Event-based Location Dependent Data Services in Mobile WSNs

Transcription

1 Event-based Location Dependent Data Sevices in Mobile WSNs Liang Hong 1, Yafeng Wu, Sang H. Son, Yansheng Lu 3 1 College of Compute Science and Technology, Wuhan Univesity, China Depatment of Compute Science, Univesity of Viginia, USA 3 College of Compute Sci. and Tech., Huazhong Univesity of Science and Technology, China husthong@gmail.com, {yw5s, son}@cs.viginia.edu, lys@mail.hust.edu.cn Abstact Mobile sensos ae widely deployed in Wieless Senso Netwoks (WSNs) to satisfy emeging application equiements. Specifically, pocessing location dependent queies in mobile WSNs is still a challenging poblem due to senso mobility. We pesent an Event-based Location Dependent Quey (ELDQ) model that continuously aggegate data in specific aeas aound mobile sensos of inteests to povide event-based location dependent data sevices to the uses. ELDQs genealize seveal typical quey types and ae impotant in many applications. Howeve, existing appoaches ae incapable of efficiently answeing ELDQs. In this pape, we popose a set of techniques to pocess ELDQs while optimizing system pefomance. Cost analysis and simulation esults indicate that ou techniques geatly educe the cost of pocessing ELDQs while achieving elatively high accuacy and shot esponse time. I. INTODUCTION ecently, mobile sensos ae intoduced into the Wieless Senso Netwoks (WSNs) to povide vaious data sevices in many applications [15, 16, 17, 18]. Howeve, location dependent data sevice queies whose esults depend on the cuent locations of the taget mobile sensos still cannot be efficiently answeed by existing appoaches. Specifically, we conside a class of location dependent data sevice queies that peiodically collect and aggegate the data within the specific aea aound the mobile senso nodes that detect the event. Such queies equie continuous event detection as well as location dependent data aggegation on the fly, and hence need specialized optimization techniques to be pocessed efficiently. Fo example, conside succos using obots to seach and escue suvivos in Sichuan eathquake. They need to quey the infomation about the teain and suvivos aound the escue obot that detects life evidence as it moves in the unknown and dangeous envionment. Base on the quey esults, succos can accuately locate the suvivos and find the best escue oute. As anothe example, health povides may concen about the ai pollutants atio within a cetain aea aound sensitive people who need peventive monitoing. The quey can be Give me the aveage pecentage of ai pollutants within 1 m aound the pesons whose blood oxygen levels ae lowe than 9% sampled evey 1 minutes in the next hous. This quey is tiggeed by the event that mobile sensos data satisfy the use-defined pedicate. Multiple mobile sensos may become centes of vaious taget aeas (called quey aeas), and they ae defined as efeence sensos. The set of efeence sensos may keep changing because diffeent mobile sensos may detect the event evey sample peiod. As a esult, quey aeas change accoding to the set of efeence sensos as well as thei locations. We categoize this type of queies as Event-based Location Dependent Quey (ELDQ) which povides a fine ganulaity quey model that only collects and aggegates the data fom the senso nodes in continuously changing quey aeas instead of the whole senso netwok. ELDQ genealizes many typical quey types in WSNs. Fo instance, a typical aggegation quey is an ELDQ in which quey aeas ae fixed, and a location dependent quey is an ELDQ without event detection and aggegation. In fact, thee ae seveal challenges in pocessing ELDQs: (1) Accessing mobile sensos in a obust and efficient manne in mobile WSNs. Although ZebaNet [18] has addessed the poblem of tacking and accessing mobile sensos, its system model and design goals diffe fom ous. We need to design a scheme fo efficiently disseminating queies to mobile sensos by stationay senso infastuctue while minimizing the total cost. () Detemination of continuously changing quey aeas. In detemining up-to-date quey aeas, the centalized methods [1,, 3, 6] incu significant tansmission ovehead and latency, because quey aeas continuously change in a distibuted manne with mobile sensos locations and the event detected each sampling peiod. (3) Efficient data aggegation in continuously changing quey aeas. Pevious in-netwok aggegation methods [1,, 3] cannot efficiently aggegate data in continuously evolving aeas. (4) Multiple concuent queies may cause message collisions and system oveload. Motivated by the above challenges, we popose a set of techniques to pocess ELDQs in mobile WSNs. Stationay sensos ae fist built into a tee stuctue with the base station as oot. Use s quey is sent to the base station which then disseminates the quey to each mobile senso using the senso tee. Afte eceiving the quey, mobile sensos keep monitoing the event evey sample peiod. Mobile sensos that detect the event become efeence sensos and stat to popagate the quey to the stationay sensos in the quey aeas. The stationay sensos that eceive the quey stat to collect and aggegate the equied

2 data. They adaptively choose outes to tansmit aggegation esults back to the base station accoding to cuent quey aeas. Such adaptation geatly educes the tansmission cost and quey esponse time. Note that thee ae possibly multiple concuently unning queies issued to the base station. They ae optimized both at the base station and at the mobile sensos to educe the total pocessing cost. We make the following contibutions in this pape: To ou best knowledge, it is the fist mechanism to define and pocess event-based location dependent queies in mobile WSNs. An ELDQ is a geneal quey which exists in many applications and can not be efficiently answeed by pevious wok. Fo efficient quey dissemination to mobile sensos, we popose an adaptive poxy selection algoithm to minimize the update cost of the poxy oute (defined in Section 4.), when the mobile sensos change thei poxies. In-netwok quey popagation and Location-based Innetwok Aggegation (LIA) algoithms ae poposed to efficiently pocess ELDQs. To optimize multiple queies, a two-level multi-quey optimization algoithm that filtes hidden queies at the base station and instantly ewites concuently tiggeed queies at each mobile senso is poposed. We give a theoetical analysis and an extensive evaluation of the ELDQ pocessing pefomance. ELDQ exhibits