Routing Paper Wsn

8/2/2019 Routing Paper Wsn

1/23IEEE Wireless Communications December 20046 1536-1284/04/$20.00 2004 IEEE

This research was sup-

ported in part by the

ICUBE initiative of Iowa

State University, Ames,

and the Hashemite Uni-

versity, Zarqa, Jordan.

1In this article, we con-

sider routing toward a BS

only.

W I REL ESS SENSOR NETW ORKS

INTRODUCTIONDue to recent technological advances, the manu-facturing of small and low-cost sensors hasbecome technically and economically feasible.These sensors measure ambient conditions inthe environment surrounding them and thentransform these measurements into signals thatcan be processed to reveal some characteristicsabout phenomena located in the area aroundthese sensors. A large number of these sensorscan be networked in many applications thatrequire unattended operations, hence producinga wireless sensor network (WSN). In fact, theapplications of WSNs are quite numerous. Forexample, WSNs have profound effects on mili-tary and civil applications such as target fieldimaging, intrusion detection, weather monitor-ing, security and tactical surveillance, distributedcomputing, detecting ambient conditions such as

temperature, movement, sound, light, or the

presence of certain objects, inventory control,and disaster management. Deployment of a sen-sor network in these applications can be in ran-dom fashion (e.g., dropped from an airplane in adisaster management application) or manual

(e.g., fire alarm sensors in a facility or sensorsplanted underground for precision agriculture).Creating a network of these sensors can assistrescue operations by locating survivors, identify-ing risky areas, and making the rescue teammore aware of the overall situation in a disasterarea.

Typically, WSNs contain hundreds or thou-sands of these sensor nodes, and these sensorshave the ability to communicate either amongeach other or directly to an external base station(BS). A greater number of sensors allows forsensing over larger geographical regions withgreater accuracy. Figure 1 shows a schematicdiagram of sensor node components. Basically,

each sensor node comprises sensing, processing,transmission, mobilizer, position finding system,and power units (some of these components areoptional, like the mobilizer). The same figureshows the communication architecture of aWSN. Sensor nodes are usually scattered in asensor field, which is an area where the sensornodes are deployed. Sensor nodes coordinateamong themselves to produce high-quality infor-mation about the physical environment. Eachsensor node bases its decisions on its mission,the information it currently has, and its knowl-edge of its computing, communication, and ener-gy resources. Each of these scattered sensornodes has the capability to collect and routedata either to other sensors or back to an exter-nal BS(s).1A BS may be a fixed or mobile nodecapable of connecting the sensor network to anexisting communications infrastructure or to theInternet where a user can have access to thereported data.

In the past few years, intensive research thataddresses the potential of collaboration amongsensors in data gathering and processing, andcoordination and management of the sensingactivity was conducted. In most applications,sensor nodes are constrained in energy supplyand communication bandwidth. Thus, innovativetechniques to eliminate energy inefficiencies that

shorten the lifetime of the network and efficient

JAMAL N. AL-KARAKI, THE HASHEMITE UNIVERSITY

AHMED E. KAMAL, IOWA STATE UNIVERSITY

ABSTRACTWireless sensor networks consist of small

nodes with sensing, computation, and wirelesscommunications capabilities. Many routing,

power management, and data dissemination pro-tocols have been specifically designed for WSNswhere energy awareness is an essential designissue. Routing protocols in WSNs might differdepending on the application and network archi-tecture. In this article we present a survey ofstate-of-the-art routing techniques in WSNs. Wefirst outline the design challenges for routingprotocols in WSNs followed by a comprehensivesurvey of routing techniques. Overall, the rout-ing techniques are classified into three cate-gories based on the underlying networkstructure: flit, hierarchical, and location-basedrouting. Furthermore, these protocols can beclassified into multipath-based, query-based,

negotiation-based, QoS-based, and coherent-based depending on the protocol operation. Westudy the design trade-offs between energy andcommunication overhead savings in every rout-ing paradigm. We also highlight the advantagesand performance issues of each routing tech-nique. The article concludes with possible futureresearch areas.

ROUTING TECHNIQUES IN

WIRELESS SENSOR NETWORKS: A SURVEY

WSNs consist of

small nodes with

sensing, computation,

and wirelesscommunications

capabilities. Many

protocols have been

specifically designed

for WSNs where

energy awareness

is an essential

design issue.


2/23IEEE Wireless Communications December 2004 7

use of the limited bandwidth are highly required.Such constraints combined with a typical deploy-ment of large number of sensor nodes posemany challenges to the design and managementof WSNs and necessitate energy-awareness at alllayers of the networking protocol stack. Forexample, at the network layer, it is highly desir-able to find methods for energy-efficient routediscovery and relaying of data from the sensornodes to the BS so that the lifetime of the net-work is maximized.

Routing in WSNs is very challenging due tothe inherent characteristics that distinguish thesenetworks from other wireless networks likemobile ad hoc networks or cellular networks.First, due to the relatively large number of sen-sor nodes, it is not possible to build a globaladdressing scheme for the deployment of a largenumber of sensor nodes as the overhead of IDmaintenance is high. Thus, traditional IP-basedprotocols may not be applied to WSNs. Further-more, sensor nodes that are deployed in an adhoc manner need to be self-organizing as the adhoc deployment of these nodes requires the sys-tem to form connections and cope with the resul-

tant nodal distribution, especially as theoperation of sensor networks is unattended. InWSNs, sometimes getting the data is moreimportant than knowing the IDs of which nodessent the data. Second, in contrast to typical com-munication networks, almost all applications ofsensor networks require the fbw of sensed datafrom multiple sources to a particular BS. This,however, does not prevent the flow of data to bein other forms (e.g., multicast or peer to peer).Third, sensor nodes are tightly constrained interms of energy, processing, and storage capaci-ties. Thus, they require careful resource manage-ment. Fourth, in most application scenarios,nodes in WSNs are generally stationary after

deployment except for maybe a few mobilenodes. Nodes in other traditional wireless net-works are free to move, which results in unpre-dictable and frequent topological changes.However, in some applications, some sensornodes may be allowed to move and change theirlocation (although with very low mobility). Fifth,sensor networks are application-specific (i.e.,design requirements of a sensor network changewith application). For example, the challengingproblem of low-latency precision tactical surveil-lance is different from that of a periodic weathermonitoring task. Sixth, position awareness ofsensor nodes is important since data collection isnormally based on the location. Currently, it isnot feasible to use Global Positioning System(GPS) hardware for this purpose. Methods basedon triangulation [1], for example, allow sensornodes to approximate their position using radiostrength from a few known points. It is found in[1] that algorithms based on triangulation ormultilateration can work quite well under condi-tions where only very few nodes know their posi-tions a priori (e.g., using GPS hardware). Still, itis favorable to have GPS-free solutions [2] forthe location problem in WSNs. Finally, data col-lected by many sensors in WSNs is typicallybased on common phenomena, so there is a highprobability that this data has some redundancy.

Such redundancy needs to be exploited by the

routing protocols to improve energy and band-width utilization. Usually, WSNs aredata-centricnetworks in the sense that data is requestedbased on certain attributes (i.e., attribute-basedaddressing). An attribute-based address is com-posed of a set of attribute-value pair query. Forexample, if the query is something like [tempera-ture > 60F], sensor nodes that sense tempera-ture > 60F only need to respond and reporttheir readings.

Due to such differences, many new algo-rithms have been proposed for the routing prob-lem in WSNs. These routing mechanisms havetaken into consideration the inherent features ofWSNs along with the application and architec-

ture requirements. The task of finding and main-taining routes in WSNs is nontrivial since energyrestrictions and sudden changes in node status(e.g., failure) cause frequent and unpredictabletopological changes. To minimize energy con-sumption, routing techniques proposed in the lit-erature for WSNs employ some well-knownrouting tactics as well as tactics special to WSNs,such as data aggregation and in-network pro-cessing, clustering, different node role assign-ment, and data-centric methods. Almost all ofthe routing protocols can be classified accordingto the network structure as flit, hierarchical, orlocation-based. Furthermore, these protocols canbe classified into multipath-based, query-based,negotiation-based, quality of service (QoS)-based, and coherent-based depending on theprotocol operation. In flat networks all nodes playthe same role, while hierarchical protocols aimto cluster the nodes so that cluster heads can dosome aggregation and reduction of data in orderto save energy. Location-based protocols utilizeposition information to relay the data to thedesired regions rather than the whole network.The last category includes routing approachesbased on protocol operation, which vary accord-ing to the approach used in the protocol. In thisarticle we explore these routing techniques inWSNs that have been developed in recent years

and develop a classification for these protocols.

n Figure 1. The components of a sensor node.Processing unitPosition finding system MobilizerTarget

Sensor node

BS

User

Internet

ProcessorStorage

Sensing unit

Sensor

Transmissionunit

Powergenerator

ADC Transceiver

Power unit


3/23IEEE Wireless Communications December 20048

Then we discuss each of the routing protocolsunder this classification. Our objective is to pro-vide deeper understanding of the current routingprotocols in WSNs and identify some openresearch issues that can be further pursued.

