A realtime devicefree localization system using correlated RSS measurements
 Zhiyong Yang^{1},
 Kaide Huang^{1},
 Xuemei Guo^{1} and
 Guoli Wang^{1}Email author
https://doi.org/10.1186/168714992013186
© Yang et al.; licensee Springer. 2013
Received: 5 December 2012
Accepted: 2 July 2013
Published: 9 July 2013
Abstract
Devicefree localization (DFL) with wireless sensor networks (WSN) is an emerging technology for target localization, which has received much attention in the area of Internet of Things. Received signal strength (RSS) measurements are the key to realize DFL and mainly affects the localization performance. Most existing approaches need to measure the RSS of all the wireless links in WSN, which take much time on measurement process and localization algorithm due to the large amounts of RSS data, thus they are inefficient, especially in the case of target tracking. In this paper, by making full use of the consecutiveness of motion, we present an efficient measurement strategy based on a small set of correlated wireless links. Furthermore, a lightweight compressed maximum matching select (CMMS) algorithm is proposed to localize target, which only needs a smallscale matrixvector product operating for one estimation. The proposed approach can significantly reduce the number of RSS measurements and improve the realtime capability of the DFL system. Experimental results demonstrate the superior performance of the proposed method in the context of target localization and tracking.
Keywords
1 Introduction
Internet of Things (IoT) concerns about the seamless interaction of objects, sensors, and computing devices[1]. With the integration of wireless sensor networks (WSN) and the Internet, the IoT is fast becoming a reality. IoT is applicable to various areas, including business logistics, home automation, and healthcare[2]. Tracking is an important aspect of the healthcare domain[3]. Devicefree localization (DFL)[4] is an emerging method for localizing and tracking target with WSN, which does not need equipping the target with any wireless device. Hence, the DFL technology would not inconvenience the target or make it uncomfortable. The DFL also can be used in other applications such as intrusion detection, nighttime security monitoring, and emergency rescue, where the traditional localization scheme that target needs to equip with a wireless device to transmit or receive wireless signals will become invalid. The location information is extremely useful in these applications, as it may provide lifesaving benefits for the emergency responders. Therefore, the study on realizing efficient realtime DFL with WSN is necessary and significative.
There are lots of wireless links within the deployment area of the WSN. When an object moves into the area, it may shadow some of the links and reflect, absorb, diffract, or scatter some of the transmitted power, which will change the received signal strength (RSS) of the shadowed links. The object locating at a different location will shadow different links, so we can realize DFL based on RSS measurements. Since there are too many wireless links in a WSN, the measurements of all the wireless links will cause some disadvantages in resources consumption, system latency, and estimation processing. This article focuses on using efficient measurement strategy and lightweight algorithm to realize realtime DFL.
Wilson and Patwari[4–7] formulated the DFL as a radio tomography imaging (RTI) problem and utilized regularization method to solve it. Moussa and Youssef[8, 9] modeled the DFL as a machine learning problem and adopted fingerprintmatching method to solve the problem. Zhang et al.[10–14] proposed a signal dynamic model and used the geometric method and probabilistic cover algorithm based on dynamic clustering to localize targets. All these works require sufficient RSS measurements of wireless links, which are inefficient, have highresource consumption, and sometimes are not even possible. In this paper, based on the consecutiveness of motion, we propose an efficient measurement strategy that only needs to measure a few wireless links. Furthermore, we consider the information of target locations as a sparse signal and reconstruct it via compressive sensing (CS) method. CS has been applied to realize DFL in[15]; however, the convex optimization approach was adopted to reconstruct the sparse signal, which is computationally expensive. Meanwhile, the reconstruction is based on a randomly selected set of links, and this leads to inefficiency and low consistency. Wang et al.[16] presented a novel Bayesian greedy matching pursuit (BGMP) algorithm to solve the DFL problem based on the enumeration region built on prior information. However, the algorithm is still computationally expensive.
