A real-time device-free localization system using correlated RSS measurements

Transcription

1 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 RESEARCH Open Access A real-tme devce-free localzaton system usng correlated RSS measurements Zhyong Yang, Kade Huang, Xueme Guo and Guol Wang * Abstract Devce-free localzaton (DFL) wth wreless sensor networks (WSN) s an emergng technology for target localzaton, whch has receved much attenton n the area of Internet of Thngs. Receved sgnal strength (RSS) measurements are the key to realze DFL and manly affects the localzaton performance. Most exstng approaches need to measure the RSS of all the wreless lnks n WSN, whch take much tme on measurement process and localzaton algorthm due to the large amounts of RSS data, thus they are neffcent, especally n the case of target trackng. In ths paper, by makng full use of the consecutveness of moton, we present an effcent measurement strategy based on a small set of correlated wreless lnks. Furthermore, a lghtweght compressed maxmum matchng select (CMMS) algorthm s proposed to localze target, whch only needs a small-scale matrx-vector product operatng for one estmaton. The proposed approach can sgnfcantly reduce the number of RSS measurements and mprove the real-tme capablty of the DFL system. Expermental results demonstrate the superor performance of the proposed method n the context of target localzaton and trackng. Keywords: Internet of Thngs; Wreless sensor networks; Devce-free localzaton; Compressed maxmum matchng select; Effcent measurement 1 Introducton Internet of Thngs (IoT) concerns about the seamless nteracton of objects, sensors, and computng devces [1]. Wth the ntegraton of wreless sensor networks (WSN) and the Internet, the IoT s fast becomng a realty. IoT s applcable to varous areas, ncludng busness logstcs, home automaton, and healthcare [2]. Trackng s an mportant aspect of the healthcare doman [3]. Devcefree localzaton (DFL) [4] s an emergng method for localzng and trackng target wth WSN, whch does not need equppng the target wth any wreless devce. Hence, the DFL technology would not nconvenence the target or make t uncomfortable. The DFL also can be used n other applcatons such as ntruson detecton, nghttme securty montorng, and emergency rescue, where the tradtonal localzaton scheme that target needs to equp wth a wreless devce to transmt or receve wreless sgnals wll become nvald. The locaton nformaton s extremely useful n these applcatons, as t may provde *Correspondence: sswgl@mal.sysu.edu.cn School of Informaton Scence and Technology, Sun Yat-sen Unversty, Guangzhou , Chna lfe-savng benefts for the emergency responders. Therefore, the study on realzng effcent real-tme DFL wth WSNsnecessaryandsgnfcatve. There are lots of wreless lnks wthn the deployment area of the WSN. When an object moves nto the area, t may shadow some of the lnks and reflect, absorb, dffract, or scatter some of the transmtted power, whch wll change the receved sgnal strength (RSS) of the shadowed lnks. The object locatng at a dfferent locaton wll shadow dfferent lnks, so we can realze DFL based on RSS measurements. Snce there are too many wreless lnks n a WSN, the measurements of all the wreless lnks wll cause some dsadvantages n resources consumpton, system latency, and estmaton processng. Ths artcle focuses on usng effcent measurement strategy and lghtweght algorthm to realze real-tme DFL. Wlson and Patwar [4-7] formulated the DFL as a rado tomography magng (RTI) problem and utlzed regularzaton method to solve t. Moussa and Youssef [8,9] modeled the DFL as a machne learnng problem and adopted fngerprnt-matchng method to solve the problem. Zhang et al. [10-14] proposed a sgnal dynamc model and used the geometrc method and probablstc 2013 Yang et al.; lcensee Sprnger. Ths s an Open Access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted.

