Outdoor Motion Localization Algorithm Based on Random Probability Density Function

Transcription

1 do: / Oudoor Moon Localzaon Algorhm Based on Random Probably Densy Funcon Dan Zhang Eas Chna Jaoong Unversy, Nanchang , Jangx, Chna Absrac In hs paper, a arge localzaon algorhm based on random probably densy funcon (RPDFTL) s proposed. The basc dea of RPDFTL s o use a seres of weghed parcles o predc he poseror dsrbuon space of he oudoor obecs locaon, each new me accordng o he sensor measuremen daa o balance and locae he arge. By nroducng error olerances (error oleran), RPDFTL sore and send he arge locaon daa, makng he obec poson nformaon daa error of he convergence pon whn he conrollable range, hereby grealy reducng he nework communcaon load. The smulaon resuls show ha he RPDFTL algorhm can acheve beer locaon effec and nework load balance a a lower cos compared wh he exsng posonng algorhms and rackng proocols, and hus exend he nework lfe. Keywords: Wreless Sensor Neworks, Targe Locaon, Random Probably Densy Funcon, Coverage problem. 1. INTRODUCTION In he wreless sensor nework (WSN), sensor nodes generally have he ably of even sensng, measuremen, daa collecon and processng. Node n he nework can mplemen nformaon communcaon n self-organzed dsrbued manner. In ypcal WSN applcaons, a large number of sensor nodes are deployed n he even area, hey are lnked hrough he wreless mul-hop nework organzaons o collaborae o complee a specfc purpose of daa collecon, ransmsson and processng asks, such as envronmenal monorng and raffc conrol of cvlan areas, and mlary purpose nruson deecon. The MTL (moble arge localzaon) s one of he classc applcaons of wreless sensor neworks. In oudoor applcaons, he sensor node has he ably o deec he relave poson of he obec, and can make use of he neghbor nodes Informaon communcaon and daa fuson (daa fuson) o esmae he arge poson, hs process s called localzaon (localzaon); and n he process of movng obecs, s locaon and movemen nformaon and oher mporan daa needs along he movemen raecory (hand-off ), And hs knd of research opc belongs o he rackng problem of movng obecs. To acheve he purpose of daa collecon, s also necessary o ransfer he obec movemen nformaon o he snk node hrough a pah n he nework opology so ha he user can monor and conduc goal behavor analyss. In order o mee he applcaon requremens, here are some scholars and nsuons o sudy he effecve MTL sraegy. The research focuses and dffcules are as follows: Frs, he desgn and use of effecve posonng algorhm, such as fuson measuremen (range-based (Trlaeraon, wdely used n GPS and radar devces) (Babcock and Sgal and Zhuang, 2012; Lnse and Kleemann and Hasse, 2014; Grchds and Konsandn and Whworh, 2014), and locaon-free algorhm based only on he connecvy performance of nodes (Peyraud and Béalle and Renaul, 2013; Ogueofor and Anedu and Eofor, 2013; Coa-Ruz and Rosles and Rvas-Perea, 2013; Pe and Hawkes and Kook, 2013); secondly, he problem of coverng he moon area of he obec and he correspondng node deploymen model (Carl and Panzer and Pascucc, 2014; Bozz and Cell and Vcn, 2015; L and Hedley and Collngs, 2015; Ayach and Boudaoud and Marque, 2014). In addon, how o develop effecve and low energy consumpon rackng algorhm and effcen and sable daa fuson sraegy s he key o affec he rackng effec of oudoor spors obec. There are also some mporan researches Resuls (Kapkranan and Zapolsky and Doman, 2014; Popov and Pope, 2014; Navarro-Marnez, 2014). The random probably densy funcon s a Bayesan nference process of sequenal Mone Carlo mehod, whch s wdely used n robo localzaon (Saleh and Bushe and Shahbazan, 2013). Is basc dea s o use a seres of weghed sample o represen he spaal dsrbuon of he poson of he obec and he "forecasng - updang" loop process s used o realze he posonng and erave rackng of he oudoor obecs. In hs paper, we focus on he locaon algorhm of oudoor moon obec, and propose a arge localzaon mehod RPDFTL whch s based on he fuson of random probably densy funcon. Compared wh oher research work, RPDFTL uses an error oleran mehod o sore and send he obec he daa error of he obec poson nformaon gahered n he snk node s whn a conrollable range, and he raecory of he obec s 340

