Energy efficient prediction clustering algorithm for multilevel heterogeneous wireless sensor networks

Transcription

1 Abstract: nergy effcent predcton clusterng algorthm for multlevel heterogeneous wreless sensor networks Tang Lu a,b,c,* Jan Peng b,c Jn Yang d Chunl Wang e a College of Fundamental ducaton, Schuan Normal Unversty, Chengdu 6168, Chna b State key Laboratory of Networkng and Swtchng Technology, Bejng Unversty of Posts and Telecommuncatons, Bejng 1876, Chna c College of Computer Scence, Schuan Unversty, Chengdu 6165, Chna d Department of Computer Scence, Leshan Normal Unversty. Leshan 614, Chna e nglsh Department, Chengdu Unversty of Informaton Technology, Chengdu 615, Chna In desgnng wreless sensor networks, t s mportant to reduce energy dsspaton and prolong network lfetme. In ths paper, a new model wth energy and montored objects heterogenety s proposed for heterogeneous wreless sensor networks. We put forward an energy-effcent predcton clusterng algorthm, whch s adaptve to the heterogeneous model. Ths algorthm enables the nodes to select the cluster head accordng to factors such as energy and communcaton cost, thus the nodes wth hgher resdual energy have hgher probablty to become a cluster head than those wth lower resdual energy, so that the network energy can be dsspated unformly. In order to reduce energy consumpton when broadcastng n clusterng phase and prolong network lfetme, an energy consumpton predcton model s establshed for regular data acquston nodes. Smulaton results show that compared wth current clusterng algorthms, ths algorthm can acheve longer sensor network lfetme, hgher energy effcency and superor network montorng qualty. Key words: Wreless sensor networks; Heterogeneous; Clusterng algorthm; nergy predcton 1 Introducton Over recent years, wreless sensor networks (WSN) [1] wth nodes equpped wth a large number of small energy devces have become a hot research and have a wde range of potental applcatons ncludng envronmental montorng, mltary detecton, health montorng, ndustral control and home networks [-5]. But n practcal applcatons, n order to meet the demands of varous applcatons for the technologes of sensor networks, ncreasng attentons have been attracted to the researches on heterogeneous wreless sensor networks HWSN [6]. HWSN s composed of dfferent types of sensor nodes, whch are n a wde range of applcatons [7,8]. In fact, the heterogenety s common n the wreless sensor networks [9]. For HWSN, t should be gven prorty to reduce energy dsspaton n network operaton, mprove network load and stablty and prolong the network lfetme. nergy consumpton n networks can be effectvely reduced by organzng clusterng sensor nodes, so many energy-effcent routng protocols are desgned on the bass of the clusterng structure. Currently, a number of dstrbuted clusterng protocols are proposed. In accordance wth the networks, homogeneous or heterogeneous, to whch the protocols are adaptve, clusterng protocols can be categorzed nto homogeneous clusterng protocols and heterogeneous clusterng * Correspondng author. College of Fundamental ducaton, Schuan Normal Unversty, No. 5, North JngAn Road, Chengdu 6168, PR Chna. Tel./fax: mal addresses: crkey@163.com (T. Lu), penguest@163.com (J.Peng), jnnyang@163.com (J.Yang), lzzywang@cut.edu.cn (C. Wang).