2 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 2 of 12 cover algorthm based on dynamc clusterng to localze targets. All these works requre suffcent RSS measurements of wreless lnks, whch are neffcent, have hgh-resource consumpton, and sometmes are not even possble. In ths paper, based on the consecutveness of moton, we propose an effcent measurement strategy that only needs to measure a few wreless lnks. Furthermore, we consder the nformaton of target locatons as a sparse sgnal and reconstruct t va compressve sensng (CS) method. CS has been appled to realze DFL n [15]; however, the convex optmzaton approach was adopted to reconstruct the sparse sgnal, whch s computatonally expensve. Meanwhle, the reconstructon s based on a randomly selected set of lnks, and ths leads to neffcency and low consstency. Wang et al. [16] presented a novel Bayesan greedy matchng pursut (BGMP) algorthm to solve the DFL problem based on the enumeraton regon bult on pror nformaton. However, the algorthm s stll computatonally expensve. In ths artcle, for realzng effcent real-tme DFL system wth wreless network, we frst provde the rado tomography magng model for relatng the varances of RSS measurements of wreless lnks to the spatal locatons of the targets. Based on the RTI model, we formulate the DFL ssue as a sparse sgnal reconstructon problem. Then we propose a novel and effcent measurement strategy based on the correlated lnks whch are determned wth possble regon bult on the prevous reconstructon. We also propose a compressed maxmum matchng select (CMMS) algorthm for fast reconstructon of the sgnal. It only utlzes RSS measurements of the correlated lnks and reconstructs the sgnal wthn a restrcted subspace. Hence, the runnng tme of the algorthm s reduced and the reconstructon performance s mproved smultaneously, whch completely meet the need for real-tme DFL applcatons. The remander of ths paper s organzed as follows. In the next secton, we wll dscuss some related works. Secton 3 ntroduces the RTI model and formulates the DFL ssue as a compressed RTI (CRTI) problem wth CS theory. Secton 4 presents the effcent measurement strategy and the CMMS algorthm. The experments and results are showed n Secton 5. Fnally, we conclude the paper n Secton 6. 2 Related works Localzaton of targets based on receved sgnal strength n WSN s a promsng technque whch has receved extensve attenton [17-21]. However, lttle attenton has been gven to the real-tme CS-based DFL and effcent measurement method. In ths secton, we brefly summarze the most relevant research on the DFL and the CS-based DFL. Devce-free localzaton was frst ntroduced by Zhang et al. [10] and Youssef et al. [9]. Zhang et al. [10-13] presented a dynamc model to descrbe the relaton between the RSS varance and the target locaton, then utlzed geometrc method and the dynamc cluster-based probablstc cover algorthm to solve the DFL problem. They also proposed a real-tme DFL system [14]. They dvded the trackng area nto dstnct sub-regons, wth each regon assgned wth a separate rado channel, and used the support vector regresson model to locate the target n each sub-regon. Youssef et al. [8,9] adopted the fngerprntmatchng method to realze DFL. The target s locaton was estmated by comparng the current RSS measurements wth the traned database. Although the above methods acheve reasonable performance, they need to buld a separate tranng measurement database before realzng the DFL. Tranng measurements ncrease exponentally wth the ncrease of the number of wreless lnks and targets. Moreover, the database wll be unavalable when the envronment changed. Wlson and Patwar [5,6,22,23] frstly modeled the DFL as a RTI problem, then they carred out n-depth research on relatng the temporal lnk sgnature wth the target s locaton. They utlzed the regularzaton method to solve the ll-posed nverse problem n the reconstructon of the rado tomographc mage. Ther studes lad the foundaton for future research on the rado tomographc magng and encouraged other researchers to start to work n ths drecton. Chen et al. [24] adopted an auxlary partcle flter to realze trackng of devcefree target based on the RSS measurements. These works requre that there should be suffcent number of wreless lnks be measured to guarantee the reconstructon performance, otherwse, the reconstructon performance wll drop sgnfcantly. On the bass of these works, we formulate the DFL as a CRTI problem and propose an effcent measurement strategy and a lghtweght algorthm to realze real-tme trackng. To the best of our knowledge, Kanso and Rabbat [15] adopted convex optmzaton algorthm to reconstruct the sparse mage, whch s the frst work that adopts CS theory to solve the DFL problem. However, the computaton complexty of the l 1 mnmzaton algorthm s too hgh and not sutable for wreless network. Wang et al. [16] also utlzed the CS theory to solve the DFL problem. They lmted the regon where the target may be located wth pror nformaton of last reconstructon, then used the proposed BGMP algorthm to solve the smplfed DFL problem. The BGMP algorthm teratvely seeks the contrbuton of each pxel n the restrcted regon and fnally locates the target on the pxel whch has the bggest contrbuton value. BGMP essentally s a fuson of the orthogonal matchng pursut (OMP) [25] algorthm and back-projecton algorthm. However, the algorthm s also computatonally expensve and not very sutable