2 predced by usng he rackng daa wh he error n he posonng process. When updang, f he absolue dfferenal value of he poson s dfferen from he predced daa s whn he range, hen whou updang, or he RPDFTL uses he curren posonng daa o refresh he snk daa and he rackng daa. Smulaon expermen resuls show ha he RPDFTL localzaon mehod proposed n hs paper can reduce he daa loss a he expense of small loss n he accuracy of locang (Accuracy s nversely proporonal o he value) o sgnfcanly reduce he nework raffc (nearly 60% o 70% of node energy savngs), and o mprove nework load balancng. In addon, he smulaon expermens also show ha, The SAC sraegy can furher reduce he communcaon overhead beween cluser and cluser and reduce he lfeme of nework (reduce he number of clusers by 20% ~ 30%) whou reducng he accuracy of locaon. Secon 1 descrbes he relaed work and he nnovaon of hs paper, whch s dfferen from he exsng leraures. Secon 2 descrbes he problem and he nework model of hs paper and some basc erms. Chaper 3 and 4 respecvely gve ou he random probably densy funcon of oudoor dynamc obec localzaon algorhm RPDFTL and he sensor node self-organzaon sraegy and daa fuson mehod SAC. The smulaon expermens of RPDFTL algorhm and SAC sraegy are gven n Secon 5. The las secon summarzes he whole paper and plans he fuure work. 2. RELATED WORK The characerscs of daa acquson and self-organzng nework communcaon n wreless sensor neworks make very suable for he monorng and posonng of oudoor movng obecs. To measure he hardware accordng o he exra dsance or angle, he posonng mehod s dvded no fuson measuremen and no measuremen. The exsng fuson measuremen algorhm s based on he fuson of sensor measuremen daa, such as me dfference of arrval (TDOA), angle of arrval (AOA), and sgnal srengh ndex, RSSI), ec. The poson of he obec can be esmaed accordng o he rdmensonal localzaon (mullaeraon), ec. The valdy of he fuson algorhm depends on he accuracy of he sensor measuremen daa, and he developmen of manufacurng echnology has made he measuremen resuls can reach cm level. Because he localzaon algorhm of fuson measuremen needs addonal measuremen devces, some researchers have proposed a localzaon algorhm ha only depends on node connecvy, such as Cenrod algorhm (Peyraud and Béalle and Renaul,2013), DV-hop mehod (Ogueofor and Anedu and Eofor, 2013), Graden localzaon algorhm (Coa-Ruz and Rosles and Rvas-Perea, 2013) And APIT algorhm (Pe and Hawkes and Kook, 2013), ec. Cenrod s a lghwegh locaon mehod n whch an anchor node of known locaon nformaon broadcass s locaon nformaon o s neghbor nodes. Unlocalzed nodes calculae her receved and he geomerc cener of he anchor nodes of he broadcas nformaon s used as s locaon poson. VV-hop s a fuson dsance vecor roung mehod (Ogueofor and Anedu and Eofor, 2013), whch uses he floodng sraegy o broadcas he nformaon of he anchor nodes and he number of hops arrvng a each unlocaed node (Hop-coun), he sysem hen esmaes he average dsance of each hop n he nework. The unlocaed nodes calculae her poson accordng o he mullaeral posonng mehod. Graden uses he echnque of esmang he average dsance per hop of he nework by DV-hop, Dfferen esmaon algorhms are used (Coa-Ruz and Rosles and Rvas-Perea, 2013).He e al proposed APIT locaon mehod (Pe and Hawkes and Kook, 2013). Frsly, he whole nework s dvded no many rangles accordng o he anchor nodes, and he non-locang nodes perform a PIT (pon-n- Trangulaon) o deermne