SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks

Transcription

1 : A Stateless Protocol for Real-Tme Communcaton n Sensor Networks Tan He a John A Stankovc a Chenyang Lu b Tarek Abdelzaher a a Department of Computer Scence b Department of Computer Scence & Engneerng Unversty of Vrgna Washngton Unversty n St Lous {tanhe, stankovc, zaher}@cs.vrgna.edu [email protected] Abstract In ths paper, we present a real-tme communcaton protocol for sensor networks, called. The protocol provdes three types of real-tme communcaton servces, namely, real-tme uncast, real-tme area-multcast and real-tme area-anycast. s specfcally talored to be a stateless, localzed algorthm wth mnmal control overhead. End-to-end soft real-tme communcaton s acheved by mantanng a desred delvery speed across the sensor network through a novel combnaton of feedback control and non-determnstc geographc forwardng. s a hghly effcent and scalable protocol for sensor networks where the resources of each node are scarce. Theoretcal analyss, smulaton experments and a real mplementaton on Berkeley motes are provded to valdate our clams. 1. Introducton Many exctng results have been recently developed for large-scale sensor networks. These networks can form the bass for many types of smart envronments such as smart hosptals, battlefelds, earthquake response systems, and learnng envronments. Whle these potental applcatons reman dverse, one commonalty they all share s the need for an effcent and robust routng protocol. The man functon of sensor networks s data delvery. We dstngush three types of communcaton patterns assocated wth the delvery of data n such networks. Frst, t s often the case that one part of a network detects some actvty that needs to be reported to a remote base staton. Ths type of communcaton s called uncast. Alternatvely, a base staton may ssue a command or query to an area n the sensor networks. For example, t may ask all sensors n the regon of a damaged nuclear plant to report radaton readngs, or command all lghts n a gven area to turn on. Ths type of communcaton motvates a dfferent routng servce where one end-pont of the route may be an area rather than an ndvdual node. We call ths area-multcast. Fnally, snce sensors often measure hghly redundant nformaton, n some stuatons t may be suffcent to have any node n an area respond. We call a routng servce that provdes such capablty, area-anycast. provdes the aforementoned three types of communcaton servces. Snce sensor networks deal wth real world, t s often necessary for communcaton to meet real-tme constrants. In survellance systems, for example, communcaton delays wthn sensng and actuatng loops drectly affect the qualty of trackng. To date, few results exst for sensor networks that adequately address real-tme requrements. In ths paper we develop a protocol that supports soft real-tme communcaton based on feedback control and stateless algorthms for large-scale sensor networks. We evaluate va smulaton usng GloMoSm [15] and compare t to fve other ad hoc routng protocols: DSR [5], AODV [10], [13] and two scaled down versons of. The performance results show that 1) reduces the number of packets that mss ther end-to-end deadlnes, 2) reacts to transent congeston n the most stable manner, and 3) effcently handles vods [6] wth mnmal control overhead. We also mplement on the Berkeley motes [4]. The results show that helps balance the traffc load to ncrease the system lfetme. 2. State of the Art Several routng protocols have been developed for ad hoc wreless networks. Sensor networks can be regarded as a sub-category of such networks, but wth a number of dfferent requrements. In sensor networks, locaton s more mportant than a specfc node s ID. For example, trackng applcatons only care where a target s located, not the ID of the reportng node. In sensor networks, such locaton-awareness s necessary to make the sensor data meanngful. Therefore, t s natural to utlze locaton-aware routng. A set of locaton based routng algorthms have been proposed. Fnn [2] proposed a greedy geographc forwardng protocol wth lmted floodng to crcumvent the vods nsde the network. GPSR [6] by Karp and Kung use permeter forwardng to get around vods. Geographc dstance routng (GEDIR) [13] guarantees loop-free delvery n a collson-free network. LAR [7] by Young-Bae Ko mproves the effcency of the on-demand routng algorthms by restrctng routng packet floodng n a specfed request zone.

