OPT Onlne Person Trackng System for Context-awareness n Wreless Personal Network Xuel An R. Venkatesha Prasad Jng Wang I.G.M.M. Nemegeers EEMCS, Delft Unversty of Technology, The Netherlands {x.an, j.wang3,vprasad,gnas.n}@ew.tudelft.nl ABSTRACT Context aware systems are the order of the day. Wth the advent of low cost sensor networks and systems ambent ntellgence s becomng more and more sought after feature. In ths work, we present an Onlne Person Trackng (OPT) system for ndoor envronment whch s amed to provde locaton nformaton to the context aware applcatons n Personal Network. We present our extensve measurements and expermental study of the popular Tmote-Sky devces whch use IEEE 80.5.4 standard. We use our expermental results to desgn a prototype for a real-tme person trackng system wth mnmal number of anchor motes. A Weghted Mnmum Mean Square Error (W-MMSE) localzaton algorthm s used n our system. Ths paper thoroughly records the efforts nvolved n desgnng and prototypng such a system.. INTRODUCTION The rapd progress n technology has enabled a faster development of many dstrbuted wreless sensor and actuator networks. Usually, a Wreless Sensor Network (WSN) s deployed for montorng envronment and for dsaster recovery and response systems. Some smple examples are: a smoke detector and actuator for ndoor applcatons, forest fre response, health montorng system, etc. Wth the emergence of newer solutons for anywhere and everywhere connectvty [] and wth the concept on Ambent Intellgence [] a potental task of a WSN s also to support the context-awareness n the networks. New paradgms securely connectng many heterogeneous nodes and/or networks are beng proposed recently. European projects on Wreless Personal Network (WPN) [3] and Future Home Networks [4], are some of the examples where the context-aware applcatons are thought to be an essental part of the applcatons n the moblty enabled networks. In a WPN the user and all hs devces are constantly and securely connected and the servces/applcatons are adapted so that the sessons are transported seamlessly wthout the user s nterventon dependng on the context or stuaton. The context-aware solutons try to explot nformaton Ths work s partly funded by IST MAGNET-Beyond and IOP Future Home Network. regardng the geographcal locaton, the tme of the day, avalable equpments, hstory of user s nteracton/usage, envronmental changes and the presence of other people. They provde the user wth the servce whch s best suted to the person s present stuaton. Locaton awareness also sustans mportant functonaltes such as sesson transfer, self-organzaton and mantenance of the network. One of the mportant nputs for a context aware applcaton s the knowledge of the physcal locaton of the person. Thus there s a necessty to nvestgate nto an effcent mplementaton of localzaton n a WPN. Specal sensng devces/nodes can be deployed for ths purpose. However t would be better f many sensng devces, already present, can be exploted for fndng the locaton along wth ther other usual task of fndng the parameters of the surroundngs. There are many well proven technques readly avalable such as Global Postonng System (GPS). It s usually very effectve for outdoor envronment. However, due to the dependency on expensve and specal hardware as well as the naccuracy and fallblty caused by the nterference and mult-path fadng nsde the buldngs, GPS s not sutable for ndoor localzaton. Some dscussons on ths can be found n [9]. The other method for localzaton s to use the Receved Sgnal Strength Indcator (RSSI), whch s a readly avalable resource n any RF-based WSN. Snce the rado power s senstve to the antenna orentaton, nterference and obstacles n the surroundngs, RSSI s not consderably relable. However n ndoor envronments for nexpensve deployment we may stll try to use RSSI to a certan degree of accuracy. Further readly deployed sensors used n some partcular context can be used for generatng the context by usng them for localzaton. Ths should be nexpensve and easy to mplement. Thus n ths paper we try to explore the mplementaton of a low cost localzaton system that can work wth sparsely deployed sensors. We employ t for low complexty Onlne Person Trackng (OPT) system for ndoor applcatons to use t as an nput for Context generaton. A wdely used WSN platform Tmote-Sky s employed n our prototype. We compare three algorthms to optmze the RSSI-based Popularly t s called as Onlne Professor Trackng system snce one of the professors was tracked wth ths experment.
