Attribute Allocation in Large Scale Sensor Networks

Transcription

1 Attrbute Allocaton n Large Scale Sensor Networks Ratnabal Bswas Kaushk Chowdhury Dharma P. Agrawal OBR Research Center for Dstrbuted and Moble Computng, Dept. of ECECS, Unversty of Cncnnat, Cncnnat, OH {bswasr,kaushr,dpa}@ececs.uc.edu ABSTRACT Wreless sensor network s an emergng technology that enables remote montorng of large geographcal regons. In ths paper, we address the problem of dstrbutng attrbutes over such a large-scale sensor network so that the cost of data retreval s mnmzed. The proposed scheme s a data-centrc storage scheme where the attrbutes are dstrbuted over the network dependng on the correlatons between them. The problem addressed here s smlar to the Allocaton Problem of dstrbuted databases. In ths paper, we have defned the Allocaton Problem n the context of sensor networks and have proposed a scheme for fndng a good dstrbuton of attrbutes to the sensor network. We also propose an archtecture for query processng gven such a dstrbuton of attrbutes. Fnally, we present smulatons results to llustrate the condtons under whch our proposed archtecture s benefcal. To the best of our knowledge, ths s the frst attempt to study the attrbute allocaton problem of dstrbuted databases n the context of sensor networks. Categores and Subect Descrptors C.2.1 [Computer Communcaton Networks]: Network Archtecture and Desgn; C.3 [Computer Systems Organzaton]: Specal Purpose and Applcaton-based Systems. General Terms Algorthms, Performance, Desgn, Expermentaton. Keywords Allocaton problem, Data-centrc storage, Query dssemnaton, Data retreval, Aggregaton, In-network processng, Mnmum spannng tree, Correlaton tree, Hard threshold, Soft threshold. 1. INTRODUCTION A wreless sensor network s a dense network of a large number of low cost mnature wreless sensor nodes drven by a lmted battery resource. At the node level, data communcaton s the 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. DMSN, August 29, 2, Trondhem, Norway. Copyrght 2 ACM //8...$.. domnant component of energy consumpton, and hence protocol desgn for sensor networks s geared towards reducng communcaton n the network. In ths paper, we have desgned a scheme for determnng a dstrbuton of attrbutes n a sensor network that mnmzes the communcaton cost nvolved n queryng the network. Sensor networks are revolutonzng remote montorng applcatons because of ther ease of deployment, ad hoc connectvty and cost effectveness. Such networks mght be expected to serve multple applcatons smultaneously. For example, gven a geographcal area, the user mght want to deploy a sensor network that asssts n ecosystem montorng, weather montorng, precson agrculture etc. For each applcaton the user would want the network to sense specfc physcal attrbutes (e.g. humdty, temperature, lght etc. for weather montorng applcaton; temperature, lght, presence of chemcals etc. for precson agrculture applcaton and so on.) and consequently would expect the sensor network to respond to some user-defned queres. Snce such large-scale sensor networks would be expected to serve a substantal number of queres smultaneously for several applcatons, the number of attrbutes sensed by the network would also be substantal. The proposed scheme s desgned for mnmzng cost of data retreval from large-scale sensor networks servng such real-lfe scenaros. The paper s organzed as follows. Secton 2 lsts some related research n the area of data storage n sensor networks. Secton 3 defnes the problem that ths paper addresses and gves an outlne of how such a dstrbuton of attrbutes can be used to facltate query processng n sensor networks. The methodology for determnng a good dstrbuton of attrbutes s presented n Secton 4. The smulaton results are dscussed n Secton, whle Secton 6 concludes the paper wth future research agenda. 