2 also utlzes geographc locaton to make localzed routng decsons. The dfference s that s desgned to handle congeston and provde a soft real-tme communcaton servce, whch are not the man goals of prevous locaton-based routng protocols. Moreover, provdes an alternatve soluton to handle vods other than approaches based on planar graph traversal [6] and lmted floodng [2]. Several real-tme protocols have been proposed for sensor networks. SWAN [1] uses feedback nformaton from the MAC layer to regulate the transmsson rate of non-real-tme TCP traffc n order to sustan real-tme UDP traffc. RAP [9] uses velocty monotonc schedulng to prortze real-tme traffc and enforces such prortzaton through a dfferentated MAC Layer. Woo and Culler [14] proposed an adaptve MAC layer rate control to acheve farness among nodes wth dfferent dstances to the base staton. All of these algorthms work well by locally degradng a certan porton of the traffc. However, ths knd of local MAC layer adaptaton cannot handle long-term congeston where routng assstance s necessary to dvert traffc away from any hotspot. provdes a combnaton of MAC layer and network layer adaptaton that effectvely deals wth such ssues. To the best of our knowledge, no routng algorthm has been specfcally desgned to provde soft real-tme guarantees for sensor networks. Reactve routng algorthms such as AODV [10] and DSR [5] mantan routng nformaton for a small subset of possble destnatons, namely those currently n use. If no route s avalable for a new destnaton, a route dscovery process s nvoked. Route dscovery broadcasts can lead to sgnfcant delays n a sensor network wth a large network dameter. Ths lmtaton makes on-demand algorthms less sutable for real-tme applcatons. 3. Desgn Goals Our desgn s nspred by the observaton that unlke wred networks, where the delay s ndependent of the physcal dstance between the source and destnaton, n mult-hop wreless sensor networks, the end-to-end delay depends on not only sngle hop delay, but also on the dstance a packet travels. In vew of ths, the key desgn goal of the algorthm s to support a soft real-tme communcaton servce wth a desred delvery speed across the sensor network, so that end-to-end delay s proportonal to the dstance between the source and destnaton. It should be noted that delvery speed refers to the approachng rate along a straght lne from the source toward the destnaton. Unless the packet s routed exactly along that straght lne, delvery speed s smaller than the actual speed of the packet n the network. For example, f the packet s routed n the opposte drecton from the destnaton, ts speed s negatve. Our algorthm ensures that ths condton never occurs. Upon ths soft real-tme delvery servce, provdes three types of real-tme communcaton servces, namely, real-tme uncast, real-tme area-multcast and realtme area-anycast, for sensor networks. In dong so, satsfes the followng desgn objectves. 1. Stateless Archtecture. The physcal lmtatons of sensor networks, such as large scale, hgh falure rate, and constraned memory capacty necesstate a stateless approach. only mantans mmedate neghbor nformaton. It doesn t requre a routng table as n DSDV [11] nor per-destnaton states as n AODV [10]. Thus, ts memory requrements are mnmal. 2. Soft Real-Tme. Sensor networks are commonly used to montor and control the physcal world. provdes a unform delvery speed across the sensor network to meet the requrement of real-tme applcatons such as dsaster and emergency survellance n sensor networks. 3. Mnmum MAC Layer Support. doesn t requre real-tme or QoS aware MAC support. The feedback control scheme employed n allows t to be compatble wth all exstng best effort MAC layers. 4. QoS Routng and Congeston Management. Most reactve routng protocols can fnd routes that avod network hot spots durng the route acquston phase. Such protocols work well when traffc patterns don t fluctuate durng a sesson. However, these protocols (e.g. [5]) are less successful when congeston patterns change rapdly compared to the sesson lfetme. When a route becomes congested, such protocols ether suffer a delay or ntate another round of route dscovery. As a soluton, uses a novel backpressure re-routng scheme to re-route packets around large-delay lnks wth mnmum control overhead. 5. Traffc Load Balancng. In sensor networks, the bandwdth and energy are scarce resources compared to a wred network. Because of ths, t s valuable to utlze several smultaneous paths to carry packets from the source to the destnaton. uses non-determnstc forwardng to balance each flow among multple concurrent routes. 6. Localzed Behavor. Pure localzed algorthms are those n whch any acton nvoked by a node should not affect the system as a whole. In algorthms such as AODV, DSR and TORA, ths s not the case. In these protocols a node uses floodng to dscover new paths. In sensor networks where thousands of nodes communcate wth each other, broadcast storms may result n sgnfcant power consumpton and possbly a network meltdown. To avod that, all dstrbuted operatons n are localzed to acheve hgh scalablty. 7. Vod Avodance. In some scenaros, pure greedy geographc forwardng may fal to fnd a greedy path to the destnaton, even when one actually exsts. handles the vod the same way as t handles congested areas and guarantees that f there s a greedy route between the source and destnaton, t wll dscover t.

