A Revised Received Signal Strength Based Localization for Healthcare

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1 , pp A Revsed Receved Sgnal Strength Based Localzaton for Healthcare Wenhuan Ch 1, Yuan Tan 2, Mznah Al-Rodhaan 2, Abdullah Al-Dhelaan 2 and Yuanfeng Jn 1* 1 Department of Mathematcs, Yanban Unversty, Yan , Chna 2 Department of Computer Scence, Kng Saud Unversty,Saud @qq.com,[email protected],[email protected], [email protected], [email protected],[email protected] * Abstract Locaton-awareness s mportant for healthcare, and can be appled to the varous consumer applcatons. The receved sgnal strength (RSS) based localzaton technque has advantages of needng no addtonal hardware and smple to be mplemented nbuldng applcatons. Receved sgnal strength ndcaton fngerprntng (RSSIFP) s an ndoor localzaton technque. However, the RSS s affected by rado sgnals reflectons, shadowng, and fadng. To solve ths problem, an effectve ndoor localzaton method of revsed RSSIFP s proposed to reduce the devaton durng ndoor RSSIFP localzaton. The proposed algorthm uses the RSSIFP based on the poston probablty grd. Before poston, the RSSIFP data are revsed accordng to anchor node sgnal and tme tag. The K-nearest neghbor (KNN) and weghted centre localzaton method s adopted n poston predcton. A test-bed only ncludng common consumer electronc equpments such as wreless access pont (AP), Zgbee node and smart cell-phone s deployed. Performance results show that the proposed algorthm outperforms other algorthms n the healthcare envronments. Keywords: Localzaton, RSSI fngerprntng revsed sgnal, KNN, Weghted centre localzaton, Healthcare 1. Introducton Montorng s the most mportant basc functon for healthcare. Knowng the patent s locaton s useful for montorng assstance. The localzaton n e-health envronment s ndoor localzaton, whch s dfferent from GPS based outdoor poston. Wreless sensor network s an promnent tool to dentfy moble node s postons n absolute or relatve coordnates systems, whch has been receved ncreasng attenton[1-3]. It s common of usng wreless sgnal strength to estmate the locatons of the movng devces based on the fxed reference devces wth ther locaton nformaton. The classc methods to estmate the ndoor locaton are tme-of-arrval (TOA) [4], tme-dfference-ofarrval (TDOA) [5], angle-of-arrval (AOA) [6], and receved sgnal strength (RSS) [7]. Whle the synchronzaton error wll exst forever, TOA method wll lead to exactly accurate because t s based on sgnal travel tmes between nodes. Avodng ths tme error, TDOA method uses the dfference of the sgnals arrval tme between anchor nodes, but an extra hardware (anchor nodes) s requred. The angles between unknown node and a number of anchor nodes are used n the AOA method to estmate the locaton. TDOA, AOA also need extra hardware. However, the RSS based technque needs no addtonal hardware and t s smple to be mplemented for n-buldng applcatons, and t has establshed the mathematcal model on the bass of path loss attenuaton wth dstance. RSS ndcaton (RSSI) based localzaton s smple and the cost of mplementaton s Correspondng Author ISSN: IJMUE Copyrght c 2015 SERSC

2 much cheaper than TOA and AOA-based methods. It s sutable to be employed n the WSN envronment. Unfortunately, t s not easy for rado-based localzaton, because sgnal has reflecton, dffracton and scatterng characters. The studes showed rado waves degradng effects of reflectons, shadowng, and fadng cause the large varablty of RSS [8-11]. As a result, RSS based localzaton methods accuracy s not guaranteed due to sgnals errors and nstablty. However, there need no extra equpment attracts many researchers eyes. In fact, most of smart moble phones have a bult-n RSSI, whch provdes RSS measurement wthout any extra cost. Utlzng RSS method s smple, easy measure advantage and avodng sgnal nstable shortage, we wll present a RSS methods combne few anchor nodes, whch can lead to accurate locaton estmaton n ths paper. The anchor node s used to correct the errors caused by sgnal s nstablty. RSSI fngerprnts are captured and k-nearest neghbor (KNN) algorthm s adopted to fnd k locatons that have a smlar fngerprnt. The accuracy of the proposed method s estmated n experments. 