Energy and QoS aware Routing in Wireless Sensor Networks

Transcription

1 Energy and QoS aware Routng n Wreless Sensor Networks Kemal Akkaya and Mohamed Youns Department of Computer Scence and Electrcal Engneerng Unversty of Maryland, Baltmore County Baltmore, MD 225 kemal youns@cs.umbc.edu Abstract. Many new routng protocols have been proposed for wreless sensor networks n recent years. Almost all of the routng protocols consdered energy effcency as the ultmate objectve snce energy s a very scarce resource for sensor nodes. However, the ntroducton magng sensors has posed addtonal challenges. Transmsson of magng data requres both energy and QoS aware routng n order to ensure effcent usage of the sensors and effectve access to the gathered measurements. In ths paper, we propose an energy-aware QoS routng protocol for sensor networks whch can also run effcently wth best-effort traffc. The protocol fnds a least-cost, delay-constraned path for real-tme data n terms of lnk cost that captures nodes energy reserve, transmsson energy, error rate and other communcaton parameters. Moreover, the throughput for non-realtme data s maxmzed by adjustng the servce rate for both real-tme and non-real-tme data at the sensor nodes. Such adjustment of servce rate s done by usng two dfferent mechansms. Smulaton results have demonstrated the effectveness of our approach for dfferent metrcs wth respect to the baselne approach where same lnk cost functon s used wthout any servce dfferentaton mechansm. Keywords: Sensor networks, QoS routng, energy-aware routng, real-tme traffc. Introducton Recent advances n mcro-electro-mechancal systems (MEMS) and low power and hghly ntegrated dgtal electroncs have led to the development of mcro sensors [][2][3][4][5][5][7]. Such sensors are generally equpped wth data processng and communcaton capabltes. The sensng crcutry measures ambent condtons related to the envronment surroundng the sensor and transforms them nto an electrc sgnal. Processng such a sgnal reveals some propertes about objects located and/or events happenng n the vcnty of the sensor. The sensor sends such sensed data, usually va rado transmtter, to a command center ether drectly or through a data concentraton center (a gateway). The gateway can perform fuson of the sensed data n order to flter out erroneous data and anomales and to draw conclusons from the reported data over a perod of tme.

2 The contnuous decrease n the sze and cost of sensors has motvated ntensve research n the past few years addressng the potental of collaboraton among sensors n data gatherng and processng va an ad hoc wreless network. Networkng unattended sensor nodes s expected to have sgnfcant mpact on the effcency of many mltary and cvl applcatons, such as combat feld survellance, securty and dsaster management. A network of sensors can be used to gather meteorologcal varables such as temperature and pressure. These measurements can be used n preparng forecasts or detectng harsh natural phenomena. In dsaster management stuatons such as earthquakes, sensor networks can be used to selectvely map the affected regons drectng emergency response unts to survvors. In mltary stuatons, sensor networks can be used n survellance mssons and can be used to detect movng targets, chemcal gases, or presence of mcro-agents. However, sensor nodes are constraned n energy supply and bandwdth. Such constrants combned wth a typcal deployment of large number of sensor nodes have necesstated energy-awareness at the layers of networkng protocol stack ncludng network layer. Routng of sensor data has been one of the challengng areas n wreless sensor network research. Current research on routng n wreless sensor networks mostly focused on protocols that are energy aware to maxmze the lfetme of the network, scalable for large number of sensor nodes and tolerant to sensor damage and battery exhauston [2][4][8][9][][][2]. Snce the data they deal wth s not n large amounts and flow n low rates to the snk, the concepts of latency, throughput and delay were not prmary concerns n most of the publshed work on sensor networks. However, the ntroducton of magng sensors has posed addtonal challenges for routng n sensor networks. Transmsson of magng data requres careful handlng n order to ensure that end-to-end delay s wthn acceptable range. Such performance metrcs are usually referred to as qualty of servce (QoS) of the communcaton network. Therefore, collectng sensed magng data requres both energy and QoS aware routng n order to ensure effcent usage of the sensors and effectve access to the gathered measurements. QoS protocols n sensor networks have several applcatons ncludng real tme target trackng n battle envronments, emergent event trggerng n montorng applcatons etc. Consder the followng scenaro: In a battle envronment t s crucal to locate, detect and dentfy a target. In order to dentfy