3 Note, whle does not use routng tables, does utlze locaton nformaton to carry out routng. Because of ths, we assume that each node s locaton-aware. 4. Protocol mantans a desred delvery speed across sensor networks by both dvertng traffc at the networkng layer and locally regulatng packets sent to the MAC layer. It conssts of the followng components: An API A neghbor beacon exchange scheme A delay estmaton scheme The Stateless Non-determnstc Geographc Forwardng algorthm (SN) A Neghborhood Feedback Loop (NFL) Backpressure Reroutng Last mle processng As shown n Fgure 1, SN s the routng module responsble for choosng the next hop canddate that can support the desred delvery speed. NFL and Backpressure Reroutng are two modules to reduce or dvert traffc when congeston occurs, so that SN has avalable canddates to choose from. The last mle process s provded to support the three communcaton semantcs mentoned before. Delay estmaton s the mechansm by whch a node determnes whether or not congeston has occurred. And beacon exchange provdes geographc locaton of the neghbors so that SN can do geographc based routng. The detals of these components are dscussed n the subsequent sectons, respectvely. Backpressure Reroutng Beacon Exchange API UnCast MultCast AnyCast Last Mle Process SN Neghbor Table MAC Fgure 1. Protocol NFL Delay Estmaton 4.1. Applcaton API and Packet Format The protocol provdes four applcaton-level API calls: AreaMultcastSend (poston, radus, packet): Ths servce dentfes a destnaton area by ts center poston and radus. It sends a copy of the packet to every node nsde the specfed area wth a speed above a certan desred value. AreaAnyCastSend (poston, radus, packet): Ths servce sends a copy of the packet to at least one node nsde the specfed area wth a speed above a certan desred value. UncastSend(Global_ID, packet): In ths servce the node dentfed by Global_ID wll receve the packet wth a speed above a certan desred value. SpeedReceve(): ths prmtve permts nodes to accept packets targeted to them. Though s a real-tme protocol, we don t use deadlne as a parameter n our API. ams at provdng a unform packet delvery speed across the sensor network, so that the end-to-end delay of a packet s proportonal to the dstance between the source and destnaton. Wth ths servce, real-tme applcatons can estmate end-to-end delay before makng admsson decsons. Delay dfferentaton for dfferent classes of packets s left as future work. There s a sngle data packet format for the protocol, whch contans the followng major felds: PacketType: the type of communcaton: Area Multcast, AreaAnyCast or Uncast. Global_ID: only used n Uncast communcaton to dentfy a destnaton node. Destnaton Area: Descrbes a three-dmensonal space wth a center pont and radus n whch the packets are destned. TTL: Tme To Lve feld s the hop lmt used for last mle processng. Payload Neghbor Beacon Exchange Smlar to other geographc routng algorthms, every node n perodcally broadcasts a beacon packet to ts neghbors. Ths perodc beaconng s only used for exchangng locaton nformaton between neghbors. We argue that the beaconng rate can be very low when nodes nsde the sensor network are statonary or slow movng. Moreover, pggybackng [6] methods can also be exploted to reduce ths beacon overhead. In addton to perodc beaconng, uses two types of on-demand beacons, namely a delay estmaton beacon and a backpressure beacon, to quckly dentfy the traffc changes nsde the network. The functonalty of two beacons wll be dscussed n secton 4.3 and 4.6, respectvely. As shown n the evaluaton (secton 5.4), our ondemand beacon scheme ntroduces only a small overhead n exchange for a fast response to congeston. In, each node keeps a neghbor table to store nformaton passed by the beaconng. Each entry nsde the table has the followng felds: (NeghborID, Poston, SendToDelay, ExpreTme). The ExpreTme s used to tmeout ths entry. If a neghbor entry s not refreshed after a certan tmeout, t wll be removed from the neghbor table. SendToDelay s a delay estmaton to the neghbor node dentfed by the NeghborID feld. The detals of set-

4 tng ths value are dscussed n the next secton Delay Estmaton We use sngle hop delay as the metrc to approxmate the load of a node. We notce that the delays experenced by broadcast packets and uncast packets are qute dfferent due to dfferent handlng nsde the MAC layer and that uncast packet delay s more approprate for makng routng decsons. In a scarce bandwdth envronment, we cannot afford to use probng packets to estmate the sngle hop delay. Instead we use the data packets passng ths node to perform ths measurement. Delay s measured at the sender, whch tmestamps the packet enterng the network output queue and calculates the round trp sngle hop delay for ths packet when recevng the ACK. At the recever sde, the duraton for processng an ACK s put nto the ACK packet. The sngle-trp tme s calculated by subtractng recever sde processng tme from the round trp delay experenced by the sender. We compute the current delay estmaton by combnng the newly measured delay wth prevous delays va the exponental weghted movng average (EWMA) [8]. Propagaton delay s gnored. We argue that ths delay estmaton s a better metrc than average queue sze for representng the congeston level of the wreless network, because the shared meda nature of the wreless network allows the network to be congested even f queue szes are small Stateless Non-determnstc Geographc Forwardng (SN) Before elaboratng on SGNF, we ntroduce three defntons: The Neghbor Set of Node : NS s the set of nodes that are nsde the rado range of node. Note, we do not assume that the communcaton radus s a perfect crcle. works wth rregular rado patterns. D L-L_Next L j FS Fgure 2. NS and FS defntons The Forwardng Canddate Set of Node : A set of nodes that belong to NS and are