Improving Unreliable Mobile GIS with Swarm-based Particle Filters

Transcription

1 Improving Unreliable Mobile GIS wih Swarm-based Paricle Filers Fama Hrizi, Jérôme Härri, Chrisian Bonne EURECOM, Mobile Communicaions Deparmen Campus SophiaTech, 450 Roue des Chappes Bio, France {hrizi, haerri, ABSTRACT Accurae Mobile Geographic Informaion Sysem (GIS) is a major building block of many applicaions, paricularly in Inelligen Transporaion Sysems (ITS). In his conex, GPS provides posiion informaion of each vehicle, while immediae surrounding informaion is gahered hrough he exchange of beacons. Ye, he ITS environmen is characerized by frequen losses of GPS signal and beacons. Esimaion/racking based on Kalman or Paricle filers could be an alernaive o suppor he precision of he Mobile GIS, bu boh approaches are equally sensiive o missing and unreliable daa. In his paper, we propose GSF, a Glowworm Swarm Opimizaion o paricle filers, adding he bio-inspired capabiliies of Glowworms o converge o muliple poenial esimaes, when unreliable mobile GIS lack precise updaes. We firs analyze he performance of GSF by considering perfec condiions. Second by considering GPS signal loss, packe loss and posiioning errors. Simulaion resuls show ha our approach achieves is design goal of improving he precision of he mobile GIS. GSF performs beer han sandard paricle filer scheme in erms of posiion accuracy, and his a a reduced complexiy and fair convergence ime. Caegories and Subjec Descripors: H.2.8 [Informaion Sysems]: Daabase Managemen: Daabase applicaions: Spaial daabases and GIS; G.3 [Mahemaics of Compuing]: Probabiliy and Saisics: Probabilisic algorihms (including Mone Carlo); C.2.1 [Compuer-Communicaion Neworks]: Nework Archiecure and Design- Wireless Communicaion; I.6.6 [Simulaion and Modeling]: Simulaion Oupu Analysis. Keywords: Mobile GIS, GPS, Paricle Filer, Swarm Inelligence, ITS. EURECOM indusrial members: BMW Group, Cisco, Monaco Telecom, Orange, SAP, SFR, STEricsson, Swisscom, Symanec, Thales. The auhors would furher acknowledge he suppor of he French DGCIS/OSEO for he FOT Projec SCOREF. Permission o make digial or hard copies of all or par of his work for personal or classroom use is graned wihou fee provided ha copies are no made or disribued for profi or commercial advanage and ha copies bear his noice and he full ciaion on he firs page. To copy oherwise, o republish, o pos on servers or o redisribue o liss, requires prior specific permission and/or a fee. ACM SIGSPATIAL MobiGIS 12, November 6, 2012, Redondo Beach, CA, USA. Copyrigh 2012 ACM /12/11...$ INTRODUCTION Mobile Geographical Informaion Sysem (GIS) is an emerging echnology combining GIS and he capabiliies of mobile communicaion o access, rerieve, fusion and aler remoely sored daa. Geographic daa can be obained no only by means of radiional GIS mehods such as acquisiion from GPS, digial road maps and local daabases, bu also via daa coming from wireless echnologies. This firs allows remoe access o large size daabases, bu also enables daa collecion and sorage from mobile GIS applicaions o remoe daabases. In ITS, GPS daa and beacon messages represen he major sources of geographic informaion for he mobile GIS. GPS provides local posiion informaion of each vehicle, while geographic informaion of he surrounding environmen (called commonly awareness ) is colleced from he beacons exchanged beween nodes. A high GIS precision is srongly required by many applicaions, noably ITS raffic safey. For insance, in case of collision avoidance applicaion, accurae geographic informaion of each vehicle and heir neighbors has o be provided by he mobile GIS in order o efficienly evaluae he risk of collision wih poenial vehicles. Ye, GPS signals and wireless communicaion are known o be unreliable and uncerain. Beacon losses due o high fading and inerferences may occur frequenly, and GPS signals may be missed or received wih large errors. In such cases, exrapolaing unreliable or missing daa using posiion racking represens a soluion for he Mobile GIS. Tracking has been sudied exensively in he las decades and several racking approaches have been proposed. Bayesian filers i.e. Kalman filers[2] and paricle filers [8] are he mos known ones. The limiaions in hese approaches are ha hey rely on reliable and consan posiion updaes eiher from GPS or from beacons, as well as on assumpions ha fuure moions no varying much from he previous ones. Ye, he unreliable characerisics of