A Multi-information Localization Algorithm in Wireless Sensor Networks and its Application in Medical Monitoring Systems

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1 A Multi-informtion Loliztion Algorithm in Wireless Sensor Networks n its Applition in Meil Monitoring Sstems Zin PEI Hngzhou Norml Universit Qinjing College; Hngzhou; Chin ; Emil: peiz2004@hoo.om.n Ting Zhu Deprtment of Computer Siene Stte Universit of New ork P.O. Box 6000 Astrt Noe loliztion tehnolog is one of the importnt supporting tehnolog for wireless sensor network.in most pplitions,suh s ojet monitor n trk, lotion router, etermining the phsil positions of sensor noes is the si requirements.the pper proposes loliztion lgorithm for wireless sensor networks se on lotion informtion etween multiple noes, The simultion results in pper show tht the loliztion lgorithm is effetive, the ur of the lgorithm is high, n its lultion is simple so tht suitle for ll sizes of WSN. Furthermore, oring to this lgorithm n the requirements of the meil monitoring sstem, this pper propose relile n effetive struture for wireless sensor networks, n hieve goo result. Kewors-Loliztion, Wireless Sensor Networks, Anhor noe I. INTRODUCTION Although tehnologil vnes improve the helth re sstems roun the worl, ut mn prolems still exist. Ptient hve to spen lot of time ess to meil servies, lrge numer of meil filities re still using outte informtion sstems, it les to misignosis n other prolems. In ition, otors re highl moile, the often wnt to get meil informtion in ifferent lotions in ver short time, whih n enhne effiien, improve ptient re onitions, while reuing the error[1]-[2]. Beuse of the prtiulrit of helth re fiel, people hve ver strong esire for reform with wireless sensor network tehnolog, prtiulr noe loliztion tehnolog[3]. The urrent loliztion proess in wireless sensor network inlues three forms[4]-[21]: the first one is tht ou first nee to otin the istne from unknown noe to the multiple (t lest three) nhor noes, or the reltive ngle informtion with the neighoring nhor noe, n then use mthemtil metho to lulte their phsil lotion[5]-[9], Suh s RSSI, TOA, TDOA, AOA. The seon is, oring to informtion of network onnetivit, suh s network onnetivit, noe onnetivit or the numer of hops from unknown noe to the nhor noe to hieve its own position, suh s Weighting Threshol Centroi Positioning[10]-[19], DV-HOP, APIT. The thir is the senrio nlsis, suh s lotion n positioning nlzing the photo, this pproh requires prior speifi onfigurtion of the sstem, n n e off-line mesurements, ut the nlsis of imges requires gret mount of omputtion proess, is not suitle wireless sensor networks. II. MULTI-DIMENSIONAL LOCALIZATION ALGORITHMS IN WIRELESS SENSOR NETWORK A Algorithms Introue The preision of Rnge-Free Metho is ver low when the noes re istriute loosel[19]-[21], it is not suitle for meil monitoring sstems[22-26]. This pper presents wireless sensor network loliztion lgorithm for loose istriution environment. To filitte the illustrtion, this pper mkes the following ssumptions. It is known tht there re three unknown noe A, B, C, n n nhor noe D(X, ) within eploment re, All these four noes n estimte istnes n ngles with nother, the error for istne estimtion is est, n the error for ngle is θ est, onrete steps of lgorithm is s follow: Step 1: Error threshol setting. When setting error threshol it must onsier set speifi purposes of noes. In monitoring sstem, the noes n e ivie into two ler, the noes in the first ler representing ptient s lotion, the noes in the seon ler represent lotions of ifferent prts of o. If the noe lotion is representing ptient s lotion, euse the istne etween noes is so long, error threshol m e ver ig, n lultion n e simpl. If the noe lotion is representing lotions of ifferent prts in o, ue to the noes jent so lose, so the error threshol n e ver smll. Here, this pper onl represents the lultion of the first ler noes lotion to esript the lgorithm, it lso n get noes lotion in the seon ler sme lgorithm fter noes lotion in first ler is lulte. This pper set the error threshol is THR here. Step 2: Initil lotion of the unknown noe /11/$ IEEE