2. Related Works The maorty of studes on RSS based localzaton have been performed [12]. However, the effect of spurous dsturbances on the accuracy of RSS-based ndoor localzaton s stll exsted. The RSS-based localzaton methods need no extra hardware, alternatvely, they store coordnate nformaton whch s related to RSSI fngerprnts. The RADAR [13] system llustrates the prncple of RSS-based localzaton. Fgure 1. Overvew of RSSI Fngerprntng As shown n Fgure 1, there are two phases. In the fngerprnts capture phase, a map of RSSI fngerprnts s constructed for a gven floor. The floor has been dvded nto a grd of cells. For each cell, the fngerprnt can be collected. In locaton predcton phase, portable devce wll read RSSI and use classfcaton technques to predct ts locaton cell that has a smlar fngerprnt. Then, the predcted locaton wll be shown on the map. Based on RSS fngerprnt, there exst several algorthms that can be used to determne the poston of a target through RSS measurements. There are two types methods: geometrc methods and statstcal method. Mnmum maxmum method (mn max) s the classc geometrc method and the maxmum lkelhood method s the representatve of statstcal method. Statstcal method and artfcal neural network method are proposed n [7] for dstance measure. The experments show that the most mportant factor for dstance estmaton s the transmsson power accordng to the relevant dstance. Mn-Max algorthm s 274 Copyrght c 2015 SERSC

3 proposed to overcome the large attenuaton measurement error for n-buldng wreless applcatons[2]. A parameter varatons tolerance method s proposed n [14] and t s more accurate as tme ncreases. A Sgma-Pont Kalman Smoother (SPKS)-based locaton algorthm, whch extend Kalman flter s proposed and t shows ts superor n localzaton accuracy [15]. Honglang and Meng [16] proposed a transmt-power adaptve localzaton algorthm based on partcle flterng for sensor networks asssted by multple transmtpowers. As we know, RSSI value vares over tme due to non-neglgble multpath fadng, especally n ndoors envronments. Ths wll affect locaton measurement accuracy. As a perfect soluton, dfferent fngerprnts n physcal space have dfferent sgnal values can decde poston. Especally, the sgnal values are far apart whch leads to good locate precson. For a system desgner, t s most mportant to construct a map or fngerprnt database for a gven envronment. As usually, an optmzaton fngerprnt map wll be decdes by the number of AP (Access pont), the locaton of AP, and the sze of lattce. In ths pont, researchers have consdered the use of strategcally postoned anchors, theoretcal modelng of sgnal space, and calbraton to quckly buld the database [17]. The RSSI database s buld once a tme, but the localzaton accuracy s dfferent accordng to dfferent algorthm. Brett and Chn [12] have surveyed several RSSIFP systems and dvded localzaton algorthms nto determnstc and probablstc methods. In whch, k-nearest neghbor and Bayesan algorthms represent determnstc and probablstc methods accordngly. Also, found AP number affected the localzaton accuracy from 2.2m 38% wth three APs [13] to 3m 90% wth 33APs [17]. Future more, Barsocch et al [18] found that APs tend to drft n and out of range frequently, and proved only a subset of APs s used for localzaton. 