a supeio pefomance in tems of enegy efficiency, quey latency and quey accuacy unde vaious netwok conditions and outpefoms all compaed techniques. The est of this pape is oganized as follows. Section discusses the elated wok. Section 3 gives the system model and quey definition. Section 4 pesents a set of techniques fo pocessing ELDQs. In section 5, we analyze the theoetical cost of pocessing ELD Section 6 poposes a two-level multi-quey optimization algoithm. The ELDQ pocessing pefomance is evaluated though simulation in Section 7. Section 8 concludes the pape. II. ELATED WOK Pocessing location queies ove moving objects have been studied extensively in both centalized and distibuted database system [4, 14]. Howeve, these solutions can not be applied diectly to pocess data sevice queies in WSNs with constained esouces and distibuted topology. The wok on quey pocessing in WSNs can be classified into two types: the solutions that conside the senso mobility o not. Typical data aggegation appoaches collect and aggegate data fom stationay sensos within a fixed aea. A well-known example is ACquisitional Quey Pocessing (ACQP) in senso netwoks based on TinyDB []. Tiny Aggegation Sevice (TAS) [1] gives an in-netwok solution to answe aggegation queies ove stationay sensos whee data ae aggegated in the netwok and sent back though the outing tee to the base station. Howeve, TAS can only answe simple and declaative aggegation queies. A ecent wok [3] poposes a Two-Phase Self-Join (TPSJ) scheme to efficiently pocess self-join quey fo event detection in senso netwoks. Howeve, the above appoaches do not conside senso mobility and can not efficiently pocess location dependent queies. Some ecent wok stats to addess senso mobility fo quey pocessing in mobile WSNs such as CNFS [15], CountToent [16], ICEDB [17] and MobiQuey [19]. Howeve, these appoaches eithe lack the in-netwok optimization [17, 19] o intoduce additional ovehead in quey pocessing though maintaining a patial map of the netwok [15] and infoming all the netwok nodes of the aggegate quey esult [16]. Moeove, thei system models and quey types ae diffeent fom ous. Fo example, ELDQ is moe challenging than the simple data collection quey in CNFS and ICEDB that collects data fom a cetain souce node o nodes in a fixed aea and the geneic aggegation quey in CountToent that aggegates senso data in a fixed aea, because ELDQ needs to aggegate senso data in a continuously changing quey aea. MobiQuey fails to optimize in-netwok pocessing cost fo mobile spatialtempoal queies. Multi-quey optimization in WSNs is studied in some ecent wok including MQO [1] whee communication cost is optimized based on complexity analysis and TTMQO [11] whee the base station goups incoming queies into synthetic queies based on the cost model to save the total cost of quey pocessing. Howeve, these methods only conside simple aggegation queies without senso mobility and do not take the unique popeties of ELDQs into account such as distibuted event detection and continuously changing quey aeas. A. System Model III. PELIMINAY (a) System model (b) Application Scenaio Figue 1. System model and scenaio We assume each stationay senso has a unique identifie and is awae of its own location though positioning devices (e.g. GPS) o localization techniques like [8]. Thee is a low-level mechanism, such as beacon messages, that enables each stationay senso to know its neighbos. Mobile sensos cuent velocities and locations ae measued by themselves using GPS. In Figue 1(a), the whole aea is divided into seveal sub aeas; each sub aea has a base station that is esponsible fo injecting use s queies to the senso netwok and sending the quey esults

3 back to the use. The white cicles ae the stationay sensos and the gay cicles ae the mobile sensos. In this pape, we leave the issues in inte-base station communication as futue wok and discuss the situation in one sub aea. Figue 1(b) shows an application scenaio of the system. The small cicle is the mobile body senso s tansmission ange, and the lage cicle is the quey aea. In this scenaio, the body senso keeps monitoing the unning peson s health status. When it detects the peson s blood oxygen level is lowe than a theshold, it tigges and popagates the quey to the gas sensos in the quey aea. Those gas sensos aggegate the ai pollutants data and epot the esults back to the base station. B. Quey Definition ELDQ is defined as the following fomat: SELECT [aggegation opeatos] S 1.att 1 S 1.att i FOM Senso 1 AS S 1 INSIDE (adius, (SELECT S.loc FOM Senso AS S )) STAT ON EVENT 1 (S.att i ) STOP ON EVENT (S.att i ) SAMPLE PEIOD Sp FO Duation whee aggegation opeatos include SUM, AVG, MAX, MIN and COUNT; att i is the data type of the senso, 1 i<n, whee n is the imum numbe of data types a senso can sample; EVENT 1 epesents the tiggeing conditions of the quey and EVENT epesents the stopping conditions of the quey; sample peiod and duation means mobile sensos should detect the event each sample peiod fo a specific duation. As pesented in [4], most of the location constaints can be expessed in tems of INSIDE, so only INSIDE is consideed in the quey. We only discuss the pocessing of the ELDQs with aggegation opeato AVG, because such ELDQs equies not only the data but also the numbe of all the sensos in the quey aea thus is usually the most challenging quey type. In fact, ou mechanism can also efficiently pocess the ELDQs with othe aggegation opeatos. IV. POCESSING ELDQS In this section, we discuss pocessing an ELDQ in a distibuted and concuent way to educe the total pocessing cost. Stationay sensos ae fist built into a tee stuctue with the base station as oot simila to the appoach in [6]. As a esult, each stationay senso s level in the tee is popotional to the tansmission hops fom the base station to itself, which minimizes the numbe of sensos to be tavesed in quey dissemination. Each stationay senso maintains a Minimum Bounding ectangle (MB) which bounds the imum extents of all its child nodes location in the tee [9]. The ovelapping aeas among MBs ae minimized to decease the numbe of messages fo accessing senso nodes. Each stationay senso also maintains a neighbo stationay senso nodes list including each neighbo s id and location. The above infomation is updated by each stationay senso using peiodical beacon messages. Note that the topology of stationay sensos does not evolve fequently, so the cost of maintaining