2 Anhor noe D mesure the istne n the ngle to unknown noes A respetivel, the istne estimting vlue is n ngle estimting vlue is θ. An then unknown noes A mesure the istne n the ngle to nhor noe D respetivel, the istne estimting vlue is n ngle estimting vlue is θ. so the estimte oorintes of A noe is (x 1, 1 ): x 1 1 = X + = + + θ + θ os + θ + θ os The noe oorintes (X, ) mst e in the region of Eqn(2), ssuming tht it is the estimte re of oorintes of the noe A, enote ( X, ). + θ + θ 2π X = ( X + ( ± est)os( ± θ est)) + θ + θ 2π = ( + ( ± est)os( ± θ est)) Similrl, we n lulte the estimte lotion (x 1, 1 ) n (x 1, 1 ) of the noe B, C. An their estimte re ( X, ), ( X, ) of the rel oorintes (X, ) n (X, ). Step 3: Compute the istne n ngle informtion etween noes n nhor noes, n etween noes n nos. Noe A, B, mesure the istne n etween them eh other respetivel, n mesure the ngle θ n θ etween them eh other respetivel, then the istne n ngle from the noe A to noe B n e etermine s : (1) (( + ) / 2, θ + ( θ + θ 2 π) / 2) (3) An then the istne n ngle from the noe B to noe A n e etermine s: (( + ) / 2, θ + ( θ + θ 2 π) / 2) (4) It is esil to lulte estimte vlue of the istne n ngle etween A, B, C, D eh other. Step 4: Further noe lotion lulting (1) Regr noe C s n unknown noe, noe A (x 1, 1 ), B (x 1, 1 ) n D (X, ) s known noes, the lotion of the noe C n e estimte. (2) Here the lotion of noe C n e estimte with Noe A, with the estimte oorintes of noe A (x 1, 1 ), n estimte istne n ngle from noe A to the noe C, the lotion of noe C n e estimte s ( x, ). (3) Determine whether (x, ) (2) is in the estimte region ( X, ) of noe C, if it is in this re, the estimte the lotion (x 2, 2 ) = (x, ), if it is not in, then the estimte lotion (x 2, 2 )=( x + x +, 1 n 1, 1 n 1, ). (4) Similrl, the lotion of noe C n e estimte with noe B (x 1, 1 ) n D (X, ) s (x 3, 3 ) n (x 4, 4 ). (5) The verge of lotion of x, ), noe A is (,, n n x x + x + x + x n, =, =.The lotion of noe A n noe B n e estimte s ( x, ),( x, ) sme w. Then the ERR n n e lulte s Eqn(5) ERR = ( x x ) + ( ) n n, n 1, n, n 1, + ( x x ) + ( ) n, n 1, n, n 1, + ( x x ) + ( ) n, n 1, n, n 1, In Eqn, n represents the numer of les. If ERR n >ERR n-1, it is men the results in n-1 irle is the est result. Think of the stringen of lgorithms, if n more thn setting numer or ERR n <THR, results in lst irle n represent lotion of ll of the noes. Else strt from the first step gin. B Evlution We implement n evlute our loliztion lgorithms using Mtl. The simultion prmeters re s follow: The mximum rius of wireless ommunitions of eh noe re the sme, is set to 100m, it n to regulte wireless ommunitions network verge onnetivit justing the rius of the noes, the hnnel pit etween noes is C=50k/s, pket length is 4000it/s, ontrol pket length is 100its. RSSI istne estimting error is 20%, η=3. In this experiments, the t is the verge t of severl simultion experiments. In the experiment, we tke the following prmeters to mesure the performne of loliztion lgorithm: (1) Anhor noe ensit: the rtio of the numer of nhor numer to the numer of ll sensor noes in the network, These nhor noes re mnul set, their lotions re no errors. (2) Loting positioning ur: it is present the rtio of the error (the istne etween the estimte lotion n the tul lotion) n the wireless rnge of the noe (ommunition rius). An lotion error is efine s follow: (5)