Although there are some previous efforts onsurveying the characteristics, applications, andcommunication protocols in WSNs [3, 4], thescope of the survey presented in this article isdistinguished from these surveys in many aspects.The surveys in [3, 4] addressed several design

issues and techniques for WSNs describing thephysical constraints on sensor nodes, applica-tions, architectural attributes, and the protocolsproposed in all layers of the network stack.However, these surveys were not devoted torouting only. Due to the importance of routingin WSNs and the availability of a significantbody of literature on this topic, a detailed surveybecomes necessary and useful at this stage. Ourwork is a dedicated s tudy of the network layer,describing and categorizing the differentapproaches to data routing. In addition, we sum-marize routing challenges and design issues thatmay affect the performance of routing protocols

in WSNs. The rest of this article is organized asfollows. We discuss routing challenges anddesign issues in WSNs. A classification and com-prehensive survey of routing techniques in WSNsis presented. A summary of future researchdirections on routing in WSNs is discussed. Wethen conclude with final remarks.

ROUTING CHALLENGES ANDDESIGN ISSUES IN WSNS

Despite the innumerable applications of WSNs,these networks have several restrictions, such aslimited energy supply, limited computing power,

and limited bandwidth of the wireless links con-necting sensor nodes. One of the main designgoals of WSNs is to carry out data communica-tion while trying to prolong the lifetime of thenetwork and prevent connectivity degradation byemploying aggressive energy management tech-niques. The design of routing protocols in WSNsis influenced by many challenging factors. Thesefactors must be overcome before efficient com-munication can be achieved in WSNs. In the fol-lowing, we summarize some of the routingchallenges and design issues that affect the rout-ing process in WSNs.

Node deployment: Node deployment in WSNsis application-dependent and can be either man-ual (deterministic) or randomized. In manualdeployment, the sensors are manually placedand data is routed through predetermined paths.However, in random node deployment, the sen-sor nodes are scattered randomly, creating an adhoc routing infrastructure. If the resultant distri-bution of nodes is not uniform, optimal cluster-ing becomes necessary to allow connectivity andenable energy-efficient network operation. Inter-sensor communication is normally within shorttransmission ranges due to energy and band-width limitations. Therefore, it is most likely thata route will consist of multiple wireless hops.

Energy consumption without losing accuracy:

Sensor nodes can use up their limited supply of

energy performing computations and transmit-ting information in a wireless environment. Assuch, energy-conserving forms of communicationand computation are essential. Sensor node life-time shows a strong dependence on battery life-time [5]. In a multihop WSN, each node plays adual role as data sender and data router. Themalfunctioning of some sensor nodes due topower failure can cause significant topologicalchanges, and might require rerouting of packetsand reorganization of the network.

Data reporting method: Data reporting inWSNs is application-dependent and also dependson the time criticality of the data. Data reportingcan be categorized as either time-driven, event-driven, query-driven, or a hybrid of all thesemethods. The time-driven delivery method issuitable for applications that require periodicdata monitoring. As such, sensor nodes will peri-odically switch on their sensors and transmitters,sense the environment, and transmit the data ofinterest at constant periodic time intervals. Inevent-driven and query-driven methods, sensornodes react immediately to sudden and drasticchanges in the value of a sensed attribute due to

the occurrence of a certain event, or respond toa query generated by the BS or another node inthe network. As such, these are well suited totime-critical applications. A combination of theprevious methods is also possible. The routingprotocol is highly influenced by the data report-ing method in terms of energy consumption androute calculations.

Node/link heterogeneity: In many studies, allsensor nodes were assumed to be homogeneous(i.e., have equal capacity in terms of computa-tion, communication, and power). However,depending on the application a sensor node canhave a different role or capability. The existenceof a heterogeneous set of sensors raises many

technical issues related to data routing. Forexample, some applications might require adiverse mixture of sensors for monitoring tem-perature, pressure, and humidity of the sur-rounding environment, detecting motion viaacoustic signatures, and capturing images orvideo tracking of moving objects. Either thesespecial sensors can be deployed independentlyor the different functionalities can be included inthe same sensor nodes. Even data reading andreporting can be generated from these sensors atdifferent rates, subject to diverse QoS con-straints, and can follow multiple data reportingmodels. For example, hierarchical protocols des-ignate a cluster head node different from thenormal sensors. These cluster heads can be cho-sen from the deployed sensors or be more pow-erful than other sensor nodes in terms of energy,bandwidth, and memory. Hence, the burden oftransmission to the BS is handled by the set ofcluster heads.

Fault tolerance: Some sensor nodes may failor be blocked due to lack of power, physicaldamage, or environmental interference. The fail-ure of sensor nodes should not affect the overalltask of the sensor network. If many nodes fail,medium access control (MAC) and routing pro-tocols must accommodate formation of newlinks and routes to the data collection BSs. This

may require actively adjusting transmit powers

One of the main

design goals of

WSNs is to carry out

data communication

while trying to

prolong the lifetime

of the network andprevent connectivity

degradation by

employing

aggressive energy

management

techniques.



and signaling rates on the existing links to reduceenergy consumption, or rerouting packetsthrough regions of the network where moreenergy is available. Therefore, multiple levels ofredundancy may be needed in a fault-tolerantsensor network.

Scalability: The number of sensor nodesdeployed in the sensing are a may be on theorder of hundreds or thousands, or more. Anyrouting scheme must be able to work with thishuge number of sensor nodes. In addition, sen-

sor network routing protocols should be scalableenough to respond to events in the environment.Until an event occurs, most sensors can remainin the sleep state, with data from the few remain-ing sensors providing coarse quality.

Network dynamics: In many studies, sensornodes are assumed fixed. However, in manyapplications both the BS or sensor nodes can bemobile [6]. As such, routing messages from or tomoving nodes is more challenging since routeand topology stability become important issues,in addition to energy, bandwidth, and so forth.Moreover, the phenomenon can be mobile (e.g.,a target detection/ tracking application). On the

other hand, sensing fixed events allows the net-work to work in a reactive mode (i.e., generatingtraffic when reporting), while dynamic events inmost applications require periodic reporting tothe BS.

Transmission media: In a multihop sensornetwork, communicating nodes are linked by awireless medium. The traditional problems asso-ciated with a wireless channel (e.g., fading, higherror rate) may also affect the operation of thesensor network. In general, the required band-width of sensor data will be low, on the order of1100 kb/s. Related to the transmission media isthe design of MAC. One approach to MACdesign for sensor networks is to use time-division

multiple access (TDMA)-based protocols thatconserve more energy than contention-basedprotocols like carrier sense multiple access(CSMA) (e.g., IEEE 802.11). Bluetooth technol-ogy [7] can also be used.

Connectivity: High node density in sensornetworks precludes them from being completelyisolated from each other. Therefore, sensornodes are expected to be highly connected. This,however, may not prevent the network topologyfrom being variable and the network size fromshrinking due to sensor node failures. In addi-tion, connectivity depends on the possibly ran-dom distribution of nodes.

Coverage: In WSNs, each sensor node obtainsa certain view of the environment. A given sen-sors view of the environment is limited in bothrange and accuracy; it can only cover a limitedphysical area of the environment. Hence, areacoverage is also an important design parameterin WSNs.

Data aggregation: Since sensor nodes maygenerate significant redundant data, similarpackets from multiple nodes can be aggregatedto reduce the number of transmissions. Dataaggregation is the combination of data from dif-ferent sources according to a certain aggregationfunction (e.g., duplicate suppression, minima,maxima, and average). This technique has been

used to achieve energy efficiency and data trans-

fer optimization in a number of routing proto-cols. Signal processing methods can also be usedfor data aggregation. In this case, it is referredto asdata fusionwhere a node is capable of pro-ducing a more accurate output signal by usingsome techniques such as beamforming to com-bine the incoming signals and reducing the noisein these signals.

Quality of service: In some applications, datashould be delivered within a certain period oftime from the moment it is sensed, or it will be

useless. Therefore, bounded latency for datadelivery is another condition for time-con-strained applications. However, in many applica-tions, conservation of energy, which is directlyrelated to network lifetime, is considered rela-tively more important than the quality of datasent. As energy is depleted, the network may berequired to reduce the quality of results in orderto reduce energy dissipation in the nodes andhence lengthen the total network lifetime.Hence, energy-aware routing protocols arerequired to capture this requirement.

ROUTING PROTOCOLS IN WSNSIn this section we survey the state-of-the-artrouting protocols for WSNs. In general, routingin WSNs can be divided into flat-based routing,hierarchical-based routing, and location-basedrouting depending on the network structure. Inflat-based routing, all nodes are typicallyassigned equal roles or functionality. In hierar-chical-based routing, nodes will play differentroles in the network. In location-based routing,sensor nodes positions are exploited to routedata in the network. A routing protocol is con-sidered adaptive if certain system parameterscan be controlled in order to adapt to currentnetwork conditions and available energy levels.

