Multi-sensor Data Fusion Based on Consistency Test and Sliding Window Variance Weighted Algorithm in Sensor Networks

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1 DOI:198/CSIS116174S Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks Ja Shu 1,, Mg Hog 1,3, We Zheg 1,3, L-M Su,4, ad Xu Ge 1,3 1 Iteret of Thgs Techology Isttute, Nachag Hag Kog Uversty, Nachag Cha shua@xtgovc School of Software, Nachag Hag Kog Uversty, Nachag Cha zhegwe_chu@16com 3 School of Iformato Egeerg, Nachag Hag Kog Uversty, Nachag Cha hog19863@hotmalcom 4 Isttute of Software Chese Academy of Scece, Nachag Cha sulm@sscasacc Abstract I order to solve the problem that the accuracy of sesor data s reducg due to zero offset ad the stablty s decreasg wreless sesor etworks, a ovel algorthm s proposed based o cosstecy test ad sldg-wdowed varace weghted The teral ose s cosdered to be the ma factor of the problem ths paper Ad we ca use cosstecy test method to dagose whether the mea of sesor data s offset So the abormal data s ameded or removed The, the result of fused data ca be calculated by usg sldg wdow varace weghted algorthm accordg to ormal ad ameded data Smulato results show that the msdagoss rate of the abormal data ca be reduced to 3% by usg mproved cosstecy test wth the threshold set to [5, 15], so the abormal sesor data ca be dagosed more accurately ad the stablty ca be creased The accuracy of the fused data ca be mproved effectvely whe the wdow legth s set to Uder the codto that the abormal sesor data has bee ameded or removed, the proposed algorthm has better performaces o precso compared wth other exstg algorthms Keywords: wreless sesor etworks, data fuso, cosstecy test, sldg wdow, varace weghted

2 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge 1 Itroducto Wreless sesor etwork (WSN) whch s costtuted by a large umber of mcro-sesor odes deployed the motored area ca sese, collect, ad process the formato of motored obects the coverage area The the processed data s set to the observer through the mult-hop self-orgazed etwork [1] Sce the odes are geerally battery-powered ad deployed a harsh evromet area, some of whch are ot avalable for huma, t s urealstc to replace battery for cotuous power supply The odes wll de as log as the eergy s draed out The etwork may work abormal or eve falure oce some odes are dead Moreover, the exteral oses ad teral oses ca affect the accuracy of the sesor data The exteral oses clude electromagetc radato, temperature ad pressures, ad teral oses clude the decrease of stablty ad the zero offset some sesors whch have bee used for a log tme Wth the help of mult-sesor data fuso algorthms, the precso of data ca be mproved I order to solve the problem that the precso of data fuso s low due to zero drft ad the drop of the stablty for part of the sesor whe multple sesor odes measurg o the same target Ths paper troduces a multsesor data fuso method based o cosstecy test ad sldg-wdowed varace weghted sesor etworks Frstly, we propose a sesor measuremet model, ad the model ca smplfy the core problem to the result of teral ose The we preset a cosstecy test wth the ew cofdece dstace to dagose whether the mea of teral ose s shft uder the sldg wdow mode, so that the abormal sesor whch wll lead to zero drft ca be ameded or removed codtoally Fally, we make data fuso processg of the ormal sesors measured value ad some certa ameded abormal sesors by sldg wdow sample varace weghted method, so that more precse data ca be obtaed Related Studes Data fuso WSN s dfferet from tradtoal oes sce the ablty of ode s lmted Nodes are battery-powered, the ablty of CPU s weak, ad wreless commucato s ustable Therefore, Tradtoal complex ad hgh eergy-cosumg fuso algorthms are ot sutable for WSN There exst some algorthms, such as weghted average algorthm, Kalma Flter [] ad Bayes estmato [3] to solve the problem Weghted average algorthm s wdely used as data fuso WSN sce t s smple ad easy Lterature [4] proposed a varace weghted algorthm, ad proved that varace weghted estmator s mmum ubased estmato value of mea varace The algorthm seems smple, but the varaces of all odes eed to be gve frstly Batch estmato algorthm s proposed lterature [5], t s a kd of weghted average algorthm It dvdes all sesors to two batches, ad the uses varace weghted algorthm for fuso after Ths paper s sposored by the Natoal Nature Scece Foudato of Cha (No677355), ad Jagx Key Techology R&D Program (No9BGA1) 198 ComSIS Vol 1, No 1, Jauary 13