3. Research Methodology As we know, the RSS s nfluenced by reflectons, shadowng, and fadng of rado waves. The ndoor envronment s complex and n whch the wave s a Non-Lne-Sght transfer. In the same poston, the RSS s dynamcally changeable accordng to dfferent tme, temperature, humdty, and movement obects etc. So, tradtonal RSSI fngerprnts used to locaton predcton results errors. The stablty of recevng sgnal strength s essental for poston predcaton. The general flow of revsed sgnal strength localzaton method s: RSSI fngerprnt collecton, RSSI revsng, poston predcton. A. Infrastructure of Test-Bed Fgure 2 depcts the floor plan of expermental ste n ubqutous lfe care research center, where the floor sze s 6m 9m. The system nfrastructure was composed of one moble node (.e., the target), one fxed node (.e., anchor node), four APs n the corner of room. The floor was dvded nto 260 test ponts located on a grd wth a resoluton of 50 cm. The fxed node was hanged on the center of celng whch s 260-cm hgh, whch s represented wth squares n Fgure 2. There s a home gateway server recevng the localzaton request and the RSS change message. In practce, the moble node sends a message to home gateway when ts receved RSSI s changed. The home gateway requests anchor node to send RSSI nformaton when t receves message from moble node. The home gate way receves RSSI measurement from moble node and anchor node, and then, t makes moble locaton predcton based on stored RSSI fngerprnt and determnstc algorthm. Copyrght c 2015 SERSC 275

4 9000mm Celng Home Gateway 6000mm APs Anchor Node RSSI fngerprnt poston (a) Network Area (RP Grd) Floor (b) APs Anchor Node Moble node Fgure 2. Healthcare Test-Bed (a) System Deployment, (b) Fngerprnt Poston Grd Cell B. Data Collecton In order to mnmze the exchange of messages and the data processng tme, data collecton should be performed on the anchor node and moble node, whle data processng should be executed n home gateway as a personal computer allows an exhaustve statstcal analyss. In ths phase, the RSSI fngerprnt data are collected for 260 test ponts. In order to dentfy all avalable APs, the spectrum all channels are scanned. Also, the tme varyng effect should be removed and a robust pcture of the APs sgnal strength are bult. We use WF applcaton programmng nterface (API) to capture the RSSI values n dbm. The measurng process was repeated 100 tmes for each of the 260 test pont locatons, resultng n a total amount of RSS values collected. The acheved measurement repeatablty was qute hgh,.e., always wthn dbm. The average value of each AP s used to represent as RSSI fngerprnt. For the 260 RP (Reference Pont) ponts, the RSSI nformaton was grasped every 1 s over a perod of 2 mn. After each 2 mn scannng, the average RSSI for each AP for a certan RP poston s nserted nto the fngerprnt database. A truncated example of ths fle s shown n Table 1. Table 1. Fngerprnt RSSI Values (n dbm) Num RP AP1 AP2 AP1 AP2 tme (0,0) (0,1) (1,0) (12,19) : : : :56 C. Fngerprnt Correcton The sgnal s fluctuant even though the ndoor envronment s unchanged. The sgnal strength s normal dstrbuted wthout any nterference. So, n fngerprnt database, we use mean value over 100 tmes as the RSS value to overcome the sgnal s nstablty. But for the localzaton, we need measure the RSS and make predcton mmedately; average value s not acceptable for real tme requrement. Even though the envronment s unchanged, the RP s RSSI change wth the sgnal power from AP tme to tme. So, we need construct a map functon from real tme sgnal power to RSSI fngerprnt data. Whle n fngerprnt measurement, we capture the anchor node s fngerprnt A 0 and RP s fngerprnt FP n a mean value. We note t as A 0 [ a 1, a 2,, a n ], whle a s the mean RSSI value from the -th AP, n s the number of AP. The RSSI fngerprnt data s noted Copyrght c 2015 SERSC