2. RELED WORK There have been dfferent approaches for storng data n sensor networks. Earler sensor network systems stored sensor data externally at a remote base staton (External Storage) or locally at the nodes whch generated them (Local Storage). Recently there has been a paradgm shft so that a technque called data-centrc storage now allows events to be stored at specfc rendezvous ponts wthn the network that queres can access drectly. Shenker et al. [7] proposed the DCS scheme and have shown that DCS outperforms other approaches such as External Storage (ES) and Local Storage (LS) under certan crcumstances. Ratnasamy et al. [6] have also proposed a Geographc Hash Table (GHT) to hash events nto geographc coordnates. In DIFS [2], Greensten et al. have desgned a spatally dstrbuted ndex to facltate range searches over attrbutes, whle L et al. [], have bult a dstrbuted ndex (DIM) for multdmensonal range queres of attrbutes. 27

2 In ths paper, we also propose a data-centrc storage scheme for dstrbutng attrbutes over a sensor network dependng on the correlatons between attrbutes and hence translate the allocaton problem of dstrbuted databases to the context of sensor networks. The proposed approach dffers from the exstng datacentrc approaches [2][][6] n that t attempts to dstrbute attrbutes nstead of specfc user-defned events. We reason as follows. If an attrbute s ncluded n many events, then the values for that attrbute have to be replcated and stored at dfferent places n the network for each ndvdual event. In our proposed approach, the attrbute need not be replcated at multple places and hence saves communcaton cost nvolved n storng and replcatng data. Instead every attrbute s stored at a predefned locaton wthn the network such that attrbutes that are a part of the same query are stored near each other to facltate data retreval. Thus t s smlar to the Allocaton Problem of dstrbuted databases where a set of attrbutes need to be dstrbuted over a number of stes such that n servng a predefned set of queres the communcaton between stes s mnmzed. In a smlar manner, n ths paper we present a scheme for storng attrbutes n the sensor network such that the communcaton nvolved n servng a predefned set of user queres can be mnmzed. 3. DISTRIBUTED TRIBUTE STORAGE Sensor networks can be envsoned as a large dstrbuted database where the sensor nodes generate named data aganst userspecfed queres [1]. In ths secton we defne the Allocaton Problem of dstrbuted databases n the context of sensor networks and llustrate how our proposed approach asssts n query processng. 3.1 Problem Defnton Assume that there are a set of sensed attrbutes A = {A 1, A 2,, A m } and a network S of sensor nodes whch have to serve a set of queres Q = {Q 1, Q 2,,Q q }. We attempt to fnd a dstrbuton of A to S such that the energy consumed n servng the set of queres Q s mnmzed. Ths s smlar to the Allocaton Problem of dstrbuted databases whch can be stated as Gven a relaton R (.e. a set of attrbutes), a set of queres Q, and a set of stes S where queres n Q run, the allocaton problem nvolves fndng the optmal dstrbuton of R to S that mnmze the cost of evaluatng Q. For databases, the Allocaton Problem nvolves fndng dsont fragments (group of attrbutes whch are accessed together) that are dstrbuted n ndependent stes. On the other hand, for sensor networks all the attrbutes are dstrbuted over the same two-dmensonal geographcal area, such that the communcaton nvolved n dssemnaton of query and retreval of data for the set of queres Q s mnmzed. As n case of dstrbuted databases, some statstcs about query runs (e.g. query access frequency, attrbute affnty) are requred to solve the allocaton problem. We thus assume that a set of prortes { p, p, 1 2 Κ, p q } for the queres { Q, Q, 1 2 Κ, Q q } s gven. The prorty p of a query Q could depct ether the probablty wth whch query Q s ssued per unt tme or the frequency of tuples generated for query Q per unt tme or a combnaton of both. These values may be set by the system desgner to specfy the relatve prortes of queres and are normalzed such that q p = 1 = 1. Each query Q can be modelled by the set of attrbutes ncluded n the query.e. Q = { A, A, Κ, A k } 1 2 ( A { A, A, 2 Κ, A }, p 1 k). We also assume that the sensor p 1 m =,..., nodes are unformly deployed over a rectangular regon. We thus ntend