estmaton. For lmted accuracy applcatons the results obtaned are useful to generate some of the context for a WPN wth a lesser number of devces. Our system s smple and nexpensve for mplementaton. In ths work we have tred to explan our experences n mplementng an onlne localzaton servce that may be used n the larger framework of a WPN. The rest of the paper s organzed as follows: n the next secton some earler related work s presented. In Secton 3 we explan our set up and data collecton. Secton 4 has dscusson on varants of Mnmum Mean Square Error (MMSE) algorthms. In Secton 5 we explan offlne and onlne locaton estmaton system. In Secton 6 we conclude and dscuss our future plans.. EARLIER STUDIES Localzaton systems adopt many approaches that are suted for dfferent problems. Hghtower and Borrello [7] developed the taxonomy to develop, evaluate, and dentfy opportuntes for locaton-sensng technques n general. When we look nto the approaches n detal, dfferent postonng algorthms vary dependng on the knds of raw data used [8]. The man technques are Angle-of-Arrval (AoA), Tme-of-Arrval (ToA), and RSSI. Many earler reported studes use AoA and ToA based technques and they show that a suffcent accuracy can be acheved n fndng the range. Addtonal mprovement can be obtaned usng Ultrasound and Ultra Wde Band (UWB) technologes wth ToA measurements [9, 0] or usng AoA asssted ToA systems []. A survey on UWB based localzaton can be found n []. However, these systems requre extra hardware support lke antenna arrays, ultrasound and UWB transcevers to measure the tme taken for the rado wave to propagate between the devces. Hence t ncreases the complexty of the devces and becomes a bt more expensve. If we want to use the smple technques and devces such as exstng devces for sensors where smplcty and low cost are fundamentally a must we have to use some smple technques avalable. Nonetheless t s fraught wth a trade off concernng the accuracy of the measured coordnates of the locaton. RSSI, as an alternatve way, s therefore extensvely studed for localzaton. However the RSSI measurements are susceptble snce t s easly affected by the envronment such as the multpath-fadng and obstructons. Therefore the dstance estmatons may be naccurate. Thus earler nvestgatons have extensvely focused on the estmaton algorthms to mprove the range usng RSSI measurements. Range-free localzaton algorthms are proposed to mnmze the naccuracy of the estmated dstance usng RSSI. In ths scheme rather than usng the nformaton concernng the absolute dstance, the geographc relatonshp between target mote and anchor motes whch are normally aware of ts own postons are used. For example, APIT [3] employs a novel area-based range-free approach to perform locaton estmaton by solatng the envronment nto trangular regons between anchor nodes. Where as n ROCRSSI [4], each sensor node uses a seres of overlappng rngs to narrow down the possble area n whch t resdes. However these algorthms are manly proposed for a broader area wth a large scale deployment of sensor network. Further they do not specfcally consder an ndoor envronment. Due to the complexty of these algorthms, much of the effort s on the smulaton studes [3, 4, 5] to evaluate the many of the deas rather than actual experments. Most prototype work s done wthn the sgnature-based systems, such as RADAR [6] and MoteTrack [7], whch are emprcal localzaton schemes that matches the rado sgnature, acqured by a roamng devce. A database s used to map the sgnature found by the roamng devce to know the locaton. These systems can acheve accuracy wth a mean error dstance of m to 3m [6, 7]. However, the lmtaton of ths scheme s the laborous work of the collectng the RF sgnatures and hence results n lack of scalablty. An expermental study such as PetTracker [8] uses smple algorthms that approxmate the target locaton by pckng the locaton of the neghbor anchor mote that receves the hghest RSSI from the target mote. The estmaton accuracy s very much dependent on the anchor motes locaton and densty of motes n the area. Another category s the rangebased algorthms whch make use of the converted dstances from RSSI values by usng trangulaton and typcally wth MMSE to estmate the target s poston [9]. In the case of a WPN the context-awareness applcatons usually lke to know the place where the person s at present who owns the WPN. Thus t s not requred to be very accurate to the last centmeters but t should gve an dea about the presence of the person approxmately, say, n whch room on the floor the person s at present. When he moves t should be possble to fnd the next room he s gong to. Snce we want to mplement many other applcatons based on the locaton as an nput to contextaware servers, t s requred to be as lghtweght as possble along wth the ambent ntellgence