QoS Aware Geographic Opportunistic Routing in Wireless Sensor Networks

Transcription

1 QoS Aware Geographic Opportuistic Routig i Wireless Sesor Networks Log Cheg, Member, IEEE, Jiawei Niu, Member, IEEE, Jiaog Cao, Seior Member, IEEE, Sajal K. Das, Seior Member, IEEE, ad Yu Gu, Member, IEEE Abstract QoS routig is a importat research issue i wireless sesor etworks (WSNs), especially for missio-critical moitorig ad surveillace systems which requires timely ad reliable data delivery. Existig work exploits multipath routig to guaratee both reliability ad delay QoS costraits i WSNs. However, the multipath routig approach suffers from a sigificat eergy cost. I this work, we exploit the geographic opportuistic routig () for QoS provisioig with both ed-to-ed reliability ad delay costraits i WSNs. Existig protocols are ot efficiet for QoS provisioig i WSNs, i terms of the eergy efficiecy ad computatio delay at each hop. To improve the efficiecy of QoS routig i WSNs, we defie the problem of efficiet for multicostraied QoS provisioig i WSNs, which ca be formulated as a multiobjective multicostrait optimizatio problem. Based o the aalysis ad observatios of differet routig metrics i, we the propose a Efficiet QoS-aware () protocol for QoS provisioig i WSNs. selects ad prioritizes the forwardig cadidate set i a efficiet maer, which is suitable for WSNs i respect of eergy efficiecy, latecy, ad time complexity. We comprehesively evaluate by comparig it with the multipath routig approach ad other baselie protocols through s- simulatio ad evaluate its time complexity through measuremet o the MicaZ ode. Evaluatio results demostrate the effectiveess of the approach for QoS provisioig i WSNs. sigificatly improves both the ed-to-ed eergy efficiecy ad latecy, ad it is characterized by the low time complexity. Idex Terms Wireless sesor etworks; Multicostraied QoS; Geographic opportuistic routig; INTRODUCTION Wireless sesor etworks (WSNs) have bee desiged ad developed for a wide variety of applicatios, such as eviromet or habitat moitorig, smart battlefield, home automatio, ad traffic cotrol etc. []. A sesor etwork cosists of spatially distributed autoomous sesor odes, to cooperatively moitor physical or evirometal coditios. These sesor odes usually operate o limited o-rechargeable battery power, ad are expected to last over several moths or years. Therefore, a major cocer is to maximize the etwork lifetime, i.e., to improve the eergy efficiecy for WSNs. Sice the sesor ode ormally has limited processig speed ad memory space, it is also required that the algorithm ruig o sesor devices has a low computatioal cost. Providig reliable ad timely commuicatio i WSNs is a challegig problem. This is because, the varyig wireless chael coditios ad sesor ode failures may cause etwork topology ad coectivity chagig over time []. Uder such coditios, to forward a packet reliably at each hop, it may eed multiple retrasmissios, resultig i udesirable log delay as well as waste of eergy. Therefore, may existig works [3] [9] have bee proposed to improve the routig reliability ad latecy i WSNs with ureliable liks. QoS (Quality of Service) provisioig i etwork level Log Cheg is with the State Key Lab of Software Developmet Eviromet at Beihag Uiversity ad Sigapore Uiversity of Techology ad Desig. Jiawei Niu is with the State Key Lab of Software Developmet Eviromet, Beihag Uiversity, Beijig 9, Chia. Jiaog Cao is with Hog Kog Polytechic Uiversity, Hog Kog. Sajal K. Das is with Missouri Uiversity of Sciece ad Techology, USA. Yu Gu is with Sigapore Uiversity of Techology ad Desig, Sigapore. refers to its ability to deliver a guarateed level of service to applicatios []. The QoS requiremets ca be specified i the form of routig performace metrics, such as delay, throughput or jitter. For periodic eviromet reportig applicatios, delivery delay is ot critically sigificat as log as the sesory data arrives at the sik ode. While for other missiocritical applicatios, e.g., target trackig ad emergecy alarm, reliable ad timely delivery of sesory data is crucial i the success of the missio. I this case, QoS routig for both the ed-to-ed reliability ad delay guaratees becomes oe of the importat research issues i WSNs. However, due to the seemigly cotradictory multiple costraits (e.g., reliability, latecy ad eergy efficiecy) ad dyamics i WSNs, oly soft QoS provisioig is attaiable []. The soft QoS refers to meetig the QoS requiremets with probability, it is also cosidered to be good eough regardless of the fact that it is ot possible to guaratee a particular level of service. Qos provisioig i this work meas the soft QoS provisioig uless otherwise specified. Several existig works, MMSPEED [], MCMP [], EQSR [7], ECM [], ad DARA [9] propose to utilize multiple paths betwee the source ad sik for multicostraied QoS provisioig i WSNs. Data trasmissio alog multiple paths ca achieve certai desired reliability, ad the delay QoS requiremet is met as log as ay oe packet copy arrives at the destiatio before the deadlie. Compared with the sigle path routig with retrasmissio to guaratee delivery reliability, the multipath approach may probably provide shorter ed-to-ed delay by removig the retrasmissios. However, it has followig two major disadvatages: ) Sedig a packet over multiple paths ievitably iduces sigificat

