The Hybrid Mobile Wireless Sensor Networks for Data Gathering

Transcription

1 The Hybrd Moble Wreless Sensor Networks for Data Gatherng Bao Ren Bejng Unv. of Posts & Telecommuncatons, Bejng 876, Chna Jan Ma Noka Research Center, Bejng 3, Chna Canfeng Chen Noka Research Center, Bejng 3, Chna ABSTRACT Introducng heterogeneous moble devces, such as moble phones nto the large scale sparse wreless sensor networks s a promsng research drecton. These devces actng as moble snks offer many benefts to the network. For nstance, they help to mprove scalablty, mantan load balance, conserve energy, prolong network lfetme and mplement commercally. The paper nvestgates the mpacts of dfferent features and behavor of moble snks on the hybrd wreless sensor networks. Analyss and smulaton results show that, nstead of deployng moble snks as much as possble, choosng approprate number, transmsson range, velocty and gatherng mode of the snk nodes can sgnfcantly decrease the average end-to-end data delvery delay and mprove the energy conservaton. The comparsons of performance metrcs between fxed snks and moble snks are also made n sparse networks along wth the results that moble snks can brng hgher data success rate and energy balance. Categores and Subject Descrptors C.2. [COMPUTER-COMMUNICATION NETWORKS]: Network Archtecture and Desgn Wreless communcaton, Dstrbuted networks General Terms: Algorthms, Performance, Desgn Keywords: Wreless sensor networks, Hybrd network, Moble phones, Energy conservaton, Delay. INTRODUCTION Advances n devce technology, rado transcever desgns and ntegrated crcut along wth effcent network protocols have enabled the emergence of Wreless Sensor Networks [,2], whch Supported by NSFC Grant No. 9243, 6422; Bejng Muncpal Key Dscplnes XK3438; mwsn project sponsored by NOKIA Chna Research Center Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. To copy otherwse, or republsh, to post on servers or to redstrbute to lsts, requres pror specfc permsson and/or a fee. IWCMC 6, July 3 6, 6, Vancouver, Brtsh Columba, Canada. Copyrght 6 ACM /6/7...$5.. brng a varety of new applcatons ncludng habtat montorng, sol qualty montorng, detecton of hazardous chemcals and forest fres, mltary survellance, sesmc actvty, etc. Such a network s comprsed of a large number of sensor nodes deployed over a large area and collects nformaton from the nodes for long perods of tme. Tradtonal wreless sensor networks are regarded as a specfc applcaton and extenson based on pure ad-hoc networks [], where the dense dstrbuton of sensor nodes and mult-hop transmsson over the whole network are ther outstandng characterstcs. Although many dstngushed researches have been carred out, some dsadvantages stll exst nherently on performance metrcs, such as poor scalablty, weak energy balance, as well as low network lfetme. Explotng hybrd archtecture n wreless sensor network s a promsng research regme n the last few years. Hybrd wreless sensor network s usually comprsed of some knds of heterogeneous devces, whch manly act as snks responsble for gatherng and forwardng data from underlyng sensor nodes. Some of them are energy-rch or rechargeable, some are capable of communcaton wth better capablty and some are moblty enabled. These features can not only mprove the network performance such as energy-effcency, throughput, relablty and scalablty, but also extend the potental applcatons and make commercal mplementaton easy. Fg. Hybrd sensor network wth moble phone and cellular nfrastructure In recent years, more and more novel portable devces such as moble phone, PDA and Laptop are ganng more popularty. They are at the same tme sutable for hybrd wreless sensor network thanks to ther powerful computng and communcatng capabltes. In partcular, we thnk moble phone s the preferred alternatve snce t s not only much popularzed but also wth comprehensve support of cellular nfrastructure. An example for such archtecture s shown n Fg., whch we refer to as Hybrd Moble Wreless Sensor Networks. Some smlar concepts, such as mult-ter archtecture, heterogeneous network as well as herarchcal network are also presented n some lteratures [,4]. 85