3 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks calculatg the sample mea ad the sample varace of each batch Adaptve varace-weghted method s proposed lterature [6] uder the premse that exteral ose s stable t s proposed to solve the problem that the varace of each sesor s ukow wth the help of the varace of sample Iterato method s used to aggregate mult-sesor data lterature [7], t s based o adaptve varace weghted algorthm Ad the result shows that good precso ca be obtaed Lterature [8] adopts wdow varace weghted algorthm ths drecto ad llustrate ts dea o wdow sze defto accordg to dfferet ose chage I regard to the characterstcs of sesor ose abrupto, Lterature [9] proposed the varace weghted algorthm based o adaptve wdow legth It dvded ose estmate curve to smooth zoe ad abrupt zoe by detectg ose chage sesor data, meawhle t uses correspodg wdow sze to revse mult-sesor fuso value ad mproves fal aggregatg accuracy accordg to dfferet curve level Cosstecy test method focuses o the problem that there wll be a devato whe varous types of sesors measurg o the same target, t tests ad removes the sesor wth larger devato to reduce the mpact o fuso Nowadays there are maly some cosstecy test methods based o relato matrx ad dstrbuto graph, ad accordg to how to determe the relato matrx, the former method whch s based o relato matrx ca be dvded to three parts:1 It s a cosstecy test method based o relato matrx whch s determed by the cofdece matrx [1], whch s establshed o kow measuremet model ad ose as the Gaussa ose, the relato matrx s obtaed by calculatg the cofdece dstace betwee each two odes, ad the, ths method tests the samplg value wth larger devato by graph theory approach;it s a cosstecy test method based o relato matrx whch s determed by degree of support [11] Based o the measuremet model, ths method quatfes the support degree of the measured value of each two sesors by a expoetal decay fucto Ad determes the samplg value wth larger devato by the experece threshold value; 3 It s a cosstecy test method based o relato matrx whch s determed by statstc dstace [1], the method s stll establshed o kow measuremet model ad ose as the Gaussa ose, defes the statstc dstace of observatos of each two sesors based o the multvarate ormal dstrbuto to determe the relato matrx, ad obtas sesor set wth the bggest mutually support through drected graph theory; 4 It s a cosstecy test method based o relato matrx whch s determed by emprcal threshold [13], the method determes the trust degree of observatos of each two sesors by the curve fucto wth a emprcal threshold to obta the relato matrx, ad determes the weght of each sesor observatos by the largest egevector of the matrx, fally makes the fuso processg Relyg o Moffat dstace to defe relatoal matrx, the cosstecy check approach [14] uses Moffat ad volvg crteros to compute both relatoal matrx ad correlated graph ad searches sesor group wth a Max support degree by ler ft method ComSIS Vol 1, No 1, Jauary

4 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge 3 System Model I wreless sesor etwork, as show Fg1, there are dscrepaces of the values o the same target measured by dfferet sesors because of the affecto from oses The oses clude exteral ose ad teral ose Target Evet Sesor Nodes Fg 1 A example of Sesor Nodes Deploymet Exteral ose s maly caused by evromet chage whch cludes temperature, pressure ad electromagetc radato Ad we use Gaussa whte ose whch s zero mea value ad dfferet varace the model defto perod Iteral ose s maly caused by the sesors themselves It s relatvely stablzed, ad t s ot chaged a short perod For example, there exsts zero offset because of sheddg of elemet wrg, bur- ad smlar factors It s usually usg Gaussa whte ose whch s ozero mea value ad costat varace the model defto perod The zero drft pheomea are assumed as the measured values of some sesors are smaller or larger tha ormal oes The decreasg stablty of sesors s showed as the large udulatory property of the measured values Lterature [15] proposes a sesor measurg model z x ad a ose model ~ N(,1 ) Cosderg the chage of both exteral ose ad the cosstecy of ose amog dfferet sesors, a ew sesor measurg model s show as formula (1) z x( k) (1) z (k) s the k th measured value of sesor x (k) s the k th real value of target obect, ad t s a costat f k s gve ~ N(, ) s the k th exteral ose, t s chaged wth tmes ~ N(, ) s the teral ose of sesor, t s stable It s ot chaged wth tmes a short perod ComSIS Vol 1, No 1, Jauary 13