2 protocols. Due to the dynamc and complex nature of energy confguraton and network evoluton, t s very dffcult to desgn a clusterng protocol whch can save energy and provde relable data transmsson n heterogeneous networks. nvronmental montorng applcatons usually nvolve a varety of data collecton wthn the same montorng range. Some data collecton s of regularty. For nstance, nformatons such as temperature and humdty should be reported at a fxed nterval, and the data sent every tme s of equal length, whle the report of fre data does not show such regularty and each tme, the length of reported data s dfferent accordng to the degree of the fre. In ths paper, a new heterogeneous sensor networks model wth heterogenety of montored objects and energy heterogenety of all nodes s proposed. For the heterogeneous networks wth such propertes, n order to make more ratonal use of network energy and prolong the lfetme of the networks, ths paper presents an nergy-ffcent Predcton Clusterng Algorthm (PCA). PCA gets nformed of the mutual dstance between nodes through broadcastng n the ntal stage of nodes clusterng. It determnes node energy factor by comparng the energy of a node wth the average energy of other nodes wthn the communcaton range and determnes communcaton cost factor accordng to the rato of the average energy consumed n one communcaton wthn all nodes and the deal average energy consumpton after the node becomes the cluster head. The probablty for nodes to become cluster heads s drectly related to energy factor and communcaton cost factor. All nodes n the networks take turns as cluster heads to acheve unform energy consumpton. In order to save energy consumed by broadcastng energy nformaton n each round of nodes clusterng, an energy predcaton model s establshed for nodes whose data collecton (such as temperature, humdty, etc) s of regularty n tme nterval and message length. Consderng the changes n networks envronment and errors between calculated and actual node energy consumpton, set the nodes do not need to broadcast ther energy nformaton f the dfference between the node resdual energy n the ntal stage at the current round and the predcted value at the last round s wthn a certan range. Smulaton results show, PCA can acheve longer lfetme, hgher energy effcency and superor network montorng qualty compared wth other clusterng protocols such as LACH, SP and DFCM. The rest of ths paper s organzed as follows. In Secton, the related work s dscussed. In Secton 3, we present the networks model, energy model and the performance measures for wreless sensor networks. Secton 4 exhbts the detals of PCA. In Secton 5, we evaluate the performance of PCA va smulatons and compare the results wth LACH,SP and DFCM. Fnally, Secton 6 concludes the paper and future work s ponted out. Related Work LACH [1] s one of the most popular dstrbuted cluster-based routng protocols n wreless sensor networks. LACH assumes that all nodes are schemed wth the same ntal energy and each node generates a random probablty between -1. Network operaton tme s dvded nto many tme slots, known as round. One round conssts of two stages, namely ntalzaton and stablty. In the ntalzaton phase, LACH carres out cluster head selecton. In order to balance the load of all network nodes, LACH elects about cluster head nodes n each round, where s the proporton of optmal cluster heads. If the probablty of a node s less than the followng probablty threshold, t becomes the cluster head.

3 T popt 1 1 popt r mod, p opt, f G otherwse (1) Where, r s the current number of rounds, G s a set of cluster head nodes whch fal to make cluster heads n the latest round mod 1 opt r p. When cluster heads are chosen, all these head nodes broadcast ths message to other nodes. Accordng to the strength of receved messages, nodes determne whch cluster head they would jon and nform the correspondng cluster head. Based on TDMA approach, cluster heads allocate tme slot to cluster members and the networks proceeds nto a stable phase, n whch each node sends the montored data back to the cluster head node n the correspondng slot and the cluster head node transfers the receved data to the Base Staton (BS) after aggregaton. So far, one round comes to the end and starts the next. In ths way, each node has opportunty to become a cluster head node dsspatng more energy. However, LACH has some constrants, ncludng: (1) t does not take nto account the optmzaton of the number of cluster heads. The probablty for a random node to become a cluster head s p, and therefore the number of cluster heads s proportonal to the number of nodes; () as cluster heads are randomly selected, and therefore LACH can not guarantee cluster heads are unformly dstrbuted n the networks. Meanwhle, the probablty threshold does not take nto account the energy factor. LACH algorthm therefore must base tself on two assumptons so as to acheve unform energy consumpton at per node: (1) the ntal energy of each node s equal; () the energy consumed at each node when actng as the cluster head s equal. Therefore, t s dffcult to apply LACH algorthm to an actual networks applcaton. Many researchers have done profound work probng nto HWSN. In [1], authors mproved LACH algorthm and put forward an algorthm of electng cluster heads accordng to the resdual energy. However, each node needs to know the total energy of the current network to determne whether t can become the cluster head, whch requres support of routng protocols and therefore dstrbuted mplementaton s dffcult to acheve. Ths algorthm s called LACH-C. SP [11] s desgned for two-level heterogeneous networks n whch there are merely two knds of nodes wth dfferent ntal energes. But n the mult-level heterogeneous networks, nodes ntal energy s randomly determned wthn a certan range, so SP does not sut for such a heterogeneous envronment. For further researches, a heterogeneous network model n term of dfferent ntal energes s dscussed n [6,1-14]. In [15], the authors ntroduced a cluster head electon method usng fuzzy logc to overcome the defects of LACH. They nvestgated that the network lfetme can be prolonged by usng fuzzy varables. In [16], authors proposed HC protocol. Ths protocol selects cluster heads based on the weghted probablty of each node related to the ntal energy, the more ntal energy, the hgher probablty the node wll be selected as a cluster head. However, ths protocol can not predct energy consumpton, so ts performance s lmted n heterogeneous networks n whch part of nodes are regular data acquston nodes. In [17], authors proposed DFCM protocol, whch apples to networks wth three dfferent

