A Data Aggregation Scheduling Algorithm with Longlifetime and Low-latency in Wireless Sensor Networks

Transcription

1 A Daa Aggregaon Schedulng Algorhm wh Longlfeme and Low-laency n Wreless Sensor eworks Zhengyu Chen,2,3 *, Geng Yang,2, Le Chen,2, Jn Wang 4, Jeong-Uk Km 5 Key Laboraory of Broad band Wreless Communcaon & Sensor eworks echnology of Mnsry of Educaon, anjng Unversy of Poss &elecommuncaons, anjng 20003, Chna 2 College of Compuer Scence & echnology, anjng Unversy of Poss &elecommuncaons, anjng 20046, Chna 3 School of Informaon echnology, Jnlng Insue of echnology, anjng 269, Chna 4 School of Compuer & Sofware, anjng Unversy of Informaon Scence & echnology, anjng 20044, Chna 5 Deparmen of Energy Grd, Sangmyung Unversy, Seoul 0-743, Korea zych@jeducn, {yangg, chenle }@njupeducn, wangjn@nuseducn Absrac In scenaros of real-me daa collecon n long-erm deployed Wreless Sensor eworks (WSs, low-laency daa collecon and long nework lfeme become key ssue We propose a Long-Lfeme and Low- Laency Daa Aggregaon Schedulng algorhm (L 4 DAS n wreless sensor neworks Frsly, we formally formulae he problem of long-lfeme and mnmum-laency aggregaon schedulng as a consraned opmzaon problem, and hen propose an approxmaon algorhm for hs problem by consrucng a degree-bounded mnmum hegh spannng ree as aggregaon ree and desgnng a maxmum nerference prory schedulng scheme o schedule he ransmsson of nodes n aggregaon ree Fnally, hrough he smulaon and comparsons, we prove he effecveness of he algorhm Keywords: WSs, Daa aggregaon, ework lfeme, laency Inroducon Wreless Sensor eworks have been used for many long-erm and real-me applcaons whch requre neworks o operae long duraons, as well as o ransm he sensed daa o snk as soon as possble herefore, boh maxmzng lfeme and mnmzng delay are he fundamenal requremens However, hese wo requremens are usually conflc wh he lmed baery power and communcaon bandwdh of sensor node Sleep-wake schedulng and daa aggregaon are he effecve mechansms o prolong he lfeme of energy-consraned sensor neworks However, boh sleep-wake schedulng and daa aggregaon can also lead o addonal daa collecon delay So, s crcal o research a problem of daa aggregaon schedulng wh long-lfeme and low-laency n WSs In WSs, he Mnmum-Laency Aggregaon Schedule (MLAS problem s o fnd 7

2 he schedule ha roues daa appropraely and has he shores me for all requesed daa o be aggregaed o snk Chen e al n [] proved ha he MLAS problem s Phard [, 2] proposed he cenralzed schedulng algorhms and proved he laency upper-bound Yu e al [3] and Xu e al [4] proposed he dsrbued schedulng mehod wh delay a mos 24D+6 +6 and 6R+ 4 me-slos respecvely However, we noe ha he upper-bounds of hese schemes are far oo pessmsc compared o he ypcal praccal behavor of her algorhms Malhora e al[5] proved he laency lower-bound and go he beer performance han he prevous algorhms by consrucng a balanced shores pah ree (BSP and usng a rankngbased heursc schedulng Energy effcency s he bgges challenge n desgnng long-lvng sensor neworks Wu e al [6] proved ha fndng a maxmum lfeme arbrary ree s P-complee, and proposed an approxmaon algorhm ha produces a sub-opmal ree Malhora e al [5] consruced a BSP o prolong nework lfeme However, he nework lfeme n [5] deermned by nework archecure and he number of nodes In hs paper, we propose a Long-Lfeme and Low-Laency Daa Aggregaon Schedulng (L 4 DAS algorhm n WSs Our man conrbuons are as follows: ( we consruc a degree-bounded mnmum hegh spannng ree as aggregaon ree whch provdes a long nework lfeme and s conducve o reduce schedulng lengh and, (2 we propose a maxmum nerference prory schedulng algorhm o schedule he ransmsson of nodes such ha he laency s approxmaely mnmzed, and (3 we carry ou exensve smulaons o verfy our algorhms, and he resuls show ha our algorhm grealy ouperforms he sae-of-ar schemes 2 Sysem Model and Problem Saemen 2 Sysem Model and Defnons We consder a WS conssng of sensor nodes v, v2,, v and snk nodev s All nodes have he same ransmsson ranger and nerference ranger I We use a undreced graph G( V, E o represen hs WS, where V = { s, v, v2,, v } denoes he se of nodes and E denoes he se of edges, e here s an edge ( v, vj E whenever her Eucldean dsance v vj r We consder he proocol nerference model n whch concurren ransmssons on wo edges u v and p q conflc wh each oher f and only f v= q, p v r I or q u ri [4,5,7] If lnk u v conflc wh lnk p q, we call ha sender u conflcs wh sender p All sensors are homogeneous whch have he same energye node and consume energye x ande rx for ransmng and recevng one b daa respecvely We adop a perfec daa aggregaon model and DMA-based schedulng proocol In order o faclae he descrpon, we gve he followng defnons: Defnon Round A Round s defned as he process of gaherng daa from all 72