5 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks Assumg that exteral ose ad teral ose are mutual depedet, the measurg model ca be smplfed as formula () z x( k) () ~ N(, ) s tegrated ose It s supposed that sesors measure a same target smultaeously Each sesor cotas a sldg wdow whose legth s W for storg samplg values the frst W tmes The k th measured value of sesor s z (k) * The sldg wdow s sample mea s z (k) ad ts sample varace s S 4 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks 41 The tradtoal cosstecy test algorthm Luo ad hs assstats proposed a cosstecy test to solve the problem of cosstece of measured value from the sesors whch measure o the same target Based o the sesor measurg model whch s establshed ths algorthm, the error sesor data s removed after calculatg the cofdece dstace of each two sesors ad establshg the relatoshp matrx betwee the sesors, ad the the optmal statstcal decso makg methods are used for fuso Cofdece Dstace [1] : There are sesors whch measure the same target, ad the measured data of all sesors x 1, x,, x ca be obtaed, f x follows Gaussa dstrbuto ad the correspodg desty fucto s 1 P ( x) The cofdece dstace ca be obtaed by formula (3) x d P ( x x ) dx A x (3) P ( x x ) 1 1 x x exp (4) Where d the formula (3) represets for the cofdece dstace betwee sesor ad sesor, s the varace of sesor, A s the area eclosed by the codtoal probablty desty curve, x x, x x ad x - axs, show Fg ComSIS Vol 1, No 1, Jauary 13 1

6 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge A x x Fg The schematc dagram of cofdece dstace The smaller the value of d s, the closer value of sesor to sesor s I order to smplfy the calculato, Luo has troduced the error fucto (5), ad d s show as formula (6) erf ( ) exp( z ) dz (5) d x x erf ( ) (6) Cofdece dstace matrx [1] : The cofdece dstaces of each two sesors ca be obtaed They costtute the matrx defed as the cofdece dstace matrx D D d11 d1 d1 d d d 1 d1 d 1 d Relato Matrx [1] : Sce the threshold d s gve, the relatoshp value of each two sesors ca be calculated by formula (8) We costtute the matrx defed as the relato matrx R 1 r R d d r11 r1 r 1 (7) (, 1,,, ) (8) r r r 1 I the relato matrx r1 r 1 r R (9), r represets for the support degree of the sesor to sesor r r expresses that sesor ad sesor do t ComSIS Vol 1, No 1, Jauary 13

7 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks have ay relatoshp Whe oe of r ad r equals ad aother equals 1, t meas that the relatoshp betwee them s weak If r r 1, t meas that sesor ad sesor have a strog relatoshp Thus, the largest supported sesor set s obtaed through the relato matrx, ad the the optmal estmato methods are used for the last aggregato But there are several problems to be solved wreless sesor etworks: 1) Accordg to the cofdece dstace from formula (5), each sesor's varace s eed to be gve for the calculato of cofdece dstace of each two sesors, t s ca be descrbed by tegrated ose varace, but t ca be obtaed wreless sesor etworks So t s ot sutable ) Sce the cofdece dstace from formula (5) ad (6) cotas tegral calculato, the calculato s so complex that t s ot sutable wreless sesor etwork 3) The method does t propose a approach how to get the largest supported sesor set Therefore, we make the followg mprovemets o the algorthm 4 A ew defto of cofdece dstace Accordg to formula (), we ca get the followg coclusos: the dfferece betwee the values ca be obtaed by the k th data of sesor ad, ad t follow the Gaussa dstrbuto, that s z x( k) ~ N(, ) (1) z x( k) ~ N(, ) The problem whch s to udge whether oe of sesor or cur zero offset ca be trasformed to the problem of the hypothess testg of the mea dfferece for two samples of ormal dstrbuto: H : (11) H : 1 Uder the sgfcace level, the test statstc T s obtaed (1) [ z x( k) z x( k)] T u 1 / x(k) s a costat, so formula(13) ca be obtaed ComSIS Vol 1, No 1, Jauary 13 3