In this article, for realizing efficient realtime DFL system with wireless network, we first provide the radio tomography imaging model for relating the variances of RSS measurements of wireless links to the spatial locations of the targets. Based on the RTI model, we formulate the DFL issue as a sparse signal reconstruction problem. Then we propose a novel and efficient measurement strategy based on the correlated links which are determined with possible region built on the previous reconstruction. We also propose a compressed maximum matching select (CMMS) algorithm for fast reconstruction of the signal. It only utilizes RSS measurements of the correlated links and reconstructs the signal within a restricted subspace. Hence, the running time of the algorithm is reduced and the reconstruction performance is improved simultaneously, which completely meet the need for realtime DFL applications.
The remainder of this paper is organized as follows. In the next section, we will discuss some related works. Section 3 introduces the RTI model and formulates the DFL issue as a compressed RTI (CRTI) problem with CS theory. Section 4 presents the efficient measurement strategy and the CMMS algorithm. The experiments and results are showed in Section 5. Finally, we conclude the paper in Section 6.
2 Related works
Localization of targets based on received signal strength in WSN is a promising technique which has received extensive attention[17–21]. However, little attention has been given to the realtime CSbased DFL and efficient measurement method. In this section, we briefly summarize the most relevant research on the DFL and the CSbased DFL.
Devicefree localization was first introduced by Zhang et al.[10] and Youssef et al.[9]. Zhang et al.[10–13] presented a dynamic model to describe the relation between the RSS variance and the target location, then utilized geometric method and the dynamic clusterbased probabilistic cover algorithm to solve the DFL problem. They also proposed a realtime DFL system[14]. They divided the tracking area into distinct subregions, with each region assigned with a separate radio channel, and used the support vector regression model to locate the target in each subregion. Youssef et al.[8, 9] adopted the fingerprintmatching method to realize DFL. The target’s location was estimated by comparing the current RSS measurements with the trained database. Although the above methods achieve reasonable performance, they need to build a separate training measurement database before realizing the DFL. Training measurements increase exponentially with the increase of the number of wireless links and targets. Moreover, the database will be unavailable when the environment changed. Wilson and Patwari[5, 6, 22, 23] firstly modeled the DFL as a RTI problem, then they carried out indepth research on relating the temporal link signature with the target’s location. They utilized the regularization method to solve the illposed inverse problem in the reconstruction of the radio tomographic image. Their studies laid the foundation for future research on the radio tomographic imaging and encouraged other researchers to start to work in this direction. Chen et al.[24] adopted an auxiliary particle filter to realize tracking of devicefree target based on the RSS measurements. These works require that there should be sufficient number of wireless links be measured to guarantee the reconstruction performance, otherwise, the reconstruction performance will drop significantly. On the basis of these works, we formulate the DFL as a CRTI problem and propose an efficient measurement strategy and a lightweight algorithm to realize realtime tracking.
To the best of our knowledge, Kanso and Rabbat[15] adopted convex optimization algorithm to reconstruct the sparse image, which is the first work that adopts CS theory to solve the DFL problem. However, the computation complexity of the ℓ_{1} minimization algorithm is too high and not suitable for wireless network. Wang et al.[16] also utilized the CS theory to solve the DFL problem. They limited the region where the target may be located with prior information of last reconstruction, then used the proposed BGMP algorithm to solve the simplified DFL problem. The BGMP algorithm iteratively seeks the contribution of each pixel in the restricted region and finally locates the target on the pixel which has the biggest contribution value. BGMP essentially is a fusion of the orthogonal matching pursuit (OMP)[25] algorithm and backprojection algorithm. However, the algorithm is also computationally expensive and not very suitable for realtime localization and tracking of targets. Furthermore, the above works choose a set of wireless links to reconstruct the sparse signal and they still need to measure all of the wireless links.
In this paper, we proposed a novel efficient measurement strategy, which only needs to measure a small set of correlated links so it is efficient and energy saving. To our best knowledge, this is the first work which realizes efficient measurement in realtime DFL system. We also proposed a lightweight CMMS algorithm to localize target, which only needs a smallscale matrixvector product and a sorting operation. Hence it runs fast and is suitable for realtime system.
3 Model and problem formulation
In this section, we provide a RTI model for relating the variance of the measured RSS value of each wireless links to the location of the target, and then we introduce the CS theory and formulate the DFL problem as a compressive RTI question.
3.1 System model
where