5 as: FP = fp fp fp fp fp fp fp fp fp 1,1 1,2 1, n 2,1 2,2 2, n m,1 m,2 m, n Where m s the RP pont number. Whle the moble node sends out localzaton requrement, the moble node and the anchor node get the RSSI values as and accordngly. We use the newly receved RSSI fngerprnt FP data FP. ' a ' fp, fp, a ' A M m1 m2 m n [,,, ] ' ' ' ' A [ a1, a2,, a n ] of the anchor node compared wth A 0 to correct the (1) Where, 1, n Through ths step, the mean RSSI based fngerprnt database FP s transferred nto a tme based fngerprnt database FP. In ths system, the corrected fngerprnt data are stored as a mult-copy wth tme tag. As a result, there are many fngerprnt copes n the system. As the sgnal wll change over tme, the localzaton can make precse predcton wth dfferent fngerprnts accordng to tme stamp. 1, m D. Poston Predcton The localzaton problem based on fngerprnt can shortly be formalzed as follows. Consder the set of nodes,.e., each one wth a fxed and known N RP, RP,..., RP 1 2 n poston (hence the name reference poston). The reference poston s descrbed n 2-D localzaton snce the thrd dmenson usually s not of prmary nterest n an ndoor envronment. Thus, the poston of a reference s, where and are evaluated wth respect to orgn O. Let denote the poston of a moble node of unknown coordnates. The goal of an RSS based localzaton algorthm s to provde estmate p ( x, y) xy, of poston p Start p gven the vector RP ( x, y ) M m1 m2 m n [,,, ]. x y Moble node and anchor node capture sgnal strength ' M, A Tme tag exst && A value exst False True Extract FP data accordng to tme tag Correct FP data wth help of A Fnd nearest neghbor usng KNN Poston estmaton usng Weghted Centrod Localzaton End ' FP RP p ( x, y) Fgure 3. The Flow Chart of Localzaton Copyrght c 2015 SERSC 277

6 We can dvde the poston predcton nto two steps: the KNN (k-nearest neghbor) reference nodes selecton, and the poston estmaton. (1) KNN based RP Selecton The reference node set s gotten based on the RSSI dstance between moble node s and any RP pont s. M m1 m2 m n [,,, ] n Ds ( fp m ) 1, 2 FP [ fp, fp, fp ],1,2, m (2) Also, we know the strong sgnal strength s useful to localzaton but the weak sgnal wll cause more devaton. It means dfferent sgnal strength has dfferent contrbuton for poston predcton. The weght of each RSS s calculated as: mn w n m 1 (3) Whle the RSSI s a negatve value, the weaker strength the RSSI s smaller, but the absolute value s bgger. So, usng represents the mportance of. mn So, the weghted sgnal strength dstance s: m Ds ( fp, m ) n n n 1 m 1 The KNN algorthm s used to calculate the sgnal dstances, whch between moble node and RP ponts, and choose the smallest four dstances RP ponts as the neghbor ponts. (2) Poston Estmaton Poston estmaton s calculatng the moble node based on the 4 nearest neghbor RP nodes mentoned above. We adopt weghted centrod localzaton (WCL) [19] algorthm to calculate the centrod of the coordnates of the so-called KNN RP poston, whch are stored n fngerprnt database. More specfcally, the estmated poston of the moble node s gven by: p 1 k RP k 1 2 p( x, y) RP ( x, y ) (5) Where k s the number of the nearest neghbor RP poston of moble node. It s worth notcng that the centrod results the center of the RP coordnates. It means that the p s the center of ts four nearest ponts. Actually, equaton (5) s most lkely to be unsatsfed n practce because t assumes all the neghbor nodes are equally near the moble node, n [19], the ntroducton of a functon, whch assgns a greater weght to the ponts closest to the moble node, was proposed. The result s the WCL algorthm, whch estmates the poston of the target node as: k Dsk p ( RP * ) k 1 Ds 1 m (4) (6) 4. Expermental Results We mplemented the proposed localzaton algorthm based on the Wf. In our Lab, there are four wreless access pont whch are deployed to send out sgnal and one Zgbee based node hung n the celng to receve the sgnal. The user s equpped wth smart cell 278 Copyrght c 2015 SERSC