to dstrbute the m attrbutes over ths rectangular feld of sensor nodes so that the cost queryng the network s mnmzed. To do so, we splt the rectangular area nto a grd G of sze m m and each attrbute A s allocated to a partcular grd cell. Let f : A G be the mappng correspondng to the optmal dstrbuton. In order to mnmze the total cost of servng the set of queres Q, the mappng f should mnmze the communcaton nvolved n dssemnatng the query and aggregatng the resultant tuples for each query. To mnmze the cost nvolved n evaluatng a query (.e. aggregatng resultant tuples), the attrbutes belongng to the same query should be stored near each other n the grd. Also to mnmze the communcaton nvolved n dssemnatng the query from the snk and reportng the results back to the snk, the attrbutes should be stored as close to the snk as possble. We thus need to determne a good dstrbuton f for a gven set of queres and ths dstrbuton f should be made avalable to all the nodes n the network so that every node s aware as to where every attrbute s stored n the network. The method of determnng the functon f s explaned n Secton 4. Snce the proposed method for computng functon f s topology ndependent, each sensor node may compute f ndependently. However to reduce the overhead nvolved n performng the same redundant computaton all over the network, the functon f may be computed at the snk and then sent to all the nodes n the network. For now, we assume that such a functon f s avalable to all the nodes n network and analyze how t facltates query processng n a large sensor network. 3.2 Query Processng As mentoned n Secton 3.1, the entre network s dvded nto a m m grd. We assume that each sensor node s locatonaware [1][11] and hence can determne whch grd-cell t belongs to. The number of sensor nodes n each grd cell depends on the densty and dstrbuton of nodes n the sensor network. Gven a dstrbuton functon f (as computed n Secton 4), each attrbute A k s then assgned a grd cell f(a k ) of the network. The grd cell f(a k ) s responsble for storng values of the attrbute A k. Thus even though the attrbute A k s sensed all over the network (assumng complete sensng coverage), the values of the attrbute are stored n the grd cell f(a k ) and every node n the network knows n whch grd cell each attrbute s stored. Let us now consder the attrbute A k and the grd cell f(a k ) where t should be stored. Only some of the nodes n the grd cell f(a k ) actually store the values for the attrbute A k. These nodes are called the storage nodes for A k. The number of such nodes requred for an attrbute A k depends on the amount of data values correspondng to attrbute A k as also the storage capacty of each sensor node. To facltate data retreval from these storage nodes, one node n every grd cell s apponted as the control node. The control node s responsble for fetchng data from the storage nodes usng specalzed ndexes that t mantans. To dssemnate a query Q, the snk node frst determnes the nearest grd cell housng any of the attrbutes n the query and sends the query to 28

3 that grd cell. On recevng the query, the control node of the correspondng grd cell then uses ts stored ndexes to retreve the requred values of the stored attrbute from the respectve storage nodes. It then computes the optmal route of dssemnatng the query to the grd cells storng the remanng attrbutes of the query. The optmal route for retrevng the remanng attrbutes belongng to Q would essentally be the mnmum spannng tree onng the grd cells that store the attrbutes n Q. The complexty nvolved n calculatng ths route s mnmal (any mnmum spannng tree algorthm may be used) snce a query wll not contan a large number of attrbutes. The computed route s then used for routng the query as well as routng the resultant data tuples back to the snk. Whle routng the data back to the snk, the control nodes of the grd cells housng the respectve attrbutes use aggregaton schemes [3][4] and n-network processng mechansms [8] to further mnmze the amount of data transfer n the network. The effcency n query processng and nformaton retreval usng such an