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Abstract

The passive object localization (POL) problem in wireless sensor networks aims to determine the location of a target without any device attached for receiving or transmitting signal. This problem is challenging as there is very limited information available for deriving the target location. By combining the diffraction and scattering models, we propose a link sensing adaptive approach to POL, which first decides the target position attribute based on the signal strength and then localizes the target in different modes. We conduct rigorous localizability analyses and design a unit localization area scheme to achieve a higher level of localization accuracy. The efficacy of the proposed method is evaluated through comprehensive experiments in real life network environments.

1. Introduction

Localization has been a traditional research topic in wireless sensor networks (WSN), aiming to determine the location of a target for various purposes such as battlefield surveillance, environment monitoring, and target tracking [1]. In general, the techniques for localization are divided into two broad categories, namely, active object localization (AOL) and passive object localization (POL). The essential difference between them is whether the target of interest is required to carry some device to receive or transmit signal. AOL imposes this requirement such that the target location can be relatively easily determined by other anchor sensor nodes [2, 3], while POL [4–7] assumes that the target does not carry any assisting device, as constrained by either the target itself or the deployment environment. Due to the limited target information available, POL brings forth a greater challenge than AOL.

POL is important in certain applications spanning from acquiring the trajectory of enemy vehicles in the battlefield to learning about the habitat of wild animals in the forest. Typical examples include the animal monitoring project in [8], where the location information of animals needs to be provided for biological experts for the study of animals' habitat, as shown in Figure 1. In such application scenarios, it is infeasible or sometimes impossible to attach any device to the target and hence the best one could do is to leverage predeployed sensor nodes for target localization. As the applications of wireless sensor networks (WSN) proliferate into various new domains, POL has attracted a great deal of attention from many researchers in recent years. However, it still remains a challenge especially in outdoor and wide-area deployments. Many existing efforts employ artificial intelligence techniques such as machine learning to tackle the problem of POL and have been successful in certain contexts [9]. However, these approaches typically require a prior knowledge of the signal level in the environment, which increases the complexity of and limits the scope of their applications.

Figure 1

Wild animal monitoring scenes in the Qinling Mountains [8].

In this paper, we investigate the passive localization problem based on the received signal strength indicator (RSSI) in WSN. We propose a link sensing-adaptive approach to passive object localization, referred to as LSA-POL. Based on the signal propagation model, we first decide the target position attribute and then locate the target using different approaches. The contributions of our work are threefold. (i) We study and define the location attributes of a target, which are used for deriving the LSA approach. (ii) We propose LSA-POL with different location attributes to improve localization accuracy. (iii) We analyze target localizability and design a localization area unit (LAU) scheme to reduce the localization complexity and save the overall network energy cost for a prolonged network lifetime.

The rest of the paper is organized as follows. Section 2 describes the related work. Section 3 defines the problem of POL. Section 4 designs LSA-POL. Section 5 presents the simulation and experimental results. Section 6 concludes our work and sketches a plan on future work.

2. Related Work

We conduct a survey of the most relevant research efforts on localization techniques in WSN.

A number of radio frequency- (RF-) based localization systems have been proposed in the literature [10–12]. LANDMARC, proposed by Green Orbs in 2004, is a location sensing prototype that uses the radio frequency identification (RFID) technology for indoor localization. The location of a tracked object is estimated based on the k nearest reference tags [10]. In MoteTrack developed by Harvard researchers in 2005, the location of each mobile node is obtained by the received radio signal strength signatures from several beacon nodes, and the signature database is stored in every beacon node [11]. RADRA, designed by Microsoft Research Institute in 2000 and updated in 2009, is another important system that operates by combining the signal strength information at multiple base stations based on empirical measurements [12]. These localization systems are able to obtain a high localization accuracy in many application scenarios. However, they belong to the category of active localization that requires the target to carry a wireless device, hence limiting their applications.

In contrast to the aforementioned approaches, Kaltiokallio et al. proposed an intrusion detection system (IDS) that does not require the target to carry such wireless devices. Instead, they use the RSSI signal for intrusion detection, but not for object localization [13]. In the signal dynamics model proposed by Zhang et al. [14–16], the reference nodes are deployed as a grid array with an interval of 1 or 2 meters. When the target is present in one grid, it would affect several links between the reference nodes near the grid, and the degree of signal variations on different links is used to estimate the position of the object. In Liu's work [17–19], the authors proposed an approach that does not require the tracked objects to carry any devices as well, and the distance between two tags is set to be 1 meter. Hence, when the monitoring region is large, the cost and communication overhead would be high. These methods belong to the category of passive localization, featuring a short link distance and an ideal and dense deployment of nodes in the monitoring region. In recent years, most of the device-free radio-based localization (DFL) schemes are based on machine learning and grid matching. The support vector regression (SVR) system has been proposed to locate targets with a triangular deployment [14], but the system requires a training process before the localization phase. In [5], Wilson and Patwari proposed the radio tomographic imaging (RTI) system that requires an inerratic deployment (the localization area is divided into grids with a set of transceivers along each boundary) and the weight matrix records the distance of communication.

In this paper, we propose the LSA-POL scheme that locates a target in an LAU that does not require any training process as opposed to [17, 18]. Compared with the work in [15, 16], the proposed LSA-POL method covers a larger monitoring area and is more suitable for practical applications. In the process of localization, LSA-POL requires a smaller amount of data and a lower demand of bandwidth for wireless transmission than the approach in [5] and achieves a higher level of localization accuracy than Zhang's work [14, 16] under the same simulation setup.

3. Passive Object Localization Problem

In POL, the target of interest does not carry any device to receive or transmit signal, hence presenting very limited data for localization except for some basic information about the predeployed nodes, such as their location and transmit/receive power, based on which more information could be derived. For example, we can calculate the distance of communication using the locations of the nodes and calculate the RSSI value using the distance of communication and transmit/receive power [20].