3 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 3 of 12 for real-tme localzaton and trackng of targets. Furthermore, the above works choose a set of wreless lnks to reconstruct the sparse sgnal and they stll need to measure all of the wreless lnks. In ths paper, we proposed a novel effcent measurement strategy, whch only needs to measure a small set of correlated lnks so t s effcent and energy savng. To our best knowledge, ths s the frst work whch realzes effcent measurement n real-tme DFL system. We also proposed a lghtweght CMMS algorthm to localze target, whch only needs a small-scale matrx-vector product and a sortng operaton. Hence t runs fast and s sutable for real-tme system. 3 Model and problem formulaton In ths secton, we provde a RTI model for relatng the varance of the measured RSS value of each wreless lnks to the locaton of the target, and then we ntroduce the CS theory and formulate the DFL problem as a compressve RTI queston. 3.1 System model In a WSN, f the number of nodes s K, then the number of the unque two-way wreless lnks s M = (K 2 K)/2. For smplcty, we llustrate a WSN n Fgure 1 wth all the 20 wreless nodes unformly dstrbuted n a square permeter. When wreless nodes communcate, the RSS y (t) of a partcular lnk at tme t s denoted as where y (t) = P S (t) F (t) L v (t), (1) P s the transmtted power n decbel, S (t) s the shadowng loss n decbel caused by the targets whch attenuate the sgnal, F (t) s the fadng loss n decbel due to constructve and destructve nterference of narrow-band sgnals n mult-path communcaton, L s the statc loss n decbel due to antenna patterns, dstance, and devce nconsstences, v (t) s the measurement nose. For two tme nstants t a and t b, the change of the RSS measurement y s y = y (t b ) y (t a ) = S (t b ) S (t a ) + F (t b ) F (t a ) + v (t b ) v (t a ) (2) whch can be rewrtten as y = S + n, (3) where the nose s the groupng of fadng and measurements n = F (t b ) F (t a ) + v (t b ) v (t a ). (4) Fgure 1 An llustraton of an RTI network wth 20 nodes. As descrbed n (3), we can see that y s prmarly determned by the shadowng loss dfference of the two tme nstants. We examned the shadowng effect of the target on sngle lnk measurement. As seen n Fgure 2, a target was located n the WSN, and we took the lnks l 1, l 2,andl 3 for comparson. Fgure 2b shows thevaluesofrssmeasurementwhenahumanstood n the poston as showed n Fgure 2a and when wthout any human n the deployment WSN area. We can see that f a wreless lnk s shadowed by a target, ts RSS value wll sgnfcantly change from when t s not shadowed. Hence we can use the shadowng model to realze DFL. In the shadowng model, whch s the most wdely adopted model, the nose n s caused by tme-varyng measurements mscalbraton of the recever, by the contrbuton of thermal nose, and by the varatons n the multpath channels. The statstcs of the nose n has been examned n [5], whch s constant wth tme. Hence, the calbraton (when no movng targets exsted n the wreless network feld) could be able to establsh t as the baselne. Then one can use the changes of RSS measurements to realze DFL. Now we provde the RTI model. The montorng area of the WSN s dvded nto N square cells whch can be seen as pxels, as ndcated n Fgure 3. We use the word pxel to represent the cell n the followng sectons, and the locaton of each pxel s represented by the center pont of the correspondng cell. The shadowng loss y of lnk can be approxmated as a sum of attenuaton that occurs n each pxel. Snce the contrbuton of each pxel to the attenuaton of each lnk s dfferent, a weghtng s