underlyng archtecture s acheved at the cost of mantanng updated values of all attrbutes n the respectve grd cells where they are stored. To reduce the overhead nvolved n ths mantenance a soft threshold scheme may be used. Whenever a sensor node senses an attrbute, t determnes whether the dfference between the prevously sensed value and the new value s more than a predefned soft threshold. If the dfference s greater than the soft threshold, the new value needs to be reported to the grd cell housng the correspondng attrbute. However nstead of sendng update messages for every ndvdual node fluctuaton, the sensor node frst sends an update message to the control node of ts own grd cell. The control node then wats for a predefned tme nterval called the update epoch T k for the sensed attrbute A k. All the update messages for the attrbute A k that the control node receves from nodes n ts own grd cell durng the epoch T k are then combned nto aggregate update message(s) and sent to the grd cell f(a k ) housng A k. Increasng the soft threshold and the update epoch reduces the frequency wth whch the attrbutes are updated. Reducng the number the update messages n turn reduces the communcaton overhead nvolved n mantanng the updated values of the attrbutes n the grd cells where they are housed. However ncreasng the soft threshold and update epoch also reduces the probablty of the grd cell f(a k ) havng updated values of A k as sensed n the network. Ths n turn reduces the probablty that queres accessng A k would receve the current values for A k as sensed by the network whch mght be a serous problem for crtcal data. Thus dependng on how crtcal an attrbute s, a sutable soft threshold and update epoch may be chosen to reduce the update overhead. Ths secton descrbed how queres can be processed once the attrbutes are stored n the network n a dstrbuted manner. All the underlyng routng of query and data messages can be done usng geographcal routng schemes lke GPSR [9]. In the next secton we present a heurstc for obtanng a good dstrbuton of attrbutes over a sensor network gven a set of queres along wth ther assocated prortes. 4. TRIBUTE ALLOCION METHODOLOGY Havng dscussed the benefts of dstrbutng attrbutes over a sensor network and how t asssts n effcent query processng, we now focus on the methodology for determnng a good dstrbuton of attrbutes such that the total cost of servng a set of user-defned queres s mnmzed. The methodology has two phases. In the frst phase, the query prortes are used to determne correlatons between each par of attrbutes. In the second phase the correlatons are used to determne the dstrbuton of attrbutes to the rectangular sensor network. To llustrate the proposed methodology, let us consder an example set of queres as lsted n Table 1. Table 1. Lst of Queres n ascendng order of prortes Q P A Q P A Q A 6 Q 1.44 A 2 Q 29 A 12,A 19 Q 6.2 A 7 Q 24 A 1,A 16 Q 8.63 A, A 8,A 11 Q 27 A 12,A 18 Q A 1, A 6, A 11, A 16 Q 18 A 4,A 8,A 12, Q A 14,A 2,A 6 A 19 Q 12 A 13,A 16 Q A 3, A 7 Q 23. A 1,A 6,A 11 Q 4.18 A,A 7,A 9 Q 1. A 11 Q A 1 Q 17.7 A 19, A 3, A 7, A 11, A 1 Q A 8, A 16, A 4, A 12, A 2 Q 28.7 A Q 13.1 A 19 Q 21.8 A 16 Q A 8 Q 2.1 A 8,A 13 Q A 16, A 2, A 4 Q 3.11 A 9,A 18,A 7, A 2 Q 11.1 A 4,A 7,A 1 Q Q 9.1 A 14,A 17 Q 7 Q.118 A 6,A 16 A 1,A 3,A, A 7,A 9 A 2,A 1,A 17, A Phase 1: Determnng correlatons Query prortes can be used to determne correlatons between attrbutes. If a par of attrbutes s a part of a hgh prorty query, then we consder the attrbutes to have a hgh correlaton between them snce they would be accessed together very frequently. Smlarly, f a par of attrbutes s never accessed together n the same query, the attrbutes may be consdered to not have any correlaton between them. The dstrbuton functon should then store attrbutes wth hgher correlatons closer to each other. The correlatons between all the attrbutes can be represented by a tree of attrbutes where the edge weghts between a par of attrbutes represent the correlaton between them. Usng ths data structure and ts represented correlatons, the dstrbuton of attrbutes can