3 a target, we should employ magng sensors. After locatng and detectng the target wthout the need of magng sensors, we can turn on those sensors to get for nstance an mage of the target perodcally for sendng to the base staton or gateway. Snce, t s a battle envronment; ths requres a real-tme data exchange between sensors and controller n order to take the proper actons. However, we should deal wth real-tme data, whch requres certan bandwdth wth mnmum possble delay. In that case, a servce dfferentaton mechansm s needed n order to guarantee the relable delvery of the real-tme data. Energy-aware QoS routng n sensor networks wll ensure guaranteed bandwdth (or delay) through the duraton of a connecton as well as provdng the use of the most energy effcent path. To the best of our knowledge, no prevous research has addressed QoS routng n sensor networks. In ths paper, we present an energy-aware QoS routng mechansm for wreless sensor networks. Our proposed protocol extends the routng approach n [2] and consders only end-to-end delay. The protocol looks for a and maxmzes the throughput for best effort traffc s found. Our protocol does not ntroduce any extra overhead to the sensors. In the balance of ths secton we descrbe the sensor network archtecture that we consder and summarze the related work. In secton 2, we analyze the complexty of the QoS routng problem n sensor networks and descrbe our approach. Secton 3 ncludes smulatons and evaluatons of the protocol. Fnally we conclude the paper n secton 4 and outlne our future research... Sensor Network Archtecture A set of sensors s spread throughout an area of nterest to detect and possbly track events/targets n ths area. The sensors are battery-operated wth dverse capabltes and types and are empowered wth lmted data processng engnes. The avalablty of magng sensors s of partcular nterest due to the Sensor nodes Gateway Node Command Node Command Node delay-constraned path wth the least possble cost. The cost functon whch captures remanng and Command Node transmsson energy and error rate, s defned for each lnk. Alternatve paths wth bgger costs are tred untl one, whch meets the end-to-end delay requrement Command Node Fg. : Three-ter sensor network archtecture

4 qualty of servce constrants assocated wth data generated by such sensors. The msson for these sensors s dynamcally changng to serve the need of one or multple command nodes. Command nodes can be statonary or moble. In a dsaster management envronment, coordnaton centers are typcal statonary command nodes, whle paramedcs, fre trucks, rescue vehcles and evacuaton helcopters are examples of moble command nodes. A gateway node s a less energy-constraned node deployed n the physcal proxmty of sensors. The gateway s responsble for organzng the actvtes at sensor nodes to acheve a msson, fusng data collected by sensor nodes, coordnatng communcaton among sensor nodes and nteractng wth command nodes. We are consderng both the gateway and sensor nodes as statonary. All the sensors are assumed to be wthn the communcaton range of the gateway node. The archtecture s depcted n Fg. The sensor s assumed to be capable of operatng n an actve mode or a low-power stand-by mode. The sensng and processng crcuts can be powered on and off. In addton both the rado transmtter and recever can be ndependently turned on and off and s worth notng that most of these capabltes are avalable on some of the advanced sensors, e.g. the Acoustc Ballstc Module from SenTech Inc. [3]. The gateway node s assumed to know ts locaton, e.g. va the use of GPS. The descrbed system s archtecture rases many nterestng ssues such as msson-orented sensor organzaton, network management, gateway to command node communcaton protocol, support of QoS traffc generated by magng sensors, etc. Whle many of these ssues are studed n the context of wreless networkng research, the naturally resource constraned sensor-based envronment makes these techncal ssues untradtonal and challengng. For example energy effcency has to be a core objectve of the system desgn, a factor that has not been consdered for typcal networks. In ths paper, we only focus on the energy-aware and QoS routng of sensor data among the communcatng nodes. Whle the gateway wll take charge of sensor organzaton based on the msson and avalable energy n each sensor, we assume knowledge of whch sensors need to be actve n sgnal processng, e.g. usng the approaches presented n [][4]. the transmsson power can be programmed for a requred range. It s also assumed that the sensor can act as a relay to forward data from another sensor. It