2 The paper nvestgates the mpacts of the number, velocty, transmsson radus and gatherng mode of moble snks on largescale and sparse wreless sensor networks, n whch the advantage of hybrd archtecture s outstandng. We are manly concerned wth the performance metrcs of end-to-end data delvery delay and energy balance. The moblty of snk nodes can beneft to load balance and energy balance. Better energy balance would mprove the overall network lfetme whch can be defned as the tme elapsed untl the frst node depletes ts energy. The sparse wreless network s characterzed by loose connectvty, n whch the process of data gatherng s manly performed by moble snks. It s much dffcult to guarantee low data delvery delay n that each sensor node has to wat for a moble snk to approach before the transfer occurs. Whereas analyss and smulaton results demonstrate that by choosng the approprate number, transmsson range and velocty of moble snks, an acceptable end-to-end delay can be acheved, whch s suffcent for many practcal applcatons. The comparsons between fxed snks and moble snks are also made wth respect to data success rate and energy balance. 2. RELETED WORKS Exstng lterature utlzes moble nodes as moble snks. In [3], R.C Shah presents a novel archtecture to collect sensor data n sparse networks. Moble snks called data MULEs are used whch pck up data from the sensors when n close range and drop off the data at access ponts. Dfferent from us, ths paper assumes a two dmensonal grd and does not consder the load balance and the lfetme of network. Yu Wang and Hongy Wu [4] focus on the Delay and Fault Tolerant Moble Sensor Network (DFT- MSN) for pervasve nformaton gatherng. They develop two approaches talored for DFT-MSN, that s, drected transmsson and optmzed floodng. Smlar to us, DFT-MSN has several unque characterstcs such as sensor moblty, loose connectvty, fault tolerablty and delay tolerablty. Fall [5] proposes the concept of a delay-tolerant sensor network (DTN). DTN would typcally be deployed to montor an envronment over a long perod of tme, and characterzed by non-nteractve sensor data traffc. Sensors are randomly scattered and organzed nto one or more clusters that may be dsconnected from each other. Sensor nformaton s typcally aggregated at the cluster heads, whch are responsble for communcatng data to outsde world. Wreless moble robots (e.g. robomote [6]), unmanned aeral vehcles can roam around the network to collect data from cluster heads. Examples of DTNs n exstence are Samm[7], Zebranet[8]. Authors n [9] explore the dea of explotng the moblty of snks for the purpose of ncreasng the lfetme of a wreless sensor network wth energy-constraned nodes. Moble snks wth predctable and controllable movement patterns are studed n [,,2]. In these approaches, the statc sensors only send out ther data when the snk s movng close enough to them. The dsadvantage of such proposals s that there wll be consderable delay n acqurng sensed data, snce a node need to wat for the snk to approach t. In order to mnmze the delay, several methods of delverng the sensng data through mult-hop communcaton to the moble snk are proposed [3,4,5]. 3. MODELS AND ASSUMPTIONS 3. Network Model The hybrd moble sensor network conssts of numerous statc sensor nodes and some number of moble snks. The locaton of the statc nodes are fxed and dstrbuted unformly at random. We concentrate on large scale and sparse network n whch the sensor nodes are deployed wth low densty. The moble snks are randomly dstrbuted at ntal tme. At later tme ther poston and veloctes are gven by the moblty model. In order to comparson, we also consder the fxed snks where the velocty of moble snks s set to zero. Each fxed snk s arranged at a grd pont to optmze the performance. 3.2 Moblty Model Smlar to random waypont model [7,8, 9], n our moblty model, each of the total m moble snks pck a drecton unformly at random from (, 2π ] and moves n that drecton for a dstance d at speed v, where d s a exponentally dstrbuton. If the snk hts the boundary of the snk, t s reflected at the boundary. Under ths model, the postons of moble nodes are ndependent of each other. The steady state dstrbuton of the moble nodes over the area s unform. The drecton of the moble node s also unformly dstrbuted n (, 2π ] all the tme [6]. 