be determned. Also attrbutes that are accessed more frequently should be stored closer to the snk. The ndvdual access probablty P(A ) of an attrbute A can be computed as follows:, = 1, Κ, m P( A ) = q P( Q ) P( A Q ), = 1 where P( Q ) = p, = 1, Κ, m and P( A Q ) 1 A Q, =, A Q Table 2 gves the ndvdual access probabltes for the attrbutes nvolved n the queres lsted n Table 1. To represent the relatve orderng of attrbutes wth respect to ther ndvdual access probabltes of attrbutes, we can further represent the attrbutes n the form of a heap so that f attrbute A s parent of A, then. We call ths heap-lke data structure the correlaton P A P( tree A ) and t gves a synoptc vew of all the correlatons 29

4 between attrbutes as also the relatve orderng of attrbutes wth respect to ther ndvdual access probabltes and thereby asssts n determnng a good dstrbuton functon f. Table 2. Lst of Attrbutes and ther access probabltes A P A P A P A A A 1.29 A 2 A A A A 1.16 A A A A A.2249 A A A A A A A To create a correlaton tree for a gven set of queres, we begn by representng each query as a tree of depth 1 and then combne these ndvdual query trees to form a comprehensve correlaton tree. Fgure 1 shows the query tree correspondng to query Q 7 lsted n Table 1. Snce the correlaton tree should have a heaplke structure, the attrbute n the query havng the maxmum access probablty s made the root of the correspondng query tree (refer to access probabltes lsted n Table 2). Hence attrbute A 19 becomes the root for query tree for Q 7. Also as mentoned before, the edge weghts depct the correlaton between the attrbutes oned by the edge. For the ntal query tree, the edge weghts are smply the query prortes. Such a query tree s created for every gven query. These query trees then need to be combned to form the fnal correlaton tree. Ths s done teratvely by selectng query trees n ascendng order of ther assocated query prortes Fgure 1. Query Tree for query Q consder the partal correlaton tree after query tree for query Q 23 has been added (refer Fgure 2). On attemptng to add query tree for Q 17, t s found that attrbute A 11 has attrbute A 19 as parent n the query tree but attrbute A 1 as parent n the partal correlaton tree. We thus need to decde whch attrbute should be parent of A 11 n the new correlaton tree. We choose the attrbute wth whch A 11 has hgher correlaton. Ths s done because n the second stage of the methodology, the attrbutes would be allocated grd cells such that they are stored closer to ther parent attrbute. Also snce every chld attrbute s stored near ts parent, the sblng attrbutes also end up beng stored farly close to each other n the grd. Thus we make the other parent as a sblng of the attrbute as shown n Fgure Fgure 3. Partal Correlaton after addng query Q 17 A trplet of values (.,1,11) called the vrtual weght s assgned to both attrbutes A 1 and A 11 to sgnfy the correlaton that attrbute A 11 has wth attrbute A 1 even though they do not share a parent-chld relatonshp n the tree. Also note that attrbute A 11 could be made a chld of attrbute A 19 because P(A 11 )<P(A 19 ). If A 19 had a hgher ndvdual probablty, then the poston of A 11 n the tree had to be adusted usng usual heap creaton algorthm. If any edge has to be deleted n the process, a cost-beneft analyss s performed to ensure that the beneft > cost, where cost and beneft are defned as follows, Cost = Sum of effectve weghts of deleted edges Beneft = Sum of effectve weghts of new edges where effectve weght w ( A, A ) of an edge ( ) T ( A A ) w, = w T (A,A ) + Σ{vrtual weghts of A } and value of a vrtual weght (v,a y,a z ) s, (v,a y,a z ) = v f A y and A z are sblngs = otherwse. T 19 A, s, A Fgure 2. Partal Correlaton after addng query Q 23 We llustrate the process usng the set of queres lsted n Table 1. For our example, frst the query tree correspondng to Q s selected and set to be the ntal partal correlaton tree. Next the query tree correspondng to query Q 29 s combned wth the partal correlaton tree. The process of combnng query trees contnues usng usual tree unon algorthms, renforcng edge weghts as requred. However specal consderaton s requred when an attrbute has dfferent parents n the query tree to be added and the partal correlaton tree respectvely. To llustrate ths, let us Fgure 4. Correlaton Tree