P_{ i } is the transmitted power in decibel,

S_{ i }(t) is the shadowing loss in decibel caused by the targets which attenuate the signal,

F_{ i }(t) is the fading loss in decibel due to constructive and destructive interference of narrowband signals in multipath communication,

L_{ i } is the static loss in decibel due to antenna patterns, distance, and device inconsistencies,

v_{ i }(t) is the measurement noise.
In the shadowing model, which is the most widely adopted model, the noise n_{ i } is caused by timevarying measurements miscalibration of the receiver, by the contribution of thermal noise, and by the variations in the multipath channels. The statistics of the noise n_{ i } has been examined in[5], which is constant with time. Hence, the calibration (when no moving targets existed in the wireless network field) could be able to establish it as the baseline. Then one can use the changes of RSS measurements to realize DFL.
where d is the distance between the two nodes of the link i, d_{ i j }(1) and d_{ i j }(2) are the distance from the center of pixel j to the two nodes, and λ is a tunable parameter describing the width of the ellipse. The width parameter λ is typically set very low in RTI, such that it is essentially the same as using the lineofsight (LOS) model, as depicted in Figure3.
where the vector y=[ △y_{1},△y_{2},…,△y_{ M }]^{ T } is a M×1 vector that represents the changes of the RSS measurements, W={w_{ i j },i=1,2,…,M, j=1,2,…,N} is the M ×N weighting matrix, x= [ x_{1},x_{2},…,x_{ N }]^{ T } is the unknown N×1 pixel vector to be reconstructed, and n= [ n_{1},n_{2},…,n_{ M }]^{ T } is the M×1 noise vector.
The weighting matrix can be calculated with (6). With sufficient link RSS measurements, we can reconstruct an image vector by solving the inverse problem in (7). The image vector describes the amount of radio power attenuation occurring due to the targets within the pixels of the WSN region. Since the pixel locations are known, RTI allows us to know where the attenuations in a WSN are occurring and, therefore, where the targets are located.
3.2 Formulation as CRTI problem
CS is an emerging theory for reconstructing sparse signals from a much lower sampling rate than Shannon/Nyquist theorem. In the deployment area of a RTI system, when a target moves into a pixel j, the pixel value x_{ j } will be nonzero, otherwise, x_{ j } will be zero. Note that after sufficient dense gridding, each target can be guaranteed to have a unique location in 1 pixel. In general DFL application, the number of targets K is considerably less than the number of pixels N. Hence, the image vector x will be a sparse signal, and it will be possible to reconstruct the x from a few measurements. This motivates us to utilize the CS theory to reconstruct the sparse signal based on (7). We will present a lightweight algorithm to solve the sparse signal reconstruction problem in the next section.
4 Efficient measurement strategy and CMMS algorithm
In this section, we describe the detailed implementation of the proposed realtime DFL system, including the efficient measurement strategy, the lightweight reconstruction algorithm CMMS, and the system scheme.
In a RTI system, if one directly use (7) to find the pixel where the target is located, almost all the links should be measured, which is time and power consuming, especially when N is large. As we know, the WSN generally is powerlimited. To solve this problem, we propose utilizing prior information of last reconstruction to restrict the range of the pixels where the target may locate and to guide the next measurements. Once we know which links need to be measured, the radio of the other nodes which does not need to participate in the measurement can be turned off, hence, we can both reduce the latency of the system and save the power of the nodes.
Algorithm 1 Possible region and correlated links construction algorithm
After calculating the set of links that needs to be measured, the host of the algorithm (generally a laptop) sends command messages to the wireless nodes through the base station node to tell them when to participate in the measurement and when to go to sleep. Therefore, in the realtime system, the nodes have two phases: one for measurement and the other for receiving command messages from the host computer. In the phase of measurement, the nodes are synchronized and time slots are assigned by the following scheme. In 1 cycle of measurement, the total number of slots is the same as the number of the nodes. Each node is assigned to transmit only in the slot in which the serial number is equal to its node ID number; in other slots, the node either receives messages from another node for measurement the RSS of the link or keeps radio in sleep phase to save power. The packet transmitted by a node includes the ID of the node and the RSS values that it has already measured. In the measurement phase, the base station node receives all the data packets and sends them to the host computer. Hence, when the last node has sent its data packet, the host computer has all the RSS values of the correlated links. Then the wireless node will turn to the second phase. All of them will keep alive and receive command messages from the base station node. Simultaneously, the host computer will use the measured RSS vector to estimate the target’s location with Algorithm 2.
Algorithm 2 CMMS algorithm
We propose the CMMS algorithm to solve the above compressed problem. The pseudocode of the CMMS algorithm is summarized in Algorithm 2. For estimating the next location of target i, the CMMS algorithm only needs to find out the pixel j that could maximize${{\mathbf{W}}_{{\mathbf{L}}_{i}{\mathbf{R}}_{i}}}^{T}\times {\mathbf{y}}_{{\mathbf{L}}_{i}}$, where R_{ i } and L_{ i } are the possible region and the correlated links of the target i, respectively;${\mathbf{W}}_{{\mathbf{L}}_{i}{\mathbf{R}}_{i}}$ is a submatrix of W that only includes the rows in L_{ i } and columns in R_{ i }; and${\mathbf{y}}_{{\mathbf{L}}_{i}}$ is the measured RSS values of the correlated links L_{ i }. Since it only needs a smallscale matrixvector product and a sorting operation, the CMMS algorithm is lightweight and has low complexity, which makes it meet the requirements of realtime systems. When two targets move too close together that it is difficult to distinguish from each other, the algorithm will make their trajectories as smoother as possible.
5 Experimental results
To evaluate the performance of the realtime DFL system, we conduct the realtime measurement and tracking experiment. In this section, we first describe our experimental setup. Then the tracking performance of moving targets is provided. Lastly, we will give some analyses and discussions.
5.1 Physical description of experiment
To avoid network transmission collisions, a simple token passing protocol is used. Each node is assigned an ID number from 1 to 20 and programmed with a known order of transmission. When a node transmits its packet, the other nodes that need to measure the RSS of the link will wake up to receive the message for acquiring the RSS value and then put it into its send buffer. When its turn to transmit arrives, the measured RSS values will be sent out. Since the base station node always receives all the packages after the last node transmitted its packet, the realtime tracking program running on the laptop will get all the needed RSS values.
In the experiment, the system is calibrated by taking RSS measurements while the network is vacant from moving targets. The RSS vector is averaged over a 120second period, which results in approximately 1,240 samples from each link. The calibrated RSS vector is saved in the laptop computer and provides a baseline against which all other RSS measurements are differenced. This process has to be done offline.
Relevant parameter settings
Parameter  Value  Description 