4 knds of heterogeneous nodes. Nodes n the networks model of ths protocol fall nto two ordnary types: one performng the functon of managng nformaton and the other collectng dfferent data(type_, type_1). type_1 have more complex hardware and software archtectures, so t has more ntal energy and greater data transfer capablty. To guarantee an optmum number of cluster heads selected n actual operatons, authors propose a stable selecton and relable transmsson protocol based on a method of energy dsspaton forecast and clusterng management. But the applcaton of ths protocol s lmted to the networks wth only two types of ordnary nodes. In [18], authors proposed RP clusterng routng protocol for HWSN. In ths paper, an evolutonary algorthm wth an approprate ftness functon s proposed wth the ntrnsc propertes of clusterng n mnd. Man dea of the proposed RP s the ncorporaton of compactness and separaton error crtera n the ftness functon to drect the search nto promsng solutons. Aganst LACH and HCR, RP can prolong network lfetme and stablty perod. However, compared wth SP, RP gans longer network lfetme, but at the expense of less stablty awareness. 3 System Model and Problem Descrpton 3.1 Heterogeneous model for wreless sensor networks To meet the demands of effcent envronmental montorng, we descrbe our HWSN model wth both dfferent ntal energes and montored objects. The basc assumptons of networks model: the networks s located n a M M square area(fg. 1), N sensor nodes are randomly dstrbuted wthn the networks, nodes are slghtly moble or statonary, and base staton s located n the mddle of the area. The networks perform the task of envronmental montorng and sensor nodes montor a varety of objects. Defne nodes montorng temperature, humdty, wnd drecton etc. as regular data acquston (RDA) nodes; these nodes send back messages of fxed length at a fxed nterval; nodes montorng fre are not regular n acqurng data and the messages sent back are not regular.

5 Fg. 1. (a) 1-node random heterogeneous network. (b) Dynamc clusterng structure by DFCM. Therefore, nodes are heterogeneous n two ways: (1) heterogeneous data-acquston -regularty: some nodes are regular n acqurng data and some are not. All regular nodes send n n tmes messages n a rotaton cycle tmes and the message szes are between, 1 ~ () the ntal energy of all nodes are heterogeneous. l l bts; Nodes communcaton lnks are symmetrc and nodes do not have any locaton nformaton, but they can calculate the dstance between nodes accordng to sgnal strength receved. Nodes n the networks are organzed n the form of clusters. Cluster heads perform the functon of data fuson and are responsble for the resultant data transmsson to the BS. There s only one BS n the networks and wreless transmsson power s controllable. Node ntal energy s randomly dstrbuted n the closed nterval [ mn, max], where mn 1 s the lower bound of the energy, max determnes the value of maxmum node ntal energy. For any node, ts ntal energy s. 3. nergy Models Ths artcle apples a smple energy consumpton model [1] to calculate energy consumpton n communcaton, gnorng energy consumpton of nodes n the process of computng, storage, etc. In the process of transmttng l bts message through dstance d, the energy consumpton of the transmtter s: Tx l, d Tx _ elec k TTx _ amp l, d 4 lelec l mpd, d d Recever s energy consumpton s _ ( ) Rx Rx elec elec lelec l fsd, d d l l l (3) () Where elec s the energy dsspated per bt to run the transmtter or the recever crcut, and and 4 are the amplfer energy that depend on the transmtter amplfer model. d fs mp d