closer to the destnaton. Formally, FS (Destnaton) = {node NS L L_next > 0} where L s the dstance from node to the destnaton and L_next s the dstance from the next hop forwardng canddate to the destnaton. These nodes are nsde the cross-hatched shaded area as shown n Fgure 2. We can easly obtan FS (Destnaton) by scannng the NS set of nodes once. NS It s worth notcng that the membershp of the neghbor set only depends on the rado range, but the membershp of the forwardng set also depends on destnaton area. Relay Speed. Relay speed s calculated by dvdng the advance n dstance from the next hop node j by the estmated delay to forward a packet to node j. Formally, j L L _ next Speed ( Destnaton) =. j HopDelay Snce n, nodes keep the Neghbor Set (NS), but don t keep a routng table or flow nformaton, the memory requrements are only proportonal to the number of neghbors. Based on the destnaton of the packet and the current FS, the Stateless Non-determnstc Geographc Forwardng (SN) porton of our protocol routes the packets accordng to the followng rules: 1. Packets are forwarded only to the nodes that belong to the FS (Destnaton). If there s no node nsde the FS (Destnaton), packets are dropped and a backpressure beacon s ssued to upstream nodes to prevent further drops (see 4.7). To reduce the chance of such drops, we deduce a lower bound of node densty that can vrtually elmnate these drops (appendx A). 2. dvdes the neghbor nodes nsde FS (Destnaton) nto two groups. One group contans the nodes that have relay speeds larger than a certan desred speed S setpont, the other contans the nodes that cannot sustan such desred speed. The S setpont s a system parameter that depends on the communcaton capablty of the nodes and desred traffc workload a sensor network should support. 3. The forwardng canddate s chosen from the frst group, and the neghbor node wth hghest relay speed has a hgher probablty to be chosen as the forwardng node. In our approach, we use a dscrete exponental dstrbuton to trade off between load balancng and optmal path length. 4. If there are no nodes belongng to the frst group, a relay rato s calculated based on the Neghborhood Feedback Loop (NFL), whch s dscussed n more detal n secton 4.5. Whether a packet drop wll really happen depends on whether a randomly generated number between (0,1) s bgger than the relay rato. In a packet s dropped only when no downstream node can guarantee the sngle hop speed set pont S setpont and droppng packets must be peformed to reduce the congeston. Though one can consder bufferng packets as an alternatve to the droppng, however, we argue that under real-tme and small memory constrans, droppng s often a better choce. SN provdes two nce propertes to help meet our desgn goals. Frst, snce SN sends packets to the downstream

5 node capable of mantanng the desred delvery speed, soft real-tme end-to-end delvery s acheved wth a theoretcal delay bound: Delay Bound = L e2e /S setpont, where L e2e s the dstance between the source and destnaton. S setpont s the unform speed to be mantaned across the sensor network. Second, SN can balance traffc and reduce congeston by dspersng packets nto a large relay area. Ths load balancng s valuable n a sensor network where the densty of nodes s hgh and the communcaton bandwdth s scarce and shared. Load balancng also balances the power consumpton nsde the sensor networks to prevent some nodes from dyng faster than others. SN provdes MAC layer adaptaton and reduces the congeston by locally droppng (or optonally bufferng) packets. Ths adaptaton s good enough to deal wth transent overshoot nsde the sensor networks. But f such congeston remans for a relatvely long tme, network layer adaptaton s desred to redrect traffc to a less congested area, whch s dscuss further n secton Neghborhood Feedback Loop (NFL) The Neghborhood Feedback Loop (NFL) s the key component n mantanng the sngle hop relay speed. The NFL s an effectve approach to mantanng system performance at a desred value. Ths has been shown n [12], where a low mss rato of real-tme tasks and a hgh utlzaton of the computatonal nodes are smultaneously acheved. Here we want to mantan a sngle hop relay speed above a certan value S setpont, a performance goal desred by the system desgner. mss rato MR Setpont MAC Feedback Neghborhood Table SELF on/off - Relay Rato Relay Controller Rato SN Delay Estmaton Beacon Back Pressure Beacon Neghbors Neghbor Nodes Fgure 3. Neghborhood Feedback Loop (NFL) We deem t a mss when a packet delvered to a certan neghbor node has a relay speed less than S setpont, or f there s a loss due to collson. The percentage of such msses s called ths neghbor s mss rato. The responsblty of the NFL s to force the mss ratos of the neghbors to converge to a set pont, namely zero. As shown n Fgure 3, the MAC layer collects mss nformaton and feeds t back to the Relay Rato controller. The Relay Rato controller calculates the relay rato and feeds that nto the SN where a drop or relay acton s made. The Relay Rato controller currently mplemented s a multple nputs sngle output (MISO) proportonal controller that takes the mss ratos of ts neghbors as nputs Beacon and proportonally calculates the relay rato as the output to the SN. Formally t s descrbed by the followng formulas. e u = 1 K f e > 0 N u = 1 f e = 0 where e s the mss rato of the neghbor nsde the FS set, N s sze of the FS set. u s the output (relay rato) to SN. And K s the proportonal gan. It should be noted that the Relay Rato controller wll be actvated only when all nodes nsde the forwardng set (FS) cannot mantan the desred sngle hop