7 phone whch acts as moble node. The server based home gateway wll receve the requests, process the data and make poston predcton. The wreless access ponts are deployed at corners as shown n Fgure 2, two on desk about 1 meter above floor, two on the floor. As there s some furntures n the home, the sgnal sent from floor has larger attenuaton as shown Fgure 4. Ths experment was executed at a fxed pont to measure the sgnals from four APs about 60 mnutes. Thus, the sgnal of AP1 and AP2 are more stable than AP3, AP AP1 AP2 AP3 AP4 RSSI (dbm) Tme (Mnute) Fgure 4. RSSI Measurements n the Moble Devce From the Fgure 4, we know the sgnal of AP1, AP2 are sutable for localzaton. However, ths s a statc envronment. For healthcare, the elders wll move and need makng predcton. So, the movng obect s not consdered means the human body s not consdered. In the next smulaton, we not only use these two APs, but we also consder the other two APs whch are affected by attenuaton largely. We make a comparson between the tradtonal fngerprnt (FP) localzaton algorthm, sgnal revsed fngerprnt (rfp) localzaton algorthm, and WCL based sgnal revsed fngerprnt (wrfp) localzaton algorthm. For measurement of the accuracy of the estmated poston for each localzaton algorthm, the root mean square dstance error (RMSE) s used. n 1 RMSE [ ( x x ) ( y y ) ] n 1 ^ ^ 2 2 Where n s the number of poston the moble node vsted, the ( x, y ) and (, ) are the real and the estmated poston of the moble node poston 30 postons ( are shown n table 2 and Fgure 5. x y (7). Durng the experment, n 30 ) are chosen randomly to verfy the algorthm s effects. The results Table 2. Localzaton Error Accordng to dfferent Number of AP Number of AP FP rfp wrfp Max error Mean error Max error Mean error Max error Mean error Table 2 shows the max poston error and mean error for each algorthm accordng to dfferent AP whch has been adopted From the results, the wrfp algorthm adoptng three AP s the best choce for our Copyrght c 2015 SERSC 279

8 experment. Also, table 2 ndcates that the localzaton precson s about 1 meter even though our grd soluton s 0.5 meter. Ths s because our AP s deployed ndoors and our experment envronment s not wde enough. In addton, the RSSI values between FP ponts are not apart enough. The no dstngushed RSSI values wll cause the localzaton error. Compared to lterature [12], our results s better. Theoretcally, the more AP wll generate hgh localzaton precson. However, for our envronment, the three AP s better than Four AP, because the AP3 and AP4 fluctuate greatly. The more fluctuant sgnal wll decrease the predcton accuracy. 2.5 FP rfp wrfp 2 RMSE Number of AP Fgure 5. Localzaton Error of Comparng Varous Algorthms Accordng to dfferent Number of AP Fgure 5 ndcates that the revsed sgnal based fngerprnt algorthm decreases the localzaton error sharply, compared to tradtonal fngerprnt algorthm. The wrpf algorthm presented here has the least localzaton error whch s less than 1 meter. In most cases, wrpf algorthm localzaton accuracy s much closed to rpf whose localzaton error s about 1 to 1.5 meters. 5. Concluson The new sgnal revsed RSSI based localzaton algorthm s proposed n ths paper. The basc approach s to dvde the network area nto the small grds and perform the localzaton usng the poston grd. Based on tradtonal RSSIFP method, an anchor node s adopted as a RSS correcton reference for the fngerprnt database. Ths anchor node can remove the sgnal attenuaton n a better way. We construct a test-bed usng smple equpments and get a good performance. The experments show that the proposed approach can acheve hgh locaton estmaton accuracy for n-buldng wreless localzaton applcatons. Acknowledgements Ths work was supported n part by Chna Postdoctoral Scence Foundaton (No. 2012M511783), Natonal Scence Foundaton of Chna (No , ), and Specal Publc Sector Research Program of Chna (No. GYHY ) and was also supported by PAPD. The authors extend ther apprecaton to the Deanshp of Scentfc Research at Kng Saud Unversty for fundng ths work through research group no. RGP Copyrght c 2015 SERSC