5 .2. Related Work In tradtonal best-effort routng throughput and delay are the man concerns. There s no guarantee that a certan performance n throughput or delay wll be ensured throughout the connecton. However, n some cases where real-tme or multmeda data are nvolved n communcaton, some performance guarantees n certan metrcs such as delay, bandwdth and delay jtter are needed. Such guarantees can be acheved by employng specal mechansms known as QoS routng protocols. Whle contemporary best-effort routng approaches address unconstraned traffc, QoS routng s usually performed through resource reservaton n a connecton-orented communcaton n order to meet the QoS requrements for each ndvdual connecton. Whle many mechansms have been proposed for routng QoS constraned real-tme multmeda data n wre-based networks [5][6][7][8][9], they cannot be drectly appled to wreless networks due to nherent characterstcs of wreless envronments and lmted resources, such as bandwdth. Therefore, several new protocols have been proposed for QoS routng n wreless ad-hoc networks takng the dynamc nature (due to moblty of the nodes) of the network nto account [2][2][22][23][24]. Some of these proposed protocols consder the mprecse state nformaton whle determnng the routes [2][2]. CEDAR s another QoS aware protocol, whch uses the dea of core nodes (domnatng set) of the network whle determnng the paths [22]. Usng routes found through the network core, a QoS path can be easly found. However, f any node n the core s broken, t wll cost too much n terms of resource usage to reconstruct the core. Ln [23] and Zhu et al. [24] have proposed QoS routng protocols specfcally desgned for TDMA-based ad-hoc networks. Both protocols can buld a QoS route from a source to destnaton wth reserved bandwdth. The bandwdth calculaton s done hop-by-hop n a dstrbuted fashon. Another protocol for wreless networks that ncludes some noton of QoS n ts routng decsons s the Sequental Assgnment Routng (SAR) [4]. The SAR protocol creates trees routed from one-hop neghbor of the snk by takng the QoS metrc, the energy resource on each path and the prorty level of each packet nto consderaton. By usng created trees, multple paths from snk to sensors are formed. One of these paths s selected accordng to the energy resources and achevable QoS on each path. In our approach, we not only select a path from a lst of canddate paths that meet the end-to-end delay requrement, but maxmze the throughput for best

6 effort traffc as well. In addton, the SAR approach suffers the overhead of mantanng the node states at each sensor node and mantanng the multple paths from each node to the snk. Our protocol does not requre sensor s nvolvement n route setup. Most of the QoS routng algorthms dscussed n ths secton are based on the moblty of the nodes and none of them consder energy awareness along wth the QoS parameters. Although they are well suted to moble ad hoc networks, the emergng complexty from moblty n such routng algorthms wll be an over-kll for the systems where nodes are not moble and have lmted resources, such as bandwdth and energy. On the other hand, routng protocols proposed specfcally for wreless sensor networks are desgned accordng to the needs of sensor networks, none of them consders any QoS or servce dfferentaton mechansm n order to handle challenges posed by magng sensors and real-tme applcatons of sensor networks. Our proposed approach tackles these challenges nto account so that both the system lfetme wll be maxmzed and QoS requrements are met. 2. Energy-aware QoS Routng Our am s to fnd an optmal path to the gateway n terms of energy consumpton and error rate whle meetng the end-to-end delay requrements. End-toend delay requrements are assocated only wth the real-tme data. Note that, n ths case we have both real-tme and non-real-tme traffc coexstng n the network, whch makes the problem more complex. We not only should fnd paths that meet the requrements for real-tme traffc, but need to maxmze the throughput for non-real tme traffc as well. Ths s because most of the crt cal applcatons such as battlefeld survellance have to receve for nstance acoustc data regularly n order not to mss targets. Therefore t s mportant to prevent the realtme traffc from consumng the bulk of network bandwdth and leave non-real-tme data starvng and thus ncurrng large amount of delay. The descrbed QoS routng problem s very smlar to typcal path constraned path optmzaton (PCPO) problems, whch are proved to be NPcomplete [25]. We are tryng to fnd least-cost path, whch meets the end-to-end delay path constrant. However, n our case there s an extra goal, whch s bascally to maxmze the throughput of non-real-tme traffc. Our approach s based on assocatng a cost functon for each lnk and used a K least cost path algorthm to fnd a set of canddate routes. Such routes are checked aganst the end-to-end constrants and the one that provdes maxmum throughput s