5 The fnal correlaton tree for the set of queres n Table 1 s shown n Fgure 4. The vrtual weghts have not been shown to ensure clarty of the fgure. 4.2 Phase 2: Allocatng Attrbutes Once the correlaton tree has been constructed, t can be used to determne the dstrbuton of attrbutes to the grd such that more frequently accessed attrbutes are closer to the snk and the attrbutes wth hgher correlatons are stored closer to each other. In ths paper, we assume that the snk can be n any random locaton of the network. Hence more frequently accessed attrbutes are stored as close to the centre of the grd snce the centre s the poston that s most easly accessble from any random poston n the rectangular regon. The attrbute havng the maxmum access probablty s allocated to the central-most grd cell. Then attrbutes are chosen teratvely n descendng order of ther access probabltes and grd cells for storng are determned usng the correlaton tree. If the attrbute does not have a parent n the correlaton tree, t s allocated the grd cell nearest to the centre. However there may be multple avalable grd cells at the same dstance from the centre. In that case, the optmal grd cell s the one for whch adacent already-allocated attrbutes have the least number of unassgned chldren. The ustfcaton for choosng such a cell s that, f a cell C s surrounded wth attrbutes that have more number of unassgned chldren attrbutes, then t would be preferable to leave C for those unassgned chldren attrbutes when other optons are avalable. Now let us consder the case where the correlaton that an attrbute has wth ts parent s the same as ts own access probablty. Ths mples that any query that accesses t also accesses ts parent. In that case, the attrbute should be stored close to ts parent. However there may be more than one cell at the same dstance from ts parent. In ths case, the optmal grd cell s the one farthest from the centre. The ustfcaton s that the avalable cells near the centre are left for attrbutes that need to be stored close to the centre. Fnally we consder the case where the attrbute has correlatons wth multple attrbutes. In such a stuaton we try to allocate the attrbute to an avalable grd cell, such that dstance of the grd from the attrbute wth whch t has correlaton s nversely proportonal to ts correlaton value. A 1 A 3 A A 11 A 18 A 8 A 18 A 7 A 9 A 13 A 2 A 19 A 16 A 1 A 12 A 17 A 2 A 6 A 14 A 4 Fgure. Allocaton of attrbutes to grd Fgure shows the allocaton of attrbutes to the grd wth the assstance of the correlaton tree of Fgure 4. Note that attrbute A 19 has hghest ndvdual probablty and hence s n the centre. Attrbutes A 1, A 17, A 2 and A 16 are then selected n descendng order of probabltes and stored near A 19. Attrbute A 7 s then stored near ts parent A 16 whle A s stored near ts parent A 7 and so on. Also note that query Q 7 that has the hghest prorty has also ts attrbutes A 2, A 1, A 17 and A 19 stored near the centre adacent to each other. Also note that some of the grd cells are empty snce the number of attrbutes n our example query set (Table 1) s less than the number of grd cells. These empty unassgned grd cells can be used later for fault tolerance and load balancng. The smulaton results demonstratng the enhanced performance of our scheme are gven n the next secton.. SIMULION RESULTS We have mplemented our proposed scheme and conducted our smulatons usng Smava [12], a general dscrete event smulator. To compare the performance of our proposed scheme, we choose the aggregaton algorthm TAG [4] n whch the sensor nodes form a spannng tree n a dstrbuted manner. The parameters used n our study are summarzed n Table 3. We have assumed that the communcaton between nodes of a grd cell and ther respectve control nodes, the snk and the control nodes, as well as nodes n dfferent levels of the aggregaton tree pack avalable data n the fewest possble packets. We assume