wheher s whn he rangular range of any hree anchor nodes can conend wh (can communcae). Repea hs operaon for all possble combnaons of s adacen anchor nodes unl he desred accuracy s reached or all combnaons are esed. In general, alhough he range-free localzaon algorhm has he advanages of low cos and lgh wegh, he exsng range-free algorhm canno mee he accuracy requremens of ceran applcaons ha requre precse posonng, and range-free posonng mehod I s ofen necessary o deploy nodes n parcular o acheve connecvy requremens, so when he nework s empy or poor connecvy n he nework, he posonng effec of he node canno mee he applcaon requremens. More mporanly, unless he arge self comes wh a sensor, oherwse he range-free approach does no apply o oudoor spors obec posonng. Wreless sensor neworks can be dvded no sac nodes and moble nodes accordng o he movemen of nodes n he nework. In he dynamc node nework, he unlocaed nodes frs need o locae he nodes by known anchor nodes, mehods used such as range-free or sequenal Mone Carlo mehods. The goal of hs paper s o develop an effcen and low energy consumpon oudoor obec rackng sraegy, n whch he organzaon and collaboraon of sensor nodes and he correspondng acve / sleep mechansms are he key o he overall sraegy. In addon o cluserng sensor nodes self-organzng form, Zhang and Cao (Popov and Pope, 2014) proposed a mehod of dynamc self-organzaon of sensor nodes no ree-lke srucures accordng o he movemen of he obec poson (ree-based), whch s a commonly used form of rackng algorhms, and he mehod of deecng, rackng and ransmng he daa. The arge node cluser organzaon sraegy does no consder how o form clusers n low energy consumpon mode and how 341

3 o reduce he number of clusers, so he energy consumpon s large. The SAC mechansm proposed n hs paper s a fuson sraegy of predcng he organzaon of cluser nodes by he predcon of he raecory of he obec, and mnmzes he burden of nework communcaon by mnmzng he number of clusers by usng he characerscs of sensor dscrezaon samplng. 3. THE DESIGN OF RPDFTL RPDFTL s a fuson of random probably densy funcon oudoor obec posonng and sae esmaon mehod, he basc dea of fuson Mone Carlo mehod and Bayesan reasonng, from he sae space samplng of a seres of ndependen varables called parcles o represen he obec poson Dsrbuon of he poseror probably, and hen accordng o he laes measuremen daa o measure he wegh of each parcle and esmae he poson nformaon. Unlke he radonal random probably densy funcon mehod s dfferen; RPDFTL uses an error olerance o conrol he convergence pon on he obec poson nformaon, and reduces he amoun of compuaon of he obec poson nformaon and he energy consumpon of he daa ransmsson n he nework Random Probably Densy Funcon In hs paper, we use x o represen of he poson of he movng obec a me and use y0: he sequence of observaons from he begnnng o he curren me, where he observaon sequence corresponds o he poson dsrbuon sequence of he obecx, x,, x.in hs paper, we consder he problem of random flerng n dynamc sae space: 0 1 x 1 F x, N Equaon (3) and equaon (4) respecvely represen he sae and he measuremen equaon. The sae p x x, and funcon F s he arge sae funcon of he equaon (3) descrbes he sae ranson probably 1 fuson me, whle equaon (4) descrbes n he case of measuremen nose, condonal probably (3) p y x, G s a nonlnear measuremen funcon, and he ndependen dynamc vecor N andv represens a random whe nose sequence n he dscree me doman, where, he sae nose s denoed as N and he measuremen nosev.if he lnear Gaussan dynamc sysem s consdered, he problem can be solved n Bayesan Theory, whle he Kalman fler s used o solve he problem. Whle he more praccal suaon n wreless sensor neworks s non-lnear and non-gaussan nose. When he number of parcles s ha N, he Mone Carlo mehod can approxmae any rue probably dsrbuon