9 References [1] C. Long, W. C. Dong and Z. Y. Zhou, "Indoor robot localzaton based on wreless sensor network", Consumer Electroncs, IEEE Transactons on, vol. 57, (2011), pp [2] K. Youngbae, K. Younggoo and P. GwTae, "Robust localzaton over obstructed nterferences for nbuldng wreless applcatons", Consumer Electroncs, IEEE Transactons on, vol. 55, (2009), pp [3] C. Y. Y and L. Y. Yuan, "A new receved sgnal strength based locaton estmaton scheme for wreless sensor network", Consumer Electroncs, IEEE Transactons on, vol. 55, (2009), pp [4] W. Yunbo, S. Goddard and L. C. Perez, "A study on the crcket locaton-support system communcaton protocols", n Electro/Informaton Technology, 2007 IEEE Internatonal Conference on, (2007), pp [5] C. Blandn, A. Ozerov and E. Vncent, "Mult-source TDOA estmaton n reverberant audo usng angular spectra and clusterng", Sgnal Processng, vol. 92, no. 8, pp. (2012), pp [6] P. Kułakowsk, J. V. Alonso, E. E. López, W. Ludwn and J. G. Haro, "Angle-of-arrval localzaton based on antenna arrays for wreless sensor networks", Computers & Electrcal Engneerng, vol. 36, no. 11, (2010), pp [7] A. Awad, T. Frunzke and F. Dressler, "Adaptve Dstance Estmaton and Localzaton n WSN usng RSSI Measures", n Dgtal System Desgn Archtectures, Methods and Tools, DSD th Euromcro Conference on, (2007), pp [8] E. Elnahrawy, L. Xaoyan and R. P. Martn, "The lmts of localzaton usng sgnal strength: a comparatve study", n Sensor and Ad Hoc Communcatons and Networks, IEEE SECON Frst Annual IEEE Communcatons Socety Conference on, (2004), pp [9] K. Langendoen and N. Reers, "Dstrbuted localzaton n wreless sensor networks: a quanttatve comparson," Computer Networks, vol. 43, pp , 11/15/ [10] D. Lymberopoulos, Q. Lndsey, and A. Savvdes, "An Emprcal Characterzaton of Rado Sgnal Strength Varablty n 3-D IEEE Networks Usng Monopole Antennas," n Wreless Sensor Networks. vol. 3868, K. Römer, H. Karl, and F. Mattern, Eds., ed: Sprnger Berln Hedelberg, 2006, pp [11] N. Patwar, J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses and N. S. Correal, "Locatng the nodes: cooperatve localzaton n wreless sensor networks", Sgnal Processng Magazne, IEEE, vol. 22, (2005), pp [12] B. Dawes and K.-W. Chn, "A comparson of determnstc and probablstc methods for ndoor localzaton," Journal of Systems and Software, vol. 84, no. 3, (2011), pp [13] P. Bahl and V. N. Padmanabhan, "RADAR: an n-buldng RF-based user locaton and trackng system", n INFOCOM Nneteenth Annual Jont Conference of the IEEE Computer and Communcatons Socetes. Proceedngs. IEEE, vol.2, (2000), pp [14] A. H. Sung and Y. Wonpl, "Envronmental-Adaptve RSSI-Based Indoor Localzaton," Automaton Scence and Engneerng", IEEE Transactons on, vol. 6, (2009), pp [15] A. S. Paul and E. A. Wan, "RSSI-Based Indoor Localzaton and Trackng Usng Sgma-Pont Kalman Smoothers", Selected Topcs n Sgnal Processng, IEEE Journal of, vol. 3, (2009), pp [16] R. Honglang and M. Q. H. Meng, "Power Adaptve Localzaton Algorthm for Wreless Sensor Networks Usng Partcle Flter", Vehcular Technology, IEEE Transactons on, vol. 58, (2009), pp [17] O. M. Badawy and M. A. B. Hasan, "Decson Tree Approach to Estmate User Locaton n WLAN Based on Locaton Fngerprntng", n Rado Scence Conference, NRSC Natonal, (2007), pp [18] P. Barsocch, S. Lenz, S. Chessa and G. Gunta, "Vrtual Calbraton for RSSI-Based Indoor Localzaton wth IEEE ", n Communcatons, ICC '09. IEEE Internatonal Conference on, (2009), pp [19] J. Blumenthal, R. Grossmann, F. Golatowsk and D. Tmmermann, "Weghted Centrod Localzaton n Zgbee-based Sensor Networks", n Intellgent Sgnal Processng, WISP IEEE Internatonal Symposum on, (2007), pp Copyrght c 2015 SERSC 281

10 Author Yuanfeng Jn, Assocate Professor Yanban Unversty, Chna, Man actvtes and responsbltes Lecture on numercal computaton & PDE Research on parallel computaton. 282 Copyrght c 2015 SERSC