such infomation is compaatively low. The ELDQ pocessing is divided into fou main steps: quey dissemination, event detection, quey popagation and data aggegation. A. Dissemination to Mobile Sensos To educe the cost, the base station disseminates the quey to mobile sensos using the tee stuctue instead of flooding as in [5]. Each mobile senso egistes to a stationay senso, called poxy, to access the senso netwok. The base station assigns each incoming quey a unique qid and convets it into the quey item (qid, attibute list, aggegation opeatos, events, adius, sample peiod, duation) and disseminate the quey item to each mobile senso along a oute fom the base station to the mobile senso s poxy (called poxy oute). Each stationay senso in the poxy oute caches the next hop to the mobile senso s poxy, so it only needs to unicast the quey item to the next hop, which geatly educes the numbe of dissemination messages. Howeve, the mobile senso changes its poxy fequently due to mobility. As a esult, the poxy oute should be updated by adding the mobile senso s poxy infomation to the new poxy oute and deleting the out-of-date poxy infomation fom the obsolete poxy oute. We ague that the numbe of update messages can be educed if mobile sensos popely select thei new poxies. An exteme situation may happen duing the peiod that the obsolete poxy oute has not been deleted immediately afte a mobile senso change its poxy. A quey may be disseminated along the obsolete poxy oute to the old poxy which has aleady lost contact with the mobile senso. In this case, afte the old poxy eceives the update message, it sends the quey up along the old poxy oute until to the node that caches the next hop to the new poxy. Then the quey is disseminated along the new poxy oute to the mobile senso. To avoid message losses, each senso in the poxy oute oveheas its next hop. If a paent node does not ovehea the quey tansmitted by the next hop, it assumes the next hop has not eceived the quey, and etansmits the quey. B. Adaptive Poxy Selection Algoithm Each mobile senso checks the connectivity with its poxy by sending peiodical handshake messages to its poxy. When a mobile senso is out of contact with its poxy, it needs to select a new poxy fom its neighbo stationay sensos. Definition 1: Conside the base station s id is S, a stationay senso S i s dissemination path is (S, S i ) which is the ode of tavesing sensos fom the base station to S i though the tee. Definition : Conside stationay senso nodes S i s dissemination path is (S, S k, S i ) and S j s dissemination path is (S, S k, S j ) whee S k is the ovelapping node of the two dissemination paths, the tee distance between S i

4 and S j is S k, S i + S k, S j. S k, S i is the numbe of senso nodes on the path (a) Tee distance 6 Figue. Vaious distances (b) Distance pojection In Figue (a), the dissemination paths fo senso node 5 and 6 ae (, 1, 3, 5) and (, 1, 4, 6) espectively, so tee distance between them is 4. When a mobile senso changes its poxy (e.g. fom 5 to 6), the poxy oute should be updated by elaying the mobile senso s infomation fom the new poxy to the ovelapping node of the new poxy oute and the old poxy oute (it is node 1 in Figue (a)). Then the obsolete poxy oute fom the ovelapping node to the old poxy is deleted. Theefoe, the numbe of update messages duing each poxy changing depends on the tee distance between the new poxy and the old poxy. We obseve that the total update cost fo a mobile senso is affected by the numbe of poxy changing in the quey s duation and the aveage tee distance between the mobile senso s old poxies and new poxies, and it can be calculated as: Npc C u = C TDi. (1) t i= 1 whee C t is the tansmission cost of each message, N pc is the total numbe of poxy changing, and TD i is the tee distance between the old poxy and the new poxy in the ith poxy changing. We popose two poxy selection algoithms to educe C u in two diections: Maximum Distance Pojection (MDP) aims to educe N pc while Minimum Tee Distance (MTD) aims to minimize TD i. As shown in Figue (b), distance between a mobile senso (the gay cicle) and a stationay senso is pojected on the mobile senso s velocity vecto. This pojection is called distance pojection. In MDP algoithm, each mobile senso egistes to the neighbo stationay senso that has imum distance pojection on its velocity diection. As a esult, mobile senso may stay in the poxy s tansmission ange fo longe time, which educes N pc in the quey s duation. When a mobile senso stats to change its poxy, it boadcasts its cuent location and velocity to its neighbo stationay sensos. Each neighbo stationay senso calculates its distance pojection with the mobile senso locally and sets a time that is invesely popotional to the distance pojection. The neighbo with imum distance pojection fist sends its distance pojection to the mobile senso and is selected as the mobile senso s new poxy. Then the mobile senso eplies a egiste message to the new poxy. Othe neighbo stationay sensos oveheaing this message will not send thei distance pojections to the mobile senso to save the tansmission cost. In MTD algoithm, each mobile senso egistes to the neighbo stationay senso that has minimum tee distance to its old poxy. Each stationay senso keeps a dissemination path that can be obtained in the senso tee s building pocedue by ecoding the ids of the sensos along the tavesing path fom the base station to itself. Theefoe, obtaining dissemination path does not intoduce exta cost. When a mobile senso needs to change its poxy, it boadcasts its old poxy s dissemination path to the neighbo stationay sensos. Each neighbo stationay senso calculates its tee distance fom the old poxy and set a time popotional to the tee distance. The neighbo node with the shotest tee distance fist sends the tee distance to the mobile senso and is selected as the mobile senso s new poxy. Howeve, multiple neighbo sensos may have the shotest tee distance fom the old poxy at the same time. The tie is solved by selecting the poxy that has minimum distance with the mobile senso (MTD&MD) o selecting the poxy that has imum pojection distance with the mobile senso (MTD&MDP). We ely on MAC laye to avoid collisions between the neighbo sensos with the same tee distance. We have compaed MDP, MTD&MD and MTD&MDP with the typical minimum distance (MD) algoithm [7] by simulation in GloMoSim whee MAC laye potocol is set to IEEE In MD algoithm, each