approxmaon compleely. One of our obecves s o esmae he sae of he curren locaon of he oudoor obec x n he gven measuremen sequence y 0:. In addon, we assume ha he arge sae obeys he Markov process, ha s, he curren sae depends only p x y o descrbe a gven observaon sequence y 0:, he on s prevous sae. Usng condonal probably 0: sae ranson probably of he obec p x x0: 1 can be expressed as p x x 1 ndependen of he sae n he case of space, he condonal probably p x y0: accordng o he Bayes rule as follows: Where he probably p y x probably dsrbuon p x y0: 1 : When he measured value s can be calculaed eravely p y, y0: 1 x P x 0: p y, y0: 1, 0: 1, 0: 1 p y y0: 1 p y0: 1 0: 1, 0: 1 0: 1 p y y0: 1 p y0: 1 p x p x y0: 1 p y y0: 1 p x y p y y x p y x p x p y y x p x y p y p x p y x defnes he sysem measuremen nose model n equaon (4); The pror descrbes he sae ranson probably densy and he knowledge model of (5) 342

4 he sysem, and can furher use he Chapman-Kolmogorov equaon accordng o he condonal probably densy of he sae of he oudoor movng obec p x 1 y0: 1 a he momen 1 The denomnaor p y y0: 1, Calculaons: p x y p x x p x y dx 0: : 1 1 n equaon (5) s he vecor of measuremens, and here s: 0: 1 0: 1 p y y p y x p x y dx The basc dea of he random probably densy funcon s o approxmae a poseror probably by usng a seres of weghed sample samples and o fuse he samples o esmae and compue he sae of he obec. When he number of samples s large enough, hese sampled samples are used nsead of he real Poseror probably dsrbuons Usng N o represen ndependen random sample spaces, he poseror probably dsrbuons of he oudoors movng obecs 0: ndependen dencally dsrbued samples (shor as d): (6) (7) p x y can be effecvely approxmaed by hese N 1 p x 0: ˆ y x x p x y0: N (8) Where x represen d sample whch s sampled from he dsrbuon p x y 0:, funcon, so he probably pˆ x y0: s close o he rue poseror dsrbuon p x y0: number N s large enough. Bu when p x y0: N 1 x x s he drac when he sample s a hgh-dmensonal probably dsrbuon, drec samplng s very dffcul or even mpossble, so s usually seleced wh a close and easy o mplemen he proposed dsrbuon (proposal dsrbuon, also known as he mporance of dsrbuon) 0: x nonlnear funcon h x for esmaon: ake any one sae varable Where x ( x ) as( x ) N ( x ) 1 When here s 1 0: q x y0: 1 p y x p x 1 p y 0: 0: q x y o sample, you can h x h x p x y0: dx p y0: x p x dx h x q x y0: 1 dx p y0: x q x y0: h x x q x y dx, A non-negave wegh for he dsrbuon of samples x, sandardzed.so, h x can be esmaed as: ( ) N h x ( x ) h( x ) 1 me o sample he daa and he laes samples a me 0: 1, 0: dsrbuon can be decomposed no he followng: wegh ( x ) can also be recursvely calculaed: (9) (10) q x x y,he proposed 0: 0: 0: 1 0: 1 0: 1, 0: q x y q x y q x x y 0 k 0: k 1, 0: k q x q x x y k 1 (11) 343

5 p x 0: 0: y 0: q x y0: 1 q x0: y0: q x x0:, y0: q x p y x p x x 1 ( x 1) q x x0: 1, y0: p y x p x ( x ) p y x p x x p x y y 1 0: 1 0: 1 0: 1 0: 1 (12) 3.2. RPDFTL Process Predcon and samplng space When s dffcul o sample he rue probably dsrbuon 0: q x x0: 1, y0: whch ncludes 0: dsrbuon, 0: 1 0:, 0: 1 p x y, a proposed p x y s usually sampled, whch s q x x y p x x y chosen as he opmal dsrbuon because he wegh ( x ) change of he parcle s he smalles n he gven sae and observed sequence y 0:. However, hs opmzaon mehod requres samplng, p x x0: 1 y and calculaon of he negral 1 1 p y x p y x p x x dx, whch s oo expensve for he WSN, and he mos drec and mos common alernave s o use he sae ranson densy p x x 1 as he proposed dsrbuon, alhough p x x 1 does no nclude he measuremen sequence and fully negrae he known sae of he obec ransfer equaon, bu s easy o mplemen under he WSN envronmen and a smaller cos samplng way. Ths paper uses he daa sored n he race daa buffer Buf o effecvely approxmae he spao-emporal daa of obec