3 nodes o he snk whch s equvalen o a DMA schedule perod conssng of me slos he duraon of each round s called he schedulng laency A each me slo, all senders and her correspondng paren nodes are scheduled n acve sae whle he remanng nodes n sleep sae Defnon 2 ework Lfeme he nework lfeme s defned as he lfeme of he frs dead node n he nework In daa aggregaon schedulng, we usually need o consruc a daa aggregaon ree, so he nework lfeme wll be he lfeme of daa aggregaon ree he lfeme L( DA of he daa aggregaon ree DA s E node L( DA = mn ( =,2, KErxD( DA, v + K( Ex Erx where D( DA, v s he degree of nodev n DA,K s he number of bs generaed by each node n a round 22 Problem Saemen We formulae he long-lfeme and low-laency daa aggregaon schedulng problem as a consraned opmzaon problem able lss he symbols used n hs opmzaon problem Symbols f v ( v, vj R( v ( v ( D able Symbols and meanngs Meanngs he se denoes he neghbors of nodev he decson varable of lnk ( v, v j a me slo ; f ( v, v = f lnk ( v, vj s scheduled o ransm a me slo ; f ( v, 0 v = oherwse j he recpen corresponds o he senderv he me slo s assgned for he ransmsson by nodev v n aggregaon ree he maxmum permed degree of each node n aggregaon ree CHD v he se denoes he chldren nodes of he objecve funcon of hs problem becomes mnmze schedulng laency subjecs o he followng consrans, f( v, v = k vk v = f( vk, v = vk v = ( v ( v f( R( v, vj = vj R( v ( v ( v f( vj, vk + f( vp, vq vj R( v, vj v vk v j vq v, vq R( v vp v, v q p v f( vk, v D vk CHD( v = (a {,2,, } (b 0 {,2,, } (c 0 {,2,, } (d (e {,2,, } j = 0 {,2,, } (2 73

4 Consran (a enforces a sngle ransmsson per node n each round Consran (b ensures ha, once a node ransms, can no longer receve daa from s chldren nodes n he same round Consran (c guaranees ha node can no ransm and receve smulaneously a he same me slo, e half-duplex operaon Consran (d ensures requremen ha here can be no nerference a he recpen node Consran (e guaranees ha each node a mos hasd chldren nodes hs schedulng problem has been proved as a P-hard problem [] We propose an approxmaon algorhm for hs problem We frs consruc a degree-bounded mnmum hegh spannng ree as aggregaon ree, and hen desgn a maxmum nerference prory schedulng scheme o schedule he ransmsson of nodes n aggregaon ree 3 Man Desgn 3 Daa Aggregaon ree Consrucon We consruc a degree-bounded mnmum hegh spannng ree as daa aggregaon ree hrough wo phases Frs, we break up he graph no clusers wh he dameer equalng o ransmsson ranger and consruc a cluser spannng ree respecng he degree consrand n each cluser hen we consruc a global ree over he clusers and connec he spannng ree n each cluser o he global ree v v 6 v 2 v 5 v c Snk c 2 v 3 v 4 Fg An example of consrucng a daa aggregaon ree Gven G( V, E, dependng on he sze of he deploymen area and ransmsson radus, we frs paron V no parwse dsjon ses [8]: ( V = VUV 2 UKU V m, for, j {,2, K, m}, VI Vj = ; (2 v, v V, {,2, K, m}, v, v r ; p q p q Where V ( {,2, K, m} s he se of nodes n cluser We paron V by essellang he deploymen area no a se of hexagonal clusers each of sde lenghr 2 and assgnng each node o a unque cluser whose cener s closes o he node We hen choose a represenave for each cluser o consue a se R= { u, u2, K, u m } whereu V, {,2, K, m} he global ree wll be consruced by hese represenaves If some represenaves can no connec wh each oher, we choose some connecng nodes C = { c, c2, K, c n } o connec hem he global ree 74