4 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 4 of 12 (a) Fgure 2 A comparson of the shadowng effect of target n WSN. In one case the network s empty from target. In the other case, there s one target that passed through by two lnks (6,14) and (10,20) n the network. (a) Measurement setup. (b) RSS values. (b) appled. Mathematcally, ths s descrbed for sngle lnk as N y = S + n = w j x j + n (5) j=1 where x j s the attenuaton occurrng n pxel j, w j s the weghtng of pxel j for lnk, andn s the total number of pxels. The weghtng w j can be calculated wth ellpse model mathematcally descrbed as w j = 1 { 1, f dj (1) + d j (2) d + λ, (6) d 0, otherwse, where d sthedstancebetweenthetwonodesofthelnk, d j (1) and d j (2) are the dstance from the center of pxel j to the two nodes, and λ s a tunable parameter descrbng the wdth of the ellpse. The wdth parameter λ s typcally set very low n RTI, such that t s essentally the same as usng the lne-of-sght (LOS) model, as depcted n Fgure 3. Supposng that M lnks are adopted to realze RTI, the changes of RSS measurements can be expressed n matrx form as y = Wx + n (7) where the vector y =[ y 1, y 2,..., y M ] T s a M 1 vector that represents the changes of the RSS measurements, W = {w j, = 1, 2,..., M, j = 1, 2,..., N} s the M N weghtng matrx, x = [x 1, x 2,..., x N ] T s the unknown N 1 pxel vector to be reconstructed, and n = [n 1, n 2,..., n M ] T s the M 1 nose vector. The weghtng matrx can be calculated wth (6). Wth suffcent lnk RSS measurements, we can reconstruct an mage vector by solvng the nverse problem n (7). The mage vector descrbes the amount of rado power attenuaton occurrng due to the targets wthn the pxels of the WSN regon. Snce the pxel locatons are known, RTI allows us to know where the attenuatons n a WSN are occurrng and, therefore, where the targets are located. Fgure 3 Illustraton of the RTI ellptcal weght model.

5 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 5 of Formulaton as CRTI problem CS s an emergng theory for reconstructng sparse sgnals from a much lower samplng rate than Shannon/Nyqust theorem. In the deployment area of a RTI system, when a target moves nto a pxel j, the pxel value x j wll be non-zero, otherwse, x j wll be zero. Note that after suffcent dense grddng, each target can be guaranteed to have a unque locaton n 1 pxel. In general DFL applcaton, the number of targets K s consderably less than the number of pxels N. Hence, the mage vector x wll be a sparse sgnal, and t wll be possble to reconstruct the x from a few measurements. Ths motvates us to utlze the CS theory to reconstruct the sparse sgnal based on (7). We wll present a lghtweght algorthm to solve the sparse sgnal reconstructon problem n the next secton. 4 Effcent measurement strategy and CMMS algorthm In ths secton, we descrbe the detaled mplementaton of the proposed real-tme DFL system, ncludng the effcent measurement strategy, the lghtweght reconstructon algorthm CMMS, and the system scheme. In a RTI system, f one drectly use (7) to fnd the pxel where the target s located, almost all the lnks should be measured, whch s tme and power consumng, especally when N s large. As we know, the WSN generally s powerlmted. To solve ths problem, we propose utlzng pror nformaton of last reconstructon to restrct the range of the pxels where the target may locate and to gude the next measurements. Once we know whch lnks need to be measured, the rado of the other nodes whch does not need to partcpate n the measurement can be turned off, hence, we can both reduce the latency of the system and save the power of the nodes. As movng s consecutve, the target s current locaton must be around the last locaton. As llustrated n Fgure 4, supposng that the target s located on x j at tme nstant t 1, then at the next tme nstant t, thetargetmust be located n a adjacent pxel of x j (the shaded pxels n Fgure 4), whch s called as possble regon n ths paper. If the dstance d j between pxel and pxel j s less than the threshold l, then the pxel s n the possble regon. The parameter l should be set as l = V max t nt,where V max s the maxmum speed of the target, and t nt s the tme nterval between two successve runnng of the estmaton algorthm. The wreless lnks that pass through the possble regon are called as correlated lnks. We can only use the correlated lnks to localze the target n the possble regon. Hence, we only need to measure the correlated lnks n the DFL system. Algorthm 1 presents the constructon process of the possble regon R and the correlated lnks of set L. Algorthm 1 Possble regon and correlated lnks constructon algorthm Requre: Index set R t 1 of pxels where targets located of the last tme nstant, Number of targets K, Measurement matrx W, The threshold l Ensure: The possble regon R, The correlated lnks set L 1: for j = 1toN do 2: for = 1toK do 3: f d jp t 1 < l then 4: R R {j}; 5: end f 6: end for 7: end for 8: for = 1toK do 9: for m = 1toM do 10: f w j > 0 then j R 11: L L {m} 12: end f 13: end for 14: end for After calculatng the set of lnks that needs to be measured, the host of the algorthm (generally a laptop) sends command messages to the wreless nodes through the base staton node to tell them when to partcpate n the measurement and when to go to sleep. Therefore, n Fgure 4 Illustraton of the possble regon and the correlated lnks.