he wireless channel and GPS signals, as well as unexpeced sudden moion changes ypically found in vehicular moions, make hose filers lose he precise locaion of esimaed vehicles. Observing ha vehicular mobiliy is joinly governed by physical and social laws, e.g. clusers are formed on he road as a resul of social needs and behaviors, and depics comparable paerns o swarm behaviors, we sugges o rely on arificial swarm inelligence o enhance racking algorihms. In his work, we propose glow-worm swarm filer (GSF), a swarm-based SIR paricle filering based on muliple hypoheses racking. The proposed soluion is o consider no only a single poenial fuure locaion bu also o consider

2 various oher poenial locaions modeling he evenual loss of GPS signal or packes in addiion o he unpredicable moion changes. A glow-worm swarm opimizaion (GSO) algorihm has been used because of is capabiliies o find muliple local opima and o cluser he search space ino various muliple hypoheses. This migh implicily improve he funcionaliy of paricle filer by augmening he diversiy of paricles and avoiding he degeneracy problem. Our approach can be applied in several GIS domain bu in his paper we invesigae he case sudy of ITS. We evaluae he performance of GSF wih a basic SIR paricle filer (laer referred as PF) scheme in erms of precision and convergence speed. Using he itetris [1] simulaion plaform and calibraed realisic vehicular scenarios, we illusrae how GSF provides beer racking accuracy requiring a lower number of paricles compared o he sandard PF. Moreover, GSF showed o achieve is design goal o ensure a rade-off beween high racking precision and fas convergence. The res of his paper is srucured as follows. Secion 2 analyses he racking problem of unreliable GIS: he sandard paricle filer is discussed wih is challenging problems hen we inroduce our racking approach GSF. Aferwards, in Secion 3, a simulaion sudy is performed evaluaing he performance of GSF compared o he basic PF. In Secion 4, we provide relaed works of posiion racking. Finally, Secion 5 repors he conclusions and provides direcions for furher research. 2. TRACKING UNRELIABLE GIS 2.1 Assumpions In he scope of his work, we assume ha each node (laer referred as ego node) manages and mainains a GIS including he self locaion informaion gahered from Global Posiioning Sysem (GPS). GPS has become one of he mos imporan daa resources of GIS. I provides 3-dimensional posiion informaion [x, y, z], velociy vecor [Vx, Vy] and ime informaion in real ime. In ITS and relaed cooperaive applicaions, an up-o-dae knowledge of he surrounding conex is required as well. Therefore, each node handles a GIS for oher neighboring nodes. This is obained by he exchange of periodic beacons, i.e. broadcas messages sen a fixed or variable rae over a single hop, and including locaion and mobiliy daa e.g. geographic posiion, speed and direcion. 2.2 Problem saemen Accurae locaion informaion is becoming increasingly imporan for many applicaions. In paricular, ITS cooperaive safey applicaions require precise and up-o-dae GIS, i.e. provided by local GPS daa. Moreover, an accurae conex-aware knowledge, i.e. GIS of neighboring nodes, is needed. These wo kind of informaion are unforunaely exposed o frequen loss of precision. In some specific zones in he road, he GPS recepion migh be obsruced leading o a wrong esimaion of he self sae. Moreover, due o he high fading of wireless channel in he vehicular environmen, beacon messages sen from neighboring nodes are highly influenced by channel losses. These unreliable characerisics sugges posiion racking o suppor and o enhance he precision of he GIS. Various esimaors have been proposed over he pas years. Bayesian filers i.e. Kalman filers[2] and paricle filers [8] are he mos popular ones. The common idea is o creae a poserior disribuion of he inner moion sae considering all colleced daa i.e. from GPS or beacons. Kalman Filers have been widely used for racking in several research fields. Their key drawback comes from heir implicily assumpion ha vehicular moions follow a linear model, and ha colleced daa is subjec o Gaussian noise. Exended Kalman filers (EKF) represen a possible alernaive for he esimaion of nonlinear sysems, alhough he poserior disribuion remains approximaed by a Gaussian disribuion. When dealing wih he challenging environmen of a mobile GIS, boh Gaussian approximaions and moion linearizaions are no accurae assumpions and migh yield o low racking