2 eergy cost, which is oe of the primary desig cocers i WSNs; ) Exploitig multiple paths also itroduces more chael cotetios ad iterferece which may icrease the delivery delay as well as cause trasmissio failures []. This work addresses two issues of the multicostraied QoS routig i WSNs: ) Is the multipath routig really suitable for multicostraied QoS provisioig i WSNs? If it is ot the case, what techique ca help? We propose to exploit the geographic opportuistic routig () for QoS provisioig i WSNs. ) Ca existig protocols be directly applied to guaratee both reliability ad delay QoS costraits i a efficiet maer? Cosiderig the reliability ad delay costraits as well as the itrisic eergy costrait i WSNs, how to desig a protocol which ca achieve good balace amog eergy efficiecy, ed-to-ed delay ad reliability? Our major cotributios are listed as follows: We argue that multipath routig approach may ot be suitable to guaratee both reliability ad delay QoS costraits i WSNs. Correspodigly, we propose to exploit the opportuistic routig approach for multicostraied QoS provisioig i WSNs. We fid that existig protocol caot be directly applied for QoS provisioig i WSNs. Therefore, we ivestigate the problem of efficiet for multicostraied QoS provisioig (EGQP) i WSNs, which is formulated as a multiobjective multicostrait optimizatio problem. We provide isight ito the properties of multiple routig metrics i. Based o the theoretical aalysis ad observatios, we propose a Efficiet QoSaware () algorithm for QoS provisioig i WSNs. Through comprehesive performace comparisos, we demostrate the low time complexity ad effectiveess of for multicostraied QoS provisioig i WSNs. The rest of the paper is structured as follows. Sectio reviews related work. Sectio 3 ad Sectio itroduce the system model ad problem formulatio, respectively. The aalysis of routig metrics is preseted i Sectio. I Sectio, algorithm is proposed. Simulatio results are show i Sectio 7. We coclude the paper i Sectio. A coferece paper [3] cotaiig some prelimiary results of this paper has appeared i IEEE MASS. RELATED WORK. Geographic opportuistic routig Opportuistic routig aims to improve wireless performace by exploitig spatial diversity i dese wireless etworks []. A umber of opportuistic routig protocols have bee proposed [] [] i the literature. Geographic opportuistic routig () [9] is a brach of the opportuistic routig, where locatio iformatio is available at each ode. I opportuistic routig, at the etwork layer a set of forwardig cadidates are selected while at the MAC layer oly oe ode is chose as the actual relay based o the receptio results i a a posteriori maer. The cadidate selectio ad relay priority assigmet at each hop are the two importat issues []. I GeRaF [9], the relay priority amog forwardig cadidates is simply assiged accordig to the sigle-hop packet progress provided by each potetial forwarder. More recetly, several opportuistic routig algorithms [] are desiged for dutycycled WSNs. The authors i [] aalyze the opportuistic routig gai uder the presece of lik correlatio ad itroduce a lik-correlatio-aware opportuistic routig scheme. Nevertheless, oe of existig works address exploitig for the multicostraied QoS provisioig i WSNs such as the oe preseted i this paper.. QoS provisioig i WSNs Uique characteristics of WSNs, such as eergy supply ad computig power limitatios of sesor devices, ureliable wireless liks, ad data-cetric commuicatio paradigm, pose challeges i the area of QoS provisioig i WSNs. Early studies o QoS aware routig i WSNs maily focus o oe QoS requiremet, either delay or reliability. The authors i [3] preset a eergy-aware QoS routig protocol for wireless video sesor etworks. The proposed protocol fids a set of QoS paths for real-time data trasmissio with certai edto-ed delay requiremets. I SPEED [] ad RAP [], the otio of packet speed is itroduced for QoS-aware geographic routig i WSNs. The packet speed reflects the local urgecy of a packet by cosiderig both the ed-to-ed delay ad commuicatio distace costraits i WSNs. For missio-critical applicatios, ot oly the ed-to-ed delay costrait should be met, but also certai packet delivery reliability is expected to be guarateed. The authors i [] [9] propose to exploit the multipath diversity for QoS provisioig with both reliability ad delay costraits i WSNs. Typically, the ed-to-ed QoS requiremets are partitioed ito the hop QoS requiremets ad each hop adaptively seeks multipath forwardig based o local estimatio. If the hop requiremet ca be achieved at each hop, the ed-to-ed QoS requiremet ca also be met with a higher probability. By cotrollig the umber of forwardig paths for each hop, the soft QoS requiremet i reliability is guarateed. 3 SYSTEM MODEL We cosider a multi-hop WSN i a two-dimesioal plaar regio. We assume the etwork is desely deployed, i.e., each ode has plety of eighbors. Nodes kow the geographical locatio iformatio of their direct eighbors ad where the destiatio is []. We assume that the MAC layer provides the lik quality estimatio service, e.g., the packet receptio ratio (PRR) iformatio o each lik ca be obtaied by coutig of the lost probe messages or data packets [7]. Each ode is aware of the PRR values to its oe-hop eighbors. To keep cosistecy, we follow the variable defiitios about i []. Assumig ode i is sedig a data packet to the sik ode (deoted as Dest), ad j is oe of i s eighbors which is closer to the sik tha i. Defie a ij i Eq. as the sigle-hop packet progress (SPP) to the Dest whe a packet is forwarded by eighbor j. C i is defied as the available ext-hop forwarder set of ode i, where all odes i C i have positive SPPs. a ij = Dist(i, Dest) Dist(j, Dest), ()

3 where Dist(i, Dest) is the Euclidia distace betwee ode i ad the Dest. Let p ij deote the PRR betwee ode i ad j. For ay eighbor j, ode i maitais the pair iformatio (a ij, p ij ) i its eighbor table. Let F i (F i C i ) deote the selected forwardig cadidate set of ode i, i which all odes are cooperatively ivolved i the local forwardig task whe ode i seds a data packet to the sik ode. The procedure is described as followig: whe ode i has a data packet to sed to the sik ode via multi-hop commuicatio, it selects the forwardig cadidate set F i based o its local kowledge of available ext-hop forwarder set C i. The ode i broadcasts the data packet where the list of cadidates ad their priorities are icluded i packet header. These cadidates follow the assiged priorities to relay the packet opportuistically. For each cadidate, if havig received the packet correctly, it will start a timer whose value depeds o its priority. The higher the priority is, the shorter the timer will be. The forwardig cadidate whose timer expires will reply a ACK, to otify the seder as well as