mobile senso selects the neaest stationay senso as its new poxy. In ou simulation, stationay senso nodes and 3 mobile senso nodes ae unifomly distibuted in a squae aea as the mobile sensos mobility patten using andom way point with pause time 3 seconds. The simulation time is set to 4 minutes and each mobile senso node checks the connection with its poxy evey 1 seconds. We design two expeiments with diffeent node densities and sensos tansmission anges. In the fist expeiment, we set the simulation aea to 6m 6m and each mobile o stationay senso s tansmission adius to 1m. Fom Figues 3(a) and 4(a), we can see that MDP esults in the minimum numbe of poxy changing and update messages. The advantage becomes moe obvious when mobile sensos imum speed inceases. Because MDP algoithm polongs each mobile senso s esident peiod within its poxy s tansmission ange thus educes the numbe of poxy changing in the quey s duation. In the second expeiment, we change the simulation aea to 1m 1m and each senso s tansmission adius to m. Howeve, we can see fom Figues 4(a) and 4(b) that although MDP minimizes the numbe of poxy changing, MTD&MDP instead of MDP esults in the minimum numbe of update messages. As the senso s tansmission adius inceases fom 1m to m, each mobile senso stays within its poxy s tansmission ange fo a longe time, which esults in less saving of the numbe of poxy changing by MDP. We can see fom Figue 3 that the imum saving of poxy changing by MDP deceases fom 49 (Figue 3(a)) to 17 (Figue 3(b)). As depicted in (1), the total update cost fo a

5 # of poxy changing MDP MTD&MD MTD&MDP MD # of poxy changing MDP MTD&MD MTD&MDP MD # of update messages Maximum speed (m/s) Maximum speed (m/s) Maximum speed (m/s) Maximum speed (m/s) (a) 6m 6m (b) 1m 1m (a) 6m 6m (b) 1m 1m Figue 3. Num of poxy changing vs. Max speed Figue 4. Num of update messages vs. Max speed MDP MTD&MD MTD&MDP MD # of update messages MDP MTD&MD MTD&MDP MD mobile senso is detemined by two factos: the numbe of poxy changing and the tee distance evey poxy changing. If the saving of the numbe of poxy changing by MDP has deceased to cetain extent, the saving of the numbe of update messages (equal to tee distance) evey poxy changing becomes a dominant facto that detemines the total update cost. That is the eason MTD&MDP becomes the optimal algoithm as each senso s tansmission adius inceases to m. Note that MDP, MTD&MDP and MTD&MD always outpefoms MD algoithm which leads to fequent updates and excessive update messages. We lean fom the above evaluations that diffeent system scenaios may lead to diffeent optimal poxy selection algoithms between MDP and MTD&MDP. We intoduce an adaptive poxy selection algoithm to choose the optimal algoithm in cetain system scenaio. To decide the optimal algoithm, thee is a taining peiod afte the senso tee is built in which each mobile senso uns both algoithms and ecods the espective total tee distance (equals to the total update messages). The algoithm that esults in the minimum total update messages is chosen as the optimal one. Such adaptive scheme can optimize the total update cost fo poxy changings. The quey s duation is usually much longe than the duation in the simulation, so the algoithm s optimization effects ae significant. C. Event Detection Each mobile senso pefoms event detection locally in a distibuted and concuent way. Afte eceiving a quey item, each mobile senso keeps sensing the equied data and checking the event evey sample peiod fo a specific duation. Once the event is detected, the mobile senso becomes the efeence senso, and its cuent location becomes the cente of the cicle quey aea. Distibuted event detection saves communication cost and enables ealie detection of events compaed to the centalized appoach whee the base station is esponsible fo event detection [1]. D. In-netwok Quey Popagation Afte a quey is tiggeed by an event, the efeence senso popagates the tiggeed quey including the efeence senso s id and cuent location, quey id, quey adius and attibute list to the stationay sensos in the quey aea. If the quey adius is shote than the efeence senso s tansmission adius, the tiggeed quey only needs to be boadcasted to the stationay sensos in the quey aea by one message. Othewise, the tiggeed quey should be popagated to all the stationay sensos in the quey aea in a multi-hop fashion. In centalized methods, queies ae popagated fom the base station to the sensos in the quey aea by flooding [11] o tee-stuctued indexes [, 3, 6], which wastes popagation messages and time. We popose an in-netwok quey popagation appoach based on each stationay senso s local decision. Each efeence senso fist boadcasts the tiggeed quey to its neighbo stationay sensos. Conside a stationay senso node S eceives the tiggeed quey. If S s paent node is not in the efeence senso s tansmission ange, it fowads the tiggeed quey to its paent node, because its paent node can not eceive the quey diectly fom the efeence senso. S s paent node can help to popagate the tiggeed quey to S s siblings that ae isolated fom othe sensos in the quey aea. If S is not a leaf node, it boadcasts the tiggeed quey to both its paent node and its child nodes that ae out of efeence senso s tansmission ange and thei MBs ovelap the quey aea. Howeve, multiple child nodes may tansmit the tiggeed quey to the same paent node. If S oveheas that one of its siblings has sent the tiggeed quey to its paent nodes, it will not tansmit the quey to its paent node to avoid duplicate tansmissions. The tiggeed quey will be eventually sent back to the base station. If the base station does not eceive the tiggeed quey fom one of its immediate child node S i while it finds that S i s MB ovelaps the quey aea, the base station will inject the tiggeed quey to S i s subtee. The eason is that some child nodes of S i may be in the quey aea but would not eceive the tiggeed quey. Theoem 1: Using in-netwok popagation, if a tiggeed quey is eceived by at least one stationay senso, it will be popagated to all the stationay sensos in the quey aea. Poof: Assume senso node S i eceives the tiggeed quey. In ou appoach, tiggeed quey is sent to senso node s paent afte being eceived. Fo any senso node S j in the quey aea, tiggeed quey will be sent fom S i to the lowest common ancesto of S i and S j in the tee, S k. Since S k s MB ovelaps S j s MB, S k will send the tiggeed quey to S j, which guaantees the coectness of Theoem 1.