7 pcked. Before explanng the detals of proposed algorthm, we ntroduce the queung model. 2. Queung Model The queung model s specfcally desgned for the case of coexstence of real-tme and non-real-tme Queung model on a partcular node Classfer Real-tme packet Non-real-tme packet Sensng only node Relayng node G Gateway node traffc n each sensor node. The model we employ s nspred from class-based queung [26]. We use G Scheduler dfferent queues for the two dfferent types of traffc. Bascally, we have real-tme traffc and non-real-tme (normal) traffc whose packets are labeled accordngly. On each node there s a classfer, whch checks the type of the ncomng packet and sends t to the approprate queue. There s also a scheduler, whch determnes the order of packets to be transmtted from the queues accordng to the bandwdth rato r of each type of traffc on that lnk. The model s depcted n Fg. 2. The bandwdth rato r, s actually an ntal value set by the gateway and represents the amount of bandwdth to be dedcated both to the real-tme and non-real-tme traffc on a partcular outgong lnk. Moreover, both classes can borrow bandwdth from each other when one of the two types of traffc s non-exstent or under the lmt. As ndcated n Fgure 3, ths r-value s also used to calculate the servce rate of real-tme and non-real-tme traffc on that Fg. 2. Queung model n a partcular sensor node partcular node, wth r µ and ( r ) µ beng respectvely the servce rate for real-tme and nonreal-tme data on sensor node. Snce the queung delay depends on ths r-value, bw = rbw j Fg. 3. Bandwdth sharng and servce rates for a sensor node we cannot calculate the end-to-end delay for a partcular path wthout knowng the r-value. Therefore we should frst fnd a lst of canddate least-cost paths and then select one that meets the end-to-end delay requrement. Our approach s based on a two-step strategy ncorporatng both lnk-based costs and end-to-end constrants. Frst we calculate the canddate paths wthout consderng the end-toend delay. What we do s smply calculate costs for = r µ N ) µ servcerat = r servcerate j bw = ( r ) bw N e ( j

8 each partcular lnk and then use an extended verson of Djkstra's algorthm to fnd an ascendng set of least cost paths. Once we obtan these canddate paths, we further check them to dentfy those that meet our end-to-end QoS requrements by tryng to fnd an optmal r-value that wll also maxmze the throughput for non-real-tme traffc. 2.2 Calculaton of lnk costs We consder the factors for the cost functon on each partcular lnk separately except the end-to-end delay requrement, whch should be for the whole path (.e. all the lnks on that path). We defne the followng cost functon for a lnk between nodes and j: cos t j = 2 CF k = c ( dst ) l j + c f ( energy j ) k = ( ) c 2 f e j where, + dst j s the dstance between the nodes and j, f ( energy j ) s the functon for fndng current resdual energy of node j, f ( e j ) s the functon for fndng the error rate on the lnk between and j. Hence, t s not part of the cost functon. Cost factors are defned as follows: depends on the envronment, and typcally equals to 2. Ths factor reflects the cost of the wreless transmsson power, whch s drectly proportonal to the dstance rased to some power l. The closer a node to the destnaton, the less ts cost factor CF and more attractve t s for routng. CF (Energy Stock)= c f ( ) energy j. Ths factor reflects the remanng battery lfetme (.e. energy usage rate), whch favors nodes wth more energy. The more energy the node contans, the better t s for routng. CF (Error rate)= c f ( ) 2 e j 2 where f s a functon of dstance between nodes and j and buffer sze on node j (.e. dst j / buffer _ sze ). The lnks wth hgh error rate wll ncrease the cost functon, thus wll be avoded. 2.3 Estmaton of end-to-end delay for a path In order to fnd a QoS path for sendng real-tme data to the gateway, end-to-end delay requrement should be met. Before explanng the computaton of the delay for a partcular path P, we ntroduce the notaton below: j CF (Communcaton Cost)= c ( ) l dst j, where c s a weghtng constant and the parameter l

9 λ : Real-tme data generaton rate for magng real-tme data rate by q nodes wll be added sensors r µ : Servce rate for real-tme data on sensor ( node r )µ : Servce rate for non-real-tme data on p q We assume that the propagaton delay s neglgble. We also assume that all the magng sensors have the same real-tme data generaton rate λ. Total realtme data rate by sensor node : The number of sensng neghbors (data generators) of node on path P : The number of relayng neghbors (data forwarders) of node on path P () λ : Real-tme data rate on sensor node Q : Queung delay on a node for real-tme ( ) T E traffc : End-to-end queung delay for a partcular path P (gnorng propagaton delay) T : End-to-end delay for a partcular path P end end T : End-to-end delay requrement for all paths requred m Nodes : The number of nodes on path P : The set of all the sensng nodes that are part of path P p nodes wll be p λ and total recursvely for each relayng only node. Then total real-tme data load on a sensor node s: () λ = pλ + q p j j= λ ( j) The average watng tme ncludng the servce tme n the queue n M/M/ model s stated as W = µ λ where µ s the lnk transmsson rate or servce rate and? s the packet arrval rate [27]. Hence, total queung delay (ncludng the servce tme), a node s: ( ) Q = ( ) r µ λ () TQ on [] We make an approxmaton to smplfy the end-toend queung delay by assumng the ncomng traffc to real-tme and non-real-tme queues are stochastcally ndependent. Thus, the end-to-end queung delay for a partcular path s: ( ) T E = Q = Path Path rµ λ ( ) = Path r µ p λ q j= p λ j ( j) Snce we gnore the propagato n delay, total end-toend delay wll be: T end end = Path rµ p λ q j= p λ ( j) j [2]