all other cadidates to cacel their timers. Subsequetly, this forwardig cadidate becomes the actual ext-hop seder i a opportuistic maer. The forwardig process repeats util the packet reaches the sik ode. If o forwardig cadidate has successfully received the packet, the seder will retrasmit the packet if the retrasmissio is eabled. Deote t k as the sigle-hop medium delay of the k th cadidate, which is the time from the seder broadcasts a packet to the k th cadidate claims it has received the packet. I, the medium delay ca be divided ito two parts. Oe part is the seder delay, which icludes the backoff delay ad the trasmissio delay of the data packet (there is o RTS/CTS exchage for the broadcast trasmissio). The secod part is the cadidate coordiatio delay, which is the time eeded for the k th cadidate to ackowledge the seder ad suppress other potetial forwarders. The sigle-hop medium delay is defied as Eq., where the sigal propagatio delay is igored. t k = T Backoff + T DAT A + k (T SIF S + T ACK ), () where T Backoff icludes the Distributed Iterframe Space (DIFS) ad a radom backoff time for the seder to acquire the chael, T SIF S is the value of Short Iterframe Space (SIFS). T DAT A ad T ACK are the trasmissio delays for sedig a data packet ad a ACK, respectively. Fig. shows a example illustratig the sigle-hop medium delay with three prioritized forwardig cadidates. We kow t k is a icreasig fuctio of k, i.e., the forwardig cadidate with higher priority has shorter coordiatio delay tha the lower priority cadidate. This is because the lower priority forwardig cadidates always wait higher priority cadidates to relay the packet first. PROBLEM FORMULATION Let D ad R deote the ed-to-ed delay ad reliability QoS costraits, respectively. Due to the ureliable wireless lik coditios, the complexity of obtaiig the exact ed-to-ed lik state iformatio is beyod the computatio ad eergy tolerace of sesor odes []. However, per hop iformatio Seder DIFS st Cadidate d Cadidate 3 rd Cadidate Seder Delay t DATA t SIFS t 3 Coordiatio Delay Fig.. A example illustratig the sigle-hop medium delay with three prioritized forwardig cadidates is coveiet to acquire ad maitai at a low overhead cost. Therefore, we partitio the ed-to-ed QoS requiremets ito the hop requiremets to achieve the soft QoS provisioig. Let d i ad r i represet the hop QoS requiremets for delay ad reliability at ode i, respectively. Deote rh i as the estimated remaiig hop cout from ode i to the sik. e i deotes the elapsed delay experieced by a packet from the source ode to the curret forwardig ode i, which ca be obtaied by piggybackig the elapsed time at each hop to the packet so that the followig ode ca kow the remaiig time without globally sychroized clock []. We have the partitioed hop QoS requiremets at ode i. ACK SIFS ACK SIFS ACK d i = D e i rh i (3) r i = rh i R () The remaiig hop cout rh i is estimated as Eq.. rh i = Dist(i,Dest), adv(h () i ) where adv(hi ) is the estimated average sigle-hop packet progress sice the packet has bee forwarded from the source ode, defied as Eq.. h i is the curret hop cout of the forwarded packet at ode i. adv(h i ) = (hi ) h i, (h i ), () adv(hi )+adv(hi) where adv(h i ) is the sigle-hop packet progress of the h th i hop forwardig. adv() is the estimated sigle-hop packet progress at the source ode, which ca be set as the expected packet progress give the available ext-hop forwarder set. Let π j (F i ) = {j, j,..., j } be oe permutatio of - odes i F i, ad the order idicates that odes will attempt to forward the packet with priority (j >j > >j ). The expected sigle-hop reliability er i (π j (F i )), sigle-hop media delay ed i (π j (F i )), ad sigle-hop packet progress ea i (π j (F i )) achieved by for ode i give the ordered forwardig cadidate set π j (F i ) are show i Eq. 7, Eq., ad Eq. 9, respectively. ed i (π j (F i )) = er i (π j (F i )) = m= p ij m, (7) k= ea i (π j (F i )) = k (t jk p ijk k= m= p ijm ) + t j m= p ijm, () k (a ijk p ijk m= p ij m ), (9) where p ijm = p ijm ad p ij =. is the umber of forwardig cadidates i F i. p ijk ad a ijk are the PRR ad

4 sigle-hop packet progress betwee odes i ad j k, respectively. I Eq. ad Eq. 9, p ijk m= p ij m k is the probability of the k th forwardig cadidate i π j (F i ) takes the forwardig task. t jk is the sigle-hop medium delay of the k th forwarder i π j (F i ). We defie espeed i (π j (F i )) as the expected sigle-hop packet speed for ode i give the ordered forwardig cadidate set π j (F i ), which is calculated by dividig the sigle-hop packet progress to the sigle-hop media delay, as i Eq.. espeed i (π j (F i )) = k (a ijk p ijk p m= ijm ) k= k (t k p ijk p m= ijm )+t p m= ijm k= () The hop QoS requiremets d i ad r i will be adaptively adjusted accordig to the actual accumulated delay ad packet progress over precedig liks. Ituitively, we kow that: ) Icreasig the sigle-hop packet speed ca relax both the hop QoS requiremets, thus the ed-to-ed delay ad reliability QoS requiremets are more likely guarateed as the packet is progressed toward the destiatio; ) Although icreasig the umber of forwardig cadidates would result i higher reliability, cosiderig the eergy costrait of sesor devices, it is expected to ivolve less forwardig cadidates to save eergy cost i WSNs (e.g., those eighbors that are ot ivolved ito local forwardig i may tur off their radios for eergy savig). From the discussio above, we formulate EGQP problem as a multiobjective multicostrait optimizatio problem. Problem formulatio: Subject to F i C i max espeed i (π j (F i )) mi F i ed i (π j (F i )) d i er i (π j (F i )) r i Dist(j m, j k ) Rage j m F i, j k F i, where Rage is the eighbor detectio rage betwee eighborig odes. For opportuistic routig, the odes i forwardig cadidate set should be able to overhear from each other. Otherwise, the packet duplicatio problem would occur ad there will be multiple data copies beig trasmitted []. Therefore, we have the costrait Dist(j m, j k ) Rage. Note that if the delay costrait is too restrictive to be achievable, we