for respondng n an unobtrusve and nvsble way []. Moreover, we would not have the luxury of havng hgher densty of anchor motes except a few motes that are possbly used also for collectng some envronmental varables. The range-based MMSE estmaton algorthm leverages smplcty wth suffcent accuracy n the context of WPN. It has an addtonal advantage that t can be mplemented n real-tme consderng the lmt on memory. We proposed a weghted MMSE algorthm wth a smple wall attenuaton model amng at the ndoor envronment, whch s valdated and evaluated through the real-tme feld tests. The WSN of our localzaton system s organzed as a dstrbuted system consstng of anchor motes whch are also havng sensors. A central computaton devce, a PC
connected wth one mote, processes the data and generates locaton estmaton for the context aware applcatons. 3. EXPERIMENTAL SETUP 3. Tmotes We employed a wdely used sensor network platform Tmote-sky motes whch are based on Telos Revson B platform [6]. Tmote-sky features the Chpcon CC40 rado [0] for wreless communcatons. The CC40 has an IEEE 80.5.4 complant rado. Tmote-sky s nternal antenna s an nverted-f mcro-strp whch does not have a perfect omn-drectonal pattern. The specfcaton of Tmote-sky says that the rado range s up to 50m. However, t has been found that after 6m, the packet drop percentage reduces consderably [3]. Therefore, we would lke to operate motes wthn ths range. The network stack s mplemented n TnyOS [] whch s an event-drven operatng system desgned for such sensor networks wth lmted computatonal and memory resources. The prototype was developed usng NesC [] whch s an extenson to the C programmng language desgned to be used wth TnyOS. 3. Experments and calbraton for prototypng The RSSI value s senstve to the envronment and t also depends on the power supply. Before usng the RSSI to estmate the poston of the target mote, we dd some prelmnary experments to see the dependence of the RSSI value on the antenna orentaton and the dstance. For the antenna orentaton experment, we put the transmttng and recevng motes 4m apart wthn lne-ofsght, and measured the RSSI values at 8 dfferent antenna drectons (0 o to 360 o ) n steps of 45 o. In Fg., we show that the antenna has the strongest strength at 0 o about -50 dbm, and the smallest sgnal strength at 90 o about -65 dbm. We observed that RSSI vares n a range of around 5dBm for the statc case. 0 50 40 0 70 (RSSI dbm) 300 90-40 -0 60 330 80 0 Fg. : Antenna orentaton effect For generatng the emprcal relatonshp concernng RSSI versus dstance, RSSI s measured by placng two motes n the mddle of a narrow corrdor 60m m at dfferent dstances, from 0 to 6m wth m step. As also observed 30 from the pror work [3], the varaton n measured RSSI s hgher when the motes are nearer to each other n order to get a better resoluton at the short dstance we took measurements from 0 to m wth a 0.m step. For each dstance, RSSI s measured wth the recever s antenna pontng n four dfferent drectons (0 o, 90 o, 80 o, 70 o ) wth respect to the antenna of the transmtter. Fnally the average RSSI over these four antenna orentaton was used to generate the emprcal relaton (curve). As shown n Fg. the RSSI decreases when the dstance between the transmtter and recever motes ncreases. Further, RSSI changes rapdly wthn the range of 0 to 4m. That s, the same RSSI varaton leads to a larger dstance varaton when the dstance between two motes s larger than 4m. We also plotted the maxmum emprcal curve whch s the maxmum value of RSSI over 4 antenna drectons (0 o, 90 o, 80 o, 70 o ) and flter out the ponts that volate the monotoncally decreasng trend. Ths maxmum emprcal curve s used for fxng the boundary of the exhaustve searchng for the locaton estmaton. Actually, by usng the maxmum emprcal curve, we try to nclude all the plausble ponts of the target mote locaton. 0-0 -0-30 -40-50 Emprcal Curve Maxmum Emprcal Curve j S -90 0 4 6 8 0 4 6 Dstance (m) Fg. : Emprcal relaton curve 3.3 Localzaton experment Scenaros The layout of the floor of the buldng s shown n Fg. 3. The physcal dmenson of the floor s 70m m wth a narrow corrdor of 60m m n the mddle. All the anchor motes were located approxmately 0cm above the ground n the corrdor. We frst measured and calbrated the error wth the known postons. We call ths a Controlled experment and later we used ths measurement to approxmately fnd the poston of a person n real-tme. We call ths an Onlne Person Trackng system. In our Controlled experments, all the doors along the corrdor were kept closed and we conducted our experments when the dsturbances along the corrdor were mnmal. Consequently the nterference from human actvtes was also less. Onlne person trackng experment was executed n an actual settng that also had many people movng around whle trackng the person.