10 2.4 Sngle-r Mechansm Whle we generate a formula for calculatng the endto-end delay for a partcular path, fndng the optmal r-values for each lnk as far as the queung delay s concerned, wll be very dffcult optmzaton problem to solve. Moreover, the dstrbuton of these r-values to each node s not an easy task because each value should be uncasted to the proper sensor node rather than broadcastng t to all the sensors, whch mght brng a lot of overhead. Therefore, we follow an approach, whch wll elmnate the overhead and complexty of the problem. Bascally, we defne each r-value to be same on each lnk so that the optmzaton problem wll be smple and ths unque r-value can be easly broadcasted to all the sensors by the gateway. If we let all r-values be same for every lnk then the formula wll be stated as: T end end = subject to : Path rµ p λ Tend end q j= requred p λ ( j) j Then the problem s stated as an optmzaton problem as follows: Max (( r) µ ) Path T and r < In order to fnd r-value from the above nequalty of Tend end T, for smplfcaton we consder requred fndng an r-value whch wll satsfy the last hop node s delay snce the last node wll be gettng the actual longest queung delay. As a consequence, the other nodes before the last node wll already be satsfed wth that r-value and wll use the same value. We dvde T nto m equal tme slots, requred where m s the number of nodes on a partcular path. The calculaton of r for the last hop node m s as follows: T m r = µ T requred m requred = Q λ + µ m = rµ λ ( pm + pk) k Nodes ( p + m p k k Nodes ) [3] By consderng the optmzaton problem above, we propose the algorthm shown n Fg. 4, to fnd a least-cost path, whch meets the constrants and maxmzes the throughput for non-real-tme data. The algorthm calculates the cost for each lnk, lne of Fg. 4, based on the cost functon defned n secton 3.2. Then, for each node the least cost path to the gateway s found by runnng Djkstra s shortest path algorthm n lne 2. Between lnes 5-5, approprate r- values are calculated for paths from magng sensors to the gateway. For each sensor node that has magng capablty, an r-value s calculated on the current path

11 Calculate cos,, j V t j 2 Fnd least cost path for each node by usng Djkstra 3 for each magng sensor node do 4 begn 5 Compute r from Tend end ( p ) = T requred (as above) 6 f (r s n range [,)) then 7 Add r to a lst correspondng to node 8 else K 9 Fnd K least cost paths ( ) k do for each K Fg. 4. Pseudo code for the proposed algorthm (lne 5). If that value s not between and, extended Djsktra algorthm for K-shortest path s run n order to fnd alternatve paths wth bgger costs (lne 9). K dfferent least-cost paths are tred n order to fnd a proper r-value between and (lnes -3). If there s no such r-value, the connecton request of that node to the gateway s rejected. P to the gateway k Recompute r from Tend end ( p ) = T requred 2 f (r s n range [,)) then 3 break; 4 f no approprate r s found 5 Reject the connecton 6 end 7 Fnd max r from the lst The algorthm mght generate dfferent r-values for dfferent paths. Snce, the r-values are stored n a lst; the maxmum of them s selected to be used for the whole network (lne 7). That r-value wll satsfy the end-to-end delay requrement for all the paths establshed from magng sensors to the gateway. In order to fnd the K least cost paths (.e. K shortest paths), we modfed an extended verson of Djkstra s algorthm gven n [28]. Snce, the algorthm can suffer loops durng executon; we modfed the algorthm n such a way that each tme a new path s searched for a partcular node; only nodedsjont paths are consdered durng the process. Ths elmnates loops and ensures smplcty and effcency. Ths mght also help fndng a proper r- value easly snce that node-dsjont path wll not nhert the congeston n the former path. Interested reader s referred to [28] for further nformaton. 2.5 Mult-r Mechansm Snce the sngle-r mechansm s just an approxmaton to the optmal soluton of allocatng r- values for each node by assumng a unque r-value for each node, we extended the model so that t wll allow dfferent r-values to be assgned to sensor nodes for better resource allocaton. In order to fnd dfferent r-values, each node s r-value s calculated by settng maxmum allowable queung delay for every node on the path proportonal to arrval rate of real-tme traffc to that node. The least-cost path s pcked. The gateway calculates a delay factor d by dvdng the value of the end-to-end delay d by the accumulatve arrval rates of real-tme traffc at all nodes on the path. The gateway then broadcasts the value of d to all nodes on the path so that they can use t to derve ther r-value.