8 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge T [ z z ] u 1 / (13) If the test statstc T meets the codtos, we ca reect the hypothess H, t represets that there s a large dfferece betwee sesor ad, ad oe of them may exst zero offset But the exteral ose varace ad teral ose varace ( ) of each sesor ca ot be obtaed, ad 1,,, are ot the same wreless sesor etworks I order to obta the test statstc, we troduce the coclusos of the lmt dstrbuto, a ew test statstc as show as formula (14) z z (14) T u * * 1 / S S Therefore, we ca defe a ew cofdece dstace as follow: (15) z z d d [1 P{ x }] Where S * S * d s the sgfcace level of the hypothess testg, accordg to the cosequeces of commttg two type errors, whe the value of s small, the probablty of error type II creases accordgly, that s t wll be easy to make substadard products the test sample udged to be qualfed, the to accept the orgal hypothess If the value of s large, the probablty of error type I creases, so t s easy to make qualfed products the test sample s deemed to have faled ad the to be refused Cosderg that the abormal sesor have much great mpact o fuso, we eed to mmze the probablty of error type II, that meas we should try our best to prevet abormal sesors udged to be ormal oes Therefore, the larger the value of d s, the less obvous the mea tegrated ose of sesor ad s That s, sesor ad may both belog to the ormal sesors ad may also both belog to abormal sesors But formula (15) ca ot be appled sesor odes because of complex calculato Therefore t requres a easy method to calculate sesor relatoal matrx drectly I order to solve the problem, the threshold of sgfcace level eed be gve frstly The result ca be obtaed by the codto (16), k 4 ComSIS Vol 1, No 1, Jauary 13

9 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks T T z z S u * * 1 / S wdow (16) r I correspodg to relatoal matrx factor r r,otherwse, r cosumpto 1 I ths way, t ca avod complex calculato ad reduce eergy 43 The dagoss of abormal sesors Accordg to the ew defto of cofdece dstace, the cofdece matrx D s obtaed, ad the relato matrx R s also obtaed Relato matrx s a symmetrc matrx composed by ad 1 R r r represets that the mea tegrated ose of sesor s much dfferet from sesor, therefore, oe of the sesors must be a abormal sesor r r 1 represets that the mea tegrated ose of sesor s less dfferet from sesor, that s they may be both ormal or abormal Assumg the sesor ode s the vertexes of graph G ad relatoal matrx s the adacecy matrx of graph G, hereby, we could plot the etre R correlato graph of all sesor odes Accordg to theory of resolvg maxmum clque G of graph G [16], the vertexes of clque G composed ormal sesor group A, ad the remag of them composed abormal sesor group B I order to avod udgg mstakely, t s ecessary to make sure the percetage of ormal sesors s beyod 5% Otherwse, t s possble to make a wrog udgmet Algorthm 1: program for Max Clque MaxClque(G; sze) f G = the ed f f sze>max the ed f retur max:=sze whle G!= do New record; save t f sze + G 6max the ComSIS Vol 1, No 1, Jauary 13 5

10 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge ed f retur :=m{ v G} G:=G\{v} MaxClque(G N(v); sze +1) ed whle retur Fg 3 s the relatoshp dagram G showg the degree of support of 1-7 sesors, whch ode 1, ode, ode 3 ad ode 4 costtute the maxmum clque G Therefore, we ca determe that ode 1, ode, ode 3 ad ode 4 costtute the ormal sesor set, ad ode 5, ode 6, ad ode 7 costtute the abormal sesor set Fg 3 The dagram of the degree of support for each sesor 44 The sldg wdow varace weghted algorthm ad how to amed or remove the measured data from abormal sesors The fudametal prcple of adaptve weghted algorthm: uder the codto of mmum average varace, t ca fd the best correspodg weght W of each sesor ode wth a adaptve way, ad help Ŝ acheve the best fuso result As Fg4 shows, S s the measure value of sesor odes, where =1,,, whle Ŝ s the fal fuso result Accordg to ths theory, the sldg wdow varace weghted algorthm: I wreless sesor etworks, sce the exteral ose varace ad the teral ose varace ( k ) of the k th measuremet of sesors carred by each sesor ode are ukow I order to solve ths problem, the sample varace ca be used to replace real varace, the weght of each sesor data ca be obtaed by formula (17) * 1 / S (17) * 1/ S 1 W 6 ComSIS Vol 1, No 1, Jauary 13