performance. Paricle filers insead do no make any assumpions in he moion or he poserior disribuions, and have been shown o fi well in GIS. They approximae he poserior disribuion by a se of weighed paricles corresponding o poenial esimaes of he unknown sae. Concepually speaking, in a Bayesian framework, he racking problem is composed of hree main blocks: 1. Inernal Moion Model - he inernal represenaion of he evoluion of he posiion. I is described by a mobiliy model, which is supposed o mach closely he movemen of he arge vehicles. Mahemaically, i is represened as p(x x 1) 2. Esimaion Model - he esimaion of he inernal model from observaions (gahered from GPS or beacon messages). Paricle filers could be employed o solve he likelihood funcion p(z x ). 3. Decision Making Model - he oupu of he racking. The poserior probabiliy condiioned o he observaions obained by he decision model. Mahemaically, i is represened as p(x z, z 1...z 0), and solved by funcions such as Maximum Mean Square Error (MMSE) or Roo Mean Square Error (RMSE). The effeciveness of posiion racking depends exremely on wo basic elemens. Firs, he accessibiliy of he exernal observaion (coming from beacons) is required for he good performance of he racking sysem. When a GPS signal is missing or when packe exchanged beween nodes are los due o bad channel condiions, a sudden large deviaion is expeced o occur. Mahemaically, if z are missed for several values, hen he likelihood funcion and he poserior funcion canno be evaluaed properly. Second, he reliabiliy of he inernal moion model. If i is known, predefined and well conrolled (as he case of mos of he Bayesian racking approaches), he racking problem is no complex. Unforunaely, highly dynamic environmen is characerized by frequen and sudden changes in dynamic paerns. Accordingly, he evoluion of he posiion p(z x ) deviaes significanly from he real sae. Boh aspecs are regularly experienced in mobile GIS for ITS environmens. In his work, our main focus is o improve he accuracy of he GIS and provide an efficien esimaion of he sae of he ego vehicle and oher moving neighboring nodes under such circumsances. We address in deails he firs major problem consising of missing or erroneous GPS signals, or losing packes exchanged beween nodes due o a bad channel condiions. We discuss parially as well he second issue of moion model which is subjec o sudden and unexpeced movemen changes.

3 2.3 Paricle Filering Paricle filers approximae he poserior disribuion, describing he sae of he sysem, by a finie se of weighed samples (or paricles) {(x (i), w (i) ): i=1,..,n}. Recursive Mone Carlo sampling is performed guided by a dynamic moion model. Several implemenaions have been proposed for Paricle Filering. Their main differences lie in he used resampling mehod and/or imporance disribuion. Sampling imporance resampling (SIR) PF [8] is he mos used implemenaion in racking sysems. In order o avoid he problem of degeneracy of he PF algorihm where all bu one of he imporance weighs are close o zero, resampling is used a each ime sep. The poserior disribuion is approximaed by he imporance weighs w (i) n f(x )p(x y 0: )dx n i=1 The weigh updae is given by: w (i) i=1 w (i) = 1 w (i) f(x (i) ) = w (i) 1 ( p(y x )p(x x 1 ) π(x x 0: 1, y 1: ) ) as follows: where he imporance disribuion π(x x 0: 1, y 1: ) is approximaed as p(x x 1 ). Algorihm 1 gives an overview of he SIR paricle filer operaion. Mainly, SIR consiss in hree seps, he sae predicion where he poserior probabiliy is given based on he probabilisic sysem ransiion model p(x x 1). The second sep is he updae which is performed based on he likelihood p(z z (i) 1 ). The paricles are hen resampled o generae an unweighed paricle se. Resampling is performed by drawing n paricles from he curren se wih probabiliies proporional o heir weighs and hen assigning o hem equal weighs 1/n. Algorihm 1 Pseudo-code of he SIR paricle filer 1: Iniializaion: Draw N paricles x (i) p 0(x x 1) wih equal weighs 2: Sae predicion: x (i) p(x x 1 ) 3: Weighs updae: For each paricle: Calculae w (i) 4: Normalizaion: For each paricle: Calculae w (i) 5: Resampling = p(z x (i) ) = w (i) / n i=1 (w(i) ) Figure 1 depics a deailed graphical represenaion of he evoluion of paricles of he SIR algorihm wih only n = 9 samples. Firs, paricles are iniialized or disribued according o he probabiliy disribuion p(x x 1 ). A he recepion of a new observaion, weighs are compued for he differen samples according