6 3.3 Problem Descrpton ssentally, all WSN clusterng algorthms are ntended to solve the problem of unbalanced networks load, and to acheve unform dstrbuton of energy dsspaton at all nodes, so as to prolong the network lfetme as much as possble. Therefore, PCA must take full account of the followng: (1) algorthm should be fully dstrbutve and self-organzed. Nodes determne ther own state based only on local nformaton, and each node must decde whether to become a cluster head or a member belongng to a cluster n the clusterng phase [1]; () nodes wth more resdual energy must have hgher probablty to become cluster head and t must be ensured that the cluster has a smaller communcaton cost, but energy s not the only factor for cluster head selecton; (3) cluster load balancng must be ensured; (4) PCA operates n rounds. In order to save energy consumpton when nodes broadcast n ntal clusterng phase of each round, an energy predcton model of RDA nodes s establshed. 4. PCA Clusterng Algorthm 4.1 Calculaton of dstance between Nodes Nodes n the networks can perceve ther mutual dstance accordng to attenuaton of sgnal strength n the process of transmsson. In clusterng phase, all nodes use certan transmsson energy for broadcast. For nstance, wth energy tran, node broadcasts nformaton to other nodes, ncludng ts message sendng cycle t, message length l and ts energy nformaton rec Node j detects the receved sgnal strength (receved energy) j, whle recevng messages. The relatonshp between transmsson energy and recepton energy s as follows []:. rec j, K (4) d, j tran Where K s a constant, d, j s the relatve dstance between node and node j. s dstance - energy gradent, and ts value vares from 1 to 6 accordng to the physcal envronment n whch the sensor networks operate. Thus, the dstance between and j s: d, j K (5) tran rec j, The node establsh a routng table of neghborng nodes based on receved data and save all relevant nformaton of all nodes wthn ts communcaton range. All nodes n the networks are marked by the only nteger value, whch s each node s ID. The nformaton stored n the routng table ncludes the dstance between the node and ts neghborng nodes, cluster head node s ID, the dstance to the cluster head, the current energy and predcted energy consumpton. 4. Cluster head selecton The cluster head node has to perform extra functons such as data fuson and relayng messages, so ts energy consumpton rate s much hgher than that of ordnary nodes. In order to

7 prevent some nodes from dyng too soon due to excessve energy cost, the nodes wth more resdual energy should be gven greater opportunty to become cluster heads and all nodes take ther turns to be cluster head nodes. Set p opt s the proporton of optmal cluster heads and p s the probablty for node to be selected as the cluster head. Obvously, f the current energy at all nodes s equal to each other, p opt p can ensure that all nodes de at the same tme. In energy-heterogeneous WSN, p calculaton s much more complcated. Currently, many clusterng algorthms n HWSN determne p by usng the rato of nodes current resdual energy and the average energy of the entre networks, but the latter s very dffcult to obtan [13], especally for networks n whch dfferent nodes are montorng dfferent objects. Consequently, major error s lkely to happen to the estmated average energy. Ideally, nodes are dstrbuted unformly and send back data at dentcal frequency and length. Set d tobs s the average dstance between the head node and the BS and d toch s the average dstance between member nodes n a cluster and the head node, t can be concluded that [1,1]: M dtobs.765 (6) d toch M (7) k k opt The number of optmal cluster heads s [13]: N fs M d (8) mp tobs Therefore, the proporton of optmal cluster heads s: p opt k opt (9) N In the ntal stage of clusterng, through broadcast among nodes n the networks, for any node, there are a total of n nodes wthn ts communcaton range, of whch the dstance between n 1 nodes and s d, and the dstance between n nodes and s d. So consderng the rato of the energy of and the average energy of all nodes wthn ts communcaton range,, the energy factor nfluencng the probablty of cluster heads can be obtaned: n j1 j n (1) Consder the nodes dstrbuton n the networks. If after nodes have been clustered, the