relay speed S setpont and a drop s absolutely necessary to mantan the sngle hop delay. Such a scheme ensures that re-routng has a hgher prorty than droppng. In other words, wll not drop a packet as long as there s another path that can meet the delay requrements. By reducng the sendng rate to the downstream nodes, the neghborhood feedback loop can mantan a sngle hop relay speed. However, ths MAC layer adaptaton can t solve the hotspot problem, f the upstream nodes, whch are unaware of the congeston, keep sendng packets nto ths area. In ths case, backpressure reroutng (network layer adaptaton) s necessary to reduce the traffc njected nto the congested area Back-Pressure Reroutng Backpressure re-routng s naturally generated from the collaboraton of neghbor feedback loop (NFL) routnes as well as the stateless non-determnstc geographc forwardng (SN). To be more explct, we ntroduce ths scheme wth an example (Fgure 4). 2 3 ID Delay 9 0.5S 7 0.1S S 3 0.1S Node 5's NT R Delay 10 Boo Fgure 4. Backpressure reroutng case one Suppose n the lower-rght area, heavy traffc appears, whch leads to a lower relay speed n nodes 9 and 10. Through the MAC layer feedback, node 5 wll detect that nodes 9 and 10 are congested. Snce SN wll reduce the probablty of selectng nodes 9 and 10 as forwardng canddates and route more packets to node 7, t wll reduce the congeston around nodes 9 and 10. Snce all neghbors of 9 and 10 wll react the same way as node 5, eventually nodes 9 and 10 wll be able to relay packets above the desred speed. 11

6 A more severe case could occur when all the forwardng neghbors of node 5 are also congested as shown n Fgure 5. 2 ID Delay 5 0.5S 2 0.1S 4 0.1S Node 3's NT 3 ID Delay 5 0.1S 7 0.5S Node 6's NT 5 Fgure 5. Backpressure reroutng case two In ths case, the neghborhood feedback loop s actvated to assst backpressure re-routng. In node 5, a certan percent of packets wll be dropped n order to reduce the traffc njected nto the congested area. At the same tme, an on-demand backpressure beacon s ssued by node 5 wth the followng felds. (ID, Destnaton, AvgSendToDelay) AvgSendToDelay s the average SendToDelay of all nodes nsde FS ID (Destnaton). In our example, when the destnaton s at node 13, AvgSendToDelay s the average delay from node 5 to nodes 7, 9 and 10. When a neghbor receves the back-pressure beacon from node 5, t determnes whether node 5 belongs to ts FS(Destnaton). If node 5 does, ths neghbor modfes the SendToDelay for node 5 accordng to the AvgSendToDelay. For example only node 3 wll consder node 5 as a next hop forwardng canddate to the destnaton where node 13 resdes. If node 5 s not n the FS(Destnaton), then ths neghbor gnores the backpressure beacon. Ths backpressure mechansm can reduce the chance of false congeston ndcaton, to ensure that traffc from node 4 to node 6 wll not be affected by the backpressure beacon. If, unfortunately, node 3 s n the same stuaton as node 5, further backpressure wll be mposed on node 2. In the extreme case, the whole network s congested and the backpressure wll proceed upstream untl t reaches the source, where the source wll quench the traffc flow to that destnaton. Backpressure reroutng s a network layer adaptaton used by to reduce the congeston nsde the network. In ths case no packet needs to be sacrfced. Network layer adaptaton has a hgher prorty than MAC layer adaptaton used by SN and NFL. A drop va the feedback loop s only necessary when the stuaton becomes so congested and there s no alternatve to mantanng a sngle hop speed other than droppng packets Vod Avodance Greedy geographc based algorthms have many advantages over the tradtonal MANET routng algorthms for real-tme sensor network applcatons. They do not suffer Delay Boo route dscovery delay and tend to choose the shortest path to the destnaton. Moreover wthout floodng, they have relatvely low control packet overhead. Unfortunately, they also have a serous drawback. In many cases, they may fal to fnd a path even though one does exst. To overcome ths, deals wth a vod the same way t deals wth congeston. As shown n the Fgure 6, f there s no downstream node to relay packets from node 2 to node 5, node 2 wll send out a backpressure beacon contanng felds: (ID, Destnaton, ). The upstream node 1 that needs node 2 to relay the packets to that destnaton wll set the SendToDelay for node 2 to nfnty and stop sendng packets to node 2. If node 3 doesn t exst, further backpressure wll occur untl a new route s found. It should be admtted that our scheme of vod avodance sn t guaranteed to fnd a path f there s one as n GPSR[6], but t s guaranteed to fnd a greedy path f one exsts. To mantan real-tme propertes, we don t allow backtrackng to volate our desred speed setpont. However, as we can see from the evaluaton secton 5.6, such a smple scheme can sgnfcantly reduce packet loss due to vods n hgh-densty sensor networks BackPressure VOID 4 5 Dest. Fgure 6. Vod avodance scheme 4.8. Last Mle Process Snce s targeted at sensor networks where the ID of a sensor node s not mportant, only cares about the locaton where sensor data s generated. The last mle process s so called because only when the packet enters nto the destnaton area wll such a functon be actvated. The SN module aforementoned controls all prevous packet relays. The last mle process provdes two novel servces that ft the scenaro of sensor networks: Area-multcast and Area-anycast. The area n ths case s defned by a centerpont (x,y,z) and a radus, n essence a sphere. More complex area defntons can be made wthout jeopardzng the desgn of ths last mle process. Nodes can dfferentate the packet type by the Packet- Type feld mentoned n secton 4.1. If t s an anycast packet, the nodes nsde the destnaton area wll delver the packet to the transport layer wthout relayng t onward. If t s a multcast packet, the nodes nsde the destnaton area whch frst receve the packet comng from the outsde of the destnaton area wll set a TTL. Ths allows the packet to survve wthn the dameter of the destnaton area and be broadcast wthn a specfed radus. Other nodes nsde ths destnaton area wll keep a copy of the packet and rebroadcast t. The nodes that are outsde the destnaton area wll just gnore t. The last mle process for uncast s nearly