6 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 6 of 12 the real-tme system, the nodes have two phases: one for measurement and the other for recevng command messages from the host computer. In the phase of measurement, the nodes are synchronzed and tme slots are assgned by the followng scheme. In 1 cycle of measurement,thetotalnumberofslotssthesameasthenumber of the nodes. Each node s assgned to transmt only n the slot n whch the seral number s equal to ts node ID number; n other slots, the node ether receves messages from another node for measurement the RSS of the lnk or keeps rado n sleep phase to save power. The packet transmtted by a node ncludes the ID of the node and the RSS values that t has already measured. In the measurement phase, the base staton node receves all the data packets and sends them to the host computer. Hence, when the last node has sent ts data packet, the host computer has all the RSS values of the correlated lnks. Then the wreless node wll turn to the second phase. All of them wll keep alve and receve command messages from the base staton node. Smultaneously, the host computer wll use the measured RSS vector to estmate the target s locaton wth Algorthm 2. Now, we only need to reconstruct the sgnal x R, whch contans far less elements than the source sgnal x. In addton, the magntude of the measured vector y L s also far Algorthm 2 CMMS algorthm Requre: Measurement matrx W, Number of targets K RSS Measurement vector y L, Possble regon R Ensure: Index set p t of pxels where targets located of the current tme nstant, Reconstructed sgnal x 1: Intalzaton: 2: x j 0, j {1, 2,..., N}; 3: for = 1toK do 4: W L R {w mj, m L, j R }; 5: c = W L R T y L ; 6: p t arg max cj, j j R, j / {p t 1, pt 2,..., pt 1, p t +1,..., pt K }; 7: end for 8: for = 1toK 1 do 9: for j = + 1toK do 10: f p t R j & p t j R then 11: f cos( p t 2 p t 1, p p t ) < cos( p t 2 p t 1, p p t j ) then 12: m p t, pt pt j, pt j m; 13: end f 14: end f 15: end for 16: end for 17: x p t 1, {1, 2,..., K} less than n the source vector y. Hence, the reconstructon problem n (7) s compressed to y L = W LR x R + n L. (8) We propose the CMMS algorthm to solve the above compressed problem. The pseudocode of the CMMS algorthm s summarzed n Algorthm 2. For estmatng the next locaton of target, the CMMS algorthm only needs to fnd out the pxel j that could maxmze W L R T y L, where R and L are the possble regon and the correlated lnks of the target, respectvely;w L R s a sub-matrx of W that only ncludes the rows n L and columns n R ;andy L s the measured RSS values of the correlated lnks L. Snce t only needs a small-scale matrx-vector product and a sortng operaton, the CMMS algorthm s lghtweght and has low complexty, whch makes t meet the requrements of real-tme systems. When two targets move too close together that t s dffcult to dstngush from each other, the algorthm wll make ther trajectores as smoother as possble. For clarty, the outlne of the proposed real-tme DFL method s summarzed n Fgure 5. 5 Expermental results To evaluate the performance of the real-tme DFL system, we conduct the real-tme measurement and trackng experment. In ths secton, we frst descrbe our expermental setup. Then the trackng performance of movng targets s provded. Lastly, we wll gve some analyses and dscussons. 5.1 Physcal descrpton of experment A wreless network contanng 20 nodes was deployed n a laboratory. Each node s placed 1.0 m apart along the permeter of 5 5 m 2 and 1.0 m off the ground on a trpod. A photograph of the expermental setup s shown n Fgure 6. The network comprses MICAz wreless nodes [26] made by MEMSIC (MEMSIC Inc., Andover, MA, USA). Each node operates n the 2.4G frequency band and runs the IEEE standard protocol for communcaton. A base staton node lstens all network traffc then feeds the data to a laptopcomputervaausbportforprocessng.italsosends command messages to the wreless nodes from the laptop computer. To avod network transmsson collsons, a smple token passng protocol s used. Each node s assgned an ID number from 1 to 20 and programmed wth a known order of transmsson. When a node transmts ts packet, the other nodes that need to measure the RSS of the lnk wll wake up to receve the message for acqurng the RSS value and then put t nto ts send buffer. When ts turn to transmt arrves, the measured RSS values wll be sent