o he likelihood of he observaion given he curren sae p(z x (i) ). Normalizaion and resampling are hen performed o generae new paricles according o heir weighs wih more paricles in areas wih high weighs and fewer paricles in areas wih low weighs. The sae ransiion generaes new paricles saes given he curren saes and he algorihm is resared. Figure 1: A represenaion of he evoluion of he paricles following he SIR algorihm. A weak poin of paricle filers is paricles degeneracy. Even wih a high number of paricles, i may happen ha he se of paricles loses is diversiy and deviaes from he real sae. This is emphasized by he lack of exernal observaions due o GPS signals or packes losses and/or o he unexpeced and sudden change in moion paerns, as menioned in Secion 2.2. As depiced in Figure 1, in such siuaions, he regions of likelihood are far away from he prior disribuion. Thus, mos of he paricles are generaed wih low weighs. The reason behind his degeneracy problem is ha PF mainains one single hypohesis which is srongly conneced o he moion model s assumpions. An example of a vehicular scenario is illusraed in Figure 2, based on his unique hypohesis, PF fails o rack accuraely he neighboring vehicle afer an updae loss. The paricles lose he diversiy yielding o severe divergence from he real posiion. Figure 2: An illusraion of a racking scenario where he dos illusrae he generaed PF paricles. The racked vehicle is esimaed by he PF of he ego vehicle o be in he major hypohesis. Afer missing he beacon, PF fails o predic properly he real posiion. The proposed soluion we describe nex secion is no only o consider a single poenial fuure locaion (illusraed as Major Hypohesis) bu also o consider various oher evenual locaions due o he loss of GPS signals or observaions (illusraed as Minor Hypohesis), and/or o unpredicable moion changes.

4 2.4 Glow-worm Swarm Filer (GSF) Following observed human behaviors, when we lack clues on where somehing disappeared, we look in various poenial locaions as funcion of he conex. Our filering proposal follows he same approach. Consequenly, ypical siuaions resuling from GPS signals or packes losses, or sudden change in mobiliy are modeled as alernaive hypoheses apar from he expeced single major hypohesis. Mainaining muliple hypoheses allows he filer o handle sudden changes and recover from emporary diversions. The idea is o cluser he se of paricles ino sub-groups represening each a poenial hypohesis. Therefore, we exend he SIR PF wih he bio-inspired paern, namely he Glowworm Swarm Opimizaion algorihm (GSO) [5]. Before he resampling phase, he GSO algorihm is applied. Accordingly, he PF paricles are mapped ono he glow-worms of he GSO. The algorihm is able o divide he paricles ino clusers ha can converge simulaneously o muliple opima. The se of paricles is enriched wih no only high bu also low probable hypoheses. The glow-worms behavior applies well o he idea of having muliple racking hypoheses. Figure 3 reproduces he same scenario of Figure 2 bu his ime GSF is applied, paricles are sub-divided ino 3 groups, one major hypohesis and wo minor hypoheses. Considering hese differen hypoheses, he filer, consequenly, is able o manage he loss of he beacon and mainain efficienly he racking process sable. Figure 3: An illusraion of a racking scenario. The dos in he figure represen he paricles corresponding o he posiion esimae of he vehicle. The racked vehicle is esimaed by GSF o be in he minor hypohesis. Considering unexpeced beacons losses, GSF is able o ensure good racking performance Glow-worm swarm opimizaion (GSO) In his secion, we give more deails abou he GSO algorihm. Basically, Glow-worm swarm opimizaion (GSO) [5] is a swarm inelligence based algorihm inspired by he behavior of he glow-worms in naure where he female glows o arac a male for maing. In he algorihm, a probabilisic approach is used o selec neighbors wih brigher glow. A luciferin value proporional o he inensiy of he glow Algorihm 2 Pseudo-code of he GSO 1: Parameers: n, l 0, r 0, γ, ρ, β, s, r s, n 2: Deploy N glow-worms randomly wih equal luciferin l i (0) = l 0 3: while <iermax do 4: for i = 1 N do 5: Updae phase 6: l i() = (1-ρ)l i(-1) + γ J(x i()) 7: end for 8: for i = 1 N do 9: Movemen phase 10: Deermine he lis of neighbors for glow-worm i 11: for j = 1 N do 12: Calculae moving probabiliy 13: end for 14: Selec Glow-worm j according o he probabiliy: P ij = (l j () - l i ()) / ( n k=1 (l k() l i ()))) 15: Move Glow-worm