7 the same as multcast, except the node wth a specfed global_id wll delver the packet to the transport layer. If the locaton drectory servce s precse, we can expect the addtonal floodng overhead for the uncast packets to be small. The current mplementaton of the last mle process s relatvely smple. More effcent and robust technques are desred for future research. 5. Expermentaton and Evaluaton We smulate on GloMoSm [15], a scalable dscrete-event smulator developed by UCLA. Ths software provdes a hgh fdelty smulaton for wreless communcaton wth detaled propagaton, rado and MAC layers. Table 1 descrbes the detaled setup for our smulator. The communcaton parameters are mostly chosen n reference to the Berkeley mote specfcaton. Routng AODV, DSR,,, -S, -T MAC Layer ( Smplfed DCF) Rado Layer RADIO-ACCNOISE Propagaton model TWO-RAY Bandwdth 200Kb/s Payload sze 32 Byte TERRAIN (200m, 200m) Node number 100 Node placement Unform Rado Range 40m Table 1. Smulaton settngs In our evaluaton, we compare the performance of sx dfferent routng algorthms: AODV [10], DSR [5], [13],, -S, -T. forwards a packet to the node that makes the most progress toward the destnaton. -S and -T are reduced versons of. -S replaces the SN wth a MAX- routng algorthm that geographcally forwards the packets to nodes that can provde a max sngle hop relay speed. -T replaces the SN wth a MIN-DELAY routng algorthm that geographcally forwards packets to nodes that have a mnmum sngle hop delay. Both reduced versons have no backpressure reroutng mechansms. In our evaluaton, we present the followng set of results: 1) end-to-end delay under dfferent congeston levels, 2) mss rato, 3) control overhead, 4) communcaton energy consumpton, and 5) packet delvery rato under dfferent node denstes. All experments are repeated 16 tmes wth dfferent random seeds and dfferent random node topologes. We also mplement on the Berkeley motes [4]. The results obtaned from ths testbed show a load balance feature of protocol (see secton 5.7) Sensor Network Traffc Pattern There are two typcal traffc patterns n sensor networks: a base staton pattern and a peer-to-peer pattern. The base staton pattern s the most representatve one nsde sensor networks. For example, n survellance systems, multple sensors detect and report the locaton of an ntruder to the control center. In trackng systems, a base staton ssues multple trackng commands to a group of pursuers. In a dfferent respect, the peer-to-peer pattern s usually used for data aggregaton and consensus n a small area where a team of nearby motes nteract wth each other. The end-to-end delay n the base staton pattern s the major part of delay for the sensng-actuaton loop, and s therefore, the focus of our evaluaton Congeston Avodance In a sensor network, where node densty s hgh and bandwdth s scarce, traffc hot spots are easly created. In turn, such hot spots may nterfere wth real-tme guarantees of crtcal traffc n the network. In, We apply a combned network and MAC layer congeston control scheme to allevate ths problem. To test the congeston avodance capabltes, we use a base staton scenaro, where 6 nodes, randomly chosen from the left sde of the terran, send perodc data to the base staton at the mddle of the rght sde of the terran. The average hop count between the node and base staton s about 8~9 hops. Each node generates 1 CBR flow wth a rate of 1 packet/second. To create congeston, at tme 80 seconds, we create a flow between two randomly chosen nodes n the mddle of the terran. Ths flow then dsappears at tme 150 seconds nto the run. Ths flow ntroduces a step change nto the system, whch s an abrupt change that stress-tests s adaptaton capabltes to reveal ts transent-state response. In order to evaluate the congeston avodance capablty under dfferent congeston levels, we ncrease the rate of ths flow step by step from 0 to 100 packets/second over several smulatons Fgure 7 and Fgure 8 plot the end-to-end (E2E) delay for the sx dfferent routng algorthms. At each pont, we average the E2E delays of all the packets from the 96 flows (16 runs wth 6 flows each). The 90% confdence nterval s wthn 2~15% of the mean, whch s not plotted for the sake of legblty. Under the no or lght congested stuatons, Fgure 7 and Fgure 8 show that all geographc based routng algorthms have short average end-to-end delay n comparson to AODV and DSR. There are several factors accountng for ths outcome. Frst, the route acquston phase n AODV and DSR leads to sgnfcant delays for the frst few packets, whle geographc based routng doesn t suffer from ths. We argue that wthout an ntal delay cost, geographc based routng s more sutable for real-tme applcatons lke target trackng where the base staton sends the actuaton commands to the sensor group, whch s dynamcally changng as the target moves. In such a scenaro, DSR and AODV need to perform route acquston repeatedly n order to track the target. Second, the route dscovered through