Furthermore, these protocols can be classifiedinto multipath-based, query-based, and negotia-tion-based, QoS-based, orcoherent-based routingtechniques depending on the protocol operation.In addition to the above, routing protocols canbe classified into three categories, proactive,reactive, and hybrid, depending on how thesource finds a route to the destination. In proac-tive protocols, all routes are computed beforethey are really needed, while in reactive proto-cols, routes are computed on demand. Hybridprotocols use a combination of these two ideas.When sensor nodes are static, it is preferable tohave table-driven routing protocols rather thanreactive protocols. A significant amount of ener-gy is used in route discovery and setup of reac-tive protocols. Another class of routing protocolsis called cooperat ive. In cooperative routing,nodes send data to a central node where datacan be aggregated and may be subject to furtherprocessing, hence reducing route cost in terms ofenergy use. Many other protocols rely on timingand position information. We also shed somelight on these types of protocols in this article.In order to streamline this survey, we use a clas-sification according to the network structure andprotocol operation (routing criteria). The classi-fication is shown in Fig. 2 where numbers in thefuture indicate the references.

In the rest of this section we present a

In WSNs, each

sensor node obtains

a certain view of the

environment. A given

sensors view of the

environment is

limited in both rangeand accuracy;

it can only cover a

limited physical area

of the environment.

Hence, area

coverage is also an

important design

parameter in WSNs.



detailed overview of the main routing paradigmsin WSNs. We start with network-structure-basedprotocols.

NETWORK-STRUCTURE-BASED PROTOCOLSThe underlying network structure can play a sig-nificant role in the operation of the routing pro-tocol in WSNs. In this section we survey in detailmost of the protocols that fall into this category.

Flat Routing The first category of routing proto-cols are the multihop flat routing protocols. Inflat networks, each node typically plays the samerole and sensor nodes collaborate to perform thesensing task. Due to the large number of suchnodes, it is not feasible to assign a global identi-fier to each node. This consideration has led todata-centric routing, where the BS sends queries

to certain regions and waits for data from thesensors located in the selected regions. Sincedata is being requested through queries,attribute-based naming is necessary to specifythe properties of data. Early work on data cen-tric routing (e.g., SPIN and directed diffusion[8]) were shown to save energy through datanegotiation and elimination of redundant data.These two protocols motivated the design ofmany other protocols that follow a similar con-cept. In the rest of this subsection, we summa-rize these protocols, and highlight theiradvantages and performance issues.

Sensor Protocols for Information via Negoti-ation: Heinzelman et al. in [9, 10] proposed afamily of adaptive protocols called Sensor Proto-cols for Information via Negotiation (SPIN) thatdisseminate all the information at each node toevery node in the network assuming that allnodes in the network are potential BSs. Thisenables a user to query any node and get therequired information immediately. These proto-cols make use of the property that nodes in closeproximity have similar data, and hence there is aneed to only distribute the data other nodes donot posses. The SPIN family of protocols usesdata negotiation and resource-adaptive algo-rithms. Nodes running SPIN assign a high-levelname to completely describe their collected data

(called meta-data) and perform metadata negoti-

ations before any data is transmitted. Thisensures that there is no redundant data sentthroughout the network. The semantics of the

meta-data format is application-specific and notspecified in SPIN. For example, sensors mightuse their unique IDs to report meta-data if theycover a certain known region. In addition, SPINhas access to the current energy level of thenode and adapts the protocol it is running basedon how much energy is remaining. These proto-cols work in a time-driven fashion and distributethe information all over the network, even whena user does not request any data.

The SPIN family is designed to address thedeficiencies of classic flooding by negotiationand resource adaptation. The SPIN family ofprotocols is designed based on two basic ideas:

1) Sensor nodes operate more efficiently and

conserve energy by sending data that describethe sensor data instead of sending all the data;for example, image and sensor nodes must moni-tor the changes in their energy resources.

2) Conventional protocols like flooding orgossiping-based routing protocols [11] wasteenergy and bandwidth when sending extra andunnecessary copies of data by sensors coveringoverlapping areas. The drawbacks of floodinginclude implosion, which is caused by duplicatemessages sent to the same node, overlap whentwo nodes sensing the same region send similarpackets to the same neighbor, and resourceblindness in consuming large amounts of energywithout consideration for energy constraints.Gossiping avoids the problem of implosion byjus t selecting a random node to which to sendthe packet rather than broadcasting the packetblindly. However, this causes delays in propaga-tion of data through the nodes.

SPINs meta-data negotiation solves the clas-sic problems of flooding, thus achieving a lot ofenergy efficiency. SPIN is a three-stage protocolas sensor nodes use three types of messages,ADV, REQ, and DATA, to communicate. ADVis used to advertise new data, REQ to requestdata, and DATA is the actual message itself.The protocol starts when a SPIN node obtainsnew data it is willing to share. It does so by

broadcasting an ADV message containing meta-

nnnn Figure 2.Routing protocols in WSNs: a taxonomy.Flatnetworkrouting2,3,7,1314,15,16,1839,41,49 1,8,9,12,1719,22,23,3531,26,48 25,33,4246,47Hierarchicalnetworkrouting Location-basedroutingNetwork structure

Negotiation-based

routing

3,72,10,26,28

29,34 2,20,27

Multipath-based

routing

Query-based

routing

11,44

QoS-based

routing

11,2,33

Coherent-based

routing

Protocol operation

Routing protocols in WSNs



data. If a neighbor is interested in the data, itsends a REQ message for the DATA and theDATA is sent to this neighbor node. The neigh-bor sensor node then repeats this process withits neighbors. As a result, the entire sensor areawill receive a copy of the data.

The SPIN family of protocols includes manyprotocols. The main two are called SPIN-1 andSPIN-2; they incorporate negotiation beforetransmitting data in order to ensure that onlyuseful information will be transferred. Also, each

node has its own resource manager that keepstrack of resource consumption and is polled bythe nodes before data transmission. The SPIN-1protocol is a three-stage protocol, as describedabove. An extension to SPIN-1 is SPIN-2, whichincorporates a threshold-based resource aware-ness mechanism in addition to negotiation.When energy in the nodes is abundant, SPIN-2communicates using the three-stage protocol ofSPIN1. However, when the energy in a nodestarts approaching a low threshold, it reduces itsparticipation in the protocol; that is, it partici-pates only when it believes it can complete allthe other stages of the protocol without going

below the low energy threshold. In conclusion,SPIN-1 and SPIN-2 are simple protocols thatefficiently disseminate data while maintaining noper-neighbor state. These protocols are well suit-ed to an environment where the sensors aremobile because they base their forwarding deci-sions on local neighborhood information. Otherprotocols of the SPIN family are (please refer to[3, 7] for more details): SPIN-BC: This protocol is designed for broad-

cast channels. SPIN-PP: This protocol is designed for point-

to-point communication (i.e., hop-by-hoprouting).

SPIN-EC: This protocol works similar to

SPIN-PP, but with an energy heuristic addedto it.

SPIN-RL: When a channel is lossy, a protocolcalled SPIN-RL is used where adjustments areadded to the SPIN-PP protocol to account forthe lossy channel.One of the advantages of SPIN is that topo-

logical changes are localized since each nodeneed know only its single-hop neighbors. SPINprovides more energy savings than flooding, andmetadata negotiation almost halves the redun-dant data. However, SPINs data advertisementmechanism cannot guarantee delivery of data.To see this, consider the application of intrusiondetection where data should be reliably reportedover periodic intervals, and assume that nodesinterested in the data are located far away fromthe source node, and the nodes between sourceand destination nodes are not interested in thatdata; such data will not be delivered to the desti-nation at all.

Directed diffusion: In [12], C. Intanagonwi-wat et al. proposed a popular data aggregationparadigm for WSNs called directed diffusion.Directed diffusion is a data-centric (DC) andapplication-aware paradigm in the sense that alldata generated by sensor nodes is named byattribute-value pairs. The main idea of the DCparadigm is to combine the data coming from

different sources en route (in-network aggrega-

tion) by eliminating redundancy, minimizing thenumber of transmissions, thus saving networkenergy and prolonging its lifetime. Unlike tradi-tional end-to-end routing, DC routing findsroutes from multiple sources to a single destina-tion that allows in-network consolidation ofredundant data.

In directed diffusion, sensors measure eventsand create gradients of information in theirrespective neighborhoods. The BS requests databy broadcasting interests. An interest describes a

task required to be done by the network. Aninterest diffuses through the network hop byhop, and is broadcast by each node to its neigh-bors. As the interest is propagated throughoutthe network, gradients are set up to draw datasatisfying the query toward the requesting node(i.e., a BS may query for data by disseminatinginterests and intermediate nodes propagatethese interests). Each sensor that receives theinterest sets up a gradient toward the sensornodes from which it receives the interest. Thisprocess continues until gradients are set up fromthe sources back to the BS. More generally, agradient specifies an attribute value and a direc-

tion. The strength of the gradient may be differ-ent toward different neighbors, resulting indifferent amounts of information flow. At thisstage, loops are not checked, but are removed ata later stage. Figure 3 shows an example of theworking of directed diffusion (sending interests,building gradients, and data dissemination).When interests fit gradients, paths of informa-tion flow are formed from multiple paths, andthen the best paths are reinforced to preventfurther flooding according to a local rule. Inorder to reduce communication costs, data isaggregated on the way. The goal is to find agood aggregation tree that gets the data fromsource nodes to the BS. The BS periodically

refreshes and resends the interest when it startsto receive data from the source(s). This is neces-sary because interests are not reliably transmit-ted throughout the network.