7 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 7 of 12 Fgure 5 Flow dagram of the system. out. Snce the base staton node always receves all the packages after the last node transmtted ts packet, the real-tme trackng program runnng on the laptop wll get all the needed RSS values. When the base staton node sends a message to the wreless nodes, ts package follows a certan format. The base staton node needs to send two knds of messages to the wreless nodes: the frst knd of messages tells the nodes when they need to wake up for recevng packages; we call ths knd of messages as L message. Packagesofthe L message follow the format as ndcated n Fgure 7. The second knd of message s a command that tells the nodes start to measure; we call ths knd of message as start message, whch s set as a smple 3 bytes packet Once the nodes receve the start message, they wll start ther tmer and the node wth ID number 1 wll transmt frst. The start message can also synchronze the wreless nodes. Fgure 6 The setup of the experment.

8 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 8 of 12 EE ID XX XX XX EE ID XX XX EE... Fgure 7 The packet format of the L message. Delmter Node ID Slot number that the node needs to wake up for recevng message In the experment, the system s calbrated by takng RSS measurements whle the network s vacant from movng targets. The RSS vector s averaged over a 120-second perod, whch results n approxmately 1,240 samples from each lnk. The calbrated RSS vector s saved n the laptop computer and provdes a baselne aganst whch all other RSS measurements are dfferenced. Ths process has to be done off-lne. The default parameters are as follows: the pxel sze s m, and the possble regon threshold s l = 0.75 m; hence, the possble regon ncludes 9 pxels. In the experments, the parameters were set as gven n Table Trackng performance In order to verfy the proposed method, three experments were conducted: one for one target trackng, the others for two targets trackng. Each experment was repeated two tmes: one experment for real-tme trackng wth the CMMS algorthm and one that measured the RSS of all the wreless lnks for off-lne processng wth the other algorthms. In the experments, the expermenter moves at a typcal walkng pace on a predefned path at a normal walkng speed of 1.2 m/s. A metronome and unformly placed markngs on the floor help the expermenter to take constant-szed steps at a regular tme nterval. The actual locaton of the expermenter s nterpolated usng the start and stop tme and the known marker postons. In the frst experment, a target moved around the square s shown n Fgure 8. The fgure compares the true trace wth the estmated ones obtaned by CMMS, l 1,regularzaton, and BGMP. In the experment, we assume that the startng locaton of the target s known, whch s reasonable n target trackng. It should be ponted out that Table 1 Relevant parameter settngs Parameter Value Descrpton w 0.5 Pxel wdth (m) λ Wdth of weghtng ellpse (m) α 4 Regularzaton parameter l 0.75 Threshold for calculate possble regon (m) there were two consecutve locatons estmated by the algorthms for each pxel that the target traveled. It s obvous that the proposed CMMS algorthm could acheve better trackng performance than the other algorthms. The detaled statstcal characters of the localzaton errors of dfferent algorthms are summarzed n Table 2. We can see that the average error of the proposed CMMS s 0.09 m whch s smaller than the errors of the regularzaton (0.27 m) and BGMP (0.15 m) and even smaller than the error of the l 1 (0.12 m). The maxmum error of thecmmssalsosmallerthantheonesoftheotheralgorthms. We addtonally tested the average runnng tme of dfferent algorthms for once estmaton on a dual-core 2.6 GHz PC. The proposed CMMS algorthm consumed about 0.06 ms, whch