Fg. 3: Floor layout Fg. 5: Data Packet format Fg. 4: Localzaton experment sub-scenaros In control experments, sx motes were used, and sx subscenaros are deployed n the experments. These scenaros can be dvded nto two sets dependng on the arrangement of anchor motes. The frst set we call t as a trangle set wth 3 anchor motes formng an magnary trangle. It s referred as Scenaro T-8, T-4, and T- correspondng to the dfferent horzontal dstances, d, between anchor motes as shown n Fg. 4. The dstance d s 8m for Scenaro T-8, 4m for T-4 and m for T-. The second set s a rectangle set wth 4 anchor motes formng an magnary rectangle. Three Scenaros, R-8, R-4, and R- represents the dstances, d, 8m, 4m, and m correspondngly. For all these dfferent scenaros, Mote-T, the target mote s placed at fve dfferent locatons.e., Locatons,,3,4, and 5 as shown. 3.4 Data Collecton Anchor motes broadcast BEACON packet perodcally at rate of 4 packets/second. When the target mote receves the BEACON packet, t measures the RSSI value. A collecton of RSSI value s used to form a new DATA packet and s sent on uncast to the base staton that s to the mote whch s connected to a PC through USB seral port. Dependng on how often we want to estmate the poston, number of RSSI values sent n a DATA packet and frequency of packets sent would vary. Snce the postons of the anchor motes are known the routng s predefned and at tmes t s multhop. The packet format s shown n Fg. 5. A Java applcaton s developed to lsten to the USB seral port to collect raw data receved by the base staton mote. Poston estmaton s made after each 00 RSSI samples were collected by the Java applcaton. We use the average RSSI of the 00 measurements to estmate the locaton. 4. ALGORITHMS USED IN THE PROTOTYPE In our prototype we used three algorthms for makng a comparson of the performance and we selected the one that offers least error. The frst one s the conventonal MMSE [9] and the next two algorthms are slghtly modfed verson of the MMSE to acheve better results. We present our expermental results wth a thorough dscusson n Secton 5. and we compare the results. In the subsequent sectons we frst dscuss the algorthms that we ntent to use. 4. Algorthm : Conventonal MMSE (C-MMSE) Mnmum Mean Square Error (MMSE) s a popular algorthm whch s employed for target locaton estmaton usng the emprcal relaton of dstance versus RSSI. We reproduce the MMSE algorthm here for the sake of completeness. We use fxed anchor motes to send beacons to the movng target mote to fnd nstantaneous RSSI value. The locatons of the anchor motes are known apror. Let us assume that N anchor motes are used for montorng, and d s the estmated dstance between target Mote-T from an anchor mote (, =,,3,... N ), whch s located at. By defnng the error estmaton functon as: N () = = where, = f x, y ) = d ( x x ) + ( y y ) () ( e e e e and ( xe, ye) s the estmated poston n two-dmensonal coordnates, whch s suffcent n most of our cases, the estmated poston ( xe, ye) s obtaned by mnmzng over a cross sectonal regon whch depends on the known error bounds on the dstances usng emprcal relaton. A
note on fxng the boundary of the cross sectonal area s gven n Secton 4.4. For the RSSI-based estmaton method wth more number of anchor motes resultng n more d s does not guarantee a hgher accuracy. In many nstances, ths may result n bgger error range. RSSI averagng over many nearest neghbours does not yeld better performance (see [6]). Thus n our prototype we use only three strongest receved sgnal strengths for estmaton n the algorthms presented n the sectons to follow. For example n Fg. 6(a), the geographcal poston ( x, y) of the anchor motes A, B and C are known apror. The estmated poston E s obtaned by 3 mnmzng =. Ths s the wdely used MMSE = method whch we call here as C-MMSE method. The complexty of C-MMSE s much dependent on the number of anchor motes nvolved n estmatng ( xe, y e). We can mprove the accuracy by adoptng some ntellgent and smple varatons of ths method as gven below. 