rackng. Accordng o he las wo obec poson nformaon n Buf and s locaon me, we combne he movemen paern of obec (such as velocy and movng drecon) and he me of he obec's poson, and hen use he daa n he daa buffer Buf o approxmae he spao-emporal daa effecvely. Samplng whn he specfed range, f he number of effecve parcles n he range N s below he hreshold N, he samplng range s expanded unl N eff N Fgure 1 shows he samplng range A S (he shaded area n he fgure) when he velocy range of he obec V, V s known and he maxmum dsplacemen angle. The poson of he obec a me follows a unform dsrbuon n he secor: P L L, L eff V V (13) 2 2 Where, L and L are Buf for he las wo poson daa (Noe: L and L are no necessarly for he connuous me pon of he spaal poson nformaon), he momen 1 he approxmae poson of he obec ds L L 1. L -1 L -2 <e V- V+ L L Real locaon Approxmaed locaon Fgure 1. Samplng space 344

6 If N eff N you exend he samplng range (such as ncreasng he angle offse value ), afer he specfed samplng space, N parcles ndependen of he same dsrbuon x 1,, N x randomly dsrbued n he regon. Unlke he radonal sochasc probably densy funcon, RPDFTL uses error olerance o sore and predc daa, ha s, when he devaon beween he arge locaon daa and he predced value s whn he algorhm, he algorhm does no updae he daa a he snk sde. As shown n Fgure 1, a he me 2, he obec's poson L 2 s he devaon from he predced value (dashed lne) whn he olerance range; he algorhm uses he predced value as he daa for sorng and he nex predcon. The process connues unl he Eucldean dsance beween he locaon value and he predced value s greaer han, The RPDFTL reflushes he locaon nformaon and repors he convergence pon. The RPDFTL can sgnfcanly reduce he communcaon overhead n he nework by olerang a posonal error ha reduces he number of daa refreshes n he nework. The resuls of he smulaon expermen wll demonsrae ha A saemen Parcle wegh esmaon A me, he curren cluser head he node CH sends a measuremen reques o all 1-hop neghbor nodes Nb CH ; S S Nb CH wakes up afer recevng he reques and eners he measuremen mode; all he nodes of he obec measure he dsance o he obec and reurn he resul o he nodes CH, le us denoe S M as he measured values of he nodes S a me, and assume ha he measuremen error of each node 2 follows he Gaussan dsrbuon N 0, Whch,hen p y x N S S m Nm 1 1 M M x exp Nm s he number of effecve measuremens; ha S x esmaes, ha s (14) M x represen node S for he parcle ds S x, herefore, no he formula (12),he wegh of he -h parcle can be calculaed eravely by he followng formula:: N m x exp x p y x And hen normalze he wegh of he -h parcle: Therefore, he poson of he obec a me s esmaed o be: x S S M M x x 1 1 x N N L x x 1 The algorhm n Fgure 4 descrbes he obec localzaon process accordng o he RPDFTL algorhm. 1. Inpus: x 1 N, 1,, N; x ~ P x ; L0=cenrod of all deecng sensors; Oupus: he locaon of arge a each me sep ; he wegh of each parcle 3. For =1,,T do 4. For =1,,N do x P L L L //Eq.(13); 5. Samplng parcle ~ 0, 6. Calculae wegh x usng Eq.(15); x 1 7. Normalze wegh x N x 1 8. =+1; 9. End for 10. Esmae arge s locaon L 11. =+1; ; x. (15) (16) (17) 345

7 12. reurn L and 13. End for x ; 4. SIMULATION EXPERIMENT The smulaon expermen s carred ou on he wreless sensor nework smulaor SIDne-SWANS. The smulaor s equpped wh MAC lnk layer sandard proocol, and he nework adops face roung proocol. The expermen compares he RPDFTL wh he exsng localzaon algorhms MSL and Cenrod (Peyraud and Béalle and Renaul, 2013): 1) Comparng he errors of he hree oudoor obec localzaon algorhms; 2) Sudyng he mpac of velocy of he obec, he node densy,, Sensor nose (sensor nose) and he number of parcles (number of parcles) on he performance of dfferen algorhms; 3) analyze and compare RPDFTL sraegy nework raffc for oher algorhms, from he nework energy consumpon