All sensor nodes in a directed-diffusion-basednetwork are application-aware, which enablesdiffusion to achieve energy savings by selectingempirically good paths, and by caching and pro-cessing data in the network. Caching canincrease the efficiency, robustness, and scalabili-ty of coordination between sensor nodes, whichis the essence of the data diffusion paradigm.Other usage of directed diffusion is to sponta-neously propagate an important event to somesections of the sensor network. Such a type ofinformation retrieval is well suited only to persis-tent queries where requesting nodes are notexpecting data that satisfy a query for a durationof time. This makes it unsuitable for one-timequeries, as it is not worth setting up gradientsfor queries that use the path only once.

The performance of data aggregation meth-ods used in the directed diffusion paradigm isaffected by a number of factors, including thepositions of the source nodes in the network, thenumber of sources, and the communication net-work topology. In order to investigate these fac-tors, two models of source placement (shown inFig. 4) were studied in [12]. These models are

called the event radius (ER) model and the ran-

The goal is to find a

good aggregation

tree that gets the

data from source

nodes to the BS.

The BS periodically

refreshes andresends the interest

when it starts to

receive data from

the source(s). This is

necessary because

interests are not

reliably transmitted

throughout thenetwork.



dom sources (RS) model. In the ER model, asingle point in the network area is defined as the

location of an event. This may correspond to ave hi cl e or som e ot he r phe no men on be in gtracked by the sensor nodes. All nodes within adistance S (called the sensing range) of thisevent that are not BSs are considered to be datasources. The average number of sources isapproximately S2n in a unit area network withn sensor nodes. In the RS model, k of the nodesthat are not BSs are randomly selected to besources. Unlike the ER model, the sources arenot necessarily clustered near each other. Inboth models of source placement, for a givenenergy budget, a greater number of sources canbe connected to the BS. However, each one per-forms better in terms of energy consumption

depending on the application. In conclusion, theenergy savings with aggregation used in directeddiffusion can be transformed to provide a greaterdegree of robustness with respect to dynamics inthe sensed phenomena.

Directed diffusion differs from SPIN in twoaspects. First, directed diffusion issues dataqueries on demand as the BS sends queries tothe sensor nodes by flooding some tasks. InSPIN, however, sensors advertise the availabilityof data, allowing interested nodes to query thatdata. Second, all communication in directed dif-fusion is neighbor to neighbor with each nodehaving the capability to perform data aggrega-tion and caching. Unlike SPIN, there is no needto maintain global network topology in directeddiffusion. However, directed diffusion may notbe applied to applications (e.g., environmentalmonitoring) that require continuous data deliv-ery to the BS. This is because the query-drivenon-demand data model may not help in thisregard. Moreover, matching data to queriesmight require some extra overhead at the sensornodes.

Rumor routing: Rumor routing [13] is a vari-ation of directed diffusion and is mainly intend-ed for applications where geographic routing isnot feasible. In general, directed diffusion usesflooding to inject the query to the entire net-

work when there is no geographic cri ter ion to

diffuse tasks. However, in some cases there isonly a small amount of data requested from the

nodes; thus, the use of flooding is unnecessary.An alternative approach is to flood the events ifthe number of events is small and the numberof queries is large. The key idea is to route thequeries to the nodes that have observed a par-ticular event rather than flooding the entire net-work to retrieve information about the occurringevents. In order to flood events through the net-wo rk , th e ru mo r ro ut in g al go ri th m emp lo yslong-lived packets called agents . When a nodedetects an event, it adds the event to its localtable, called an events table, and generates anagent. Agents travel the network in order topropagate information about local events to dis-tant nodes. When a node generates a query for

an event, the nodes that know the route mayrespond to the query by inspecting its eventtable. Hence, there is no need to flood thewhole network, which reduces the communica-tion cost. On the other hand, rumor routingmaintains only one path between source anddestination as opposed to directed diffusionwhe re dat a ca n be ro ut ed th ro ug h mu lt ip lepaths at low rates. Simulation results showedthat rumor routing can achieve significant ener-gy savings compared to event flooding and canalso handle a nodes failure. However, rumorrouting performs well only when the number ofevents is small. For a large number of events,the cost of maintaining agents and event tablesin each node becomes infeasible if there is notenough interest in these events from the BS.Moreover, the overhead associated with rumorrouting is controlled by different parametersused in the algorithm such as time to live (TTL)pertaining to queries and agents. Since thenodes become aware of events through theevent agents, the heuristic for defining the routeof an event agent highly affects the performanceof next-hop selection in rumor routing.

Minimum Cost Forwarding Algorithm: TheMinimum Cost Forwarding Algorithm (MCFA)[8] exploits the fact that the direction of routingis always known (i.e., toward the fixed external

BS). Hence, a sensor node need not have a

nnnn Figure 3.An example of interest diffusion in a sensor network.(a) Propagate interest

Source Sink

Source Sink

(b) Set up gradients

(c) Send data and path reinforcement

Source Sink

The energy savings

with aggregation

used in the directed

diffusion can be

transformed to

provide a greater

degree of robustnesswith respect to

dynamics in the

sensed phenomena.



unique ID nor maintain a routing table. Instead,each node maintains the least cost estimate fromitself to the BS. Each message to be forwardedby the sensor node is broadcast to its neighbors.When a node receives the message, it checks if it

is on the least cost path between the source sen-sor node and the BS. If this is the case, itrebroadcasts the message to its neighbors. Thisprocess repeats until the BS is reached.

In MCFA, each node should know the leastcost path estimate from itself to the BS. This isobtained as follows. The BS broadcasts a mes-sage with the cost set to zero, while every nodeinitially sets its least cost to the BS to infinity(). Each node, upon receiving the broadcastmessage originated at the BS, checks to see ifthe estimate in the message plus the link onwhich it is received is less than the current esti-mate. If yes, the current estimate and the esti-mate in the broadcast message are updated. If

the received broadcast message is updated, it isresent; otherwise, it is purged and nothing fur-ther is done. However, the previous proceduremay result in some nodes having multipleupdates, and those nodes far away from the BSwill get more updates from those closer to theBS. To avoid this, MCFA was modified to runa backoff algorithm at the setup phase. Thebackoff algorithm dictates that a node will notsend the updated message until a * lc t imeunits have elapsed from the time at which themessage is updated, where a is a constant andlc is the link cost at which the message wasreceived.

Gradient-based routing: Schurgers et al. [14]proposed another variant of directed diffusion,called gradient-based routing (GBR). The keyidea in GBR is to memorize the number of hopswhen the interest is diffused through the wholenetwork. As such, each node can calculate aparameter called the height of the node, which isthe minimum number of hops to reach the BS.The difference between a nodes height and thatof its neighbor is considered the gradient on thatlink. A packet is forwarded on a link with thelargest gradient. GBR uses some auxiliary tech-niques such as data aggregation and trafficspreading in order to uniformly divide the trafficover the network. When multiple paths pass

through a node, which acts as a relay node, that

relay node may combine data according to a cer-tain function. In GBR, three different data dis-semination techniques have been discussed: A stochastic scheme, where a node picks one

gradient at random when there are two or

more next hops that have the same gradient An energy-based scheme, where a nodeincreases its height when its energy dropsbelow a certain threshold so that other sensorsare discouraged from sending data to thatnode

A stream-based scheme, where new streamsare not routed through nodes that are cur-rently part of the path of other streamsThe main objective of these schemes is to

obtain balanced distribution of the traffic in thenetwork, thus increasing the network lifetime.Simulation results of GBR showed that GBRoutperforms directed diffusion in terms of totalcommunication energy.

Information-driven sensor querying and con-strained anisotropic diffusion routing: Tworouting techniques, information-driven sensorquerying (IDSQ) and constrained anisotropicdiffusion routing (CADR), were proposed in[15]. CADR aims to be a general form of direct-ed diffusion. The key idea is to query sensorsand route data in the network such that informa-tion gain is maximized while latency and band-width are minimized. CADR diffuses queries byusing a set of information criteria to select whichsensors can get the data. This is achieved by acti-vating only the sensors that are close to a partic-ular event and dynamically adjusting data routes.The main difference from directed diffusion isthe consideration of information gain in additionto communication cost. In CADR, each nodeevaluates an information/cost objective androutes data based on the local information/costgradient and end-user requirements. Estimationtheory was used to model information utility. InIDSQ, the querying node can determine whichnode can provide the most useful informationwith the additional advantage of balancing theenergy cost. However, IDSQ does not specifical-ly define how the query and information arerouted between sensors and the BS. Therefore,IDSQ can be seen as a complementary optimiza-tion procedure. Simulation results showed that

these approaches are more energy-efficient than

nnnn Figure 4. Two models used in a data-centric routing paradigm such as directed diffusion: a) event radiusmodel; b) random source model.(a) Source nodeSink nodeSink (b)SinkThe key idea in GBR

is to memorize the

number of hops

when the interest is

diffused through the

whole network.