3.3 Traffc Model In dense sensor network, data gathered from envronment s forwarded to snk node n mult-hops fashon. Although t can provde low data delvery delay, the energy consumpton per bt s much hgher due to the mult-hop forwardng of one packet. We adopt the lmted k-hop scheme for data gatherng from sensor nodes, as shown n Fg.2. That s, the data transmsson of a sensor node wll not happen untl at least one moble snk approach to t wthn at most k-hop dstance. In general, gatherng data wthn one-hop neghborhood can mnmze the average energy consumpton coupled wth more delay, whle k-hop forwardng would lead to more overhead for routng. 4. ANALYSES 4. Delay Analyss In our analyss, tme s dvded nto consecutve slots, so that delay s counted by the number of tme slots. In addton, snce Fg. 2 one-hop and lmted mult-hop transmsson the moble snks are responsble for gatherng data from ther nearby sensor nodes n one-hop or k-hop fashon, and the number of moble snks s very small as compared to sensor nodes, the end to end data delvery delay s domnated by the duraton 86

3 durng whch sensor nodes are watng for a snk to approach. So n terms of delay, ths watng duraton s our prmary concern. Now we wll deduce the mean value of delay that a moble snk frstly come nto the range of a sensor node. The results show that the delay for whch sensor node has to wat s related to the number and the velocty of moble snks as well as the transmsson radus of sensor nodes. Theorem Gven a sensor node S. Let m denotes the number of moble snks, r s the range of transmsson and v s the velocty of moble snks. Wth hgh probablty, the average duraton D untl whch a moble snk frst enters the feld of sensor node S s 4logm D crv m Proof: Let M be some moble snk at a dstance l from an ntended and statc sensor node S. The poston and movng drecton of M s unformly and ndependent dstrbuton. For smplcty, assume M does not change ts movng drecton untl reachng the network boundary. Then the probablty that M enters the neghborhood of S s equal to the angle subtended by the neghborhood of S at M, see Fg.3. we use the constant c as a scalng factor defned n [lemma 4, 6] and substtutes c wth cr. cr Then ths subtended angle s at least, where c <. The l m related notatons are alse llustrated n Fg.3. Defnng concentrc rngs R,..., R,... wth wdth m. Each R s centered at S and R conssts of ponts whch are at a dstance between and from S. Let X, j be a random m m varable such that X, j = f the th moble snk s n R and t wll enter neghborhood of S. X, j = otherwse. Snce the unform dstrbuton of moble snks, the probablty that M R s 2 2 ( j ( j ) ) m Fg. 3 The llustraton of notatons 2 whch s approxmately 2jm. Moreover, each moble snk n R has a probablty of at least cr j j m j of reachng S, the event X, j has an expected value of 2cr. Thus X m, j are ndependent Bernoull random varables. Defne j d X( d) = m j= = X, j, where d s a varable denotng the number of rngs far from S. In order to obtan a bound on d such that X( d ) >, Use the second Chernoff bound Equaton [2] as [6], we get the probablty that X( d ) < for d = 4f log m/ rc s f less than m, whch means 4f logm Pr D m crv m Thus a sensor node can take tme less than 4logm to wat for crv m a moble snk reachng t wth hgh probablty. The result follows. Then the servce probablty that a sensor s wthn the coverage of at least one moble snk crv m p =. Furthermore, 4logm ether ncreasng the radus r of sensor and moble snk, or ncreasng the number m as well as the velocty v of moble snk can all decrease the watng duraton D to some extent. 4.2 Velocty Impact on Delay Hgh velocty can ncrease the probablty for the sensor and moble snk meet wth each other, on the other hand, t can result n moble snks passng through the effectve regon of a sensor node so fast that there s no adequate tme to perform contnuous transmsson. The data delvery delay would ncrease nevtably snce one message has to be sent more than one tme. So, ncreasng velocty wll ncrease the servce probablty whereas decrease the servce duraton of each tme. Gven velocty v and a moble snks can move nto the transmsson regon of a sensor node from any drecton, the average travel dstance through the regon s equal to r π. The avalable tme for message transmsson s proportonal to r π. v That s, the travel tmes that are requred to fnsh a ntegrated v message s proportonal to. In [4], the authors assume r π message length of L, channel bandwdt w, the number of tme