ca oly reduce the ed-to-ed delay i a best effort approach. PROPERTIES OF DIFFERENT METRICS I this sectio, we will aalyze ad have a better uderstadig of differet routig metrics i, where the proof ca be foud i the supplemetal material.. Miimum sigle-hop media delay Defiitio : For sedig ode i, defie mi{ed i (C i, )} be the miimum expected sigle-hop media delay achieved by selectig forwardig cadidates from C i. Property. : (Relay priority rule) mi{ed i (C i, )} ca oly be achieved by assigig the relay priority to each eighbor based o their PRRs to the sedig ode i, the larger the PRR is, the higher its relay priority will be. Property. : (Cotaiig property) Give the available ext-hop ode set C i, the ordered cadidate set that achieves mi{ed i (C i, )} is a subset of the ordered cadidate set that achieves mi{ed i (C i, )}. It idicates that the miimum expected sigle-hop media delay ca be obtaied through a greedy algorithm. Property. 3: (Strictly icreasig property) mi{ed i (C i, )} is a strictly icreasig fuctio of, which meas ivolvig more eighbors will icrease the expected sigle-hop media delay. If there is o costraits o the expected delivery speed ad reliability at each hop, the miimum sigle-hop media delay ca be trivially achieved by icludig oly a sigle eighbor which has the best PRR to the seder. However, this may lead to extremely low packet delivery ratio ad low sigle-hop packet progress. Property. : (Cocavity property) The miimum siglehop media delay icreases whe more odes are icluded, while the successive icreases become smaller.. Maximum sigle-hop packet progress Defiitio : Defie max{ea i (C i, )} be the maximum expected sigle-hop packet progress achieved by seder i selectig forwardig cadidates from C i. I [9], it has bee prove that max{ea i (C i, )} has followig properties: ) Relay priority rule: the maximum ea i (π j (F i )) ca oly be achieved by assigig the relay priority to each cadidate based o their closeess to the destiatio, that is, the larger the packet progress, the higher its relay priority. ) Cotaiig property: the ordered cadidate set that achieves max{ea i (C i, )} is a subset of the ordered cadidate set that achieves max{ea i (C i, )}. 3) Strictly icreasig property: it idicates that the more odes get ivolved i the forwardig cadidate set, the larger the max{ea i (C i, )} will be. ) Cocavity property: it meas that max{ea i (C i, )} is a cocave fuctio of, although the maximum ea i (π j (F i )) keeps icreasig whe more odes get ivolved, the gaied extra progress becomes margial..3 Maximum sigle-hop packet progress per cadidate Defiitio 3: For sedig ode i, defie max{ ea i(c i,) } be the maximum expected sigle-hop packet progress per cadidate achieved by selectig forwardig cadidates from C i. Give the size of the forwardig cadidate set, apparetly, max{ ea i(c i,) } has the same relay priority rule ad cotaiig property as the max{ea i (C i, )}. Property 3. : (Strictly decreasig property) max{ ea i(c i,) } is a strictly decreasig fuctio of, which meas the average cotributio of each ode i the local opportuistic forwardig decreases as more odes gettig ivolved.

5 . Maximum sigle-hop packet speed Defiitio : Defie max{espeed i (C i, )} be the maximum expected sigle-hop packet speed achieved by seder i selectig forwardig cadidates from C i. From the properties of max{ea i (C i, )} ad mi{ed i (C i, )}, we kow that both max{ea i (C i, )} ad mi{ed i (C i, )} are icreasig fuctios of ad have the cocavity properties. I geographic routig, a eighborig ode with larger sigle-hop packet progress is also likely to be farther away from the sedig ode ad subsequetly the PRR would be lower [3]. Thus, max{ea i (C i, )} ad mi{ed i (C i, )} have early opposite relay priority rules, which meas they are of the coflictig metrics. The ituitio is that the relay priority rule for max{espeed i (C i, )} may balace both max{ea i (C i, )} ad mi{ed i (C i, )} s relay priority rules. That is, the heuristic relay priority assigmet is based o the SP P P RR metric [3]. Sigle-hop Media Delay (ms) Ratio of SPP to Cadidate Set Size TABLE Sigle-hop packet progress (SPP) ad PRR settig Idex SP P Model Model P RR SP P P RR P RR SP P P RR Model Model [,*] [,,*] [,,3,,] [,,3,*] [,*] [,,3,*] [*] [,,*] [,,3,,] 3 (a) mi{ed i (C i, )} vs. [3] [] [,3] [,,3] Model Model [,,3,] [3,] [,,3,,] [,3,] [,,3,] [,,3,,] 3 (c) max{ ea i(c i,) } vs. Sigle-hop Packet Progress (m) Sigle-hop Packet Speed (km/s) Model Model [3] [] [,3] [3,] [,,3,] [,,3,,] [,,3] [,3,] [,,3,] [,,3,,] 3 (b) max{ea i (C i, )} vs. [3] [,3] [3,] [,,3] [,3,] [,,3,] [,,3,,] [,3,,] [,3,,,] Model [] Model 3 (d) max{espeed i (C i, )} vs. Fig.. A example illustratig the properties of EGQP s multiple objectives A example validatig our aalysis is illustrated i Fig., i which ode i is sedig a data packet to the sik. Assume it has available ext-hop odes. We set t k ( k)ms based o the measuremet values i the s- simulator, which will be itroduced i detail i Sectio 7. The SPP-PRR settigs for two differet lossy lik models are listed i Table. The figures i square brackets represet the selected optimal forwardig cadidates ad their relay properties. From Fig., the umerical results clearly justify the proved properties of differet metrics i. It also shows that the chages of mi{ed i (C i, )}, max{ea i (C i, )} ad max{ ea i(c i,) } are related with the SPP-PRR models.. Discussio The above aalysis leads to the followig coclusio. It is ot appropriate to trasform the EGQP problem ito a sigle objective optimizatio problem directly. This is because, if we choose max espeedi(ci,) as a sigle objective, oly those odes with higher PRR but likely smaller siglehop packet progress will be selected ito F i to meet the reliability costrait with the least umber of forwardig cadidates. I such case, usually, there exist multiple Pareto optimal solutios [3], rather tha a optimal solutio. I the literature, differet approaches have bee suggested to solve this complex problem, e.g., multiobjective evolutioary algorithms [33]. However, cosiderig the limitatio