6 E. Location-based In-netwok Aggegation In geneic in-netwok aggegation algoithms [1,, 3], each senso always tansmits the data to its paent node. When quey aeas keep changing, many tansmissions will be wasted because some paent nodes ae not in the quey aeas thus can not pefom local aggegation. Note that each senso in the quey aea should tansmit at least one message since it needs to send its data. Howeve, sensos out of the quey aea usually do not need to send any messages except elaying the aggegated data to the base station. Thus, tansmission cost can be saved by educing the numbe of sensos that paticipate in the aggegation and ae not in the quey aeas. We popose a Location-based In-netwok Aggegation (LIA) algoithm to enable each senso to dynamically choose the next hop senso to tansmit data based on the efeence senso s cuent location. A senso node tansmits data to its paent node in eithe of the two situations: (1) paent node is in the quey aea; () no neighbo nodes with equal o highe level ae in the quey aea, except the neighbo nodes that tansmit data to this senso. Othewise, the senso node tansmits the data to the neighbo node with the highest level in the quey aea. If thee ae moe than one such neighbo nodes, the senso node choose the neaest one to guaantee the link quality. As a esult, fewe senso nodes ae involved in the aggegation and moe data ae aggegated locally. Each non-leaf senso node should wait until it has head fom its child nodes in the quey aea befoe aggegating and fowading the aggegation esults. It is possible that seveal senso nodes at the same level tansmit data to each othe at the same time. To avoid the collisions, each non-leaf senso s wait inteval is set to be popotional to the numbe of its child nodes in the quey aea. Because senso nodes with moe child nodes in the quey aea may aggegate moe sensos data locally, they need a longe inteval to wait fo othe sensos data to save tansmission cost. Since each senso has a neighbo list containing each neighbo node s id and location, it can calculate the distance between each child node s location and the efeence senso s cuent location which has been eceived in the quey popagation. If the distance is shote than the quey adius, the cuent child node is in the quey aea. Afte scanning the whole neighbo list, a senso can locally get the up-to-date numbe of its child nodes that ae in the quey aea. Assume the imum depth of the senso tee is d, the non-leaf senso node is at level l and has i child nodes in the quey aea ( i n), whee n is the imum numbe of child nodes in the tee. If each senso s sensing and pocessing time is T sp and tansmission time is T t, the senso s wait inteval WI can be calculated as: WI = ( d l)( Tt + Tsp) + i Tt () WI consides the longest hops d l that the non-leaf senso node fowads the tiggeed quey to the leaf nodes in the quey aea which then tansmit thei data back to it. Afte eceiving the tiggeed quey, each non-leaf senso in the quey aea calculates its own wait inteval WI and tansmits the aggegation esult with the numbe of sensos that contibute to the esult to the next hop afte WI expies, while each leaf senso in the quey aea tansmits its data to the next hop without waiting. The sensos out of the quey aea send the aggegation esults to the next hops immediately afte eceiving them. These senso nodes continue to aggegate and fowad the data in this manne, until the quey esults aive in the base station. V. COST ANALYSIS Since enegy consumption of the senso netwok is dominated by adio tansmissions, we give a theoetical analysis of the tansmission cost of ELDQ pocessing (cost(q)) including the cost of dissemination (diss(q)), popagation (pop(q)) and aggegation (agg(q)). cost(q) = diss(q) + pop(q) + agg(q) (3) An ELDQ Q can be decomposed into attibute set att, event e, sample peiod sp, adius and duation d. Assume N stationay senso nodes and N m mobile senso nodes unifomly distibuted in a cicle aea with adius and the base station at its cente. Q s dissemination cost includes the cost of quey dissemination and poxy changing. Assume the aveage quey aival time is t seconds pe quey, diss(q) is: Nm Npc diss(q) = d C + + t ( l( Mj) 1) TDi (4) t j= 1 i= 1 whee l(m j ) is the level of mobile senso M j s poxy. The pobability density of any location within the cicle aea is 1 π. Theefoe, the distance between the mobile senso (the cente of the cicle in Figue 6) and the base station (point b in Figue 6) has the following pobability distibution: π 1 P( ) = ' d'dθ = π Thus the pobability density function of is: f () = a S.t P = e, b c Q i. d S.t Figue 5. Theoetical model

7 If is shote than the mobile senso s tansmission adius, pop(q) is. Othewise, pop(q) includes the cost of popagation within the quey aea and popagation fom bounday nodes to the base station. Bounday nodes ae the senso nodes in the quey aea whose paents ae not in the quey aea. The numbe of popagation messages within the quey aea can be appoximated to the numbe of sensos in the ing aea between the mobile senso s tansmission ange and the quey aea. As shown in Figue 5, we can egad the numbe of popagation messages fom bounday nodes to the base station as the numbe of senso nodes in the shadowed aea abc. The numbe of popagation messages out of this aea is elatively small. The expectation of the aea of abc is: E(Aea abc ) = Q. accos d = 3 + accos accos Given the node density N π and the senso s tansmission adius S.t, the popagation cost of Q is then: pop(q) = C t p(e) ( Q. d sp )( N π ) (E(Aea abc ) + π ( S. t ) ), Q. > S. t (5) whee p(e) is the pobability that the event