slots requred to transmt a message L. Then wth a servce s = w probablty p, the servce tme of the message s a random varable wth Pascal dstrbuton. That s, the probablty that the message can be transmtted wthn no more than x tme slots, s x s s+ s FX ( x) = p ( p) = s Ths s the Pascal dstrbuton wth mean value of s. Under an p average Posson arrval rate λ and a Pascal servce tme wth p μ =. Data generaton and transmsson can be modeled as s an M/G/ queue. But [4] ddn t take the mpact of snk moblty on delay nto consderaton. Heren we substtute the value μ as follows: wr π p μ = Lv The average message delvery delay can be expressed as follows: f. 87

4 2 2 2 ρ + λρ D = ρ + λ 2( ρ), λ where ρ =. For smplcty, we neglect the mpact of arrval μ rate and set λ =, thus D = μ. On one hand, large v can mprove the servce probablty p, on the other hand t ncreases the requred tmes of mobles snks reachng t n order to fnsh a message transmsson. Both sdes of the mpacts should be consdered when choosng the approprate velocty value of moble snks. 5. SIMULATIONS Extensve Smulatons have been carred out. We evaluate three dfferent performance metrcs whch are crucal to mprove the qualty of servce and prolong the lfetme of wreless sensor network. The average data delvery delay s referred to the duraton from data generaton to data recepton by moble snks. The data success rate s the rato of the number of message generated by sensor nodes to the number of message receved by moble snks. For evaluatng the lfetme of the network, we also consder the mnmal nodal remanng energy, whch s set to ntally. Totally 5 sensor nodes are deployed unformly at random n the determned area. Varyng transmsson radus s chosen properly to assure some degree of connectvty of network. Moble snks move accordng to moblty model mentoned n Average Delay (Unts) hop 2 hops 4 hops 6 hops Number of Moble Snks Fg. 4 Impact of the number of moble snks on delay secton 3.2. The data generaton of each sensor nodes follows a Posson process wth an average arrval nterval of s. Frstly we vary the number of moble snks to observe the change of average data delvery delay. Apparently the end to end delay would decrease wth the ncrease of the number of moble snks as shown n Fg.4. Ths trend would become stable when the number s near to 4, whch s approxmately equal to the square root of the number of sensor nodes ( 5 39 ). It can also be regarded as a trade-off pont at whch some degree of low delay can be guaranteed at cost of the least number of moble snks. At the same tme, addng addtonal expensve moble snks would not sgnfcantly reduce the delay any more. From Fg. 4 we can also see that ncreasng the number of the hops s another effectve approach to obtan low delay, especally when the fracton of moble snks s small. Wth the ncrease of the number of moble snks, the performances correspondng to 4-hop and 6-hop fashons are prone to get closer, and the larger hop would result n extra overhead for routng and data forwardng. Fg.5 shows the energy expendture under varous numbers of moble snks. When adoptng the one-hop fashon, the energy consumpton s constant, also t s mnmum compared to those by other mult-hop forwardng. Comparng wth the performance obtaned by the one-hop fashon, we can see that the 6-hop fashon can acheve the lowest degree of delay but the most energy s consumed as seen n Fg.4 and Fg 5. The mpact of velocty of moble snks on data delvery delay s evaluated n Fg.6, n whch the x axs value means the rato of the velocty of moble snks to the transmsson radus of nodes. The change trend of delay n the fgure verfes the results deduced from secton 4. and secton 4.2. At begnnng, wth the ncrease of the velocty of moble snks, the average delay s decreasng Average Delay (unts) moble snks moble snks 5 moble snks Rato of Velocty to Radus of Sensor Nnodes Fg. 6 Impact of velocty of moble snks on delay 35 Energy Consumpton (per message) hop 2 hops 4 hops 6 hops Number of Moble Snks Fg. 5 Energy consumpton under varyng number of moble snks Data Success Rate moble snks moble snks 5 moble snks Rato of Velocty to Radus of Sensor Nodes Fg. 7 Data success rates under varyng veloctes 88