i oward j: x i (+1) = x i () + s*((x j () - x i ()) / ( x j () - x i () )) 16: Updae neighborhood range: r i d (+1) = min (rs, max (0, r i d ()+β(n - N i() ))) 17: end for 18: end while is associaed wih each agen. GSO sars wih a random deploymen of an iniial populaion of glow-worms in he search space wih equal quaniy of luciferin l 0 and wih he same neighborhood decision r 0. Each ieraion consiss of a luciferin updae phase followed by a movemen phase based on a ransiion rule. The luciferin value is updaed based on previous luciferin value and a funcion J(x) ha evaluaes he finess of he previous posiion of he glow-worms. The pariculariy of GSO is is decenralized decision making aspec. The movemen phase depends on he posiion of neighbor where he glow-worm will move. The algorihm of GSO is furher explained in Algorihm GSF algorihm In GSF, as shown in Algorihm 3, GSO is applied o paricles before he resampling phase. Paricles, considered as glow-worms, move owards neighbors wih high weighs. This resuls in a creaion of several sub-groups wih various weighs in he soluion space. The paricles resampling is hen performed for each sub-group. Only paricles wih highes weighs in each sub-group will survive. Figure 4 depics a represenaion of an example of he evoluion of paricles according o GSF algorihm. The GSO algorihm is performed before resampling phase. Paricles move owards neighbors wih high weighs. Accordingly, differen groups are buil corresponding o muliple hypoheses. The resampling process is hen applied o each group wih more paricles in areas wih high weighs and less paricles in areas wih low weighs. The sae ransiion predics new paricle saes given he curren paricle saes and he algorihm is resared. 3. EVALUATIONS In his secion, we evaluae he performance of our new racking sysem aiming a enhancing he GIS accuracy. We aemp o examine he effeciveness of our GSF algorihm o rack efficienly he sae of boh he ego vehicle and he moving neighboring nodes considering he wo cases wih

5 in he vehicle rajecory in such environmen. The laer represens he high dynamic aspec of vehicular environmen. The simulaion experimens are based on four scenarios. We have used wo calibraed realisic scenarios from he ciy of Bologna. Furhermore, wo arificial scenarios: urban and highway have been designed. The firs realisic scenario called Acosa Pasubio joined models an urban environmen composed of muliple inersecions. The second one, consiss in a highway. The Figures 5 and 6 illusrae he Acosa Pasubio joined and he highway itetris scenarios. The configuraion parameers of our simulaions are shown in Table 1. Figure 4: A represenaion of he evoluion of paricles according o GSF algorihm. Algorihm 3 Pseudo-code of he GSF 1: Iniializaion: Deploy paricles randomly 2: Sae updae: Apply mobiliy model 3: Weighs updae 4: Normalizaion 5: Move paricles according o GSO algorihm 6: Resampling aking ino accoun he several sub-groups creaed by GSO and wihou GPS (or packes) losses and under dynamic consrains. Moreover, GPS posiioning errors resuling from he precision of he navigaion sysem are considered. We perform a comparison of GSF wih he sandard paricle filer scheme. We consider vehicular environmen as a case sudy o model dynamic sysems. In he following, we inroduce he simulaion seup and he configuraion of mobiliy and nework scenarios. We presen hen he se of performance merics we have measured, and finally he resuls of our experimens. 3.1 Simulaion seup We have carried ou a se of simulaions o examine he performance of our proposed sysem under various realisic condiions. We have used itetris [1], he inegraed ITS simulaion plaform. itetris enables he simulaion of V2X communicaions and he modeling of vehicular mobiliy paerns and raffic condiions. Simulaions have been carried ou on he basis of one ego vehicle ha runs GSF and he basic PF o compare boh filers performances. Accordingly, simulaions of GPS signals and beacons losses are performed using he same scenario. In he firs case, by considering he scenario as self racking of he ego vehicle. In he second case, by running he same algorihm of he neighboring node on he ego node. To simulae he loss, we proceed by suppressing he beacons ransmissions in some given ime seps. We apply hree differen schemes of beacon updae loss (or GPS signal loss): one loss ou of en updaes per second, wo successive losses and hree successive losses Mobiliy scenarios We have considered boh urban and highway raffic environmens. The former models he unpredicable and he uncerain mobiliy due o he brual change ha can occur Figure 6: itetris Highway mobiliy scenarios. Scenario Size Mean Speed Figure 5: itetris Acosa Pasubio joined mobiliy scenarios. Arificial- Urban Arificial- Highway itetris- Urban itetris- Highway x:2000m y:100m x:10000m y:0m x:2126m y:2117m x:69000m y:53000m [m/s] Max Speed [m/s] Max Accel [m/s2] Max Deccel [m/s2] Table 1: Configuraion parameers of he mobiliy scenarios Nework scenario In our communicaion scenario, we consider ha beacons are sen a he nework layer periodically. Table 2 gives an