8 average dstance between nodes wthn the cluster and cluster head s far, a hgh communcaton cost s nevtable for one communcaton wthn the cluster. Set round s the average energy consumed n one communcaton between each node n the cluster and node after has been selected as the cluster head. round n 1 4 ljelec lj fsd, j lk elec lkmpd, k j1 k1 n n On an deal occason that nodes n the networks are unformly dstrbuted and every data (11) transmsson send data dentcal n length l, the number of nodes n each cluster s N k opt. If the dstance from m 1 nodes to the cluster head s d, m nodes d, the rato of these two types of nodes s : d d d (1) Therefore, the number of these two types of nodes s: N kopt m1 1 (13) m N kopt 1 (14) The random dstrbuton of nodes can be vewed as a Posson pont process [1]. Ideally, there are n ponts n crcle A and ther locatons whch are unformly dstrbuted n A are mutually ndependent random varables. d s a random varable, presentng the dstance from a pont ( x, y ) to the crcle centre pont. The expectaton of all the ponts n the crcle to the center pont s: d x y ( x, y )dxdy (15) A A crcle can be obtan after any radus revolves around the center, so consder the dstrbuton of ponts on a random radus. Ponts are dstrbuted unformly n the crcle, and accordngly, the densty of ponts s proportonal to radus squared. Therefore, the probablty densty of ponts on a random radus s: x f ( x) (16) R Where R s radus length. Therefore, the calculaton of d can be smplfed to:

9 R d xf x dx (17) By formula (16) and (17), the average dstance expectaton of nodes whose dstance to the cluster head s less than d s: x d x x d d s: d 1 d (18) d 3 The average dstance expectaton of nodes whose dstance to the cluster head s more than x d x x d d d d s consume d d (19) d d d 3 Therefore, deally the average energy consumpton wthn one data transmsson n the cluster elec fs elec mp l m1 d m 1 d N () k opt By formula (11) and (), communcaton cost factor C whch has nfluence on probablty of cluster head electon s: consume C (1) round Integratng node energy factor and communcaton cost factor, the followng formula can be used to calculate the probablty for node to become the cluster head node: opt p p C () Where and are the calculaton factors regulatng the proporton of energy factor and communcaton cost factor n calculaton p, 1. The constrants of LACH threshold formula T should be mproved n two steps: (1) to promote T nto mult-level heterogeneous networks; () n PCA, to take energy factor and the communcaton cost factors nto account and to mprove calculaton method of T, as s shown n formula (3):

10 p 1 C s 1 r dv C 1 p T 1 p r mod, p, f G (3) otherwse Where rs s the number of rounds when a node fals to be selected as the cluster head. Once the node elected, r s s reset to. 4.3 energy consumpton predcton mechansm Obvously, after the networks complete a round, a new node need to be selected as the cluster head. Because t s necessary to re-evaluate the energy factor and the communcaton cost factor so as to determne the probablty for the node to become the cluster head, the current node resdual energy must be obtaned. The easest way s that all nodes n the networks carry out a broadcast through the method utlzed n the frst round of clusterng. However, consderable energy wll be consumed when broadcastng n each round of clusterng, so ths paper establshes an energy consumpton predcton mechansm for RDA nodes. In r 1 round, t takes n j tmes for any node j to send messages wth a length l j to cluster head node and the dstance between and j s d, j. Snce each node keeps relevant nformaton of all nodes wthn communcaton range and ther mutual dstance, any node wthn node j communcaton range can calculate the energy consumpton of node j n r 1 round. j r1 _ comsume, n l l d d d n l l d, d d j j elec j fs, j, j 4 j j elec j mp, j, j (4) Accordng to the current energy of node j and formula (4), the resdual energy of node j can be predcted at the begnnng of r round when r 1 round starts. (5) jr _ predcton jr1 jr1 _ comsume Due to reasons such as networks envronment changes, when r round starts, all nodes need to be re-clustered and new cluster head node need to be elected. Node j determnes whether ts current resdual energy s close to the resdual energy predcted n the last round or not. jr _ predcton 1 (6) jr If s less than constant, the energy predcaton error can be tolerated. In the ntal phase of r round, node j does not broadcast ts energy nformaton and the remanng nodes update node j energy nformaton n the routng table accordng to calculaton results.