5 sgnfcantly. The reason s that ncreasng the velocty can ncrease the servce probablty that some moble snks come nto the range of a sensor nodes. Consequently the watng duraton s shortened greatly. However, ths trend s reversed when the number of moble snks exceeds some value of about. Then the value of delay s prone to ncrease. Ths s attrbuted to the fact that, the ncreased velocty would shorten the duraton of passng through the effectve regon of a sensor node. It results n no adequate tme to perform contnuous transmsson and hence the message (see secton 4.2, heren we use message nstead of packet snce a packet can not be parttoned when transmttng) has to be segmented and sent over several tmes by several snks. At ths moment the ncreased servce probablty can not offset the decreased servce tme. So t s mportant to choose a reasonable velocty of moble snks when deployng network and gatherng data. Smlar delay change trends can also be found under dfferent numbers of moble snks n Fg.6. Too large value of velocty s not practcal n realty, we use t here only for the evaluaton of delay performance. Fg. 7 depcts the data success rates correspondng to varous veloctes. It shows the same trend as descrbed n Fg. 6. A message wth large delay would be dscarded from the queue n sensor nodes. We also make the comparson of data success rates between fxed snks and moble snks n spare network, where the sensor nodes are not fully connected wth each other. In ths case, the data success rate produced by moble snks s much better than that by fxed snks, as shown n Fg. 8. One of the advantages of moble snks s that they can approach to such sensor nodes that are dsconnected wth others, that s, those sensor nodes are solated. Usng fxed snks, the data success rate decreases snce those solated node can not fnd a path to delvery ther data to any snk. Sucess Data Rate Average Delay (unts) fxed snks moble snks, hop moble snks, 2 hop moble snks, 4 hop moble snks, 6 hop Number of Snk Nodes Fg. 8 Data success rates n loose-connectvty network 5 moble snks 2 moble snks Radus of Sensor Nodes Fg. 9 Impact of the transmsson radus on delay Data Success Rate moble snks 2 moble snks Radus of Sensor Nodes Fg. Data success rates under varyng transmsson radus Mnmal Remanng Energy fxed snks moble snks wth one hop moble snks wth four hops Number of moble and fxed snks Fg. Mnmal nodal Remanng Energy In general, the large transmsson radus can obtans the better network performance such as low delay and hgh data success rate. In ad-hoc networks, although large transmsson radus can reduce the end to end delay (for example, hops), t also leads to the decrease of throughput of pre node [7] snce the nterference from each other. But n the fashon of lmted k-hop transmsson, the nterference s reduced and localzed, so we don't have to consder t too much. Furthermore, addng transmsson radus can also ncrease the servce probablty and prolong the servce tme. But note that, sensor nodes are resource constraned and large transmsson power would consume more energy. Fg. 9 depcts the mpact of the transmsson radus on delay. Fg. shows the data success rates under varyng transmsson radus. Both of them present the smlar characterstcs of performance enhancement. Fg. shows the mnmal nodal remanng energy. More remanng energy of each node means the prolonged lfetme of wreless sensor network. The advantage brought by moble snks s that t can acheve load balance over all the sensor nodes. The load balance s assocated wth energy balance, whch s unreachable by fxed snks. The moble snks wth one-hop fashon gves the best energy reservaton and t doesn't scale wth the number of moble snks. The four-hop fashon also performs well. The performance correspondng to fxed snks s poor snce the pure mult-hop forwardng can lead to more energy consumpton of some sensor nodes around the fxed snks, whch s referred to as traffc or routng hot spots. 89