of sesor devices o processig power ad memory space, it is ot appropriate to execute such algorithms o sesor odes. Thus, a efficiet cadidate selectio ad prioritizatio algorithm with low computatioal cost is desirable. DESIGN. Motivatio The pareto priciple (also kow as the - rule i the field of ecoomics) states that, for may evets, roughly % of the effects come from % of the causes [3]. We observe that there also exists similar pareto priciple i. That is, most forwardig tasks for each hop are take by the first two or three cadidates i the ordered forwardig cadidate set. This idicates that it may oly eed to order a very small umber of cadidates to obtai a close optimal solutio i our desig, by which the algorithm s time complexity ca be effectively reduced. Packet Speed Ratio TABLE Number of forwardig cadidates ivolved i the proposed algorithm i []. # of available Avg. # of forwardig Stadard ext-hop odes cadidates Deviatio Average Stadard Deviatio 3 Number of Forwardig Cadidates (a) Ratio of the expected oe hop packet speed to the optimal value Expected Reliability Average Stadard Deviatio 3 Number of Forwardig Cadidates (b) The expected oe hop reliability Fig. 3. A umerical example illustratig the similar pareto priciple i

6 Here, we give a umerical example illustratig the similar pareto priciple i, as show i Fig. 3. We vary the umber of available ext-hop odes C from to, where C is the available ext-hop forwarder set of a sedig ode. For each potetial relay ode, the sigle-hop packet progress is radomly geerated. We use N akagami distributio to model the atteuatio of wireless sigals, ad derive the PRR with certai distace betwee the seder ad receiver. The results have bee averaged over rouds. Table illustrates the umber of forwardig cadidates ivolved i the proposed cadidate selectio ad prioritizatio algorithm i []. We ca see that it ivolves too may forwardig cadidates, e.g., whe C =, the algorithm will choose.7 forwardig cadidates out of o average. The, we choose oly the first % of the available ext-hop odes to calculate the siglehop packet speed, where cadidates are descedigly sorted accordig to SP P P RR. From Fig. 3, it is see that the first % of available ext-hop odes ca achieve more tha % of the optimal values o average.. descriptio Iput: available ext-hop ode set C i ( C i ); hop QoS requiremets: d i, r i ; α ad β, k = max{α, mi{β,. C i }}. Output: the forwardig cadidate set F i. F i {c }, C i C i {c }; while C i do 3 if meet QoS requiremets the retur F ; else if CheckRage(F i, c )==false the //c deotes the first ode i C i ; it should be withi the trasmissio rage of ay ode i F i ; 7 C i C i {c }; cotiue; 9 else if F i k the for i= to F i do temporarily isert c as the i th item i F i ; get the optimal isert positio i i term of maximizig espeed i (π j (F i )); ed 3 Isert(c, i, F i ); //fially isert c as the i th item i F i ; C i C i {c }; else 7 C i C i {c }; Apped(c, F i); //Apped c as the last item i F i; 9 ed ed Algorithm : Cadidate selectio ad prioritizatio at forwardig ode i i The above theoretical aalysis ad observatios motivate us to propose a tailored cadidate selectio ad prioritizatio algorithm i for QoS provisioig i WSNs. Whe ode i is sedig a data packet to the sik ode, it selects ad prioritizes forwardig cadidates based o the scheme as proposed i Algorithm. The it forwards the data packet followig the procedure as itroduced i Sectio 3. The desig of Algorithm is described as follows. We itroduce two adjustable parameters α ad β, which represet the miimum ad maximum umber of cadidates to be prioritized, respectively. will oly prioritize the first k available ext-hop odes based o the observatio of the similar pareto priciple i, where k = max{α, mi{β,. C i }}. For sedig ode i, cadidates i C i are descedigly sorted accordig to the SP P P RR metric. Iitially, we iclude the first ode of C i ito F i ad removes it from C i (Lie ). The, we check odes i C i i sequece, where c always deotes the first ode i C i. Whe itedig to add c ito F i, it should be withi the trasmissio rage of ay ode i F i (Lie ). Otherwise, it will be elimiated from C i (Lie 7). If there is o packet duplicatio (i.e., c ca overhear ay ode i F i ), we search the best place to isert c ito F i, where the espeed i (π j (F i )) is maximized (Lie 9-Lie ). The searchig procedure is to try every possible isertig positio i F i, ad calculate the expected sigle-hop packet speed values. For the remaiig odes i C, cadidates will be selected to meet the hop Qos requiremets at a miimum cost, i.e., simply appedig to F i (Lie 7-Lie ). Whe the umber of available ext-hop odes icreases i dese etworks, the time complexity of is approximate O( C i ). I Algorithm, although the first ode i C i is icluded ito F i directly (Lie ), it is ot ecessary the first cadidate of F i fially. This is because the ewly added odes may still have higher priorities ad thus beig iserted ahead. 7 EVALUATION I this sectio, we first preset simulatio results to show that achieves a good balace amog eergy cost, edto-ed delay ad reliability. The we show the effectiveess of for multicostraied QoS provisioig i WSNs usig s- simulator [3], by comparig it with the multipath routig approach ad with two other baselie protocols. Lastly, we show the low time complexity of through measuremets o the MicaZ (with low-power -bit microcotroller) ode. All the results have bee averaged over rus. 7. Simulatio settigs I the implemetatio of our simulatio, sesor odes are placed i a m m square area. We defie the ode desity as the umber of odes deployed i the field. A sik ode is positioed at (m, m), ad the source ode is located at (m, m). A data packet geerated by the source ode is forwarded toward the sik over multiple hops. The sesor ode trasmissio rage is take m. Two parameters i, α ad β are set to ad, respectively. The MAC layer protocol is a modified versio of the CSMA/CA MAC i s-, T SIF S = us, T DIF S = us. Wireless liks qualities are derived from the Nakagami fadig model, where PRR is related with the distace betwee two odes [3].