12 Then d wll be calculated as follows: Trequred d = [4] q p p + j * λ Path j= Each sensor node wll calculate ts r-value r by usng From [], d as follows: d = r µ λ * p + q j= p j reflects the fact that some routes become nvald and cluster-wde reroutng may be mmanent. Average lfetme of a node: Ths gves a good measure of the network lfetme. A routng algorthm, whch maxmzes the lfetme of the network, s desrable. Ths metrc also shows how effcent s the algorthm n energy consumpton. Average delay per packet: Defned as the average r q λ = + * p + p d * µ µ j= j [5] tme a packet takes from a sensor node to the gateway. Most energy aware routng algorthms try Then the problem wll be to maxmze the total throughput on each partcular path: Max ( r ) where r <. Path 3. Expermental Results The effectveness of the energy-aware QoS routng approach s valdated through smulaton. Ths secton descrbes the performance metrcs, smulaton envronment, and expermental results. 3.. Performance Metrcs We have used the followng metrcs to capture the performance of our QoS routng approach: Tme to frst node to de: When the frst node runs out of energy, the network wthn the cluster s sad to be parttoned. The name network parttonng to mnmze the consumed energy. However, the applcatons that deal wth real-tme data s delay senstve, so ths metrc s mportant n our case. Network Throughput: Defned as the total number of data packets receved at the gateway dvded by the smulaton tme. The throughput for both realtme and non-real-tme traffc wll be consdered ndependently. 3.2 Envronment Setup In the experments we have consdered a network of randomly placed nodes n a meter square area. The gateway poston s determned randomly wthn the boundares of deployment area. A free space propagaton channel model s assumed [29] wth the capacty set to 2Mbps. Packet lengths are Kbt for data packets and 2 Kbt for routng

13 and refresh packets. Each node s assumed to have an ntal energy of 5 joules. The buffers for real-rme data and normal data have default sze of 2 packets [3]. A node s consdered non-functonal f ts energy level reaches. For the term CF n the cost functon, we used the lnear dscharge curve of the alkalne battery [3]. For a node n the sensng state, packets are generated at a constant rate of packet/sec. Ths value s consstent wth the specfcatons of the Acoustc Ballstc Module from SenTech Inc. [3]. The real-tme packet generaton rate ( λ ) for the nodes, whch have magng capablty s greater than the normal rate. The default value s 6 packets/sec. A servce rate (µ ) of 2 packets/sec s assumed. Each data packet s tme-stamped when t s generated to allow the calculaton of average delay per packet. In addton, each packet has an energy feld that s updated durng the packet transmsson to calculate the average energy per packet snce our cost functon defned for each lnk s usng remanng energy as part of the cost. A packet drop probablty s taken to be.. Ths s used to make the smulator more realstc and to smulate the devaton of the gateway energy model from the actual energy model of nodes. We assume that the network s tasked wth a targettrackng msson n the experment. The ntal set of sensng nodes s chosen to be the nodes on the convex hull of sensors n the deployment area. The set of sensng nodes changes as the target moves. Snce targets are assumed to come from outsde the area, the sensng crcutry of all boundary nodes s always turned on. The sensng crcutry of other nodes are usually turned off but can be turned on accordng to the target movement. We also assume that each sensor node s capable of takng the mage of a target to dentfy t clearly and can turn on ts magng capablty on demand. Durng smulaton, a small subset of current actve nodes, whch are the closest nodes to the target, are selected to turn on ther magng capablty. Therefore, the magng sensor set may change wth the movement of the target.