5 should be consruced sasfyng he followng consran, mn max hops( u, vs R V u R s max deg u D u R ( where hops( u, vs s he hop dsance from u o snk s v, ( (3 deg u s he degree of u As shown n Fg, each crcle represens a cluser he nodes fromv ov 6 are represenaves whch correspond o 6 clusers respecvely c andc 2 are connecng nodes Doed lnes wh arrows connec hese nodes ogeher o form he global ree In each cluser, we consruc a cluser spannng ree rooed a represenave node whle respecng he degree consran D he Algorhm of consrucng daa aggregaon ree s shown n Algorhm Algorhm Consrucng Daa Aggregaon ree Inpu: G( V, E, snk, D 2 ; Oupu: aggregaon ree DA Sep : Paron V no parwse dsjon sesv, V 2,, V m Sep 2:Choose represenaves R= { u, u2, K, u m } and connecng nodes C = { c, c2, K, c n } sasfyng formula (3; Sep 3: Consruc global ree G rooed a snk by connecng nodes n boh R andc ; Run Sep 4 o Sep 6 for each cluser, consruc m cluser spannng rees; Sep 4: For cluser, V = { v, v2, K, vj} and represenave node u, nalze spannng ree = ( V, E, V { u } E ; Sep 5: Choose n nodes ({ v,, v n} from V as chldren nodes of u, where deg n= D G ( u ( deg G ( u s he degree of nodeu n he global ree G ; Sep 6: Choose nodes from { v,, vn} as paren nodes, hen choosng D chldren nodes from V /{ v,, v n} for each paren node, repea hs process unl all nodes n V = { v, v 2, K, v } are scheduled; j Sep 7: Combne global ree G wh all cluser spannng rees, m = I DA B = 32 Aggregaon Schedulng A daa aggregaon schedule wh delay can be defned as a sequence of sender ses S, S2,, S sasfyng he followng condons: ( S I S =, j; j (2 v, vj Sl, l =,2,,, v andv j do no conflc wh each oher; k (3 A me slok, each sender ns k ransms daa o s paren node nv U j= Sj 75

6 In hs subsecon, we desgn a maxmum nerference prory schedulng scheme o schedule he nodes n aggregaon ree A a me slo, nodes are elgble o be scheduled as senders f hey are leaf-nodes no beng scheduled a earler me slo or nermedae nodes whose all chldren nodes have been scheduled a earler slos We defne a sef of such nodes as an elgble schedule se When a node s scheduled o ransm, he number of recevers whch can be nerfered by hs node s defned as he nerference nensy of hs node For an elgble schedule se F and he sess, S 2,, S ha has been scheduled a earler slos, we can calculae he nerference nensy DI( u of node u (for u F as DI( u = e ( V S e F { par } u j u u j= wheree s he neghbors of node u n graphg, u u n aggregaon ree, and I U I (4 V paru s he paren node of node USj represens he se of nodes ha have no been j= scheduled a me slo For he elgble schedule sef a me slo, we frs assgn he node wh he larges nerference nensy o sender ses, hen choose he node ha has he larges nerference nensy n F S and also do no conflc wh he nodes ns We connue hs process unl here do no exs node nf S sasfyng conflc-free schedule Algorhm 2 gves he processes of schedule Algorhm 2 Aggregaon Schedulng Inpu: G= ( V, E, daa aggregaon ree DA, snk; Oupu: Ses of sender S, S2,, S ; Inalze: =, = ; Repea execung Sep o Sep 4 unl all nodes have been scheduled Sep :Calculae he elgble schedule se F a me slo ; Sep 2: v= arg max DI( u, S = { v} ; Sep 3: f u F u ( F S p= arg max DI( u and node p do no conflc wh he nodes ns, S { p} ; repea execung Sep 3 unl here do no exs node nf S sasfyng conflc-free schedule; Sep 4: OupuS, = + 4 Smulaon Resuls We evaluae he performance of our algorhm usng smulaons We randomly deploy sensor nodes n a 200m 200m feld wh a snk locaed a (00m, 00m All sensor nodes have he same ransmsson range and nerference radus he 76