w  0.5  Pixel width (m) 
λ  0.035  Width of weighting ellipse (m) 
α  4  Regularization parameter 
l  0.75  Threshold for calculate possible region (m) 
5.2 Tracking performance
In order to verify the proposed method, three experiments were conducted: one for one target tracking, the others for two targets tracking. Each experiment was repeated two times: one experiment for realtime tracking with the CMMS algorithm and one that measured the RSS of all the wireless links for offline processing with the other algorithms. In the experiments, the experimenter moves at a typical walking pace on a predefined path at a normal walking speed of 1.2 m/s. A metronome and uniformly placed markings on the floor help the experimenter to take constantsized steps at a regular time interval. The actual location of the experimenter is interpolated using the start and stop time and the known marker positions.
Comparison of localization error and execution time
Algorithm  Median (m)  Average (m)  Stand deviation (m)  90% (m)  Max (m)  CPU time (ms) 

CMMS  0  0.09  0.19  0.50  0.50  0.06 
ℓ _{1}  0  0.12  0.23  0.50  0.71  28 
Regularization  0  0.27  0.29  0.71  0.71  0.3 
BGMP  0  0.15  0.24  0.50  0.71  5 
Based on the proposed efficient measurement strategy, each node needs to receive 2.3 packets on average from the other nodes and additional 3 packets from the base station node for one estimation. Without utilizing the efficient measurement strategy, each node needs to receive 19 packets from all the other nodes. Each node needs to send one packet in the two situations. Therefore, one can save nearly 88% of the RSS measurements and about 72% of the radio communications using the proposed efficient measurement strategy.
5.3 Analyses and discussions
We first analyze the latency of the system. Since the total number of the nodes is 20, there 20 time slots in the measurement phase. A MICAz node takes 7 ms on average to transmit a packet with 51 bytes, so we assigned each time slot with 8 ms. The measurement phase needs 20×8=160 ms=0.16 s. In the first experiment, the number of links each node needs to measure on average is 2.3, following the packet format in Figure7; each node needs up to 5 bytes on average in the packet of the L message. The maximum length of the payload in TinyOS packet is over 51 bytes, so the L message can be sent in two packets. The time required by the base station node sending L message and start message to the nodes is up to (2+1)×7=21 ms. The time needed for the host running the reconstruction algorithm is less than 1 ms. In total, once estimation of the location of the target needs up to 160+21+1=182 ms≈0.2 s. In summary, our system can reach the realtime tracking with the latency of about 0.2 s, which significantly outperforms previous tracking systems[10, 11, 14]. The latency will increase as enlargement of the deployment area with more nodes. Based on the above analysis, we can see that the system latency will increase at less than 10 ms with each additional node. In general application, two estimation in 1 s is enough. On this condition, the system can be extended up to 50 nodes. The system can also be extended by clustering nodes into different regions which are assigned with separate channels and share timeslots as introduced in[11]. Hence, the system is scalable.
The width of the pixel is an important parameter in RTI problem, which correlates highly with the resolution. We believe that it is mainly dependent on the application. In the experiments, we set it at 0.5 m, which can meet the requirement of general applications. In addition, increasing or decreasing of the width of the pixel will not significantly change the latency of the system.
6 Conclusion
In this article, we designed and implemented a realtime DFL system, which is based on efficient measurement strategy and lightweight reconstruction algorithm. The measurement strategy makes use of the last localization result to predict a possible region of the target, then finds the wireless links which travel through the possible region to establish the set of correlated links. The system only needs to measure the RSS of the correlated links via cooperating with the bastion station node. As far as we know, we are the first to realize realtime DFL based on measurements of correlated links. Furthermore, the proposed CMMS algorithm only needs a smallscale matrixvector product operation to reconstruct the signal and localize the target. In summary, we realized an efficient and energysaving realtime DFL wireless network system. Experimental results demonstrate the effectiveness of our approach and confirm that the CMMS algorithm could achieve satisfactory localization and tracking results.
Declarations
Acknowledgements
This work has been financially supported by the National Natural Science Foundation of China (grant no. 61074167).
Authors’ Affiliations
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