7 7. Evaluatio metrics We choose six mai evaluatio metrics to evaluate the effectiveess of for QoS provisioig i WSNs. Ed-to-ed Delay: the time take for a packet to be trasmitted from the source ode to the sik ode. Give the ed-to-ed delay QoS requiremet, this metric measures the o-time packet delivery ratio. Packet Delivery Ratio: the ratio of the amout of packets received by the destiatio to the total amout of packets set by the source. Data Trasmissio Cost: measured as the total umber of data trasmissios for a successful ed-to-ed data delivery. Cotrol Message Cost: defied as the total umber of cotrol message trasmissios for sedig a sigle packet to the destiatio, such as RTS, CTS ad ACK. Sigle-hop Packet Progress: the ratio of the sum of siglehop packet progress i each hop to the umber of hops i a simulatio ru. Lik Quality Per Hop: the average lik quality for successful data trasmissio at each hop. 7.3 The balace achieved by We compare with two extreme solutios uder the grid topology with differet ode desities: ) maximizig the packet speed as the sigle objective (refer to as Max-Speed); ) miimizig the umber of forwardig cadidates as the sigle objective (refer to as Mi-F). The reliability requiremet is set.99. For the extreme solutios (exhaustive searchig method), the computatio delay would be very high as the ode desity icreases. To esure the effectiveess, we set 9 as the maximum umber of the available ext-hop ode set, that is, at most the first 9 odes from the eighbor list will be selected ad ordered. Fig. (a) plots the average packet speed achieved by E- Q ad the two extreme solutios uder differet ode desities. We ca see that provides very close packet speed to the Max-Speed scheme. Fig. (b) shows that ivolves a little more forwardig cadidates tha the Mi- F scheme, which serves as the lower boud of the size of forwardig cadidate set. From the results i Fig., we ca see that provides a good balace betwee the packet speed ad the eergy cost for. Avg. Packet Speed (km/s) 9 7 Max-Speed Mi-F (a) Avg. packet speed Avg Max-Speed Mi-F (b) Avg. # of forwardig cadidates Fig.. Compariso with two extreme solutios i terms of the packet speed ad umber of forwardig cadidates uder differet ode desities 7. The effectiveess of for QoS provisioig i WSNs I term of the effectiveess for multicostraied QoS provisioig i WSNs, we compare the performace of agaist the multipath routig approach, amed. We also report the evaluatio results of two other baselie protocols, the sigle path geographic routig () [3] with up to 3 MAC layer retrasmissios, ad the covetioal [9], where all the available ext-hop odes are ivolved i the local forwardig ad the relay priority is assiged accordig to the sigle-hop packet progress provided by each cadidate. Sice our aim is to compare the multipath routig approach with the opportuistic routig approach for multicostraied QoS provisioig i WSNs, we implemet the commo fuctio of the multipath approach [] []. To save the eergy cost, at each hop, the seder selects the ext-hop odes with the miimum umber of splitted paths to meet the reliability QoS requiremet. I this sectio, differet radomly coected topologies for each ode desity settig are geerated usig the setdest tool i s-, where the ode desity rages from to. 7.. Impact of the ode desity I this test, we evaluate the performace of uder differet ode desities by varyig the umber of odes from to. The reliability QoS requiremet is set.9. Fig. (a) reports the ed-to-ed delay uder differet ode desities. achieves shorter ed-to-ed delivery delay tha that of. This is because, follows the maximum sigle-hop packet progress relay priority rule. While the miimum sigle-hop media delay has the early opposite rule, as discussed i Sectio. It is show that is iflueced by differet ode desities, ad its ed-to-ed delay is much higher tha others. The reaso is that usig multiple paths itroduces more chael cotetios which sigificatly degrades ed-to-ed delay performace. Note that oly the successful ed-to-ed trasmissios are couted i the results. will yield worse result if the retrasmissio limit is icreased to achieve the comparable reliability to other protocols. We also observe that the ed-to-ed delay of ad does ot chage much as the ode desity icreases. This is partly because the ode desity settig is high, ad there are eough forwardig cadidates at each hop. Fig. (b) illustrates the ed-to-ed packet delivery ratio. Compared with the sigle path routig scheme, the multipath approach improves the delivery ratio by 3%. However, is more sesitive to differet radom topologies tha the approach. For example, whe the ode desity is, its delivery ratio is oly %. This is because the problem of collisios amog multiple paths arises ad the retrasmissio is disabled i. Sice the reliability costrait is set.9, oly ad meet the QoS requiremet. Fig. (c) ad Fig. (d) depict the chages of the data trasmissio cost ad cotrol message cost uder differet ode desities, respectively. Normally, the cotrol message cost is directly proportioal to the umber of data trasmissios. From Fig. (c) ad Fig. (d), it clearly shows

8 Ed to Ed Delay (s) (a) Ed-to-Ed Delay % 9% % 7% % % % 3% % % Packet Delivery Ratio (b) Packet Delivery Ratio Number of Data Trasmissios 3 (c) Number of Data Trasmissios Number of Cotrol Messages (d) Number of Cotrol Messages Sigle-hop Packet Progress (m) 3 3 (e) Sigle-hop Packet Progress Avg. Lik Quality Per Hop.... (f) Average Lik Quality Per Hop Fig.. Impact of the ode desity, the ed-to-ed reliability is set.9, ad oly the successful ed-to-ed trasmissios are couted. Number of Retrasmissios (a) Number of Retrasmissios Number of Splitted Paths (b) Number of Splitted Paths (c) Number of Forwardig Cadidates Fig.. Isights of the differet routig protocols that icurs more tha four times higher tha other approaches regardig the trasmissio cost. The sigificat eergy cost makes usuitable for multicostraied QoS provisioig i WSNs. The data trasmissio cost metric also reflects the hopcout of a routig path. I our implemetatio of, sice the miimum umber of paths are chose to meet the reliability costraits at each hop, it is more likely that eighbors with higher PRRs but smaller sigle-hop packet progresses are selected as the exthop forwardig odes. This icreases the hopcout of the ed-to-ed