and lfe expecancy, he performance of each mehod was analyzed. An mporan creron for evaluang he accuracy of localzaon algorhms n sensor neworks s he localzaon error Lerr (localzaon error).in hs expermen, he absolue error and he rao of Rs are used o err 1 s evaluae, ha s 1, L ds X X R, where X s he rue coordnae poson of he arge and he esmaed poson of he localzaon algorhm respecvely,, X s ds X X calculae he plane space dsance, Lerr s averaged o measure he performance of he algorhm n he dscree samplng me of he arge rackng. The expermen smulaes a square obec moon area, n whch 1000 sensor nodes are deployed, and he dfferen nework densy s obaned by changng he sze of he area. Nework densy s defned as he average number of nodes ha can be covered by each pon n he regon, ha s, he nework s -cover. The cener of he nework se a convergence node, he sensor samplng and communcaon frequency s 2s. All nodes of he communcaon radus s se o Rc 100m percepual radus s se o Rs 50m a percepon error of 1m ~ 20m.Mobody movemen accordng o GMMM model, smulaon of hree dfferen raes (Car, 25mph), bke (10mph), and pedesrans (4mph) were used n hs expermen. The resuls were averaged over 10 ndependen expermens. Dealed parameers are shown n Table 1. Parameer Table 1. Smulaed Parameers Value Number of nodes 1000 Communcaon range Sensng range Velocy of arge 100m 50m 4, 10, 25 (mph) Nodes densy 8, 12, 16, 24 Sensor nose e~(0,s2 ) σ=1, 5, 10 Number of parcles 5, 10, 20, 50, 100, 200 a (GMMM) 0.9 Error oleran e Samplng rae Smulaon me 1,5, 10, 15, 20 (m) 2s 2 hours 4.1. The Impac Of Targe Velocy on Performance Fgure 8 (a) depcs he effec of changes n he velocy of he movng obec on he accuracy of he hree localzaon algorhms: he RPDFTL and MSL localzaon errors ncrease as he obec movemen rae ncreases, whle he Cenrod s no affeced by he velocy In addon, he ncrease of he velocy makes he relably of he obec's hsorcal raecory daa decrease, whch furher nfluences he accuracy of he predced poson of he MSL and RPDFTL. On he oher hand, he Cenrod The accuracy of RPDFTL localzaon s hgher han ha of he oher wo mehods, and he accuracy of he algorhm s beer han ha of he oher wo mehods. Low (4mph), relave o he Cenrod ncreased nearly 50%, compared o MSL ncreased nearly 35%. Fgure 8 (b) shows he effec of error olerance parameers on he accuracy of RPDFTL posonng. The accuracy of RPDFTL decreases wh he ncrease of.as can be seen from he fgure, 10m, he RPDFTL 346

8 error s n a smaller range, and he expermenal resuls presened laer wll prove ha afer he nroducon, he raffc n he nework wll be grealy reduced Effec of node densy on performance Boh he range-based and range-free posonng algorhms are beer wh he ncrease of he node densy, because he ncrease of he node makes he dependen measuremen value (range-free s he number of conneced nodes) Fgure 9 (a) shows he effec of node densy on he posonng effec of he hree algorhms. As can be seen from he fgure, RPDFTL s respecvely dfferen from ha of he oher nodes, and he number of nodes ncreases, and he number of nodes ncreases. The accuracy of he Cenrod and MSL mehods s ncreased by nearly 35% and 23% compared o he Cenrod and MSL mehods. The ncrease n accuracy of he Cenrod and MSL s due o he ncreased number of anchor sgnals ha can be receved, whereas he RPDFTL s because here are more acve nodes n each cluser In addon, as he node densy ncreases, he RPDFTL posonng effec ncreases more han he oher wo ways, because he accuracy of he measuremen value s far greaer han us relyng on he accuracy of conneced sgnals. (a) Locang error comparson Fgure 2. Influence of he velocy (b) Influence on RPDFTL performance Fgure 3 (b) shows he nfluence of he number of nodes and parameers on he accuracy of RPDFTL posonng. Locaon error ncreases wh ncreasng of, and s nversely proporonal o he ncrease