6 Parameer Value p Channel CCH Conrol channel Simulaion Time 100s for each run Number of simulaion runs 5 imes for each scenario Propagaion model Logarihmic Disance Scenario GSF(A-Urban) 1.53 m 1.49 m 1.55 m PF(A-Urban) 2.73 m 2.44 m 2.24 m GSF(iTETRIS-Urban) 1.36 m 1.31 m 1.27 m PF(iTETRIS-Urban) 2.42 m 1.79 m 1.69 m Transmission power V2V ransmission range 20 dbm 400m Table 4: Error disance of GSF and he basic PF in case of urban scenarios for differen paricles numbers. Table 2: Configuraion parameers of he nework scenario. overview of he configuraion parameers for he communicaion scenario. Regarding he configuraion of he GSO algorihm, he parameers values resuling from [5] have been used. They are illusraed in Table 3. ρ γ β n s l Table 3: Configuraion parameers of he GSO algorihm Performance merics Performances of GSF and he basic paricle filer have been examined in erms of: 1. Error disance (ED) which is defined as he Caresian disance beween he esimae obained from filering algorihms and he real posiion: ED = (D 2 x) + (D 2 y) where D 2 x and D 2 y denoe respecively he error on X and Y posiion. This meric represens a measure of he level of accuracy of he filers. 2. Convergence ime ha denoes he filer run-ime reflecing is real-ime capabiliy and is convergence speed. 3.2 GSF performance evaluaion In his secion, we aim o evaluae he performance of our GSF algorihm and compare i wih he generic paricle filer scheme considering several raffic environmens. Mainly, we examine in his secion he level of accuracy of he filers in urban and highway scenarios. As a firs sep, we assess he performance of boh filers in loss free channel. Then, we consider beacon messages or GPS signals losses wih various raios. Finally, he impac of posiioning errors on boh racking schemes is sudied. Moreover, he resuls of he convergence ime for boh schemes are represened. Error disance and convergence ime are considered as he evaluaion merics Tracking Precision Urban Scenario. Table 4 shows he average error disance obained for boh GSF and he basic PF in case of boh realisic itetris and arificial urban scenarios. We deduce ha he performance of boh filers is enhanced when increasing he number of paricles. For urban raffic scenarios, GSF gives beer esimaion resuls less han 1.6 m. However, he basic paricle filer exceeds 2 m of posiion error. An improvemen on he Scenario GSF(A-Highway) 2.10 m 2.11 m 2.10 m PF(A-Highway) 2.68 m 2.52 m 2.19 m GSF(iTETRIS-Highway) 1.41 m 1.39 m 1.35 m PF(iTETRIS-Highway) 2.14 m 1.58 m 1.60 m Table 5: Error disance of GSF and he basic PF in case of highway scenarios. racking error (54% han he basic PF) is provided by GSF in case of urban raffic. A sligh decrease in he disance error is observed for realisic itetris scenario which can be explained by he fac ha he average speed is more imporan for arificial scenario. We conclude ha he speed is an influencing facor on he racking model which will be more shown in nex secion. Highway Scenario. The firs observaion ha can be aken from Table 5 is ha he disance error increases when he velociy of he vehicle becomes imporan. As expeced, when he number of paricles grows, he accuracy of boh filers is enhanced. Moreover, GSF ouperforms he sandard paricle filer scheme which even wih 500 paricles can no perform he same way as GSF wih 10 paricles. GSF is capable of enhancing he racking error of 12% only wih 10 paricles compared o 500 paricles for PF Impac of packe/gps loss ITS environmen is consrained o high fading channels and congesed nework which lead o a serious problem of packe loss. Moreover, i may be common in ITS environmen o miss GPS signals. The impac of his aspec on racking algorihms is worhy o invesigae. In he following, we sudy his aspec in boh urban and highway raffic environmens. Urban Scenario. From Table 6, we observe ha in all he cases he disance error from real posiion informaion grows when he packe loss raio increases. The disance error does no exceed around 2.5 m for GSF scheme however i goes up o 4.7 m in case of he basic PF. The paricles in he basic PF lose heir imporance. However, in GSF hey are spread in all possible direcions o augmen he space search. Highway Scenario. Table 7 shows he behavior of boh racking algorihms in high speed environmens. From he firs sigh, we can deduce ha for boh filers he disance error increases com-