happens duing a sample peiod. Q s aggegation cost includes the cost of aggegation within the quey aea and tansmitting aggegation esults out of the quey aea. Aggegation esults ae aggegated in seveal bounday nodes at the highest level in the quey aea befoe they ae tansmitted back to the base station. Thus, most of such bounday nodes ae in the ing aea of acde in Figue 6 with S.t as its width. The expectation of the aea of acde is: ( ) E(Aea acde ) = ( S. t) = ( S. t S. t ) π < ( S. t S. t ) accos accos d 1, Q. > S. t E(Aea acde ) = accos d = Q. accos 1 π <, Q. S. t The tansmission anges of all the bounday nodes that have aggegation esults can cove the ing aea acde with little ovelap. Thus, the numbe of these bounday nodes can be calculated as: E( Aeaacde) N b = π S. t The expectation of the aveage level of these bounday nodes can be simply calculated as: E(l avg ) = d + 1. S t 3 = S t In the LIA algoithm, each senso node in the quey aea sends its data by one message. The numbe of senso nodes in the quey aea is NQ.. Thus, agg(q) is: agg(q) = C t p(e)( Q. d sp )( NQ. +N b E(l avg )) (6) Theoetical # of agg msgs Quey adius Figue 6. Theoetical num of agg msgs The cost analysis suggests that ou techniques minimize the edundant messages in pocessing ELDQs. Fom (4), we lean that dissemination cost is mainly affected by the mobile sensos mobility patten and the system scenaio. The theoetical uppe bounds of the cost of popagation and aggegation can be obtained fom (5) and (6) and used to estimate the enegy consumption of the WSNs. Even though ou analysis is fo an idealized case based on some assumptions, the simulation esults in Section 7 match well with the theoetical cost. Fo example, the theoetical numbe of aggegation messages in Figue 6 is quite close to that in the simulation, most of the deviations ae within 5% to 15% of the expeimental value. VI. MULTI-QUEY OPTIMIZATION In many applications, multiple concuent queies may be issued to the base station. Pocessing multiple queies in an uncoopeative manne will lead to bandwidth contention and tansmission collisions. We popose a two-level multiquey optimization algoithm that filtes hidden queies at the base station and instantly ewites multiple concuently tiggeed queies at each mobile senso to educe total pocessing cost.

8 # of dissemination messages SEAD ELDQ # of popagation messages Maximum speed (m/s) Quey adius (m) Quey adius (m) (a) Num of dissemination msgs vs. Max speed (b) Num of popagation msgs vs. Quey adius (c) Num of agg msgs vs. Quey adius # of aggegation messages In-netwok LIA Pecentage of aveage eo TinyDB ELDQ Pecentage of imum eo TinyDB ELDQ Quey adius (m) Quey adius (m) # of concuent queies (d) Aveage eo vs. Quey adius (e) Max eo vs. Quey adius (f) Benefit atio vs. Num of concuent queies Figue 7. ELDQ Pocessing Pecentage of benefit atio A. Optimization at the Base Station A base station maintains a list of unning queies. When an ELDQ Q i aives at a base station, ou algoithm fist scans the quey list and compaes each pat of incoming quey Q i with the coesponding pat of each unning quey Q in the quey list. Assume a stationay senso S s attibute set is S.att, emaining duation of a unning quey Q is d. Q i is a hidden quey and need not to be injected to the senso netwok if it satisfies all the following hidden ules: (1) Q i.att att S.att, () Q i.e = e, (3) Q i. =, (4) Q i.sp sp, (5) Q i.d d. ule (1) means Q i s attibute set is included in Q s attibute set. ule () guaantees that Q i s efeence senso set is the same as Q s efeence senso set. By adding ule (3), Q i s quey aeas ae the same as Q s quey aeas. ule (4) means Q i s sample peiod is divided by Q s sample peiod. These hidden ules guaantee Q i s esult set is totally included in Q s esult set and can be filteed out by the base station. If no quey satisfies all the hidden ules with Q i, Q i is added to the list of unning queies and disseminated to mobile sensos. We should conside the following two special cases. One special case is that Q i satisfies ules (1) though (4) with Q, Q i will be ewitten to Q i with new a duation (Q i.d d). Q i will not be injected to the senso netwok until Q finishes, because it is possible that when Q i is waiting, anothe incoming quey includes Q s esults and i makes Q i unnecessay to be injected. Anothe special case is that Q i satisfies ules () though (5) with multiple unning queies in the quey list. Assume Q i satisfies ules () though (5) and the following ule with k (k ) unning queies Q 1, Q, Q k : (1) * Q i.att (Q 1.att Q.att Q k.att) S.att, Q i s esults can be obtained by dawing out the equied attibutes fom the quey esults of Q 1, Q, Q k. ule (1) * means Q i s attibute set is included in the union set of the attibute sets of Q 1, Q, Q k, so Q i also does not need to be injected to the senso netwok. Optimization at the base station based on hidden ules can minimize duplicate access to the senso netwok. B. On-line Quey ewiting Algoithm Hidden ules ae stict fo many incoming queies and optimization at the base station does not exploit the similaities among queies at a fine ganulaity. We apply an on-line quey ewiting algoithm at each mobile senso. Compaed to ewiting queies at the base station [11, 1], ou algoithm can easily decide which queies ae tiggeed evey sample peiod, thus can intelligently ewite the queies. Each mobile senso also keeps a list of unning queies. Afte a mobile senso eceives an incoming quey Q i, it sets its own sample peiod at the geatest common diviso of the sample peiods of all the queies. Q i s stat time is set to be divisible by the mobile senso s sample peiod. In this way,