3 ( x, y) e e x3, y3 ( ) (a) Conventonal MMSE E e e E 3 3 (b) Modfed MMSE Fg. 6: Illustratons for Algorthms C-MMSE and M- MMSE 4. Algorthm : Modfed MMSE (M-MMSE) M-MMSE reduces the computng tme of C-MMSE and also enhances accuracy n many cases. A qualtatve weghng concept s ntroduced n M-MMSE. Consder a stuaton where we have two anchor motes whch are near the target and the thrd one beng farther away. Snce estmated dstance from the farther mote provdes less accuracy generally than the closer anchor motes [3], among the three RSSI values, the frst two hghest RSSI values can offer a better relablty than the thrd one. Therefore, n M-MMSE, only two anchor motes wth bggest RSSI values are nvolved n the MMSE estmaton process and we get two possble estmated postons. The thrd anchor mote s used to choose the fnal estmaton from the two possble canddates. As shown n Fg. 6(b), E and E are the canddate target postons estmated by mote A and C. e and e are the estmated dfferences accordng to mote B s RSSI value. The fnal estmaton s chosen from E and E whch has mn{ e, } e. The advantage of ths method s that we have less target area whle fndng MMSE estmaton and thus t s faster, moreover snce the thrd RSSI value has lesser accuracy we wll not be usng t n the estmatons to avod hgher possblty of errors. 4.3 Algorthm 3: Weghted MMSE (W-MMSE) In M-MMSE we used the dstance calculated from the RSSI as t s wthout modfyng them. However, we used the lowest RSSI value (low relablty) only to make a mnor selecton. We extend the same dea that the relablty of the estmated dstances s lower f the anchor motes are farther to all the RSSI measurements from the anchor motes. Thus we try to nvestgate the accuracy of the poston measurements by gvng hgher prorty to RSSI values that are hgh. We modulate all the measured RSSI values and thus the dstances wth dfferent weghts dependng on how relable the measurements are. In ths algorthm, a quanttatve analyss of the relablty that s reflected n fndng the weghts s dscussed below. Actually, there s no explct way to gve a clear pcture about the relablty wth respect to the measured RSSI. We use the ndrect way that the resoluton of dstances estmated are hgher f the RSSI values are hgher. Ths s because of the hgher slope n the emprcal relaton for dstance wth respect to hgher RSSI values, referrng to Fg.. Thus we use the slopes for calculatng weghts by pecewse lnear approxmaton of RSSI versus dstance resultng n segments. Therefore, we quantfy the weghts wth the slopes of the lne segments of the emprcal curve. We fnd the varous slopes n the emprcal relaton and then we use them to modfy the dstances estmated from the emprcal relaton wth dfferent weghts. For each RSSI value we get a slope S j from the emprcal relaton for j th segment. If we have N anchor motes and out of them we select a set of three motes, that have the hghest RSSI values. Let us call ths set of these three anchor motes as M = {k, l, m}. Frst, we fnd the dstance d, M from the j emprcal relaton. S s the slope found from the emprcal curve for the mote havng an RSSI whch corresponds to the segment j n the emprcal relaton, as shown n Fg.. Then we defne, j S, M (4) w = j max{ S } where, w s the weght for anchor mote, M. Now, the C-MMSE s modfed as,
N new w = =. (5) And estmated poston ( x e, ye ) s obtaned by mnmzng. new 4.4 Boundary Selecton Prmarly for all the three proposed algorthms we use crcle overlappng method to determne the probable cross sectonal area or ponts and then an exhaustve searchng n ths area yelds an optmzed locaton dependng on the resoluton that s sought. However, precse calculaton of the overlapped area s complex and tme-consumng. In order to reduce the complexty and at the same tme to mantan the suffcent accuracy, a rough selecton of the boundary of the searchng area s requred. If the selected boundary s too wde t can ensure the optmal poston but consumes more tme snce we may search over a larger area. Whereas, f the selected boundary s too small, then computatons could be less but there s chance of excludng an optmal poston from the searchng area. Because of the antenna orentaton effect [5], the emprcal relaton between dstance and RSSI s found by averagng the RSSI values sampled from four dfferent drectons. Nonetheless, the mapped dstance found from emprcal relaton for crcular overlappng may nduce potental error because of the excluson of the real orentaton. It s shown n Fg. 7. In order to nclude all the possble locatons and at the same tme to keep the computatons as least as possble we defned maxmum emprcal relaton, that wll take care of the RSSI measurement errors (see Fg. ), for boundary selecton. d d max d max Fg. 7: Estmated area selecton For example, n Fg. 8, we use three anchor motes wth ther estmated dstances to generate three crcles. If the three crcles are partly overlappng as shown n Fg. 8 (a), the target mote has the hghest probablty of beng n the overlappng area. The searchng range b x on x-axs vares from max{ x dmax, x dmax, x3 dmax 3} to mn{ x + dmax, x + dmax, x3 + dmax3}, where, d max s the dstance found from d d max 3 3 b x d max d