14 The packet-generaton rate for magng sensors s bgger than the normal sensors; hence more packets are generated when magng sensors are employed. These packets are labeled as real-tme packets and treated dfferently n sensor nodes. The r-value s ntally assumed to be but t s recalculated as magng sensors get actvated. The default end-to-end delay requrement for a QoS path s taken to be.8 sec [32]. Targets are assumed to start at a random poston outsde the convex hull. Targets are characterzed by havng a constant speed chosen unformly from the range 4 meters/s to 6 meters/s and a constant drecton chosen unformly dependng on the ntal target poston n order for the target to cross the convex hull regon. It s assumed that only one target s actve at a tme. Ths target remans actve untl t leaves the deployment regon. In ths case, a new target s generated. 3.3 Performance Results In ths secton, we present some performance results obtaned by the smulaton. Dfferent parameters are such as buffer sze, packet drop probablty and realtme data generaton rates are consdered n order to capture the effects on the performance metrcs defned earler n ths secton. Performance comparson of three dfferent protocols Tme Tme As a baselne approach, we have used the same cost functon wth same routng algorthm (.e. Djkstra) Baselne Sngle-r Mult-r Data rate Fg. 5. Average delay per packet wth dfferent real-tme data rates Baselne Sngle-r Mult-r Data rate Fg. 6. Average lfetme of a node wth dfferent real-tme data rates wthout dong any servce dfferentaton. That s, we have not dfferentated between packets and have used only one queue n each sensor node, whch accommodates all knds of packets. Therefore, no bandwdth sharng on any path s performed. We have compared ths approach wth our sngle -r and mult-r mechansms by lookng at the average delay per packet, average lfetme of a node and tme to frst node to de. When we compare the average delay per real-tme packets generated n our model wth the average delay per packet generated n sngle queue

15 model, we observed that both mult-r and sngle-r Tme Baselne Sngle-r Mult-r Data Rate Fg. 7. Tme for frst node to de wth dfferent real-tme data rates mechansms have less average delay (See Fg. 5). Ths s due to the prorty gven to real-tme packets when transmttng to the gateway. On the other hand, mult-r mechansm performs better than sngle -r mechansm as expected. Because, every partcular node adjusts ts r-value based on the resources t has. Ths s more effcent than the sngle-r case n whch a unque r-value s mposed by the gateway for all the nodes. Furthermore, n all cases the average delay per packet ncreases for hgher rates and real-tme data causes more queung delay at each sensor node. In fgures 6 and 7, we have looked at the energy usage of the protocols. The average lfetme of a node and the tme for frst node to de decreases when real-tme data ncreases, causng the nodes to sense and transmt more packets. Snce the same cost functon s used for all protocols, the lfetme of the nodes and the tme for frst node to de are very close to each other as confrmed by fgures 6 and 7. However, the energy usage of the sngle-r mechansm s slghtly less than the others. Ths can be explaned by lookng at the throughput. For the sngle -r mechansm, sometmes an r-value for the whole network cannot be found; causng the rejecton of some connectons. Ths decreases the throughput hence fewer packets are relayed. Ths s not the case for the baselne protocol. On the other hand, for the mult-r mechansm, t s easer to fnd an r-value for a partcular node. Furthermore, the effcency n the usage of resources for mult-r mechansm causes an ncrease n the throughput especally for non-realtme data as seen n fgure 8. Such ncrease ncurs a lttle more energy consumpton n the sensor nodes. Effect of real-tme data rate on throughput and delay In order to study the performance of the algorthm for dfferent real-tme data rates, we ran smulaton for dfferent values of real-tme packet data rates. The results are depcted n fgures 8 and 9. Frst, we have looked at the non-real-tme data throughput. Whle the number of real-tme packets ncrease, t gets more dffcult to satsfy ncreasng number of QoS paths. Hence, ths can cause rejecton of paths or packet drops for non-real-tme data causng throughput for such data to decrease. However, such decrease s very less, becomng constant after a certan pont (See fgure 8).

16 Throughput Tme Date Rate Fg. 8. Non-real-tme data throughput for dfferent real-tme data rates Sngle-r Mult-r.5 Sngle-r Mult-r Data Rate Fg. 9. Non-real-tme packet delay for dfferent real-tme data rates We restrcted r-value to be strctly less than, causng the throughput for non-real-tme data ( ( r ) µ ) to stay greater than. Hence, the mechansm. In mult-r mechansm, the ncrease n the throughput of non-real-tme packets cause extra queung delay on the nodes; leadng non-real-tme packets to have more end-to-end delay. Effect of end-to-end delay requrement and real-tme date generaton rate on r-values In order to see how the algorthm behaves under strngent condtons, we vared the end-to-end delay requrement and montored how ths change affects the network r-value. The results are depcted n fgure. The network r-value goes down whle the end-toend delay requrement gets looser. Snce the delay s r-value algorthm does not sacrfce the throughput for nonreal-tme data for the sake of real-tme data. Mult-r mechansm has greater throughput than the sngle -r snce the resources are handled more effcently. Fg. 9 shows the effect of real-tme data rate on average delay per non-real-tme packet. The delay ncreases wth the rate snce packets ncur more r-value End-to-End Delay Fg.. Network r-value wth dfferent end-toend delay values queung delay and share the same amount of bandwdth. It s nterestng to note that the average packet delay for non-real-tme packets n the case of mult-r mechansm s bgger than the sngle -r Data Rate Fg.. Network r-value wth dfferent real-tme data rates not too strct, the nodes wll be able to meet the endto-end delay requrement wth a smaller r-value as