7 # Packe Tx 1/1 1/2 1/3 1/4 GSF 1.53 m 1.69 m 1.88 m 2.50 m PF 2.73 m 3.23 m 4.26 m 4.72 m Table 6: Impac of packe loss on he error disance of GSF and he basic PF in case of urban scenario. # Packe Tx 1/1 1/2 1/3 1/4 GSF 2.10 m 2.82 m 3.68 m 4.64 m PF 2.68 m 3.99 m 4.94 m 5.78 m Table 7: Impac of packe loss on he error disance of GSF and he basic PF for highway raffic scenario. Figure 8: The evoluion of he Error disance of GSF and PF wih packe loss raio (1/1) in case of highway scenario. pared o urban scenario. Moreover, when increasing he updae loss raio he error becomes more and more imporan. Figure 7 depics he evoluion of he disance error in urban scenario during simulaion ime. Boh resuls wih and wihou updaes loss (firs scheme) are ploed. We can observe ha he disance error increases in case of updae loss for he basic PF and he GSF. Moreover, we disinguish wo zones in he curve, he former where boh filers perform approximaely he same way. The laer is he zone where he basic paricle filer deviaes from he real posiion and gives more han 2m of posiion error which is due o he augmenaion of vehicle speed. In conras o paricle filer, he performance of GSF remains almos sable when varying he vehicle velociy. This is due o he inelligence in our racking scheme and is reliabiliy o provide he highes posiion accuracy. Regarding highway scenario illusraed in Figure 8, hree zones can be defined. The firs corresponds o an equivalen performance for boh filers. I is worhy o noe ha he performance of our racking model can be shown in he second zone where GSF ouperforms PF and he las one, corresponding o high velociy, where boh filers deviae from he real posiion. Figure 7: The evoluion of he Error disance of GSF and PF wih packe loss raio (1/1) in case of urban scenario Impac of sudden changes in moion model Sudden and unexpeced movemen changes influences dramaically he performance of he racking scheme. I is he subjec of invesigaion of his secion. Figure 9 depics a racking case aken from itetris Acosa scenario where he vehicle sars moving from second 32. A seconds 40 and 42 he posiion of he vehicle deviaes abruply due o wo successive lane changes. This leads o a severe deviaion of he paricle filer where he posiion error reaches 4m. However, GSF succeed o rack well he vehicle giving an average posiion error of around 1.3m. Figure 9: change. Tracking error of GSF and PF in case of lane Effec of posiioning errors on racking performance A par from GPS signal loss, anoher source of error more relaed o he precision of he navigaion sysem can be inroduced o he posiioning informaion. In his secion, we examine his aspec and we evaluae he behavior of GSF and he basic SIR PF when we consider he error of posiioning devices ha can be inroduced o he real posiion. Table 8 summarizes he obained resuls for all of he differen raffic scenarios. We can conclude ha he performance of GSF remains sable however a degradaion of PF performance can be observed. For insance he posiion error goes from 2.14m o 3.26 in case of realisic highway scenario. The obained simulaion resuls reveal ha GSF achieves is design goal of providing a good and sufficien level of accuracy for posiion as compared o he basic paricle filer scheme. GPS signals and awareness updaes losses as well as errors on posiioning have shown o be imporan and influencing facors for he precision of filers. In he nex