As such, each nodecan calculate a

parameter called the

height of the node,

which is the

minimum number

of hops to

reach the BS.



directed diffusion where queries are diffused inan isotropic fashion and reach nearest neighborsfirst.

COUGAR: Another data-centric protocolcalled COUGAR [16] views the network as ahuge distributed database system. The key ideais to use declarative queries in order to abstractquery processing from the network layer func-tions such as selection of relevant sensors and soon. COUGAR utilizes in-network data aggrega-tion to obtain more energy savings. The abstrac-

tion is supported through an additional querylayer that lies between the network and applica-tion layers. COUGAR incorporates an architec-ture for the sensor database system where sensornodes select a leader node to perform aggrega-tion and transmit the data to the BS. The BS isresponsible for generating a query plan thatspecifies the necessary information about thedata flow and in-network computation for theincoming query, and sends it to the relevantnodes. The query plan also describes how toselect a leader for the query. The architectureprovides in-network computation ability that canprovide energy efficiency in situations when the

generated data is huge. COUGAR provides anetwork-layer-independent method for dataquery. However, COUGAR has some draw-backs. First, the addition of a query layer oneach sensor node may add extra overhead interms of energy consumption and memory stor-age. Second, to obtain successful in-networkdata computation, synchronization among nodesis required (not all data are received at the sametime from incoming sources) before sending thedata to the leader node. Third, the leader nodesshould be dynamically maintained to preventthem from being hotspots (failure-prone).

AC QU IR E: In [17], Sadagopan et al . pro-posed a technique for querying sensor networks

called Active Qwery Forwarding in Sensor Net-wo rk s (A CQ UI RE) . Sim il ar to CO UG AR,ACQUIRE views the network as a dis tributeddatabase where complex queries can be furtherdivided into several subqueries. The operation ofACQUIRE can be described as follows. The BSnode sends a query, which is then forwarded byeach node receiving the query. During this, eachnode tries to respond to the query partially byusing its precached information and then for-wa rd s it to an ot he r se ns or no de . If th e pr e-cached information is not up-to-date, the nodesgather information from their neighbors within alookahead ofd hops. Once the query is resolvedcompletely, it is sent back through either thereverse or shortest path to the BS. Hence,ACQUIRE can deal with complex quer ies byallowing many nodes to send responses. Notethat directed diffusion may not be used for com-plex queries due to energy considerations asdirected diffusion also uses a flooding-basedquery mechanism for continuous and aggregatequeries. On the other hand, ACQUIRE can pro-vide efficient querying by adjusting the value ofthe lookahead parameter d. Whend is equal tonetwork diameter, ACQUIRE behaves similar toflooding. However, the query has to travel morehops ifd is too small. A mathematical modelingwas used to find an optimal value of the parame-

ter d for a grid of sensors where each node has

four immediate neighbors. However, there is nova li da ti on of re su lt s th roug h si mu la ti on . Toselect the next node for forwarding the query,ACQUIRE either picks it randomly or the selec-tion is based on maximum potential query satis-faction. Recall that either selection of the nextnode is based on information gain (CADR andIDSQ) or the query is forwarded to a node thatknows the path to the searched event (rumorrouting).

Energy-Aware Routing: The objective of the

Energy-Aware Routing protocol [18], a destina-tion-initiated reactive protocol, is to increase thenetwork lifetime. Although this protocol is simi-lar to directed diffusion, it differs in the sensethat it maintains a set of paths instead of main-taining or enforcing one optimal path at higherrates. These paths are maintained and chosen bymeans of a certain probability. The value of thisprobability depends on how low the energy con-sumption is that each path can achieve. By hav-ing paths chosen at different times, the energy ofany single path will not deplete quickly. This canachieve longer network lifetime as energy is dis-sipated more equally among all nodes. Network

survivability is the main metric of this protocol.The protocol assumes that each node is address-able through class-based addressing that includesthe locations and types of the nodes. The proto-col initiates a connection through localizedflooding, which is used to discover all routesbetween a source/ destination pair and theircosts, thus building up the routing tables. High-cost paths are discarded, and a forwarding tableis built by choosing neighboring nodes in a man-ner that is proportional to their cost. Then for-wa rd in g ta bl es ar e us ed to se nd da ta to th edestination with a probability inversely propor-tional to the node cost. Localized flooding isperformed by the destination node to keep the

paths alive. Compared to directed diffusion, thisprotocol provides an overall improvement of21.5 percent energy saving and a 44 percentincrease in network lifetime. However, theapproach requires gathering location informa-tion and setting up the addressing mechanismfor the nodes, which complicate route setupcompared to directed diffusion.

Routing protocols with random walks: Theobjective of the random-walks-based routingtechnique [19] is to achieve load balancing in astatistical sense by making use of multipath rout-ing in WSNs. This technique considers onlylarge-scale networks where nodes have very lim-ited mobility. In this protocol, it is assumed thatsensor nodes can be turned on or off at randomtimes. Furthermore, each node has a uniqueidentifier but no location information is needed.Nodes were arranged such that each node fallsexactly on one crossing point of a regular grid ona plane, but the topology can be irregular. Tofind a route from a source to its destination, thelocation information or lattice coordination isobtained by computing distances between nodesusing the distributed asynchronous version of thewell-known Bellman-Ford algorithm. An inter-mediate node would select as the next hop theneighboring node that is closer to the destina-tion according to a computed probability. By

carefully manipulating this probability, some

The objective of

random walks based

routing technique is

to achieve load

balancing in a

statistical sense and

by making use ofmulti-path routing

in WSNs. This

technique considers

only large scale

networks where

nodes have very

limited mobility.



kind of load balancing can be obtained in thenetwork. The routing algorithm is simple asnodes are required to maintain little state infor-mation. Moreover, different routes are chosen atdifferent times even for the same pair of sourceand destination nodes. However, the main con-cern about this protocol is that the topology ofthe network may not be practical.

Hierarchical Routing Hierarchical or cluster-based routing methods, originally proposed in

wireline networks, are well-known techniqueswith special advantages related to scalability andefficient communication. As such, the concept ofhierarchical routing is also utilized to performenergy-efficient routing in WSNs. In a hierarchi-cal architecture, higher-energy nodes can beused to process and send the information, whilelow-energy nodes can be used to perform thesensing in the proximity of the target. The cre-ation of clusters and assigning special tasks tocluster heads can greatly contribute to overallsystem scalability, lifetime, and energy efficiency.Hierarchical routing is an efficient way to lowerenergy consumption within a cluster, performing

data aggregation and fusion in order to decreasethe number of transmitted messages to the BS.Hierarchical routing is mainly two-layer routingwhere one layer is used to select cluster headsand the other for routing. However, most tech-niques in this category are not about routing, butrather who and when to send or process/ aggre-gate the information, channel allocation, and soon, which can be orthogonal to the multihoprouting function.

LEACH protocol: Heinzelman,et al. [5] intro-duced a hierarchical clustering algorithm forsensor networks, called Low Energy AdaptiveClustering Hierarchy (LEACH). LEACH is acluster-based protocol, which includes distribut-

ed cluster formation. LEACH randomly selects afew sensor nodes as cluster heads (CHs) androtates this role to evenly distribute the energyload among the sensors in the network. InLEACH, the CH nodes compress data arrivingfrom nodes that belong to the respective cluster,and send an aggregated packet to the BS inorder to reduce the amount of information thatmust be transmitted to the BS. LEACH uses aTDMA/code-division multiple access (CDMA)MAC to reduce intercluster and intracluster col-lisions. However, data collection is centralizedand performed periodically. Therefore, this pro-tocol is most appropriate when there is a needfor constant monitoring by the sensor network.A user may not need all the data immediately.Hence, periodic data transmissions are unneces-sary, and may drain the limited energy of thesensor nodes. After a given interval of time, ran-domized rotation of the role of CH is conductedso that uniform energy dissipation in the sensornetwork is obtained. The authors found, basedon their simulation model, that only 5 percent ofthe nodes need to act as CHs.