that the snk s at the centre of the network. To evaluate the best case performance of our proposed scheme, we assume that the attrbute quered s stored n the central grd cell (.e. nearest to the snk). To measure the worst case performance, we consder the stuaton when the snk (whch s at the centre of the regon) queres an attrbute that s stored n a grd cell further away from the centre of the network. We use energy consumpton as the metrc to compare our proposed scheme wth the aggregaton tree algorthm of TAG. To measure energy consumpton for both the schemes, we assume that only those readngs greater than the hard threshold are reported to the snk. Further for our proposed scheme we assume that the update messages are sent only when the soft threshold s breached. We have conducted experments for computng energy consumpton aganst queryng rate and attrbute fluctuaton frequency as descrbed n the followng sectons. Table 3. Smulaton Parameters Total number of nodes 1 Dmensons of the deployed area 6 6 meters Transmsson radus of a node 4 meters Data packet sze bytes Channel bandwdth 2 kbps Transmsson power.81 mw Recepton power.3 mw Number of subregons 49 Length of each subregon 9 meters Hard threshold 22 Soft Threshold.6 Probablty of fluctuaton.3 Magntude of change ± Test # 1: Varyng the query rate We frst keep the rate of attrbute fluctuatons as constant and vary the query dssemnaton rate. Fgure 6(a) shows results for the best case scenaro where the attrbute stored at the central grd cell (CR) s accessed by a perodc query. We observe that our scheme shows margnal performance degradaton at lower query rates but as the number of queres nected per unt tme ncreases, our scheme performs sgnfcantly better than the aggregaton tree () algorthm. We reason ths as follows. The cost of floodng the query down to the leaf level of the and then retrevng the nformaton requres O(n) transmssons. On the other hand, n our 31

6 Queres/Smulaton tme Fgure 6(a). Varyng Query rate (Best Case) Queres/Smulaton tme Fgure 6(b). Varyng Query Rate (Attrbute far from snk) Fluctuatons/Smulaton tme Fgure 7(a). Varyng Fluctuaton rate (Best Case) proposed scheme (and more so for our consdered topology), ths s accomplshed n a sngle transmsson between the control node n the CR and the snk. Thus we reason that that the queryng cost s mnmzed n ths confguraton. There s however, a constant overhead of updatng the storage regon based on the nodes whch show a varaton n the sensed attrbute. We note that the s preferred when the rate of query necton s less. From fgure 6(a), we see that when the query rate s at about 8 queres/smulaton tme, both the schemes show equal performance for the best case scenaro. Fgure 6(b) shows results for the scenaro where the attrbute stored at a grd cell far from the snk s accessed by a perodc query. Here the breakeven pont s reached when the rate of query s 22 queres/smulaton tme, after whch our scheme performs much better than..2 Test # 2: Varyng the fluctuaton rate In ths experment (Fgures 7(a) and 7(b)), we vary the rate of attrbute value fluctuatons whle keepng a steady query rate of 2 queres/smulaton tme. We observe that wth an ncrease n the number of fluctuatons per unt tme, the proposed scheme s no longer preferable to the scheme beyond the break-even pont. The performs at a steady energy cost as nodes do not report untl a query message s dssemnated. The mnor ncrease n energy cost of happens because, wth ncreasng rate of fluctuatons, more number of nodes have values greater than the hard threshold and hence send reports back to the snk. On the other hand, the rapd fluctuatons result n steady ncrease n the number of update messages sent from the node detectng Fluctuatons/Smulaton tme Fgure 7(b). Varyng Fluctuaton Rate (Attrbute far from snk) fluctuaton to the control node of ts own cell as well as the aggregated update messages sent to the control node of the grd cell where the attrbute s stored. Even then, n the best case scenaro (Fgure 7(a)), our scheme performs better than tll a consderable value of 34 fluctuatons /smulaton tme s reached. In the worst case scenaro (Fgure 7(b)), ths pont s reached at 1 fluctuatons /smulaton tme. The energy consumed by the s seen to be almost constant at 23- mj, ndependent of the frequency of fluctuatons. 