17 expected from equaton 3. On the other hand, whle we congest the network wth more real-tme data packets by ncreasng the real-tme data generaton rate, more bandwdth wll be requred for real-tme packets. Ths wll cause the r-value to ncrease so that each node can serve more real-tme packets (See fgure ). Effect of packet drop probablty on delay and average lfetme of a node To study the effect of packet drop probablty on performance, we vared the probablty of packet drop from. to.5. The results are depcted n fgures 2 and 3. The average delay per packet decreases Tme Sngle-r Mult-r Packet Drop Probablty Fg. 2. Average delay per packets for dfferent packet drop probabltes wth the ncreasng probablty. Ths can be explaned Tme Fg. 3. Average lfetme of a node for dfferent packet drop probabltes not all packets reach ther destnaton and thus the node energy s conserved. Effect of buffer sze on delay and average lfetme of a node Sngle-r Mult-r Packet Drop Probablty Snce, the queung model we employed uses buffers n each node and there s a lmt on the sze of those buffers, we vared the buffer sze to see f ths has any effect on the performance of the algorthm. The results are shown n fgures 4 and 5. The average delay per packet ncreases wth the buffer sze snce the throughput ncreases. Packets are not dropped when there s enough space n the buffers. Ths wll ncrease the number of packets arrvng to the gateway. The packets from far nodes wll be also able by notng that as the number of hops the packet traverse ncreases, the probablty that t wll be dropped ncreases. Ths means that the packets that arrve to the gateway are most probable to take a small number of hops and thus ncurrng less delay. As expected, the throughput decreases due to lost packets. The average node lfetme ncreases snce Tme Buffer Sze Sngle-r Mult-r Fg. 4. Average delay per packets for dfferent buffer sze

18 Tme Fg. 5. Average lfetme of a node for dfferent buffer sze to reach the gateway. More packets from far nodes mean more delay, whch eventually ncreases the average delay per packet. The ncreasng number of packets arrvng to the gateway wll also ncrease the energy consumpton by ncreasng the number of transmsson and recepton costs, therefore decreasng the average lfetme of a node. It s worth notng that, for both delay and average lfetme metrcs, mult-r mechansm performs better because of the more effcent adjustment of the packet servce rates on sensor nodes as depcted n fgures 2, 3, 4 and Concluson Sngle-r Mult-r Buffer Sze In ths paper, we presented a new energy-aware QoS routng protocol for sensor networks. The protocol fnds QoS paths for real-tme data wth certan endto-end delay requrements. In order to support both best effort and real-tme traffc at the same tme, a class-based queung model s employed. The queung model allows servce sharng for real-tme and nonreal-tme traffc. A rato r s defned as an ntal value set by the gateway and s used to calculate the amount of bandwdth to be dedcated to the real-tme and non-real-tme traffc on a partcular outgong lnk. The selected queung model for the protocol allows the throughput for normal data not to dmnsh by utlzng that servce rate on each node. Two dfferent mechansms, namely sngle -r and mult-r, for settng that servce rate on each node are presented. Sngle-r mechansm sets a network wde r- value for every sensor node. In the mult-r mechansm, the gateway broadcasts the necessary nformaton to the sensor nodes n order for them to calculate ther own r-value. The effectveness of the protocol for both mechansms s valdated by smulaton. Smulaton results have shown that our protocol consstently performs well wth respect to QoS metrcs, e.g. throughput and average delay, n comparson to a baselne non-qos aware protocol that use the same lnk cost. The mult-r mechansm has provded better end-to-end delay for real-tme packets wth a slght ncrease n energy usage. It has also ncreased the throughput for non-real-tme data packets, whch has extended the queung delay on the nodes causng an ncrease n the average delay per non-real-tme packets.

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