max 3 (a) Wth overlappng area b y b x dmax dmax 3 3 b y d max 3 (b) Wthout overlappng area Fg. 8: Boundary selecton RSSI vs dstance curve but usng the maxmum emprcal curve. If the three crcles do not have a common overlappng area, as shown n Fg. 8 (b), b x ranges from mn{ x, x, x 3} to max{ x, x, x 3}. In ths case wthout the common overlappng area, we expand the estmaton area to make t certan that potental target poston s ncluded n the search area. The same procedure s appled to fnd b y. Another consderaton s the buldng structure. The selected boundary can not be larger than the physcal dmenson of the buldng and thus t can lmt the search area. 5. RESULTS AND DISCUSSIONS We frst studed our algorthms, postons and patterns of the deployment of anchor motes n an deal stuaton and found the range of the error. We call ths as controlled experments. Later we selected the best suted algorthm and deployment pattern n our OPT prototype. 5. Controlled experments To examne the antenna orentaton effect we measured the RSSI values, wth Mote-T antenna n four dfferent angles, receved from three dfferent anchor motes, where Mote-3 s the nearest to the target mote, and Mote- s the farthest. Measured typcal RSSI values are shown n Fg. 9 wth x- axs representng the sample ndex. We can see that there t vares wthn a range of 5dBm over dfferent drectons. Whle consderng the emprcal curve, t s obvous that the antenna orentaton effect leads to bgger error on dstance estmaton when two motes are farther apart. Further t s not possble to fnd a proper antenna orentaton n an onlne system therefore we took RSSI measurements n four drectons (see Fg. 9). In the controlled experments we used these four drectons rather than eght drectons as depcted n Fg.. Fg. 0 shows the cumulatve dstrbuton functon (CDF), of the estmaton error consderng all the 6 scenaros. Each scenaro nvolves fve expermental postons and a total of 30 estmatons. W-MMSE outperforms all the algorthms and C-MMSE provdes the least performance.
For W-MMSE, 5 % of the estmated postons are wthn an absolute error of 0.6m, 75 % estmatons wthn an absolute error.7m. The results show that W-MMSE has better accuracy. -50 0 degree -90 0 0 30 40 50-50 80 degree 0 0 30 40 50-40 -50 90 degree 0 0 30 40 50 70 degree -65-75 0 0 30 40 50 Mote Mote Mote 3 Fg. 9: Effect of antenna orentaton wth three anchor motes CDF Probablty 0.8 0.6 0.4 0. C-MMSE M-MMSE W-MMSE 0 0 3 4 5 6 Error dstance(m) Fg. 0: Algorthm comparson We also consder the effect of anchor motes deployment on the performance wth respect to the accuracy n fndng the poston. Comparng the dfferent anchor deployment scenaros, based on W-MMSE, Scenaro T-4, where 3 anchor motes are used and wth horzontally 4m apart, gves the best performance, see Fg.. We fxed fve dfferent postons and these postons were revsted wth all the sx dfferent anchor mote deployment scenaros. The estmaton errors for all the postons are well wthn.5m for Scenaro T-4. The reason for the Scenaro T-4 outperformng all others s probably related to the emprcal relaton. That s when the dstance s less than 4m, the RSSI versus dstance has a relatvely a sharper slope where the RSSI value wthn ths dstance s more senstve, and can reflect the dstance calculaton effcently. It s also seen that wthn m range the RSSI value s hgher and one may thnk that keepng anchor motes at m apart may offer better result. Probablty 0.8 CDF 0.6 Scenaro T-8 Scenaro T-4 0.4 Scenaro T- Scenaro R- 0. Scenaro R-4 Scenaro R-8 0 0 3 4 5 6 Error dstance (m) Fg. : Scenaros comparson However, we used T-4 snce, we wanted to use less number of motes and our experments also showed that T- or R- dd not perform well at all. 5. Onlne Person Trackng System Prototype Based on the controlled experment n the prevous subsecton, Scenaro T-4 was chosen to deploy the anchor motes for real-tme trackng prototype. We used a total of ten motes wth the dstance between them beng 4m. As we see that W-MMSE gave a better result we employed t n our OPT system. In order to track the real-tme poston of the target mote, we developed an applcaton wth GUI to depct the floor plan of the buldng n Java programmng language. We dsplay the person who s tracked wth a red dot. When the person moves the estmaton would not be perfect however, as soon as the person halts we should be able to get an approxmate poston of the person. Some snapshot of the trackng system (wth the GUI) s shown n Fg.. Whle trackng a person who s moble, fast data processng s requred. One possble soluton s to ncrease the BEACON rate at each anchor. However, t