6. CONCLUSIONS AND FUTURE WORK In ths paper, we have proposed a scheme for determnng a dstrbuton of attrbutes to a large-scale sensor network based on the correlatons between them. Our scheme obvates the need for queres to be flooded n the network and mnmzes the average user response tme. Moreover our proposed scheme mnmzes both the query access cost and the query evaluaton cost. In addton to these benefts, havng all values of an attrbute at one place provdes helpful global context for evaluatng local data. For example, the sensed temperature values could be compared aganst the average temperature value of the network to detect fres or other local temperature spkes. Also, havng user-defned parameters lke soft threshold and update epoch allows the user to tune the performance of the system as per hs requrements. The proposed scheme works well as long as the overhead of sendng update messages does not supersede the advantage of mnmzng the query cost. Snce most real-lfe physcal phenomena are localzed, the fluctuatons can be consdered to be mostly local and not too 32

7 many. Consequently the proposed scheme should perform well n most real-lfe stuatons. As part of future work, we plan to develop detaled protocols for query dssemnaton, data updatng and retreval. We also need to ensure that these protocols are fault-tolerant and perform load balancng. 7. REFERENCES [1]. Govndan, R., Hellersten, J. M., Hong, W., Madden, S., Frankln, M. and Shenker, S. The Sensor Network as a Database. Techncal Report 2-771, USC Computer Scence Department, 22. [2]. Greensten, B., Estrn, D., Govndan, R., Ratnasamy, S. and Shenker, S. DIFS: A Dstrbuted Index for Features n Sensor Networks. In Proceedngs of the Frst IEEE Internatonal Workshop on Sensor Network Protocols and Applcatons. May 23, [3]. Intanagonwwat, C., Govndan, R. and Estrn, D. Drected Dffuson: A Scalable and Robust Communcaton Paradgm for Sensor Networks. In Proceedngs of the Sxth Annual ACM/IEEE Internatonal Conference on Moble Computng and Networkng (Mobcom 2). Boston, Massachusetts, August 2. [4]. Madden, S., Frankln, M. J., Hellersten, J. M. and Hong, W. TAG: a Tny AGregaton Servce for Ad-Hoc Sensor Networks. In Proceedngs of the th Annual Symposum on Operatng Systems Desgn and Implementaton (OSDI). Boston, MA, December 22. []. L, X., Km, Y. J., Govndan, R. and Hong, W. Mult- Dmensonal Range Queres n Sensor Networks. In Proceedngs of the 1st Internatonal Conference on Embedded Networked Sensor Systems. Nov. 23. [6]. Ratnasamy, S., Karp, B., Yn, L., Yu, F., Estrn, D., Govndan, R. and Shenker, S. GHT: A Geographc Hash Table for Data-Centrc Storage. In Proceedngs of the Frst ACM Internatonal Workshop on Wreless Sensor Networks and Applcatons (WSNA 22). Atlanta, GA, September, 22. [7]. Shenker, S., Ratnasamy, S., Karp, B., Govndan, R. and Estrn, D. Data-centrc Storage n Sensornets. ACM SIGCOMM Computer Communcaton Revew, Volume 33 Issue 1, January 23. [8]. Kumar, R., Tsatss, V. and Srvastava, M. B. Computaton Herarchy for In-network Processng. In Proceedngs of 2nd ACM Internatonal Conference on Wreless Sensor Networks and Applcatons. San Dego, CA, September 23. [9]. Karp, B. and Kung, H.T. GPSR: Greedy Permeter Stateless Routng for Wreless Networks. In Proceedngs of Sxth Annual ACM/IEEE Internatonal Conference on Moble Computng and Networkng (MOBICOM). Boston, Massachusetts, August 2. [1]. Doherty, L., Pster, K. S. J. and Ghaou, L. E. Convex Poston Estmaton n Wreless Sensor Networks. In Proceedngs of the IEEE Infocom.. Alaska, Aprl 21, [11]. HghTower, J. and Borrello, G. Locaton systems for Ubqutous Computng. IEEE Computer, Vol. 34 (Aug 21), [12]. Howell, F. and McNab, R. Smava: A Dscrete Event Smulaton Package for Java wth Applcatons n Computer Systems Modellng. In Proceedngs of the Frst Internatonal Conference on Web-based Modellng and Smulaton. Socety for Computer Smulaton, San Dego, CA, Jan