The operation of LEACH is separated intotwo phases, the setup phase and the steady statephase. In the setup phase, the clusters are orga-nized and CHs are selected. In the steady statephase, the actual data transfer to the BS takes

place. The duration of the steady state phase is

longer than the duration of the setup phase inorder to minimize overhead. During the setupphase, a predetermined fraction of nodes, p,elect themselves as CHs as follows. A sensornode chooses a random number, r, between 0and 1. If this random number is less than athreshold value, T(n), the node becomes a CHfor the current round. The threshold value is cal-culated based on an equation that incorporatesthe desired percentage to become a CH, the cur-rent round, and the set of nodes that have not

been selected as a CH in the last (1/P) rounds,denoted G. It is given by

where G is the set of nodes that are involved inthe CH election. All elected CHs broadcast anadvertisement message to the rest of the nodesin the network that they are the new CHs. Allthe non-CH nodes, after receiving this advertise-ment, decide on the cluster to which they wantto belong. This decision is based on the signalstrength of the advertisement. The non-CHnodes inform the appropriate CHs that they will

be a member of the cluster. After receiving allthe messages from the nodes that would like tobe included in the cluster and based on the num-ber of nodes in the cluster, the CH node createsa TDMA schedule and assigns each node a timeslot when it can transmit. This schedule is broad-cast to all the nodes in the cluster.

During the steady state phase, the sensornodes can begin sensing and transmitting data tothe CHs. The CH node, after receiving all thedata, aggregates it before sending it to the BS.After a certain time, which is determined a pri-ori, the network goes back into the setup phaseagain and enters another round of selecting newCHs. Each cluster communicates using different

CDMA codes to reduce interference from nodesbelonging to other clusters.

Although LEACH is able to increase the net-work lifetime, there are stil l a number of issuesabout the assumptions used in this protocol.LEACH assumes that all nodes can transmitwith enough power to reach the BS if neededand that each node has computational power tosupport different MAC protocols. Therefore, itis not applicable to networks deployed in largeregions. It also assumes that nodes always havedata to send, and nodes located close to eachother have correlated data. It is not obvious howthe number of predetermined CHs (p) is goingto be uniformly distributed through the network.Therefore, there is the possibility that the elect-ed CHs will be concentrated in one part of thenetwork; hence, some nodes will not have anyCHs in their vicinity. Furthermore, the idea ofdynamic clustering brings extra overhead (headchanges, advertisements, etc.), which may dimin-ish the gain in energy consumption. Finally, theprotocol assumes that all nodes begin with thesame amount of energy capacity in each electionround, assuming that being a CH consumesapproximately the same amount of energy foreach node. The protocol should be extended toaccount for non-uniform energy nodes (i.e., usean energy-based threshold). An extension to

LEACH, LEACH with negotiation, was pro-

T np

p r pn G( )

( mod( / )),=

1 1if

The operation of

LEACH is separated

into two phases, the

setup phase and the

steady state phase.

In the setup phase,

the clusters areorganized and CHs

are selected. In the

steady state phase,

the actual data trans-

fer to the base

station takes place.



posed in [5]. The main theme of the proposedextension is to precede data transfers with high-level negotiation using meta-data descriptors asin the SPIN protocol discussed earlier. Thisensures that only data that provides new infor-mation is transmitted to the CHs before beingtransmitted to the BS. Table 1 compares SPIN,LEACH, and directed diffusion according to dif-ferent parameters. It is noted from the table thatdirected diffusion shows a promising approach

for energy-efficient routing in WSNs due to theuse of in-network processing.Power-Efficient Gathering in Sensor Infor-

mation Systems: In [20], an enhancement overthe LEACH protocol was proposed. The proto-col, called Power-Efficient Gathering in SensorInformation Systems (PEGASIS), is a near opti-mal chain-based protocol. The basic idea of theprotocol is that in order to extend network life-time, nodes need only communicate with theirclosest neighbors, and they take turns in commu-nicating with the BS. When the round of allnodes communicating with the BS ends, a newround starts, and so on. This reduces the powerrequired to transmit data per round as the power

draining is spread uniformly over all nodes.Hence, PEGASIS has two main objectives. First,increase the lifetime of each node by using col-laborative techniques. Second, allow only localcoordination between nodes that are closetogether so that the bandwidth consumed incommunication is reduced. Unlike LEACH,PEGASIS avoids cluster formation and uses onlyone node in a chain to transmit to the BS insteadof multiple nodes.

To locate the closest neighbor node inPEGASIS, each node uses the signal strength tomeasure the distance to all neighboring nodesand then adjusts the signal strength so that onlyone node can be heard. The chain in PEGASISwill consis t of those nodes that are closest toeach other and form a path to the BS. Theaggregated form of the data will be sent to theBS by any node in the chain, and the nodes inthe chain will take turns sending to the BS. Thechain construction is performed in a greedyfashion. Simulation results showed that PEGA-SIS is able to increase the lifetime of the net-work to twice that under the LEACH protocol.Such performance gain is achieved through theelimination of the overhead caused by dynamiccluster formation in LEACH, and decreasingthe number of transmissions and reception byusing data aggregation. Although the clustering

overhead is avoided, PEGASIS still requires

dynamic topology adjustment since a sensornode needs to know about the energy status ofits neighbors in order to know where to route itsdata. Such topology adjustment can introducesignificant overhead, especially for highly uti-lized networks. Moreover, PEGASIS assumesthat each sensor node is able to communicatewith the BS direct ly. In practical cases, sensornodes use multihop communication to reach theBS. Also, PEGASIS assumes that all nodesmaintain a complete database of the location of

all other nodes in the network. The method bywhich the node locations are obta ined is notoutlined. In addition, PEGASIS assumes that allsensor nodes have the same level of energy andare likely to die at the same time. Note also thatPEGASIS introduces excessive delay for distantnodes on the chain. In addition, the single lead-er can become a bottleneck. Finally, although inmost scenarios sensors will be fixed or immobileas assumed in PEGASIS, some sensors may beallowed to move and hence affect the protocolfunctionality.

An extension to PEGASIS, called Hierarchi-cal PEGASIS, was introduced in [2] with the

objective of decreasing the delay incurred forpackets during transmission to the BS. For thispurpose, simultaneous transmissions of data arestudied in order to avoid collisions throughapproaches that incorporate signal coding andspatial transmissions. In the latter, only spatiallyseparated nodes are allowed to transmit at thesame time. The chain-based protocol withCDMA-capable nodes constructs a chain ofnodes that forms a tree-like hierarchy, and eachselected node at a particular level transmits datato a node in the upper level of the hierarchy.This method ensures data transmitting in paral-lel and reduces delay significantly. Such a hierar-chical extension has been shown to perform

better than the regular PEGASIS scheme by afactor of about 60.

Threshold-Sensitive Energy Efficient Proto-cols: Two hierarchical routing protocols calledThreshold-Sensitive Energy Efficient SensorNetwork Protocol (TEEN) and Adaptive Period-ic TEEN (APTEEN) are proposed in [21, 22].These protocols were proposed for time-criticalapplications. In TEEN, sensor nodes sense themedium continuously, but data transmission isdone less frequently. A CH sensor sends itsmembers a hard threshold, which is the thresh-old value of the sensed attribute, and a softthreshold, which is a small change in the value ofthe sensed attribute that triggers the node toswitch on its transmitter and transmit. Thus, thehard threshold tries to reduce the number oftransmissions by allowing the nodes to transmitonly when the sensed attribute is in the range ofinterest. The soft threshold further reduces thenumber of transmissions that might otherwiseoccur when there is little or no change in thesensed attribute. A smaller value of the softthreshold gives a more accurate picture of thenetwork, at the expense of increased energy con-sumption. Thus, the user can control the trade-off between energy efficiency and data accuracy.When CHs are to change (Fig. 5a), new valuesfor the above parameters are broadcast. The

main drawback of this scheme is that if the

n Table 1. Comparison between SPIN LEACH anddirected diffusion.Directed

SPIN LEACH diffusion

Optimal route No No Yes

Network lifetime Good Very good Good

Resource Yes Yes Yesawareness

Use of meta-data Yes No Yes

PEGASIS assumes

that all sensor nodes

have the same level

of energy and they

are likely to die at

the same time. Note

also that PEGASISintroduces excessive

delay for distant

node on the chain.

In addition, the

single leader can

become a

bottleneck.



thresholds are not received, the nodes will nevercommunicate, and the user will not get any datafrom the network at all.

The nodes sense their environment continu-ously. The first time a parameter from theattribute set reaches its hard threshold value, thenode switches its transmitter on and sends thesensed data. The sensed value is stored in aninternal variable called sensed value (SV). Thenodes will transmit data in the current clusterperiod only when the following conditions are

true: The current value of the sensed attribute isgreater than the hard threshold.

The current value of the sensed attribute dif-fers from SV by an amount equal to or greaterthan the soft threshold.Important features of TEEN include its suit-

ability for time-critical sensing applications.Also, since message transmission consumes moreenergy than data sensing, the energy consump-tion in this scheme is less than in proactive net-wo rk s. Th e so ft th re sh ol d ca n be va ri ed. Atevery cluster change time, fresh parameters arebroadcast, so the user can change them asrequired.