8 floodng and path reversal has relatvely more hops than greedy geographc forwardng. The reason for even hgher delay n AODV than DSR s that DSR mplementaton ntensvely uses a route cache to reduce route dscovery and mantenance cost. As shown n Fgure 8, -T has hgher delay than, -S and, because -T only uses hop delay to make routng decson and dsregards the progress each hop makes, whch leads to more hops to the destnaton n wreless mult-hop networks. Instead, under lghtly congested stuaton,, -S and tend to forward a packet at each step as close to the destnaton as possble, thereby reducng the number of hops and the end-to-end delay. Delay (MS) AODV DSR Rate (P/S) Fgure 7. E2E Delay Under Dfferent Congeston Delay (MS) S -T Rate(P/S) Fgure 8. E2E Delay Under Dfferent Congeston Under the heavy congested stuatons (Fgure 7 and Fgure 8), each routng algorthm responds dfferently. performs best. For example, reduces the average end-to-end delay by 30%~40% n the face of heavy congeston n comparson to the other algorthms consdered. The key reasons for s better performance are 1) DSR, AODV and only respond to severe congeston, whch leads to lnk falures (.e., when multple retransmssons fal at the MAC layer). They are nsenstve to long delays as long as no lnk falures occur. 2) DSR, AODV and routng decsons are not based on the lnk delays, and therefore may cause congeston at a partcular recever even though t has long delays. 3) DSR and AODV flood the network to redscover a new route when the network s already congested. 4) -T and -S don t provde traffc adaptaton. When all downstream nodes are congested, -T and -S cannot reduce or redrect the traffc to uncongested routes. 5) not only locally reduces the traffc through a combnaton of SN and Neghborhood Feedback loops n order to mantan the desred speed, but also dverts the traffc nto a large area through ts backpressure reroutng mechansm. Ths combnaton leads to lower end-to-end delay E2E Deadlne Mss Rato The deadlne mss rato s the most mportant metrc n soft real-tme systems. We set the desred delvery speed S setpont to 1km/s, whch leads to an end-to-end deadlne of 200 mllseconds. In the smulaton, some packets are lost due to congeston or forced-drops. We also consder ths stuaton as a deadlne mss. The results shown n Fgure 9 and Fgure 10 are the summary of 16 randomzed runs. Mss Rato 50% 40% 30% 20% 10% 0% AODV DSR Rate(P/S) Fgure 9. MssRato Under Dfferent Congeston Mss Rato 50% 40% 30% 20% 10% 0% -S -T Rate (P/S) Fgure 10. MssRato Under Dfferent Congeston AODV and DSR don t perform well n the face of congeston because both algorthms flood the network n order to dscover a new path when congeston leads to lnk falure. Ths floodng just serves to ncrease the congeston. only swtches the route when there are lnk falures caused by heavy congeston. The routng decson s based solely on dstance and does not consder delay. -T only consders the sngle hop delay and doesn t take dstance (progress) nto account, whch leads to a longer route. -S provdes no adaptaton to the congeston and cannot prevent packets from enterng the congeston area. Only tres to mantan a desred delvery speed through MAC and network layer adaptatons, and therefore has a much less mss rato than other algorthms. Due to ts transent behavor, stll has about a 20% mss rato when the network s heavly congested. Future work s

9 needed to reduce the convergence tme n order to mprove the performance. Comparng Fgure 9 and Fgure 10, we argue that purely localzed algorthms wthout floodng outperform other algorthms when traffc congeston ncreases. Generally, the less state nformaton a routng algorthm depends on, the more robust t s n the face of packet loss and congeston Control Packet Comparson Except for AODV, all other routng algorthms studed use a relatvely low number of control packets. Most control packets n DSR and AODV are used n route acquston. Because AODV ntates route dscovery (floodng) whenever a lnk breaks due to congeston, t requres a large number of control packets. DSR uses a route cache extensvely, so t can do route dscovery and mantenance wth a much lower cost than AODV. The only control packets used n, -S and -T (Fgure 11) are perodc beacons, whose number s constant at 750 under dfferent congeston levels. In addton to perodc beacons, uses two types of on-demand beacons to notfy neghbors of the congeston. Ths costs more control packets than the other three geographc based routng algorthms (Fgure 11). #Packets DSR -S -T Rate (P/S) Fgure 11. Control packet overhead comparson 5.5. Energy Consumpton Under energy constrants, t s vtal for sensor nodes to mnmze energy consumpton n rado communcaton to extend the lfetme of sensor networks. From the results shown n Fgure 12, we argue that geographc based routng tends to reduce the number of hops n the route, thus reducng the energy consumed for transmsson. AODV performs the worst as a consequence of sendng out many control packets durng congeston. DSR has larger average hop counts and more control packets than other geographc base routng algorthms. -T only takes delay nto account, whch leads to longer routes. Fgure 12 shows that has nearly the same power consumpton as and - S when the network s not congested. Under such stuatons, tends to choose the shortest route and does not requre any on-demand beacons. Under heavy congeston, has slghtly hgher energy consumpton than and -S, manly because delvers more packets to the destnaton than the other protocols when heavly congested. Energy consumpton (mwhr) Rate (P/S) AODV DSR -S -T Fgure 12. Energy Consumpton for transmsson 5.6. Vod Avodance