7 energy consumpons for ransmsson ( E x and recepon ( E rx are 06 and 02 nj b respecvely he nal energye node of each node s 3J he lengh of daa s 5 Byes he degree consrand n L 4 DAS s 4We generae 30 random neworks and WIRES LDAS L 4 DAS Laency (slo umber of nodes presen he averaged resuls for performng comparsons Fg 2 Laency wh dfferen number of nodes For aggregaon laency, we compare L 4 DAS wh WIRES [5] and LDAS [7] ransmsson range r s fxed o 25m, he number of nodes vares from 400 o 000 wh an ncremen of 50 As can be seen from Fg2, boh L 4 DAS and LDAS ouperform WIRES When he number of nodes s less han 650, LDAS ouperforms L 4 DAS he reason s ha when node densy s no hgh, he aggregaon ree of L 4 DAS has hgher hegh ha can ncrease he low-bound of laency [5] However, wh he ncreasng of node densy, he degree of nodes (especally snk n LDAS ncreases rapdly whch causes he low-bound of laency ncreases So, when he node densy exceeds a ceran value, L 4 DAS wll always ouperform LDAS L 4 DAS r=25 LDAS r=25 WIRES r=25 L 4 DAS r=30 LDAS r=30 WIRES r=30 Laency (slo umber of nodes Fg3 Laency wh dfferen number of nodes and ransmsson range In Fg3, he number of nodes vares from 500 o 800 wh an ncremen of 00, whle ransmsson ranger akes hree values 25 and 30 respecvely I s ndcaed from he hsogram ha wh he ncremen of he number of nodes and he ransmsson range, he mprovemen of our algorhm wll be larger hese resuls ndcae ha our algorhm s grealy preferred for large scale and hgh densy WSs 77

8 5 Conclusons and he fuure work In hs paper, we have nvesgaed he daa aggregaon problem and consdered s laency and nework lfeme for WSs n scenaros of real-me and long-erm applcaons We formulaed he problem as a consraned opmzaon problem hen, we proposed an approxmaon algorhm for hs problem Fnally, hrough he smulaon and comparsons, we proved ha our algorhm ouperforms he sar-of-ar schemes In he fuure, we wll research he dsrbued algorhms for consrucng aggregaon ree and schedulng he ransmsson of nodes Acknowledgmens hs work was suppored by he aonal Basc Research Program of Chna (973 Program under Gran o 20CB302903, he aonal Scence Foundaon of Chna under Gran o , he Innovaon Projec for posgraduae culvaon of Jangsu Provnce under Gran o CXZZ_0402, he aural Scence Foundaon of he Jangsu Hgher Educaon Insuons of Chna under Gran o KJA hs work was also suppored by he Indusral Sraegc echnology Developmen Program ( funded by he Mnsry of Knowledge Economy (MKEKorea, and by he aural Scence Foundaon of Jangsu Provnce (o BK20246 References Chen, X J, Hu, X D, Zhu, J M: Mnmum Daa Aggregaon me Problem n Wreless Sensor eworks Lecure oes n Compuer Scences, 3794/ 2005: ( Wan, P J, Huang, S C, Wang, L X, e al: Mnmum-laency Aggregaon Schedulng n Mulhop Wreless eworks In: he 0h ACM Inernaonal Symposum on Moble Ad Hoc eworkng and Compung, pp ACM Press, ew Orleans, LA, USA ( Yu, B, L, J Z, L Y S: Dsrbued Daa Aggregaon Schedulng n Wreless Sensor eworks In: he 28h Conference on Compuer Communcaons, IFOCOM 2009, pp IEEE Press, Ro de Janero, Brazl ( Xu, X H, L, X Y, Mao, X F, e al: A Delay-Effcen Algorhm for Daa Aggregaon n Mul-hop Wreless Sensor eworks IEEE ransacons on Parallel and Dsrbued Sysems (PDS, 22(: (20 5 Malhora, B, kolads, I, ascmeno, M A: Aggregaon Convergecas Schedulng n Wreless Sensor eworks Wreless eworks, 7(2: (20 6 Wu, Y, Mao, Z J, e al: Consrucng Maxmum-Lfeme Daa-Gaherng Foress n Sensor eworks IEEE/ACM ransacons on eworkng, 8(5: (200 7 Chen, Z Y, Yang, G, Xu, J e al: A Low-delay Daa Aggregaon Schedulng Algorhm Based on Leafy Spannng ree (n Chnese Journal of anjng Unversy of Poss and elecommuncaons: aural Scence, 32(: 6-- (202 8 Ghosh, A, Incel, O D, Anl Kumar, V S: Mul-Channel Schedulng and Spannng rees: hroughpu-delay rade-off for Fas Daa Collecon n Sensor eworks IEEE/ ACM ransacons on eworkng, 9(6: (20 78