decreased too. We no-tice a 37.8% decrease in average of messages num-bers. These results are only possible, because in-terest awareness guarantees that only sensors withspecifics skills answer the query.

Fig. 4. Number of messages received in LIASensorwith interest awareness.

– Number of messages with InterestAwareness and Locality Awareness

In the third simulation, we test LIASensor fullyoperational, i.e. with locality and interest aware-ness turned on. Figure (figure 5) presents the re-sults. This graph clearly shows a decreasing inmessages numbers. As explained before, localityawareness avoids messages from farther sensors,whereas interest awareness avoids useless mes-sages to the user. Thus, as expected, when wecombine these two techniques, we decrease, evenmore, the number of messages. With both inter-est and locality awareness we notice, in average, adecrease of 48% in messages numbers.

– Size of messages with Interest Aware-ness and Locality Awareness

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Fig. 5. Number of messages received in LIASensorwith interest and locality awareness.

The number of messages is compared in thefourth simulation. Figure 6 shows the results ofthis simulation. The graph represents the averagesize of a packet containing environmental data.

Fig. 6. Average packet size in LIASensor.

As expected, LIASensor reduces the size of mes-sages within a WSN. Through the graph analyzes,we can infer that LIASensor can decrease packetssize some minutes after the network deployment.LIASensor reduced the size of packets in 21.9%.This behavior is explained by the use of localityawareness, since this feature associates the sen-sor responses size with the sensor distance to theend-user.

– Network Lifetime

The last simulation tests the network lifetime.We execute this simulation until the death of thenetwork. It was considered that a network wasdead when all the sensors within the most internalconsistency degree did not respond.

Table 2 shows the results of this simulation. Theobtained results show a 16.4% of improvement.Without LIASensor, the network died after 15.26hours of execution, whereas with LIASensor, thenetwork only died after 18.26 hours of execution.

Table 2. Network lifetime with LIASensor.

These results are in accordance with the other re-sults.

As it is known, sensors spent a large amount oftheir energy when they are sensing and transmit-ting data, so by reducing the number of messagescirculating in the network, as well as, the size ofthe messages in it, the increase of the network life-time is a natural result.

6 Conclusion

All the results obtained were very encourag-ing. The simulations numbers shows that withLIASensor the network improve its lifetime. More-over, the results proved that interest and localityawareness can achieve very good results. In fact,each one of these techniques can reduce the num-ber of messages individually.

The explanation for these results can be easilyextracted from LIASensor architecture. The archi-tecture achieved to prioritize messages from sen-sors closer to the user rather than messages fromsensors farther. In addition, the reduction of theamount of information within every response alsocontributed for these good results.

LIASensor is an application layer protocol,which allows the use of bottom layer API. Thisfeature guarantees its use over any routing andMAC protocol without significant adaptations.

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