9 queies will sample at the same time, and hence shae the event detection. Although intoducing such little delay will make the fist sampling peiod stat late, fo a continuous quey, this exta latency is acceptable. In each sample peiod of the mobile senso, if multiple queies ae tiggeed at the same time, the mobile senso instantly ewites these queies into a synthetic tiggeed quey Q syn. Q syn s attibute list is the union set of all the tiggeed queies attibute list, and Q syn s quey adius is set to the longest quey adius of all the tiggeed queies. To maintain the semantic coectness, Q syn should contain efeence senso s id and each tiggeed quey s id which is mapped to the coesponding quey adius. Then, the mobile senso popagates Q syn to the sensos in the quey aea instead of popagating the tiggeed queies sepaately. Each senso that eceives Q syn sends its data and the id list of the tiggeed queies whose quey aeas it is in to the next hop senso by LIA algoithm. The id list helps the next hop senso in aggegating multiple esults fo multiple tiggeed queies. ewiting and pocessing the multiple tiggeed queies in this way educes the total cost because edundant tansmissions ae minimized by pocessing Q syn while only intoducing a little computation cost in the mobile senso. VII. PEFOMANCE EVALUATION We evaluate the pefomance of pocessing ELDQ though simulation using GloMoSim. As explained in the intoduction, thee ae no othe solutions that povide eventbased location dependent data sevices, so we sepaately evaluate the techniques including quey dissemination, innetwok quey popagation, location-based in-netwok aggegation and two-level multi-quey optimization. We use the same setting as in Section 4. whee the total aea is 6m 6m and each senso s tansmission adius is 1m. The wokload quey has a sample peiod of 1 seconds and duation of 1 seconds, and the pobability that the event happens in each sample peiod is set to 1%, so the quey is tiggeed aound 1 times at each mobile senso. Each data point in the figues has a 9% confidence inteval which comes fom the aveage esult of 1 uns. Dissemination in mobile WSNs has been studied in [5], [7] and [13]. Howeve, IDDA [13] focuses on inteest dissemination fom the mobile sink to neighbo stationay sensos, which is opposite to the dissemination diection in ou system. Moeove, the flooding appoach in [5] is not efficient compaed to the SEAD appoach [7]. Thus, we compae ou in-netwok quey dissemination appoach with SEAD. In SEAD, the mobile sink selects its neaest neighbo as its access node (MD algoithm) and sends a join quey to the souce which disseminates the data to the access node. We evaluate the total numbe of dissemination messages including the numbe of quey dissemination messages and update messages caused by poxy changing. The aveage quey aival fequency is set to 5 minutes pe quey, thus thee ae aound 4 queies being disseminated to the senso netwok in 1 seconds. As shown in Figue 7(a), ou appoach educes the total dissemination cost. The numbe of quey dissemination messages is popotional to the aveage level of poxy senso (coespond to access node in [7]), making the numbe of quey dissemination messages of these two appoaches quite close to each othe. Howeve, as discussed in Section 4., the MD algoithm used in SEAD esults in moe update messages compaed to the adaptive poxy selection algoithm used in ou appoach. Note that total dissemination cost ae lagely affected by the numbe of update messages caused by poxy changing when the quey aive ate is not vey high, so it is detemined by the poxy selection algoithm in most of the cases. As the imum speed of mobile sensos inceases, the saving becomes moe obvious. We evaluate the total numbe of popagation messages in the quey s duation. If the quey adius is shote than the mobile senso s tansmission adius, the numbe of popagation messages is because sensos in the quey aea can eceive the quey fom the mobile senso diectly. Othewise, we can see fom Figue 7(b) that the numbe of popagation messages inceases with the quey adius. We compae the LIA algoithm with the geneic innetwok aggegation algoithm [1,, 3] in which each senso aggegates the data of its child nodes in the tee stuctue. We set the imum speed of mobile sensos to 1m/s. Figue 7(c) shows that LIA algoithm always outpefoms the geneic in-netwok aggegation algoithm in the numbe of aggegation messages. Accoding to the esults, the saving of messages inceases and then deceases as quey adius inceases. The most saving happens aound the quey adius 75. As quey adius inceases, moe sensos ae involved in the aggegation, thus the saving of messages also inceases. Howeve, when the quey adius is lage enough that most of the sensos paent nodes ae in the quey aea, these sensos send thei data to thei paent nodes instead of dynamically choosing the destination. In this situation, only a pat of the sensos in the quey aea save messages by using LIA algoithm. Message saving degades if quey adius inceases above cetain theshold. Fo a continuous quey, the total saving will be significant when the quey s duation is long enough in lage scale senso netwoks. It is inteesting to note that the dissemination cost foms the main pat of the ELDQ pocessing cost, which eveals that the mobility is a dominant facto that affects the total cost. Afte evaluating each technique sepaately, we compae the accuacy of quey esults with that in TinyDB []. The accuacy is computed by dividing the eo (i.e. deviation value) by the tue value of the quey esults. Since TinyDB [] cannot answe ELDQ popely, we apply in-netwok quey dissemination and popagation appoaches to TinyDB. The only diffeence is in aggegation pat: geneic in-netwok aggegation algoithm is used in TinyDB while LIA algoithm is used in ou scheme. We evaluate the accuacy by measuing the pecentage of the aveage eo and imum eo. Figue 7(d) shows that quey esults ae moe accuate in ou scheme, because fewe sensos ae involved in the aggegation than TinyDB, educing the