APTEEN, on the other hand, is a hybrid pro-tocol that changes the periodicity or thresholdvalues used in the TEEN protocol according touser needs and the application type. InAP TE EN , th e CH s br oa dc as t th e fo ll ow ingparameters (Fig. 5b): Attributes (A): a set of physical parameters

about which the user is interested in obtaininginformation

Thresholds: consists of the hard threshold(HT) and soft threshold (ST)

Schedule: a TDMA schedule, assigning a slotto each node

Count time (CT): the maximum time periodbetween two successive reports sent by a nodeThe node senses the environment continuous-

ly, and only those nodes that sense a data valueat or beyond HT transmit. Once a node senses avalue beyond HT, it tra nsmits data only whe nthe value of that attribute changes by an amountequal to or greater than ST. If a node does notsend data for a time period equal to CT, it isforced to sense and retransmit the data. ATDMA schedule is used, and each node in thecluster is assigned a transmission s lot. Hence,APTEEN uses a modified TDMA schedule toimplement the hybrid network. The main fea-tures of the APTEEN scheme include the fol-lowing. It combines both proactive and reactive

policies. It offers a lot of flexibility by allowing

the user to set the CT interval, and the thresholdvalues for energy consumption can be controlledby changing the CT as well as the threshold val-ues. The main drawback of the scheme is theadditional complexity required to implement thethreshold functions and CT. Simulation of TEENand APTEEN has shown that these two proto-cols outperform LEACH. The experiments havedemonstrated that APTEENs performance issomewhere between LEACH and TEEN interms of energy dissipation and network lifetime.

TEEN gives the best performance since itdecreases the number of transmissions. Themain drawbacks of the two approaches are theoverhead and complexity associated with form-ing clusters at multiple levels, the method ofimplementing threshold-based functions, andhow to deal with attribute-based naming ofqueries.

Small minimum energy communication net-work (MECN) : In [23], a protocol is proposedthat computes an energy-efficient subnetwork,the minimum energy communication network(MECN), for a certain sensor network utilizinglow-power GPS. MECN identifies a relay regionfor every node. The relay region consists of

nodes in a surrounding area where transmittingthrough those nodes is more energy-efficientthan direct transmission. The enclosure of anode i is created by taking the union of all relayregions node i can reach. The main idea ofMECN is to find a subnetwork that will havefewer nodes and require less power for transmis-sion between any two particular nodes. In thiswa y, global minimum power paths are foun dwithout considering all the nodes in the network.This is performed using a localized search foreach node considering its relay region. MECN isself-reconfiguring and thus can dynamicallyadapt to node failure or the deployment of newsensors. The small MECN (SMECN) [24] is anextension to MECN. In MECN, it is assumedthat every node can transmit to eve ry othernode, which is not possible every time. InSMECN possible obstacles between any pair ofnodes are considered. However, the network isstill assumed to be fully connected as in the caseof MECN. The subnetwork constructed bySMECN for minimum energy relaying is prov-ably smaller (in terms of number of edges) thanthe one constructed in MECN. Hence, the sub-network (i.e., subgraph G) constructed bySMECN is smaller than the one constructed byMECN if the broadcast region is circular aroundthe broadcasting node for a given power setting.

Subgraph G of graph G, which represents the

nnnn Figure 5. Time line for the operation of a) TEEN and b) APTEEN.(a)Parameters

Time

Clusterhead receivesmessage

Cluster changetime

Attribute > threshold

(b)

TDMA scheduleand parameters

Time

Frame timeCluster formation

Cluster changetime

Slot fornode i

Important features

of TEEN include its

suitability for time-

critical sensing

applications. Also,

since message

transmissionconsumes more

energy than data

sensing, the energy

consumption in this

scheme is less

than in proactive

networks.



sensor network, minimizes the energy usage sat-isfying the following conditions: The number of edges in G is less than in G

while containing all nodes in G. The energy required to transmit data from a

node to all its neighbors in subgraph G is lessthan the energy required to transmit to all itsneighbors in graph G. Assume that r= (u, u1,,v) is a path between u andv that spansk 1 intermediate nodes u1, uk1 . The totalpower consumption of one path likeris given

by

wh er e u = u0 and v = uk, and the powerrequired to transmit data under this protocol is

p(u,v) = t.d(u,v)n

for some appropriate constant t, n is the pathloss exponent of outdoor radio propagationmodelsn 2, andd(u,v) is the distance betweenu and v. It is assumed that a reception at thereceiver takes a constant amount of power

denotedc

. The subnetwork computed bySMECN helps in sending messages on mini-mum-energy paths. However, the proposed algo-rithm is local in the sense that it does notactually find the minimum-energy path, it justconstructs a subnetwork in which it is guaran-teed to exist. Moreover, the subnetwork con-structed by SMECN makes it more likely thatthe path used is one that requires less energyconsumption. In addition, finding a subnetworkwith a smaller number of edges introduces moreoverhead in the algorithm.

Self-organizing protocol: Subramanian et al.[25] describes a self-organizing protocol (SOP)and an application taxonomy that was used to

build architecture to support heterogeneoussensors. Furthermore, these sensors can bemobile or stationary. Some sensors probe theenvironment and forward the data to a designat-ed set of nodes that act as routers. Router nodesare stationary and form the backbone for com-munication. Collected data are forwardedthrough the routers to the more powerful BSnodes. Each sensing node should be able toreach a router in order to be part of the net-wo rk. A ro uti ng ar ch ite ct ur e th at re qu ir esaddressing of each sensor node has been pro-posed. Sensing nodes are identifible through theaddress of the router node to which they areconnected. The routing architecture is hierarchi-cal where groups of nodes are formed andmerge when needed. The Local Markov Loops(LML) algorithm, which performs a randomwalk on spanning trees of a graph, was used tosupport fault tolerance and as a means of broad-casting. Such an approach is similar to the ideaof a virtual grid used in some other protocolsdiscussed later under location-based routingprotocols. In this approach, sensor nodes can beaddressed individually in the routing architec-ture; hence, it is suitable for applications wherecommunication to a particular node is required.Furthermore, this algorithm incurs a small costfor maintaining routing tables and keeping a

balanced routing hierarchy. It was also found

that the energy consumed for broadcasting amessage is less than that consumed in the SPINprotocol. This protocol, however, is not an on-demand protocol, especially in the organizationphase of the algorithm, and thus introducesextra overhead. Another issue is related to theformation of a hierarchy. It could happen thatthere are many cuts in the network, and hencethe probability of applying reorganization phaseincreases, which is an expensive operation.

Sensor aggregates routing: In [26], a set of

algorithms for constructing and maintaining sen-sor aggregates were proposed. The objective isto collectively monitor target activity in a certainenvironment (target tracking applications). Asensor aggregate comprises those nodes in a net-work that satisfy a grouping predicate for a col-laborative processing task. The parameters ofthe predicate depend on the task and its resourcerequirements. The formation of appropriate sen-sor aggregates were discussed in [26] in terms ofallocating resources to sensing and communica-tion tasks. Sensors in a sensor field are dividedinto clusters according to their sensed signalstrength, so there is only one peak per cluster.

Then local cluster leaders are elected. One peakmay represent one target, multiple targets, or notarget if the peak is generated by noise sources.To elect a leader, information exchangesbetween neighboring sensors are necessary. If asensor, after exchanging packets with all its one-hop neighbors, finds that it is higher than all itsone-hop neighbors on the signal field landscape,it declares itself a leader. This leader-basedtracking algorithm assumes that the unique lead-er knows the geographical region of the collabo-ration.

Three algorithms were proposed in [26]. Firstwas a lightweight protocol, Distributed Aggre-gate Management (DAM), for forming sensor

aggregates for a target monitoring task. The pro-tocol comprises a decision predicate P for eachnode to decide if it should participate in anaggregate and a message exchange scheme Mabout how the grouping predicate is applied tonodes. A node determines if it belongs to anaggregate based on the result of applying thepredicate to the data of the node as well a sinformation from other nodes. Aggregates areformed when the process eventually converges.Second, Energy-Based Activity Monitoring(EBAM) estimates the energy level at each nodeby computing the signal impact area, combininga weighted form of the detected target energy ateach impacted sensor, assuming that each targetsensor has equal or constant energy level. Thethird algorithm, Expectation-Maximization LikeActivi ty Monitoring (EMLAM), removes theconstant and equal target energy level assump-tion. EMLAM estimates the target positions andsignal energy using received signals, and uses theresulting estimates to predict how signals fromthe targets may be mixed at each sensor. Thisprocess is iterated until the estimate is sufficient-ly good.

The distributed track initiation managementscheme, combined with the leader-based track-ing algorithm described in [26], forms a scalablesystem. The system works well in tracking multi-

ple targets when the targets are not interfering,

C r p u u ci ii

k

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1

The subnetwork

constructed by

SMECN makes it

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energy consumption.In addition, finding a

sub-network with a

smaller number of

edges introduces

more overhead in

the algorithm.



and it can recover from intertarget interferenceonce the targets move apart.