3 e = N i= M+ 1 ( x x ) ( ) i i i i N In this Eqn, N presents the numer of ll noes in network. (x i, i ) present results for the loliztion lgorithm, ( x, ) present tul loliztion. i i (3) Coverge: The rtio of the numer of noes n e lote to the totl numer of noes nee e lote. noe on the ege of re m not e lote, it improves network overge. Figure 2. Coverge with the nhor noe ensit III. APPLICATION IN MEDICINE MONITORING AND SSTEM STRUCTRUE Figure 1. Lotion ur with the nhor noe ensit Fig.1 esries the ur (vertil xis) with the nhor noe ensit (horizontl xis) inresing of the Eulien lgorithm n improve lgorithms. it n e seen from the figure when the nhor noe ensit is less thn 20%, the ur of the improve lgorithm is 20% higher thn the Eulien lgorithm. It is euse the lgorithm in this pper introues irultor justment whih n improve the preision of lotion estimte. The verge lotion error of ll the noes is the flg of loop, so eh lotions justment in eh loop is onsiere the sstem fetures on the whole. The result is otine when sstem verge error in next loop is greter thn it in this loop, so tht it n pproh its optiml result of whole sstem loop loop itertive omputtion. Fig.2 esries the overge is improve with ensit of nhor noe inresing of Eulien lgorithm n improve lgorithm. Comprison with the Eulien lgorithm, the overge of the improve lgorithm is signifintl improve thn it of the Eulien lgorithm, espeill when nhor noe ensit is low, vntge of the improve lgorithm is oviousl. As shown in Figure 3.5, when the nhor noe ensit of less thn 20%, overge of Eulien lgorithm ereses rpil with the ensit of nhor noe eresing, while overge of the improve lgorithm erese little with ensit of nhor noe eresing. So the epenene of overge of the improve lgorithm to nhor noe ensit is erese. It is euse lotion mehnism of this improve lgorithm, in sstem onl the A Applition in Meiine Monitoring In the meil monitoring sstem, ll noes in sstem will e ivie into three lers. The noes in first ler re the nhor noe, the re resonle set in vrious prts of the monitoring region, suh noes hve urte lotion informtion, n re fixe, the represent the lotion of region for proviing loting referene for the noes in the seon n thir ler, n the n e istriute in vrious rooms. The noes in seon ler represents the lotion informtion of the ptient, it n e lote oring to loliztion of nhor noe in the first ler. The noes in the thir ler represent ifferent prt of the ptient, these noes n e loting in vertil surfe n Vertil respetivel the lgorithm esrie in this pper, oring to the lotion of the noes in the first ler n the seon ler. Therefore, the n e loting in threeimensionl oorintes. So tht the omputer terminl n simulte the lotion n position of the ptient e se on the lotion of ll the noes, suh s sitting or stning in room. Menwhile, the noes in the thir ler noes lso hve funtion of etetion, the n etet the ptient's hert rte, loo pressure n other phsiologil sttus. Therefore, the stff n grsp the sttus of the ptient timel, n provie the neessr servies to ptients from the sink it usull is omputer. Of ourse, these t will lso e store in the omputer, it n provie the effetive sientifi sis for the tretment. Dt trnsmission etween noes is lso ver importnt, euse of the speifi pplition environment, to trnsmit t effetivel n relil, tking into ount the energ sving, the t trnsmit sstem is three-ler struture s follow.

4 B Struture of Telemeiine Monitoring Sstem We usull introue the struture tht in ever re onl one noe ts s trnsponer. The whole network hs struture s shown in Fig.3. when A trnsponer stops working, the w of 1,2,1,2 will isonnet. It is si, ll of the noes in re A n t reeive the t, n ll of the noes in re B n t sen the t. To overome this shortge, we hve to introue n improve struture, n this struture is whih is pplie in telemeiine monitoring sstem. In this struture, we selet two noes to t s trnsponer in eh re. This two noes t sme funtion n smmetril works. It hs relile struture s shown in Fig.5. Figure 3. One Trnsponer Struture In this struture, ll of the noes in sme re were ivie in to some groups, the noes on sme ptient re in sme group. Certinl, this struture is esil to e simulte on sink. The noes in seon ler t s trnsponer for noes in thir ler, n the nhor noes t s trnsponer for noes in seon ler n thir ler. We n esil get n onlusion tht this struture is frngile, euse in the pplition, the energ onsuming is not sme etween trnsponer lws, some trnsponers n work ver long time, euse the on t nee onsume too muh energ, ut other trnsponers lre n not ommunite to nhor noes, euse the these trnsponers lre onsume too muh energ n it n not work no longer. So we introue nother struture s shown in Fig.4. Figure 4. One Trnsponer Struture With Connetion In this struture, there lso is one trnsponer in ever re, the energ onsume n tsk lo re lne mong the trnsponers, so it is muh relile thn it in Fig.3. But this struture is not relile enough lso. Beuse when the some trnsponer stops working, ll the noes in this re lso n t reeive or sen t to nhor noes. In Fig.3, Figure 5. Two Trnsponers Struture In this struture, eh ptient hs two noes s trnsponers, the lso represent the lotion of ptient. So eh re hs two trnsponers, the sttuses of these two trnsponers re equlit, n the ut in the re is ivie equlit to these trnsponers lso, the trnsponers in two ifferent res re onnete eh other in network struture. The ut in re is ivie equlit, n is omplishe the two trnsponers in this re together. So this struture s is more relile thn the struture we introue in Fig.3. When the trnsponer A in left re stop working, The ws of 1, 1, 1, 2 will isonnet, ut 50% ut in the re n e omplishe trnsponer A. If the trnsponer A n omplish ll the ut in this re, ll the ut will e omplishe trnsponer A. An sstem n still work normll. IV. CONCLUSION AND FUTURE WORK In this pper, we propose the lotion lgorithm oring to lotion informtion of numer of noes in wireless sensor networks, lotion of the unknown noes is etermine irultor justment for lotion informtion of numer of surrouning noes, n the numer of loop in the proess of refinement les is eie the men vrine of error, this lgorithm tkes into ount lotion errors of whole the wireless sensor network, so the noe lotion error is less ffete nhor noe ensit, Simultion results show tht the lotion ur of lgorithms n meet the most nees. In ition, this pper introue relile struture se on the lotion lgorithm propose in this pper n requirements of meil monitoring sstem. In this struture, two trnsponers re equl, n the ut in the re is ivie equlit to these trnsponers lso. So tht network reliilit n vliit n e protete.

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