11 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks S1 W1 S S W W Ŝ Fg 4 Model of adaptve weghted estmate fuso Algorthm : program for sample varace whle(<window_num) do f(posto<sze) the AVE average value f(posto=) the VAR=; varace= else caculate VAR New record; save t ed f ed f else f(posto=sze) the caculate AVE caculate VAR ed f ed whle retur Algorthm 3: program for sldg-wdow weght ad fuso value whle(<node_num) do f(gpnode!=) update SesorValue update SesorWeght ed f ComSIS Vol 1, No 1, Jauary 13 7

12 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge else f(gpnode!=&&node[]var<node[]var) update SesorValue update SesorWeght ed f ed whle How to amed or remove the measured data from abormal sesors: f the greatest ormal sesor set A ad abormal sesor set B are obtaed, we amed or remove uder certa codtos: * * 1) Sesor m B, f sesor A, ad S S, m z m (k) s eeded to be ameded by usg formula (18), (19) ad () z m z (18) m m m z w z (19) m A w 1/ S A * 1/ S * () * * ) Sesor m B, f sesor A, there s S S, z (k) m m s eeded to be removed smply 5 Smulato Research I order to verfy the valdty of the algorthm, OMNet++ s used for smulato Accordg to the expermetal results obtaed uder dfferet sgfcace level threshold ad the legth of wdow W, the optmal value best ad W best ca be evaluated The the precso of algorthm s compared wth three other fuso algorthms such as arthmetc average algorthm, batch estmato algorthm ad the adaptve varace weghted algorthm Cluster-based routg protocol s used the expermet, each cluster has 41 odes (cludg oe cluster head), ad the cluster head takes resposble for data fuso The sldg wdow legth of the member odes s set to W, ad sgfcace level threshold s Accordg to the model that z x( k) Gaussa whte ose whose mea s zero s used for smulatg the exteral ose (k), ad ts varace wll chage every R tmes Iteral ose ca be descrbed by Gaussa whte ose whose mea s o-zero The percet of abormal 8 ComSIS Vol 1, No 1, Jauary 13

13 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks sesor ode s P, t assumes that the teral ose s stable, ad wll ot chage as tme chages Cosderg the chagg characterstc of the target obect s actual value, x(k) s geerated radomly ad chages every R x 75 5 Mea Square Error (a) Mea Square Error 4 Average Msdagoss Rate (b) Average Msdagoss Rate Fg 5 Smulato Results uder Dfferet Sgfcace Level Threshold ad Wdow Sze W 51 The best sgfcace level threshold best I order to obta optmze sgfcace level threshold best, the parameters are set as follows: =4, N=, R =1, P =3, R x =1, ad [,1 ] ad W [,3] Fg 5(a) ad Fg 5(b) llustrate the smulato results for ths expermet The x -axs represets sgfcace level threshold ad the y - axs represets wdow sze The z -axs represets mea square error ComSIS Vol 1, No 1, Jauary 13 9

14 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge the Fg 5(a) The z -axs represets average msdagoss rate the Fg 5(b) 1) [,5], mea square error ad average msdagoss rate decreases rapdly whe s creasg The hgher the value of, the lower the probablty of Error-type-I s, t meas that the ormal sesor odes have less probablty to be dagosed as abormal odes ) [5,15 ], mea square error ad average msdagoss rate ted to be statoary The reaso s that whe chaged wth the rage, all the abormal sesors are dagosed correctly, t has less effect o mea square error ad average msdagoss rate 3) [15,1 ], mea square error ad average msdagoss rate creases rapdly as s creasg The hgher the value of, the hgher the probablty of Error-type-II s It meas that the abormal odes ca be mstakely dagosed as ormal odes easly Therefore, the optmal rage of sgfcace level threshold s [5, 15] 5 The best wdow sze W best Smlarly, order to obta the optmstc wdow sze W best,fg 6(a) ad Fg 6(b) llustrate the smulato results for ths expermet The x -axs represets wdow sze ad the y -axs represets sgfcace level threshold The z -axs represets mea square error the Fg 6(a) The z -axs represets average msdagoss rate the Fg 6(b) 75 Mea Square Error (a) Mea Square Error 1 ComSIS Vol 1, No 1, Jauary 13