11 5. Smulaton xperment 5.1 establshment of smulaton envronment Through smulaton experment, ths paper makes analyss and comparson on the performance of PCA. The experment smulates a hgh densty sensor network for envronmental montorng randomly formed wthn a 1m 1m area. After the formaton, nodes become statc, no longer movng. And 1 sensor nodes are randomly dstrbuted n ths area, wthout loss of generalty. Assumng the BS s located n the center of the area. In order to compare wth other protocols, mpact caused by random factors such as sgnal collson and wreless channel nterference s gnored. Parameters used n ths experment can be seen n Table 1. Ths paper wll compare the performance of PCA and that of LACH, SP and DFCM. All results, unless otherwse stated, are average values of 1 tmes ndependent experments. 5. xperment Results and Analyss Table 1 Parameters used n smulatons Parameter Value Network grd (,) ~(1,1) node numbers 1 Coverage radus (m) 1 Threshold dstance d (m) 75 Intal energy (J ) 1-3 elec 5nJ/bt fs 1pJ/bt/m m p.13pj/bt/m 4 Message sze Broadcast Packet Sze Round -6bts 5 bts 5 TDMA frames In PCA, and are calculaton factors regulatng the proporton of energy factor and communcaton cost factor n calculaton p, satsfyng 1. Change the values of and and observe the performance of PCA. Ths experment sets all nodes are energy-heterogeneous and the ntal energy s 1-3J. All montored objects n the network are homogeneous, excludng RDA nodes. All nodes send 4bts messages to the cluster head at TDMA tmeslot.

12 3 3 Round 8 6 the frst node de 1% nodes de 5% nodes de α Fg. Influence of and values on performance Fg. shows the death tme of the frst node, 1% nodes and 5% nodes when the values of and vary n the above crcumstances. It can be seen when the values of are n the vcnty of.74, death tme of the frst node and 1% nodes appears the latest; whle when the values of are wthn the range of , death tme of 5% nodes appears the latest. When parts of nodes n the network de, nodes densty becomes sgnfcantly lower and due to the reducton of nodes number, network load s more lkely to be uneven. Therefore, greater value of the communcaton cost factor can help mprove algorthm performance. In subsequent experments, the values of and are unfed as.7 and.3. In the above expermental envronment, PCA and LACH, SP and DFCM wll be compared and tested to analyze PCA cluster head selecton mechansm s mpact on the algorthm performance when all nodes are heterogeneous. Round PCA LCAH Number of nodes de SP DFCM Fg.3 Death tme of nodes The smulaton results n Fg. 3 show the varaton of the number of dead nodes over tme n the above expermental envronment n dfferent algorthms. It can be seen n Fg. 3, LACH can not make good use of the addtonal energy of heterogeneous nodes, the stable perod s very short and nodes de at a fxed speed rate. Compared wth LACH, SP has longer stable perods. PCA and DFCM curves are lnes wth smaller slope versus X-axs. Because PCA

13 dstrbutes energy consumpton unformly on each node n the heterogeneous network, the death tme of the frst and the last node s relatvely closer. It can be seen from Fg. 3, compared wth LACH and SP, PCA can prolong network lfe expectancy by 19% and 55%. In the above expermental envronment, change the proporton of heterogeneous nodes n the total number of nodes and observe the performance of each algorthm. Fg. 4 presents the number of rounds from the begnnng to the death of the frst node when the proporton of heterogeneous nodes vares from to 1%. In ths experment, the ntal energy of all non-energy-heterogeneous nodes s J Round of the frst node des PCA LCAH SP DFCM proporton of heterogeneous nodes Fg.4 Death tme of the frst node when the number of heterogeneous nodes changes Before the death of 1% nodes, the network can send back to the BS data of hgh qualty and relablty [13]. So Fg. 5 presents the number of rounds from the begnnng to the death of 1% nodes, namely the stable perod. 4 Round of 1% nodes de PCA LCAH SP DFCM proporton of heterogeneous nodes Fg.5 network stable perod when the number of energy heterogeneous nodes changes It can be seen that as LACH s not a clusterng algorthm for heterogeneous networks, t does not take nto account the energy dfference between nodes and nstead, all nodes are treated equally. Therefore, n LACH,wth the ncrease of the proporton of heterogeneous nodes, attanable network stable perod quckly reduces. SP can obtan 5% more stable perod than LACH, whch s bascally consstent wth the expermental results presented by [11]. As DFCM takes nto account heterogeneous energy of dfferent nodes, the death tme of ts frst node s later than SP and t gets longer stable perod than SP. PCA takes nto account the energy consumpton of nodes n the communcaton process n addton to resdual energy, so the declne rate of stable perod s sgnfcantly less than other algorthms n the process of ncreasng