of.from he resuls can be seen, he nroducon of error n a conrollable range, and can grealy compensae for he ncrease n node densy brough abou by he nework overhead. (a) Locang error comparson Fgure 3. Influence of he nodes densy (b) Influence on RPDFTL performance 4.3. The Impac of Measuremen Error on he Performance Ths se of expermens analyzes he effec of sensor measuremen errors on he performance of varous 2 localzaon algorhms, and he error of he sensor nodes e s assumed o be N 0, normal dsrbuon. Fgure 4 (a) shows ha he hree algorhms are affeced by he measuremen error. More obvously, As he 347

9 measuremen error ncreases, he accuracy of each algorhm wll decrease, bu he RPDFTL s more sensve o errors han he oher wo mehods because he RPDFTL uses he measured values of he nodes o locae he Cenrod and MSL rely only on he deecon range of he node, because he mpac of he sensor error s large, RPDFTL performance wh he error ncreases even wll no degrade o he mehod of measuremen, such as MSL. Therefore, RPDFTL more suable for hose measuremen error. If he olerance value of he snk for he obec poson error s also oo large (> 10), he posonng error wll be magnfed, as shown n Fgure 4 (b).ths group of small sensor nework. When he measuremen error s large, he expermen shows ha, whle reducng he nework communcaon burden, he nroducon of poson error olerance wll enlarge he posonng error. I can be sad ha he sensor measuremen error s large; he error of he posonng algorhm wll be amplfed, especally for he range-based, herefore, how o balance he wo parameers o balance he nework raffc and posonng error s a key. One basc prncple s ha when he node measuremen error s large, he error of he poson nformaon of he snk wll be affeced, Poson error olerance values are narrowed o balance posonng accuracy. 5. CONCLUSION (a) Locang error comparson Fgure 4. Influence of he measuremen error (b) Influence on RPDFTL performance In hs paper, an oudoor obec localzaon algorhm based on random probably densy funcon (RPDFTL) s proposed, and a sensor nework organzaon sraegy, SAC, s desgned for effecve arge rackng. RPDFTL nroduces obec locaon error olerance mechansm compared wh he radonal localzaon mehod, a he expense of a small loss of locaon accuracy, o a large exen reduce he nework raffc, and exend he lfe of WSN nework. SAC as a fuson nework coverage area sraegy, ake full advanage of he sensor homogeney and he samplng frequency dscrezaon, whch provdes a good guaranee for savng energy consumpon of he nodes. Expermen resuls show ha under he framework of movng arge locaon algorhm and arge rackng, he energy consumpon, localzaon accuracy of sensor nodes and nework lfe of sensor nework nodes and oher ndcaors relave o he curren algorhm has a greaer degree of mprovemen. REFERENCES Ayach, F. S., Boudaoud, S., Marque, C. (2014) Evaluaon of muscle force classfcaon usng shape analyss of he semg probably densy funcon: a smulaon sudy, Medcal & bologcal engneerng & compung, 52(8), pp Babcock, H., Sgal, Y. M, Zhuang, X. (2012) A hgh-densy 3D localzaon algorhm for sochasc opcal reconsrucon mcroscopy, Opcal Nanoscopy, 1(1), pp Bozz, G., Cell, L., Vcn, A. (2015) Paron densy funcon unceranes on he W boson mass measuremen from he lepon ransverse momenum dsrbuon, Physcal Revew D, 91(11), pp Coa-Ruz, J., Rosles, J. G, Rvas-Perea, P. (2013) A dsrbued localzaon algorhm for wreless sensor neworks based on he soluons of spaally-consraned local problems, IEEE Sensors Journal, 13(6), pp Carl, M., Panzer, S., Pascucc, F. (2014) A on roung and localzaon algorhm for emergency scenaro, Ad Hoc Neworks, 13, pp Grchds, P., Konsandn, L., Whworh, A. P. (2014) On he evoluon of he densy probably densy funcon n srongly self-gravang sysems, The Asrophyscal Journal, 781(2), pp

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