8 Scenario PF GSF Arificial-Urban 3.08 m 1.58 m Arificial-Highway 3.18 m 2.08 m itetris-urban 2.31 m 1.38 m itetris-highway 3.26 m 1.44 m Table 8: Impac of posiioning error on he error disance for PF and GSF and considering 10 paricles. Scenario GSF(A-Highway) 1.6 s 40.7 s s PF(A-Highway) 0.6 s 8.2 s s GSF(A-Urban) 1.3 s 33.7 s s PF(A-Urban) 0.6 s 8.2 s s Table 9: Convergence ime of GSF compared o he basic PF. secion, we sudy he performance of he filers in erms of convergence ime Convergence ime In order o evaluae he real-ime performance of he racking algorihms, he execuion ime has been measured for differen numbers of paricles. Table 9 illusraes he real execuion ime in seconds of 100s of simulaion in ns-3 for some scenarios. The basic PF ensures he lowes run ime compared o GSF for he differen scenarios which is due o he exra compuaion ha GSF algorihm inroduces. However, in order o respec real-ime requiremens of ITS acive safey applicaions and a he same ime preserve a high level of accuracy, a rade-off beween fas convergence and high precision mus be aken ino accoun. The performance of GSF wih 10 paricles showed o ensure his rade-off. 4. RELATED WORKS Tracking has been exensively sudied in many research domains. Several mechanisms have been proposed o enhance he accuracy of he sae esimaion. The mos relevan echniques of mobiliy predicion ha we found in he lieraure can be classified in wo caegories: Bayesian filers such as Kalman [2] and paricle filer [8][4], and heurisic approaches based on neural neworks [3][7] and geneic algorihms [9]. Generally, he issue wih heurisic schemes is he memory usage. For insance, geneic algorihm is characerized by a long processing ime and here is no guaranee for convergence. The performance of neural neworks depends on he learning phase which is inappropriae for dynamic environmens. Kalman filer is an opimal Bayesian filering approach whose advanage is is simpliciy in implemenaion and compuaion. Neverheless, i has been designed mosly o sui linear and Gaussian problems considering simplisic assumpions. This does no cope wih he dynamic naure of ITS sysems. Paricle filers, on he oher hand, consider non-linear and non-gaussian sysems bu also does no require very complex compuaion resources. In spie of hese advanages, paricle filers have he drawback of paricles degeneracy. Even wih a high number of paricles, i may happen ha he se of paricles loses is diversiy and deviaes from he real sae. Many opimizaion algorihms have been proposed o ackle his limiaion. For insance, he auhors in [6] propose o inroduce geneic algorihm in he basic paricle filer approach. As menioned above, his may reduce he convergence rae due o he heavy compuaion ime ha geneic algorihms require. 5. CONCLUSIONS AND FUTURE WORKS In his work, we proposed o improve mobile Geographic Informaion Sysems (GIS) for uncerain and unreliable environmens by inegraing an swarm-inspired racking mechanism. We have presened GSF (Glow-worm Swarm Filer), an improved paricle filer designed o deal wih unreliable wireless channel resuling in beacon messages and GPS signal loss on he one hand, and wih he unpredicable inernal dynamic models on he oher hand. The main feaure of GSF is he glow-worm abiliies o rack muliple hypoheses (i.e. poenial locaion esimaes), ypically required when he previously described challenges add a large unknown on he locaion esimaes. Simulaion resuls show ha GSF ouperforms he basic paricle filer and ensures a good rade-of beween high precision in posiion esimae and fas convergence. In fuure works, we plan o improve our racking sysem and paricularly he decision making model. We believe ha i could be he key o furher reduce he error o fall below ha of he GPS precision requiremens in ITS (1.5m). 6. REFERENCES [1] itetris projec. hp://wiki.ic-ieris.eu/index.php/main_page. [2] B. D. O. Anderson and J. B. Moore. Opimal filering. Prenice-Hall, Englewood Cliffs, NJ. [3] C. M. H. C. Y. Kong and M. Y. Mashor. Radar racking sysem using neural neworks. in Proc. of he Inernaional journal of he compuer, he inerne and managemen, Vol. 6, No. 2., [4] F. Gusafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P.-J. Nordlund. Paricle filers for posiioning, navigaion, and racking. in Proc. of he IEEE Transacions on in Signal Processing, [5] K. Krishnanand and D. Ghose. Glowworm swarm opimizaion for simulaneous capure of muliple local opima of mulimodal funcions. in Proc. of he Swarm Inelligence Journal, [6] S. Park, J. Hwang, K. Rou, and E. Kim. A New Paricle Filer Inspired by Biological Evoluion: Geneic Filer. World Academy of Science, Engineering and Technology, Sepember [7] L. I. Perlovsky and R. W. Deming. Neural neworks for improved racking. in Proc. of he IEEE ransacions on neural neworks, Vol. 18, No. 6., [8] B. Risic, S. Arulampalam, and N. Gordon. Beyond he kalman filer: Paricle filers for racking applicaions. Arech House Boson London, [9] P. J. Shea, K. Alexander, and J. Peerson. Group racking using geneic algorihms. in Proc. of he Inernaional Sociey Informaion Fusion, 2003.