wll ncrease the possblty of packet collson. Another possble soluton s to reduce the number of samples n each estmaton. Accordng to our experence n workng wth the prototype, the estmaton accuracy s not affected by the number of samples n a certan range. Thus n the prototype, we took 60 samples for fndng the mean RSSI nstead of 00 that was used n our controlled expermentaton. Ths acheved a good result when person was n the corrdor where the motes are placed. However, consderng the rado attenuaton by the walls, we added a wall model n the estmaton. Because all the anchor motes are deployed n the corrdor, when the person enters the offce rooms the attenuaton caused by the walls was taken nto consderaton n the estmaton. A threshold s defned to dstngush f the target mote s n the corrdor or n the offce rooms. Based on Scenaro T-4, the bggest Eucldean dstance between the target mote and the nearest anchor mote s.5m
Fg. : Real-tme trackng Java GUI as llustrated n Fg. 3. Accordng to the prelmnary experment, we take the smallest RSSI value amongst four dfferent antenna drectons at 3m as the threshold. For sngle wall, 3 dbm attenuaton s accounted. Typcally, f the target mote s estmated to be nsde the offce room, 6 0.8 dbm s added to the RSSI value to compensate the wall effect. Ths s due to two walls ncludng the compartment 0.6 wall between the target and anchor mote par. It s found by many experments that wth 6 dbm the target poston s 0.4 estmated wth less error. 0. Probablty CDF In the corrdor In the offce room 0 0 4 6 8 0 Error Dstance (m) Fg. 4: Real-tme experment performance Fg. 3: Bggest Eucldan dstance estmaton We tracked the person n real-tme, who was slowly movng from one poston to another (say wth a gap of a mnute) and recordng the poston at defnte nstants. The OPT performance was thus found by comparng the recorded real poston and the poston estmated by the OPT. In Fg. 4 we show the localzaton performance obtaned when the person s n the corrdor and when n offce rooms. Totally, 36 expermental postons were used n the corrdor experment. Consderng the results of all the 36 postons, 50% of the estmatons provde an accuracy of about m and 5% of them provde an accuracy of about.5m. When the person was n the offce room, 6 expermental postons were used. 50% of them have an accuracy of about 3.8m; and 5% of them were found to be wthn about 3.3m. And ths error s less than the typcal sze of an offce room. We also note that we used a mnmal number of motes n our system and we ddn t deploy motes nsde the offce rooms that would have reduced the error n fndng the poston. 6. CONCLUSION In ths work, we mplement a testbed for provdng locaton nformaton for ndoor applcaton. We want to use ths system for generatng nputs for Context Aware systems n a WPN. We employed the RSSI value for the estmaton of locaton. Usng the MMSE algorthm we proposed a weghted MMSE algorthm for mprovng the accuracy of the estmatons. We extensvely tested the system to show that t mproves the performance compared to the tradtonal MMSE algorthm. Comparng wth the earler results reported n [8], we have the smlar system complexty however our algorthm out performed n terms of accuracy. Compared wth the reported experments n [6] [7] based on the smlar estmaton performance our locaton system s smple snce t does not nvolve laborous work of database creaton and mappng. We also note that the vacllatng nature of RSSI measurement lmts the accuracy n our estmaton. One way to solve ths s to have a lttle hgher densty of anchor
motes to cover the offce rooms dependng on the stuaton and floor plan. A complete system has been developed. The program requres n the specfc floor plan to dsplay the estmated poston of the person on the GUI. The system can be adopted for dfferent floor plans wth some change n the codng. We are plannng to make t robust and easly modfable for dfferent envrons. Moreover ths system can be demonstrated easly. We plan to set up a demonstraton unt. Further we want to study the effect on accuracy by usng dfferent anchor mote deployment patterns wth dfferent denstes. We want to study the ntegrated context aware system that uses OPT. We plan to explore whether some more mprovsaton can be acheved wth respect to our estmatons. A more precse wall model s another area where we want to mprove. We ntend to study the effect of the power supply (or uptme) on the RSSI and n turn on the locaton estmaton. We would also lke to see the results by varyng the frequency of data collecton and processng. 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