6 6. CONCLUSIONS Incorporatng moble phones nto large-scale and sparse wreless sensor network wll form a novel hybrd moble wreless sensor network, whch offers much beneft to network performance. The paper nvestgates the mpacts of moble snks on such hybrd wreless sensor networks. The results show that, nstead of deployng as much expensve moble snks as possble, choosng approprate number, transmsson range, velocty as well as gatherng fashon of moble snks can sgnfcantly guarantee lower end-to-end data delvery delay and acheve better energy conservaton. For many applcatons such level of delay and lfetme of network s suffcent. In addton, moble snks can also brng hgher data success rate and energy balance as compared to those obtaned by the same amount of fxed snks. A promsng drecton for future work s to explore the use of cooperatve moblty. Snce moble snks usually operate under centralzed control, the cooperatve moblty s possble and can also mprove the coverage and effcency of moble snks for gatherng data, along wth the decrease of data delvery delay and energy dsspaton. An effectve data dssemnaton protocol s under consderaton whch can reduce the broadcast and routng load. Moreover, utlzng the moble snks to mprove the throughput capacty of hybrd sensor network s another challengng regme. 7. ACKNOWLEDGMENTS Thanks to Dr. Jan Ma at Noka Chna Research Center for gvng me much help to fnsh my work. 8. REFERENCES [] J. Akyldz, W. Su, Y. Sankarasubramanam, and E. Cayrc, A survey on sensor networks, IEEE Commun. Mag., vol. 4, pp. 2 4, Aug. 2. [2] Ren FY, Huang HN, Ln C. Wreless Sensor Networks. Journal of Software, 3,4(7):282~29. [3] R. C. Shah, S. Roy, S. Jan, and W. Brunette, Data MULEs: Modelng a three-ter archtecture for sparse sensor networks, n Proceedngs of the Frst IEEE Internatonal Workshop on Sensor Network Protocols and Applcatons, SNPA 3, Anchorage, AK, May 3, pp [4] Y. Wang and H. Wu, "DFT-MSN: The Delay Fault Tolerant Moble Sensor Network for Pervasve Informaton Gatherng", to appear n IEEE INFOCOM'6, Barcelona, Span, Aprl 23-29, 6. [5] K.Fall, A delay-tolerant network archtecture for challenged nternets, Intel Research Berkeley, Tech. Rep. IRB-TR-3-3, February 3. [6] G. Sbley, M.H.Rahm, and G.S.Sukhatme, Robomote: A tny moble robot platform for large-scale ad-hoc sensor networks, n Proceedngs of the IEEE Internatonal Conference on Robotcs and Automaton (ICRA-2), vol. 2. Washngton D.C, USA: IEEE, May -5 2, pp [7] D. T. N. R. Group, The samm network, cdt.luth.se/babylon/snc/. [8] P. Juang, H. Ok, Y. Wang, M. Martonos, L. Peh, and D. Rubensten, Energy-effcent computng for wldlfe trackng: Desgn tradeoffs and early experences wth zebranet, n Proceedngs of ASPLOS-X. ACM, October 2. [9] Z. M. Wang, S. Basagn, E. Melachrnouds, and C. Petrol, Explotng snk moblty for maxmzng sensor networks lfetme, n Proceedngs of the 38th Hawa Internatonal Conference on System Scences (HICSS), 5. [] A. Chakrabart, A. Sabharwal, and B. Aazhang, Usng Predctable Observer Moblty for Power Effcent Desgn of Sensor Networks, n The second Internatonal Workshop on Informaton Processng n Sensor Networks (IPSN), Palo Alto, CA, Aprl 3. [] A. Kansal, A. Somasundara, D. Jea, M. Srvastava, and D. Estrn, Intellgent flud nfrastructure for embedded networks, n Proceedngs of the 2 nd nternatonal conference on Moble systems, applcatons, and servces (MobSYS), 4. [2] W. Zhao, M. Ammar, and E. Zegura, A message ferryng approach for data delvery n sparse moble ad hoc networks, n Proceedngs of the 5th ACM nternatonal symposum on Moble ad hoc networkng and computng (MobHoc). ACM Press, 4, pp [3] S. Gandham, M. Dawande, R. Prakash, and S. Venkatesan, Energy-effcent schemes for wreless sensor networks wth multple moble base statons, n Proceedngs of IEEE GLOBECOM, Dec 3. [4] Sankaranarayanan, L.; Kramer, G.; Mandayam, N.B. Herarchcal sensor networks: capacty bounds and cooperatve strateges usng the multple-access relay channel model, st Annu. IEEE Conf. on Sensor and Ad Hoc Communcaton and Networks (SECON'4), Santa Clara, CA, USA, Oct. 4-7, 4.. [5] J.Luo and J. P. Hubaux, Jont moblty and routng for lfetme elongaton n wreless sensor networks, n Proceedngs of the 24th IEEE INFOCOM, Mar 5. [6] N. Bansal and Z. Lu, Capacty, delay, and moblty n wreless ad hoc networks, n Proc. IEEE INFOCOM, vol. 3, 3. [7] P. Gupta and P. R. Kumar, The capacty of wreless networks, IEEE Transactons on Informaton Theory, vol. 46, no. 2, Mar. [8] X. Hong, M. Gerla, G. Pe, and C. Chang. A group moblty model for ad hoc wreless networks, 999. [9] Davd B Johnson and Davd A Maltz. Dynamc source routng n ad hoc wreless networks. In Moble Computng, volume 353. Kluwer Academc Publshers, 996. [2] H. Chernorff. A measure of aymptotc e_cency for tests of a hypothess based on the sum of observatons. Annals of Mathematcal Statstcs. 9