s far less than 1 ms the regularzaton algorthm consumed 0.3 ms; the BGMP algorthm consumed approxmately 5 ms; and l 1 consumed much more about 28 ms. We can see that the CMMS needs consderably less tme than the other algorthms. Based on the proposed effcent measurement strategy, each node needs to receve 2.3 packets on average from the other nodes and addtonal 3 packets from the base staton node for one estmaton. Wthout utlzng the effcent measurement strategy, each node needs to receve 19 packets from all the other nodes. Each node needs to send one packet n the two stuatons. Therefore, one can save nearly 88% of the RSS measurements and about 72% of the rado communcatons usng the proposed effcent measurement strategy. In experments 2 and 3, two targets walked along the polylne shown n Fgure 9a,b. The target A moved from A1 to A2; smultaneously, target B moved from B1 to B2. In Fgure 9a, the dstance between target A and target B wasalwayslongerthan3pxels.wecanseethatthetrajectores estmated by the CMMS algorthm s smlar to the real ones. The average error of ths experment s 0.11 m. In Fgure 9b, target A and target B moved nto two adjacent pxels (1.25, 2.25) and (1.25, 2.75). Wecanseethat the trackng performance s gettng worse when two targets moved too close to each other. The average error of ths experment ncreases to 0.23 m. In summary, the total average error of the experments of two targets s 0.17 m, thus the expermental trackng performance s acceptable.

9 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 9 of y(m) 2.5 y(m) CMMS trajectory Real trajectory Node L1 trajectory Real trajectory Node (a) x(m) (b) x(m) y(m) 2.5 y(m) Regularzaton trajectory Real trajectory Node BGMP trajectory Real trajectory Node x(m) x(m) (c) Fgure 8 Vsualzed trackng example wth one target. (a) CMMS. (b) l 1. (c) Regularzaton. (d) BGMP. (d) 5.3 Analyses and dscussons We frst analyze the latency of the system. Snce the total number of the nodes s 20, there 20 tme slots n the measurement phase. A MICAz node takes 7 ms on average to transmt a packet wth 51 bytes, so we assgned each tme slot wth 8 ms. The measurement phase needs 20 8 = 160 ms = 0.16 s. In the frst experment, the number of lnks each node needs to measure on average s 2.3, followng the packet format n Fgure 7; each node needs up to 5 bytes on average n the packet of the L message. The maxmum length of the payload n TnyOS packet s over 51 bytes, so the L message can be sent n Table 2 Comparson of localzaton error and executon tme Algorthm Medan (m) Average (m) Stand devaton (m) 90% (m) Max (m) CPU tme (ms) CMMS l Regularzaton BGMP

10 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 10 of 12 (a) Fgure 9 Vsualzed trackng example wth two targets. (a) The dstance between the two targets was always longer than 3 pxels. (b) The two targets moved nto two adjacent pxels (1.25, 2.25) and (1.25, 2.75). (b) two packets. The tme requred by the base staton node sendng L message and start message to the nodes s up to (2+1) 7 = 21 ms. The tme needed for the host runnng the reconstructon algorthm s less than 1 ms. In total, once estmaton of the locaton of the target needs up to = 182 ms 0.2s.Insummary,oursystem can reach the real-tme trackng wth the latency of about 0.2 s, whch sgnfcantly outperforms prevous trackng systems [10,11,14]. The latency wll ncrease as enlargement of the deployment area wth more nodes. Based on the above analyss, we can see that the system latency wll ncrease at less than 10 ms wth each addtonal node. In general applcaton, two estmaton n 1 s s enough. On ths condton, the system can be extended up to 50 nodes. The system can also be extended by clusterng nodes nto dfferent regons whch are assgned wth separate channels and share tmeslots as ntroduced n [11]. Hence, the system s scalable. The wdth of the pxel s an mportant parameter n RTI problem, whch correlates hghly wth the resoluton. We beleve that t s manly dependent on the applcaton. In the experments, we set t at 0.5 m, whch can (a) ( b) Fgure 10 Performance under dfferent parameters. (a) λ. (b)l.