15 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks 4 Average Msdagoss Rate (b) Average Msdagoss Rate Fg 6 Smulato Results uder Dfferet Sgfcace Level Threshold ad Wdow Sze W Accordg to Fg 6, the mea square error creases wth the cremet of wdow sze, but the average msdagoss rate creases lttle Thus, t s clear that wdow sze W oly have mpact o the mea square error 1) W [,1],the mea square error rses sharply Because the wdow sze s the rage of R x ad R The larger wdow sze s, The lower fuso accuracy s ) W [1,3], the mea square error rse gradually, because the wdow sze already exceeds both R x ad R The devato betwee estmated sesor ose varace ad real oe reaches the hghest value Therefore, the optmstc sldg wdow sze W best = s obtaed 53 The precso comparso Fg 7 shows the smulato results of the proposed algorthm compared wth arthmetc average algorthm, batch estmato algorthm ad adaptve varace weghted algorthm uder the codto of =3, W=W best =, N=1, = best =1, R =1, P =3 ad R x =1 Accordg to Fg 7, tradtoal adaptve varace weghted approach has the Max average varace result Because t reles oly o sample varace as the caddate crtero of estmatg whether the sesor odes are ormal or ot, ad adopts weghted mea theory ad uses sample varace as the weght to obta data fuso result, whle gores the fluece o data fuso procedure trggered by Zero offset For example, f the target sesors emerge Zero offset but the stablty of ts measuremet value s o good codto, ComSIS Vol 1, No 1, Jauary 13 11

16 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge ths method wll make the weght too bg from ts possble value rage, drectly cause fuso precso depressg ad lead to the worst fuso result Batched estmato algorthm oly uses batchg method to fuse multsesor data, takes the recprocal of each sample varace as the weght ad sample average value ad eglects the fact that sesor odes lacks stablty Expermet result shows that average varace teds to hgh whch meas a low data fuso outcome; ad arthmetc mea algorthm mprove the fuso precso compared to above two approaches by usg average calculatg operato that may cover those effects brought by Zero offset ad low stablty sesors, for the weghts of sesor odes are all equal However, the fuso result remas udesrable for t do t take the problems of Zero offset ad stablty dfferece to accout Based o tradtoal adaptve varace weghted algorthm, our protocol uses codtoal amed method to correct some of the abormal sesor value whch have a good stablty, the adds them to the weghted fuso sequece of ormal sesor group makg the formato requred by data fuso large eough to ehace fuso precso Ad the result appears to have mmum average varace, estmated fuso data closer to real value ad best fuso performace compared to others The reasos are show as follows: Frstly, cosstecy test s used for dagosg the abormal sesor data; Secodly, t corrects the abormal data some degree Fally, sldg wdow weghted algorthm s used for the last fuso Arthmetc average algorthm Batch estmato algorthm Adaptve varace weghted algorthm The Proposed algorthm Mea Square Error K Fg 7 The Precso Comparso usg the Proposed Algorthm vs Arthmetc Average Algorthm, Batch Estmato Algorthm ad Adaptve Varace Weghted Algorthm 6 Cocluso A ovel sesor model ad a ew data fuso algorthm are proposed to solve the problem that the precso of measured value s low due to exteral ose 1 ComSIS Vol 1, No 1, Jauary 13