Delvery Rato 100% 95% 90% 85% 80% 75% 70% DSR -S -T Densty (nodes per rado crcle) Fgure 13. Delver rato under dfferent densty Ths experment tres to evaluate the end-to-end delvery rato of all routng algorthms under dfferent node denstes. To elmnate packet loss due to the congeston, we only use four flows wth a rate of 0.5 packets/second, these flows go from the left sde of the terran to the base staton at the rght sde of the terran. To change the densty of the network, nstead of ncreasng the number of nodes n the terran, we keep the number of nodes constant at 100, and ncrease the sde length of the square terran n steps of 50 meters. It s no surprse that DSR performs best n the delvery rato snce t s a floodng based route dscovery algorthm. Theoretcally, DSR should have 100% delvery rato (Fgure 13) as long as the network s not parttoned. All other geographc based algorthms have 100% delvery rato when the network has hgh densty (>12 nodes / per rado range). However, when the network densty s reduced below 9 nodes/ per rado crcle,, -S and - T degrade performance rapdly. Only can manage to delver 95% of ts packets to the destnaton. However, drops 5% of ts packets, because those packets need backtrackng n order reach the destnaton. If backtrackng, those packets would have a negatve delvery speed, whch

10 s not allowed by for the sake of mantanng the real-tme propertes. It should be ponted out that GPSR[6], another well known geographc based routng algorthm, permts backtrackng and can acheve 100% delvery rate as long as the network s not parttoned Implementaton on Motes We have mplemented the protocol on Berkeley motes platform wth a code sze of 6036 bytes (code s avalable at [3]). Three applcatons ncludng data placement, target trackng and CBR are bult on top of. Due to space lmtaton, we only present partal results here. In the experment, we use 25 motes to form a 5 by 5 grd. To evaluate the load balance capablty of the, we send a CBR flow from node 24 to node 0 whch s the base staton. We collect the number of packets relayed by ntermedate motes (1~23) and compare ths wth the result obtaned from protocol whch we also mplemented on the motes. tends to relay packets va a fxed route whch leads to unbalance traffc, for example, n Fgure 14, node 14 sends out 98 packets whle node 13 doesn t sent out any packets. uses non-determnstc forwardng, whch can balance energy consumpton. We argue that n sensor networks, balanced energy consumpton can prevent some nodes from dyng faster than others, therefore ncreasng the network lfetme. #Packets Relayed ID Fgure 14. Traffc Balance 6. Concluson Many excellent protocols have been developed for ad hoc networks. However, sensor networks have addtonal requrements that were not specfcally addressed. These nclude real-tme requrements and nodes whch are severely constraned n computng power, bandwdth, and memory. mantans a desred delvery speed across the network through a novel combnaton of feedback control and non-determnstc QoS-aware geographc forwardng. Ths combnaton of MAC and network layer adaptaton mproves the end-to-end delay and provdes good response to congeston and vods. Our smulatons on GloMoSm and mplementaton on Berkeley motes demonstrate s mproved performance compared to DSR, AODV,, -S and -T. s a new protocol that meets the requrements of sensor networks n real-tme stuatons. 7. Acknowledgment Ths work was supported n part by the DAPRPA IXO offces under the NEST project (grant number F C-1905), the MURI award N from ONR and NSF grant CCR References [1] G. S. Ahn, A. T. Campbell, A. Veres and L.H. Sun. SWAN: Servce Dfferentaton n Stateless Wreless Ad Hoc Networks, In Proc. IEEE INFOCOM'2002, June [2] G. G. Fnn. Routng and Addressng Problems n Large Metropoltan-scale Internetworks. ISI/RR , USC/ISI, March [3] T. He, L. Gu, B.Blum, Jun Xe. Nest Project Source Code [4] J. Hll, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pster. System Archtecture Drectons for Network Sensors. ASPLOS [5] D. B. Johnson and D. A. Maltz. Dynamc Source Routng n Ad Hoc Wreless Networks. In Moble Computng, Chapter 5, pages , Kluwer Academc Publshers, [6] B. Karp and H. T. Kung. GPSR: Greedy Permeter Stateless Routng for Wreless Networks. In IEEE MobCom, August [7] Y.B. Ko and N. H. Vadya. Locaton-Aded Routng(LAR) n Moble Ad Hoc Networks. In IEEE MobCom 1998, October [8] J. F. Kurose, K. W. Ross. Computer Networkng A Top-Down Approach Featurng the nternet. ISBN Addson Wesley Longman Inc. [9] C. Lu, B. M. Blum, T. F. Abdelzaher, J. A. Stankovc, and T. He. RAP: A Real-Tme Communcaton Archtecture for Large-Scale Wreless Sensor Networks, In IEEE RTAS 2002, September [10] C. E. Perkns and E. M. Royer. Ad-hoc On Demand Dstance Vector Routng. In WMCSA'99, February [11] C. E. Perkns and P. Bhagwat. Hghly dynamc Destnaton- Sequenced Dstance-Vector routng (DSDV) for Moble Computers, n SIGCOMM Symposum on Communcatons Archtectures and Protocols, pp , September [12] J. A. Stankovc, T. He, T. F. Abdelzaher, M. Marley, G. Tao, S. Son, and C. Lu. Feedback Control Schedulng n Dstrbuted Systems, IEEE RTSS, December [13] I. Stojmenovc and X. Ln. GEDIR: Loop-Free Locaton Based Routng n Wreless Networks, IASTED Int. Conf. on Parallel and Dstrbuted Computng and Systems, November 3-6, [14] A. Woo and D. Culler. A Transmsson Control Scheme for Meda Access n Sensor Networks, In IEEE MobCOM 2001, July [15] X. Zeng, Rajve Bagroda, and Maro Gerla. GloMoSm: a Lbrary for Parallel Smulaton of Large-scale Wreless Networks. In Proceedngs of the 12th Workshop on Parallel and Dstrbuted Smulatons -- PADS '98, May 26-29, 1998.