Virtual grid architecture routing: An energy-efficient routing paradigm is proposed in [27]that utilizes data aggregation and in-networkprocessing to maximize the network lifetime.Due to the node stationarity and extremely lowmobility in many applications in WSNs, a rea-sonable approach is to arrange nodes in a fixedtopology, as briefly mentioned in [28]. A GPS-free approach [2] is used to build clusters that

are fixed, equal, adjacent, and nonoverlappingwith symmetric shapes. In [27] , square clusterswere used to obtain a fixed rectil inear virtualtopology. Inside each zone, a node is optimallyselected to act as CH. Data aggregation is per-formed at two levels: local and then global. Theset of CHs, also called local aggregators (LAs),perform local aggregation, while a subset ofthese LAs are used to perform global aggrega-tion. However, the determination of an optimalselection of global aggregation points, calledmaster aggregators (MAs), is NP-hard. Figure 6illustrates an example of fixed zoning and theresulting virtual grid architecture (VGA) used to

perform two-level data aggregation. Note thatthe location of the BS is not necessarily at theextreme corner of the grid; it can be located atany arbitrary place.

Two solution strategies for the routing withdata aggregation problem are presented in [27]:an exact algorithm using an integer linear pro-gram (ILP) formulation, and some near-optimalbut simple and efficient approximate algorithms:a genetics-algorithm-based heuristic, a k-meansheuristic, and a greedy-based heuristic. In [29],another efficient heuristic, the Clustering-BasedAggregation Heuris tic (CBAH), was also pro-posed to minimize energy consumption in thenetwork and hence prolong the network lifetime.

The objective of all algorithms is to select anumber of MAs out of the LAs that maximizenetwork lifetime. For a realistic scenario, it isassumed in [27] that LA nodes form possiblyoverlapping groups. Members of each group sen-sie the same phenomenon; hence, their readingsare correlated. However, each LA node thatexists in the overlapping region will send data toits associated MA for each of the groups towhich it belongs. It was noted in [29 ] that theproblem of assigning MAs to LAs in CBAH issimilar to the classical bin packing problem, amajor difference being that neither the identitiesnor the amount of power each MA will be usingfor different LAs are known. In CBAH, the setof MAs are selected based on incremental filingof some bins with capacities. Besides being fastand scalable to large sensor networks, theapproximate algorithms in [27, 29] produceresults not far from the optimal solution.

Hierarchical power-aware routing: In [30],hierarchical power-aware routing was proposed.The protocol divides the network into groups ofsensors. Each group of sensors in geographicproximity are clustered together as a zone, andeach zone is treated as an entity. To performrouting, each zone is allowed to decide how itwill route a message hierarchically across theother zones such that the battery lives of the

nodes in the system are maximized. Message are

routed along the path that has the maximumover all the minimum of the remaining power,called the max-min path. The motivation is thatusing nodes with high residual power may bemore expensive than the path with the minimalpower consumption. An approximation algo-rithm, called the max-min zPmin algorithm, wasproposed in [30]. The crux of the algorithm isbased on the trade-off between minimizing thetotal power consumption and maximizing theminimal residual power of the network. Hence,the algorithm tries to enhance a max-min pathby limiting its power consumption as follows.First, the algorithm finds the path with the leastpower consumption (Pmin) by using the Dijkstraalgorithm. Second, the algorithm finds a paththat maximizes the minimal residual power inthe network. The proposed algorithm tries tooptimize both solution criteria. This is achievedby relaxing the minimal power consumption forthe message to be equal tozPminwith parameterz 1 to restrict the power consumption for send-ing one message to zPmin . The algorithm con-sumes at most zPmi n wh il e ma xi miz in g th eminimal residual power fraction.

Another algorithm that relies on max-minzPmin , called zone-based routing, is also pro-posed in [30]. Zone-base routing is a hierarchical

approach where the area covered by the (sensor)

nnnn Figure 6.Regular shape tessellation applied to the network area.

Base station

Sensor node Local aggregator node

Master aggregator node



network is divided into a small number of zones.To send a message across the entire area, a glob-al path from zone to zone is found. The sensorsin a zone autonomously direct local routing andparticipate in estimating the zone power level.Each message is routed across the zones usinginformation about the zone power estimates. Aglobal controller for message routing is assignedthe role of managing the zones. This may be thenode with the highest power. If the network canbe divided into a relatively small number ofzones, the scale for the global routing algorithmis reduced. The global information required tosend each message across is summarized by thepower level estimate of each zone. A zone graph

was used to represent connected neighboringzone vertices if the current zone can go to thenext neighboring zone in that direction. Eachzone vertex has a power level of 1. Each zonedirection vertex is labeled by its estimated powerlevel computed by a procedure, which is a modi-fied Bellman-Ford algorithm. Moreover, twoalgorithms were outlined for local and globalpath selection using the zone graph.

Two-Tier Data Dissemination: An approachin [6], called Two-Tier Data Dissemination(TTDD), provides data delivery to multiplemobile BS. In TTDD, each data source proac-tively builds a grid structure that is used to dis-seminate data to the mobile sinks by assumingthat sensor nodes are stationary and location-aware. In TTDD, sensor nodes are stationaryand location-aware, whereas sinks may changetheir locations dynamically. Once an eventoccurs, sensors surrounding it process the signal,and one of them becomes the source to generatedata reports. Sensor nodes are aware of theirmission, which will not change frequently. Tobuild the grid structure, a data source choosesitself as the start crossing point of the grid, andsends a data announcement message to each ofits four adjacent crossing points using simplegreedy geographical forwarding. When the mes-sage reaches the node closest to the crossing

point (specified in the message), it will stop.

During this process, each intermediate nodestores the source information and further for-wards the message to its adjacent crossing pointsexcept the one from which the message comes.This process continues until the message stops atthe border of the network. The nodes that storethe source information are chosen as dissemina-tion points. After this process, the grid structureis obtained. Using the grid, a BS can flood aquery, which will be forwarded to the nearestdissemination point in the local cell to receivedata. Then the query is forwarded along otherdissemination points upstream to the source.The requested data then flows down in thereverse path to the sink. Trajectory forwarding is

employed as the BS moves in the sensor field.Although TTDD is an efficient routing approach,there are some concerns about how the algo-rithm obtains location information, which isrequired to set up the grid structure. The lengthof a forwarding path in TTDD is larger than thelength of the shortest path. The authors ofTTDD believe that the suboptimality in the pathlength is worth the gain in scalability. Finally,how TTDD would perform if mobile sensornodes are allowed to move in the network is stillan open question. Comparison results betweenTTDD and directed diffusion showed thatTTDD can achieve longer lifetimes and shorterdata delivery delays. However, the overheadassociated with maintaining and recalculatingthe grid as network topology changes may behigh. Furthermore, TTDD assumed the avail-ability of a very accurate positioning system thatis not yet available for WSNs.

The above mentioned flat and hierarchicalprotocols are different in many aspects. At thispoint, we compare the different routingapproaches for flat and hierarchical sensor net-works as shown in Table 2.

Location-Based Routing Protocols In this kind ofrouting, sensor nodes are addressed by means oftheir locations. The distance between neighbor-

ing nodes can be estimated on the basis of

n Table 2.Hierarchical vs. flat topologies routing.Hierarchical routing Flat routing

Reservation-based scheduling Contention-based scheduling

Collisions avoided Collision overhead present

Reduced duty cycle due to periodic sleeping Variable duty cycle by controlling sleep time of nodes

Data aggregation by clusterhead Node on multihop path aggregates incoming data from neighbors

Simple but non-optimal routing Routing can be made optimal but with an added complexity.

Requires global and local synchronization Links formed on the fly without synchronizationOverhead of cluster formation throughout the network Routes formed only in regions that have data for transmission

Lower latency as multiple hops network formed by Latency in waking up intermediate nodescluster- heads always available and setting up the multipath

Energy dissipation is uniform Energy dissipation depends on traffic patterns

Energy dissipation cannot be controlled Energy dissipation adapts to traffic pattern

Fair channel allocation Fairness not guaranteed



incoming signal strengths. Relative coordinatesof neighboring nodes can be obtained byexchanging such information between neighbors[1, 2, 31]. Alternatively, the location of nodesmay be available directly by communicating witha satellite using GPS if nodes are equipped witha small low-power GPS receiver [28]. To saveenergy, some location-based schemes demandthat nodes should go to sleep if there is no activ-ity. More energy savings can be obtained by hav-ing as many sleeping nodes in the network as

possible. The problem of designing sleep periodschedules for each node in a localized mannerwas addressed in [32, 28]. In the rest of this sec-tion, we review most of the location- or geo-graphic-based routing protocols.

Geographic Adaptive Fidelity: GAF [28] isan energy-aware location-based routing algo-rithm designed primarily for mobile ad hoc net-works, but may be applicable to sensor networksas well. The network area is first divided intofixed zones and form a virtual grid. Inside eachzone, nodes collaborate with each other to playdifferent roles. For example, nodes will electone sensor node to stay awake for a certain

period of time, and then the rest go to sleep.This node is responsible for monitoring andreporting data to the BS on behalf of the nodesin the zone. Hence, GAF conserves energy byturning off unnecessary nodes in the networkwithout affecting the level of routing fidelity.Each node uses its GPS-indicated location toassociate itself with a point in the virtual grid.Nodes associated with the same point on thegrid are considered equiv

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