17 Mult-sesor Data Fuso Based o Cosstecy Test ad Sldg Wdow Varace Weghted Algorthm Sesor Networks ad teral ose Frstly, a mproved cosstecy test algorthm s used for dagosg sesor data ad obtag the ormal sesor set ad abormal sesor set Secodly, the abormal sesor value s ameded or removed uder some degrees Fally, sldg wdow varace weghted algorthm s proposed for the last data fuso The smulato result shows that the optmum cosstecy test threshold rage s [5, 15] ad the optmum sldg wdow sze s The results also show that t has better performaces o precso compared wth other exstg algorthms Refereces 1 Chog C Y, Kumar S P, Sesor etworks: Evoluto, opportutes ad challeges, Proceedgs of the IEEE, vol 91, pp , 3 Olfat-Saber R, Dstrbuted Kalma flter wth embedded cosesus flters, Proceedgs of Coferece o Jot CDC-ECC 5, Dec 5 3 Zhag Shu-ku, Cu Zh-mg, Gog Sheg-rog, A Data Fuso Algorthm Based o Bayes Sequetal Estmato for Wreless Sesor Network, Joural of Electrocs & Iformato Techology, vol 31, pp , 9 4 Lao X-chu, Qu M, Ma Ha-rog, Study o data fuso algorthms based o parameter-estmato, Trasducer ad Mcrosystem Techologes, vol 5, pp 7-73, 6 5 Zhag X-lag, Su You, A data aggregato arthmetc based o drected dffuso ad batch estmate for wreless sesor etwork, Cotrol & Automato, vol 6, pp ,18, 6 6 Zhog Chog-qua, Dog X-lu, Zhag L-yog, O the Estmato of Varaces for Mult-Sesor Measuremet, Joural of Data Acqusto & Processg, vol 18, pp , 3 7 Lu Yua-ze, Zhag Ja-we, L Mg-bao, Support degree ad adaptve weghted spatal-temporal fuso algorthm of mult-sesor, CCDC 1, pp , 1 8 Xao Log-yua, Zeg Chao, Adaptve secod data fuso algorthm, Trasducer ad Mcrosystem Techologes, vol 6, pp 81-83,18, 7 9 Zhag Y, Ja M-pg, The Applcato of Varace-Estmato Based o the Adaptve Wdow Legth Mult-Sesor Data Fuso, Chese Joural of Sesors ad Actuators, vol 1, pp , 8 1 Luo RC,et al Dyamc multsesor data fuso system for tellget robots IEEE Joural of Robotcs ad Automato,vol 4, pp , Zhag Je, Zhag Zog-L, Jg Bo, Su Yog, Spatal-Temporal Iformato Fuso for Mult-Source Node Cluster Based o D-S Evdetal Theory ad Fuzzy Itegral of Support Degree, Chese Joural of Sesors ad Actuators, Vol19 No6 P77-731, 6 1 Dua Zha-Sheg, Ha Chog-Zhao, Tao Tag-Fe, Cosstet Mult-sesor Data Fuso Based o Nearest Statstcal Dstace, Chese Joural of Scetfc Istrumet, vol 6 No5, pp , 5 13 Zha Ja-She, L Na, Wu Qg, Improved Algorthm of Clusterg-based Data Fuso for WSN, Chese Joural of Computer Egeerg, Vol 34 No11, pp , 8 ComSIS Vol 1, No 1, Jauary 13 13

18 Ja Shu, Mg Hog, We Zheg, L-M Su, ad Xu Ge 14 Mhaela Duta, Maus Hery, The Fuso of Redudat SEVA Measuremets, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol13, PP Nu R, Decso fuso a wreless sesor etwork wth a large umber of sesors, Proc of the Seveth Iteratoal Coferece o Iformato Fuso, Jue 4 16 Pattc R, J Ostergard, A fast algorthm for the maxmum clque problem, Dscrete Appled Mathematcs, vol 1, pp 197-7, Ja Shu s a professor school of Software, Nachag Hagkog Uversty, Cha He receved a Ms Computer Networks from Northwester Polytechcal Uversty 199 Hs research terests clude wreless sesor etwork, embedded system ad software egeerg He s the drector of Iteret of the Thgs Techology Isttute, Nachag Hagkog Uversty Mg Hog s a postgraduate studet of Nachag Hagkog Uversty Hs research terest s Wreless Sesor Network We Zheg s a lecturer school of Software, Nachag Hagkog Uversty, Cha He receved a Ph D Computer scece ad techology from Xda Uversty 1 Hs research terests clude Wreless Sesor Network, optcal etwork, etwork optmzato ad tellgece algorthm L-M Su s a researcher Chese Academy of Sceces, Software Laboratores He receved hs Ph D from Natoal Uversty of Defese Techology, Cha Hs research terest s wreless sesor etwork ad moble Ad Hoc etwork Xu Ge s a postgraduate studet of Nachag Hagkog Uversty Hs research terest s Wreless Sesor Network Receved: Jue 17, 11; Accepted: November 1, 1 14 ComSIS Vol 1, No 1, Jauary 13