routig paths. always chooses the eighbor with the maximum sigle-hop packet progress without cosiderig the lik quality. Cosiderig the retrasmissios icurred i, the trasmissio cost is still higher tha ad, although oly the successful trasmissios are couted. Fig. (e) illustrates the sigle-hop packet progress amog the four compared protocols. has the highest packet progress at each hop due to its relay priority rule. It serves as the maximum boud of this metric. It is clearly see that achieves very close performace to, which shows the very high efficiecy of. Fig. (f) plots the average data trasmissio lik qualities of differet schemes. The results reveal that teds to choose poor lik quality ode as relay, ad always selects good quality liks. ad opportuistically exploit the log distace but ureliable liks, thus, their average data trasmissio lik qualities are lower tha the result of ad higher tha that of. Fig. demostrates the system isight of each protocol ad reveal the reasos behid the correspodig statistics i Fig.. Fig. (a) shows the umber of retrasmissios occurred i. There are aroud retrasmissios i each successful ed-to-ed data delivery. Fig. (b) reveals the umber of splitted paths of i the evaluatio. I the -ode radom etwork, the data packet is replicated more tha times to meet the reliability requiremet. The umber of splitted paths is reduced as the ode desity icreases. However, the sigle-hop packet progress decreases with the icrease of ode desity, as show i Fig. (e). Fig. (c) plots the umber of forwardig cadidates ivolved i the local forwardig at each hop. The result highlights that the size of the forwardig cadidate set i is stable ad small at icreasig the ode desity compared with. 7.. Impact of the reliability QoS requiremet I this compariso, we examie the performace differeces of ad uder differet reliability QoS requiremets i -ode radom etwork. Fig. 7(a) illustrates the chages of the ed-to-ed delay at icreasig the reliability costrait from. to.99. As the reliability QoS requiremet icreases, the ed-to-ed delay of also icreases from.s to.3s, ad its variace is much higher tha that of. This idicates that the multipath approach is difficult to guaratee the o-time packet delivery ratio uder strict delay requiremet. While is robust to the reliability costrait chage, its ed-to-ed delay

9 Ed to Ed Delay (s) Number of Cotrol Messages (a) Ed-to-Ed Delay (d) Number of Cotrol Messages % 9% % 7% % % % 3% % % Packet Delivery Ratio Sigle-hop Packet Progress (m) 3 3 (b) Packet Delivery Ratio (e) Sigle-hop Packet Progress Number of Data Trasmissios Avg. Lik Quality Per Hop (c) Number of Data Trasmissios (f) Average Lik Quality Per Hop Fig. 7. Impact of the reliability QoS requiremet i -ode radom etwork, ad oly the successful ed-to-ed trasmissios are couted. maitais below.s. Fig. 7(b) shows the actual packet delivery ratio agaist the reliability QoS requiremet. As see i the figure, may suffer from the problem of overcoutig to guaratee lower reliability costraits. adaptively improves the packet delivery ratio as the reliability requiremet icreases. Whe the ed-to-ed reliability QoS costrait is., suppose the estimated hopcout is from the source ode to the sik, the partitioed hop reliability requiremet is aroud.97. However, the actual sigle-hop reachability is higher tha.97 whe multiple paths are selected to route a packet. Fig. 7(c) ad Fig. 7(d) depict the tradeoff betwee the commuicatio cost ad the reliability QoS requiremet. It clearly shows that sigificatly improves the eergy efficiecy for the QoS provisioig i WSNs. With icreasig the reliability requiremet, icurs higher data trasmissio cost ad cotrol message cost. As show i Fig. 7(e) ad Fig. 7(f), the sigle-hop packet progress ad the average data trasmissio lik quality are relatively stable at icreasig the reliability requiremet. shows obvious advatage over. Fig. plots the system isights for ad. From Fig. (a), as the reliability QoS requiremet icreases, more paths are required to guaratee the requiremet i. For, as show i Fig. (b), the average umber of forwardig cadidates icreases from to. whe icreasig the reliability requiremet from. to.99. Number of Splitted Paths (a) Number of Splitted Paths (b) Number of Forwardig Cadidates Fig.. Isights of ad From the above results, we ca see that is less iflueced by the chages of the reliability requiremet ad Avg. Computatio Delay (ms) Algorithm i [], r i =., r i = Number of Available Next-hop Nodes Fig. 9. Average computatio delay measuremet o the MicaZ ode ode desity. Together with its shorter ed-to-ed delay, lower commuicatio cost ad higher delivery ratio, we coclude that compared with the multipath routig approach, all the performaces are greatly improved by exploitig the for QoS provisioig i WSNs. However, the approach requires the cross-layer iteractio betwee the MAC ad etwork layers, ad icurs certai coordiatio overhead (such as forwardig cadidate coordiatio delay). As other opportuistic routig protocols, the overhead of is expected to be evetually compesated by the improved routig performace over a log-term of etwork operatio. 7. The time complexity of We evaluate the computatio delay of cadidate selectio ad prioritizatio at each hop i o MicaZ [37] ode sice such computatio delay caot be captured i simulatio. The measured performace is show i Fig. 9. I the experimet, we vary the umber of available ext-hop odes from 3 to. From Fig. 9, whe the hop reliability r i =., the average computatio delay of grows liearly ad slowly as the umber of available ext-hop odes icreases. Whe C i =, the delay is aroud ms, however, the cadidate selectio ad prioritizatio algorithm proposed i [] itroduces.9ms computatio delay o average. Whe the required reliability goes to.99, the s computatio delay uder differet umber of available ext-hop odes has