11 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 11 of 12 (a) Fgure 11 Imagng performance. (a) Real mage. (b)image reconstructed by CMMS. (b) meet the requrement of general applcatons. In addton, ncreasng or decreasng of the wdth of the pxel wll not sgnfcantly change the latency of the system. To evaluate the performance of the proposed method under dfferent condtons, we evaluate the algorthm wth dfferent wdth of the ellpse λ and possble regon threshold l. The weghtng parameter plays an mportant role n generatng accurate RTI mages. If the ellpse s too wde, the pxels of where attenuaton s not occurrng may be ncluded. If the ellpse s too narrow, pxels that do, n fact, attenuate a lnk s sgnal may not be captured by the model. Ths may result n a loss of nformaton that degrades the fnal reconstructon qualty. Fgure 10a llustrates the mean squared error (MSE) wth respect to dfferent λ. It s obvous that the CMMS algorthm can acheve better results when λ n the neghborhood of the pont And the MSE wth respect to dfferent possble regon threshold l s ndcated n Fgure 10b. We can see that the MSE s very large (over 1.8 m) when l = 0.5 m, because the target may move out of the possble regon whch only comprses 5 pxels. The MSE does not sgnfcantly ncrease wth the threshold l ncreasng, but a very large value of l wll ncrease both the computatonal complexty and the system latency. Essentally, the threshold l s determned by V max and t nt (l V max t nt ). If the movement speed of the target s very slow, one can set t nt wth a bg value (such as 1 or 2 s) for conservng system resources. Snce once estmaton of the target s locaton needs less than 0.2 s, the value of t nt can down to 0.2 s. On the precondton of l = 0.75 m and w = 0.5 m, the value of V max can up to 3.75 m/s, whch s much larger than the normal walkng speed. In short, one should carefully select the value of l accordng to specfc applcaton for reachng good trackng accuracy and conservng system resources. Essentally, our proposed real-tme DFL s based on the RTI theory, hence, the localzaton performance s dependng on the magng performance. We provde an magng result n Fgure 11. It can be seen that the CMMS algorthm could acheve reasonable magng performance. 6 Concluson In ths artcle, we desgned and mplemented a real-tme DFL system, whch s based on effcent measurement strategy and lghtweght reconstructon algorthm. The measurement strategy makes use of the last localzaton result to predct a possble regon of the target, then fnds the wreless lnks whch travel through the possble regon toestablshthesetofcorrelatedlnks.thesystemonly needs to measure the RSS of the correlated lnks va cooperatng wth the baston staton node. As far as we know, we are the frst to realze real-tme DFL based on measurements of correlated lnks. Furthermore, the proposed CMMS algorthm only needs a small-scale matrx-vector product operaton to reconstruct the sgnal and localze the target. In summary, we realzed an effcent and energy-savng real-tme DFL wreless network system. Expermental results demonstrate the effectveness of our approach and confrm that the CMMS algorthm could acheve satsfactory localzaton and trackng results. Competng nterests The authors declare that they have no competng nterests. Acknowledgements Ths work has been fnancally supported by the Natonal Natural Scence Foundaton of Chna (grant no ).

12 Yang et al. EURASIP Journal on Wreless Communcatons and Networkng 2013, 2013:186 Page 12 of 12 Receved: 5 December 2012 Accepted: 2 July 2013 Publshed: 9 July 2013 References 1. The Internet of Thngs, ITU Internet Reports (2005). nternetofthngs/. Accessed 15 May CFok,CJulen,GRoman,CLu,nProceedngs of the 2nd workshop on Software Engneerng for Sensor Network Applcatons, SESENA 11, Wakk, Honolulu. Challenges of satsfyng multple stakeholders: qualty of servce n the nternet of thngs (ACM New York, 2011), pp L Atzor, A Iera, G Morabto, The nternet of thngs: a survey. Comput. Netw. 54(15), (2010) 4. N Patwar, J Wlson, RF sensor networks for devce-free localzaton: measurements, models, and algorthms. Proc. IEEE. 98(11), (2010) 5. J Wlson, N Patwar, Rado tomographc magng wth wreless networks. IEEE Trans. Mob. Comput. 9(5), (2010) 6. J Wlson, N Patwar, F Vasquez, n Vrgna Tech Symposum on Wreless Personal Communcatons. Regularzaton methods for rado tomographc magng. 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