10 possibility of message losses. As the quey adius inceases, both the aveage eo and imum eo incease because message losses incease if moe sensos ae involved in the quey popagation and aggegation. In fact, the accuacy of the quey esults is affected by the messages losses in the quey dissemination, quey popagation and data aggegation. As discussed in Section 4, seveal techniques ae employed to avoid message losses. Howeve, in the hieachical tee stuctue, a single node failue can esult in the aggegated data of a senso node and its neighbo nodes being lost, making the imum eos highly vaiable in Figue 7(e). We evaluate the benefit atio of the two-level multiquey optimization algoithm with the numbe of concuently unning queies. The benefit atio is computed by dividing sum of message savings by the sum of evey quey s messages. The wokloads ae set to 1 queies with aveage aival fequency 6 seconds pe quey. These queies andomly choose thei attibute lists (lights, temp), event pobability (fom 1% to 9%), quey adii (fom 5m to 175m), sample peiods (fom 5s to 5s). We vay the aveage duation to contol the aveage numbe of concuent queies. All the queies sample peiods and duations ae divided by a smallest time unit 1 second, and thei aggegation opeatos ae set to AVG. As shown in Figue 7(f), the benefit atio inceases significantly fom aound 8% to 78% as the numbe of concuently unning queies incease fom 5 to 4. This is because geate shaing can be exploited among moe unning queies. VIII. CONCLUSIONS In this pape, we define a geneal quey type Eventbased Location Dependent Quey (ELDQ) which exists in a wide ange of data centic applications in mobile WSNs. Existing appoaches can not efficiently answe ELDQs. We poposed a set of techniques to efficiently pocess ELDQs. We also give a theoetical analysis of the ELDQ pocessing cost. The cost analysis and expeimental esults show that these techniques can educe the pocessing cost compaed to the state-of-the-at appoaches. These techniques can also be efficiently applied to typical quey types in senso netwoks. In the futue, we plan to extend ou scheme in two diections. Fist, we want to design a new event detection mechanism to accuately detect sophisticated events. Second, we will adapt ou scheme to optimize continuous pocessing cost in inte-base station scenaio. These extensions will make ou scheme even moe efficient fo answeing ELDQs. ACKNOWLEDGEMENT This eseach wok was suppoted by NSF CNS , KOSEF WCU Poject and National Pe-eseach Poject of China EFEENCES [1] S. Madden, M. J. Fanklin, J. M. Helletein, and W. Hong, "TAG: a Tiny AGgegation Sevice fo Ad-Hoc Senso Netwoks," in OSDI,. [] S.. Madden, M. J. Fanklin, J. M. Hellestein, and W. Hong, "TinyDB: An Acquisitional Quey Pocessing System fo Senso Netwoks," ACM Tansactions on Database Systems, 5. [3] X. Yang, H. B. Lim, M. T. Ozsu, and K. L. Tan, "In-Netwok Execution of Monitoing Queies in Senso Netwoks," in SIGMOD, 7. [4] S. Ilai, E. Mena, and A. Illaamendi, "Location-Dependent Queies in Mobile Contexts: Distibuted Pocessing Using Mobile Agents," IEEE Tansactions on Mobile Computing, vol. 5, 6. [5] F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, "A Two-Tie Data Dissemination Model fo Lagescale Wieless Senso Netwoks," in Mobicom,. [6] A. Soheili, V. Kalogeaki, and D. Gunopulos, "Spatial Queies in Senso Netwoks," in GIS, 5. [7] H. S. Kim, T. F. Abdelzahe, and W. H. Kwon, "Minimum-Enegy Asynchonous Dissemination to Mobile Sinks in Wieless Senso Netwoks," in Sensys, 3. [8]. Stoleu, J. A. Stankovic, and S. Son, "obust Node Localization fo Wieless Senso Netwoks," in EmNets, 7. [9] A. Guttman, "-tees: A Dynamic Index Stuctue fo Spatial Seaching," in SIGMOD, [1] D. J. Abadi, S. Madden, and W. Lindne, "EED: obust, Efficient Filteing and Event Detection in Senso Netwoks," in 31st VLDB Confeence, 5. [11] S. Xiang, H. B. Lim, K.-L. Tan, and Y. Zhou, "Two-Tie Multiple Quey Optimization fo Senso Netwoks," in ICDCS, 7. [1] N. Tigoni, Y. Yao, A. Demes, J. Gehke, and. ajaaman, "Multiquey Optimization fo Senso Netwoks," in DCOSS, 5. [13] Y. Wu, L. Zhang, Y. Wu, and Z. Niu, "Inteest Dissemination with Diectional Antennas fo Wieless Senso Netwoks with Mobile Sinks," in Sensys, 6. [14] B. Gedik, K.-L. Wu, P. S. Yu, and L. Liu, "Pocessing Moving Queies ove Moving Objects Using Motion-Adaptive Indexes," IEEE Tansactions on Knowledge and Data Engineeing, 6. [15] H. Huang, J. H. Hatman, and T. N. Hust, "Efficient and obust Quey Pocessing fo Mobile Wieless Senso Netwoks," in GLOBECOM, 6. [16] A. Kama, V. Misa, and D. ubenstein, "CountToent: Ubiquitous Access to Quey Aggegates in Dynamic and Mobile Senso Netwoks," in Sensys, 7. [17] Y. Zhang, B. Hull, H. Balakishnan, and S. Madden, "ICEDB: Intemittently-Connected Continuous Quey Pocessing," in ICDE, 7. [18] Juang, P., Oki, H., Wang, Y., Matonosi, M., et al, "Enegy Efficient Computing fo Wildlife Tacking: Design Tadeoffs and Ealy Expeiences with ZebaNet, " in ASPLOS,. [19] C. Lu, G. Xing, O. Chipaa, C.-L. Fok, and S. Bhattachaya, "A Spatiotempoal Quey Sevice fo Mobile Uses in Senso Netwoks," in the 5th IEEE Intenational Confeence on Distibuted Computing Systems, 5.