10 very little chage, from.9ms to.7ms. Thus, we coclude that is characterized by its very low time complexity. Meawhile, it also achieves the competig packet speed close to the optimal value as show i Fig. 3 ad Fig., respectively. CONCLUSION I this paper, we proposed to exploit the geographic opportuistic routig () for multicostraied QoS provisioig i WSNs, which is more suitable tha the multipath routig approach. We foud that existig protocol caot be directly applied to the QoS provisioig i WSNs. Because the computatio delay of a protocol should be also cosidered i WSNs. We studied the problem of efficiet for multicostraied QoS provisioig (EGQP) i WSNs. We formulated the EGQP problem as a multiobjective multicostrait optimizatio problem ad aalyzed the properties of EGQP s multiple objectives. Based o our aalysis ad observatios, we the proposed a Efficiet QoS-aware () algorithm for QoS provisioig i WSNs. achieves a good balace betwee these multiple objectives, ad has a very low time complexity, which is specifically tailored for WSNs cosiderig the resource limitatio of sesor devices. We coducted extesive evaluatios to study the performace of the proposed. Evaluatio results demostrate its efficacy for QoS provisioig i WSNs. ACKNOWLEDGEMENTS This work was supported i part by Research Fud of the State Key Laboratory of Software Developmet Eviromet uder Grat No. BUAA SKLSDE-ZX-7, CPSF 3M3, Natioal Natural Sciece Foudatio of Chia uder Grat No. 37, 79 ad 9, Program for New Cetury Excellet Talets i Uiversity uder Grat No. NECT-9-, SUTD SRG ISTD, SUTD-MIT Iteratioal Desig Ceter IDDA ad itrust Cyber Physical System Protectio project. REFERENCES [] J. Yick, B. Mukherjee, ad D. Ghosal, Wireless sesor etwork survey, Comput. Netw., vol., o., pp. 9 33, Aug.. [] J. Niu, L. Cheg, Y. Gu, L. 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11 Log Cheg is a postdoctoral researcher at Beihag Uiversity ad Sigapore Uiversity of Techology ad Desig. He got his PhD i the State Key Lab of Network ad Switchig Techology, Beijig Uiversity of Posts ad Telecommuicatios,. He received his B.S. degree i Computer Sciece from Xi a Telecommuicatio Istitute, Chia,, ad M.S. degree i Telecommuicatio Egieerig from XiDia Uiversity, Chia, 7. Durig the period from Ju. 9 to Dec. 9, he studied i the Hog Kog Polytechic Uiversity as a research assistat. From Ja. to Sept., he was a Joit PhD studet i the Departmet of Computer Sciece ad Egieerig, Uiversity of Texas at Arligto. His curret research iterests iclude wireless sesor etwork, Iteret of thigs, mobile computig, ad pervasive computig. Jiawei Niu received his M.S. ad Ph.D. degrees i 99 ad i computer sciece from Beijig Uiversity of Aeroautics ad Astroautics (BUAA, ow reamed as Beihag U- iversity). He was a visitig scholar at School of Computer Sciece, Caregie Mello Uiversity, USA from Ja to Feb. He is a professor i the School of Computer Sciece ad Egieerig, BUAA. He is a IEEE member ad has published more tha referred papers ad filed more tha patets. He has bee o various chairs ad TPC members for may iteratioal cofereces. He served as the Program Chair of IEEE SEC. He received New Cetury Excellet Researcher Award from Miistry of Educatio of Chia i 9. His curret research iterests iclude mobile ad pervasive computig. Sajal K. Das is curretly the head ad chair professor i Computer Sciece Departmet at Missouri Uiversity of Sciece ad Techology, ad the foudig director of the Ceter for Research i Wireless Mobility ad Networkig (CReW- MaN). He was a uiversity distiguished scholar professor of computer sciece ad egieerig at the Uiversity of Texas at Arligto. Durig -, he served the US Natioal Sciece Foudatio (NSF) as a program director i the Divisio of Computer Networks ad Systems. His curret research iterests iclude wireless ad sesor etworks, mobile ad pervasive computig, cloud computig, cyberphysical systems ad smart heath care, security ad privacy, biological ad social etworks, applied graph theory ad game theory. He has published extesively i these areas ad made fudametal cotributios. He holds five US patets ad coauthored books o Smart Eviromets: Techology, Protocols, ad Applicatios (Wiley, ), Hadbook o Securig Cyber-Physical Critical Ifrastructure: Foudatios ad Challeges (Morga Kaufma, ), ad Mobile Agets i Distributed Computig ad Networkig (Wiley, ). He is a recipiet of the IEEE Computer Society Techical Achievemet Award for pioeerig cotributios to sesor etworks ad mobile computig; IEEE Regio Outstadig Egieerig Educator Award; ad Eight Best Paper Awards icludig those at IEEE SmartGridComm, QShie 9, EWSN, IEEE PerCom, ad ACM MobiCom 99. He serves as the foudig editor-i-chief of Pervasive ad Mobile Computig joural, ad also as a associate editor ACM/Spriger Wireless Networks, Joural of Parallel ad Distributed Computig, ad Joural of Peer-to-Peer Networkig. He is the cofouder of IEEE PerCom, WoWMoM ad ICDCN cofereces. He is a seior member of the IEEE. Jiaog Cao received the BSc degree i computer sciece from Najig Uiversity, Chia, i 9, ad the MSc ad PhD degrees i computer sciece from Washigto State Uiversity, Pullma, Washigto, i 9 ad 99, respectively. He is curretly the head ad chair professor i the Departmet of Computig at Hog Kog Polytechic Uiversity, Hog Kog. Before joiig Hog Kog Polytechic Uiversity, he was o the faculty of computer sciece at James Cook Uiversity, the Uiversity of Adelaide i Australia, ad the City Uiversity of Hog Kog. His research iterest icludes parallel ad distributed computig, etworkig, mobile ad wireless computig, fault tolerace, ad distributed software architec- ture. He has published more tha techical papers i the above areas. His recet research has focused o mobile ad pervasive computig ad mobile cloud computig. He is a seior member of the Chia Computer Federatio, a seior member of the IEEE ad the IEEE Computer Society, ad a member of the ACM. He has served as a member of editorial boards of several iteratioal jourals, a reviewer for iteratioal jourals/coferece proceedigs, ad also as a orgaizig/program committee member for may iteratioal cofereces. Yu Gu is curretly a assistat professor i the Pillar of Iformatio System Techology ad Desig at the Sigapore Uiversity of Techology ad Desig. He received the Ph.D. degree from the Uiversity of Miesota, Twi Cities i. Dr. Gu is the author ad co-author of over 7 papers i premier jourals ad cofereces. His publicatios have bee selected as graduatelevel course materials by over uiversities i the Uited States ad other coutries. His research icludes Networked Embedded Systems, Wireless Sesor Networks, Cyber-Physical Systems, Wireless Networkig, Real-time ad Embedded Systems, Distributed Systems, Vehicular Ad-Hoc Networks ad Stream Computig Systems. Dr. Gu is a member of ACM ad IEEE.