14 proporton of heterogeneous nodes. Therefore, wth greater proporton of heterogeneous nodes, a more stable perod s obtaned. To go further, RDA nodes are ntroduced nto the experment. Set all nodes energy n the networks s heterogeneous and 5% nodes are RDA nodes. Meanwhle, because of factors such as changes n the envronment, 1% nodes are malfunctonng. All RDA nodes send messages 3-7 tmes n a round and the szes of messages are valued randomly between -6bts. xamne the mpact of the constant on networks stable perod Round 7 65 PCA constant ε Fg.6 Impact of constant on network stable perod Due to malfunctonng nodes n the network, errors n energy predcton are nevtable. If 1, nodes broadcast ther energy nformaton when energy predcton errors happen. In ths case, t s dffcult to acheve substantal savngs n energy consumpton. If the value of s too low, nodes do not broadcast ther energy nformaton even f bggsh errors n actual resdual energy and predcted energy happen. In ths case, nodes wth lower actual resdual energy may have hgher opportunty to become the cluster head and the length of network stable perod s thus affected. Fg. 4 shows when the value of s near.9-.93, the network acheves maxmum stable perod. RDA nodes are ntroduced nto LACH, SP, DFCM and PCA and the stable perods of all these algorthms are examned. Ths experment sets all nodes are energy heterogeneous, 5% of whch are RDA nodes, constant.93 and 1% nodes n the network are malfunctonng. The results are shown n Fg. 7: 3 Round f 1% nodes de LCAH SP DFCM PCA Fg.7 network stable perod Obvously, due to the ntroducton of energy consumpton predcton mechansm, broadcast

15 frequency n the clusterng phase n each round s effectvely reduced. Therefore, n a network heterogeneous n two ways --- ntal energes and montored objects, PCA makes sgnfcant mprovement n network stable perod compared wth the other three algorthms. However, the heterogenety of DFCM fals to take nto account RDA nodes, so when these nodes are added, the stable perod of DFCM declnes consderably Number of Messages PCA LCAH SP DFCM Round Fg.8 the number of messages receved by BS For the algorthm runnng by round, montorng qualty can be measured by the total tmes for all nodes n the network to collect data. Fg. 8 shows that all the nodes are energy heterogeneous, 5% are RDA nodes and 1% of the nodes are malfunctonng. In PCA, the number of messages receved by BS s on lnear rse for a long perod of tme, whle n other algorthms, the growth rate of the number of messages receved by BS begns to declne earler. To sum up the total number of messages sent back to BS by all nodes n these four algorthms when the network fals, the amount of data collected by PCA s much larger than that by the other three algorthms. Therefore, PCA has better network montorng qualty. 6 Concluson In ths paper, we descrbe the HWSN model wth both dfferent ntal energes and montored objects. We present an effectve energy predcton clusterng algorthm PCA for mult-level heterogeneous sensor networks. In PCA, each node ndependently selects tself as the cluster head node based on energy factor and communcaton cost factor, whch leads to the probablty of cluster head electon related to nodes current resdual energy and average communcaton cost after beng selected. At the same tme, wth the consderaton that the WSN are frequently used to montor objects such as temperature and humdty whch need to report data regularly, and the length of reported data are usually fxed, an energy consumpton predcton mechansm s establshed for RDA nodes. Smulaton results show that compared wth LACH, SP and DFCM, PCA can acheve longer lfetme, hgher energy effcency and better network montorng qualty. Its performance s superor to other protocols. In future work, research wll further mprove resdual energy predcton mechansms so as to acheve greater predcton accuracy and prolong network lfetme to the maxmum. In addton, such problems wll be consdered as message transmsson and energy predcton n networks where one node montors a varety of dfferent objects. Our ultmate goal s to apply PCA algorthm to practcal use.

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