In general, the path loss $PL (dB)$ of a wireless communication signal in free space is calculated as follows:

\begin{matrix} PL (dB) = 10 \log \frac{P_{t}}{P_{r}} = - 10 \log [\frac{G_{t} G_{r} λ^{2}}{{(4 π)}^{2} d^{2}}], \end{matrix}

(1)

where

P_{t}

denotes the transmit power,

P_{r}

denotes the receive power,

G_{t}

denotes the transmit antenna gain,

G_{r}

denotes the receive antenna gain, λ denotes the wireless signal wavelength in meters, and d denotes the distance the signal travels. Note that the RSSI is a constant when the above parameters are fixed and is subject to variation caused by the environment.

We conduct a simple experiment in an open outdoor area as shown in Figure 2 to investigate the variation of RSSI caused by a target. We observe that the RSSI remains relatively stable if no target is present in the monitoring area but experiences considerable bidirectional fluctuations, otherwise, as shown in Figure 3. We will conduct further analysis of the signal fluctuation and study the relationship between the target location and the RSSI of the influenced links in the next section.

Figure 2

Network topology.

Figure 3

RSSI fluctuation.

4. Link Sensing-Adaptive POL

We propose the LSA-POL method, whose overall structure is shown in Figure 4, including two main components: (i) define target location attributes and (ii) calculate the target-node distance based on different models according to different location attributes. We further discuss the localizability of a target and design the localization area unit scheme.

Figure 4

The overall structure of LSA-POL.

4.1. Target Location Attributes

According to the wireless communication theory, the position of a target would affect the level of fluctuation in the received signal. Depending on its position, the presence of a target may enhance or reduce the RSSI of the influenced link, which is a bidirectional effect on the fluctuation. Therefore, we classify the target location according to the fluctuation variable η, which is defined as

\begin{matrix} η = PL - {PL}_{T}, \end{matrix}

(2)

where

{PL}_{T}

is the RSSI value of the influenced link measured by the receiver and

PL

denotes the path loss in free space propagation. We determine the target location attributes based on the value of η as follows:

\begin{matrix} η > 0, the target is on the link; \\ η < 0, the target is off the link; \\ η = 0, the link is not affected . \end{matrix}

(3)

When the target is on the link, it blocks the signal transmission, thus resulting in a reduced RSSI of the link, as shown by the simulation results in Figure 5(a). When the target is off the link, the diffracted or scattered signal would increase the RSSI, as shown by the simulation results in Figure 5(b). The calculation of the target-node distance depends on different location attributes.

Figure 5

RSSI fluctuation.

4.2. Target-Node Distance Calculation

We calculate the target-node distance in two scenarios, where the target is on or off the link.

4.2.1. The Target is on the Link ( $η > 0$ )

When the target is located on the link between two nodes t and r, the RSSI value is lower than that in the free space propagation as the target blocks the shortest-path communication.

As Figure 6 shows, the area of the Fresnel zone determines the amount of the RSSI attenuation. Therefore, the receiver observes different RSSI values when the object is at different locations along the link. The signal strength $R_{t, r, o}$ received by r and sent by t can be calculated as

\begin{matrix} R_{t, r, o} = P_{t} - 10 γ_{t} \log d_{t, r} - D_{i, j, k} + n, \end{matrix}

(4)

where

P_{t}

denotes the transmit power,

d_{t, r}

denotes the distance between nodes t and r,

γ_{t}

is the attenuation rate of the signal strength in the vicinity of node t, and

D_{i, j, k}

is the diffraction gain [21] calculated as

\begin{matrix} D (dB) = 20 \log | \frac{1 + j}{2} \int_{v}^{\infty} ‍ \exp (\frac{- j π t^{2}}{2}) d t | . \end{matrix}

(5)

\begin{matrix} \frac{λ v^{2}}{2 α^{2}} = \frac{d_{1} d_{2}}{(d_{1} + d_{2})} . \end{matrix}

(6)

Figure 6

The deployment scene when the target is on the link.

In practice, the object such as a human being or an animal is not of an ideal shape with an infinite width and a fixed height, so the signal may pass by the object through different paths. We assume that there are N signal diffraction paths, based on (5); the aggregated signal strength at the receiver can be calculated as

\begin{array}{l} D (dB) \\ = \sum_{n - 1}^{N} ‍ 20 \log | \frac{1 + j}{2} \int_{v_{n}}^{\infty} ‍ \exp (\frac{- j π t^{2}}{2}) d t | \\ = \sum_{n - 1}^{N} ‍ 20 \log | \frac{(1 + j) π}{2} (\frac{1}{2} v_{n}^{2} - v_{n} + 1) \exp (\frac{- j π v_{n}^{2}}{2}) | \\ = 20 \log | {[\frac{(1 - j) π}{2}]}^{N} \exp (\frac{- j π}{2} \sum_{n - 1}^{N} ‍ v_{n}^{2}) ∐_{n - 1}^{N} \frac{v_{n}^{2} - v_{n} + 1}{2} |, \end{array}

(7)

where

v_{n}

is the diffraction coefficient of a diffraction path. It is generally hard to calculate (7) due to the unknowns on the signal diffraction paths in the other nonfirst Fresnel zones. However, the exponential function can be approximated as

\begin{matrix} \exp (\frac{- j π}{2} \sum_{n - 1}^{N} ‍ v_{n}^{2}) \approx \exp [\frac{- j π}{2} {(α v_{\max})}^{2}] . \end{matrix}

(8)

4.2.2. Target is off the Link ( $η < 0$ )

When the target is located off the link, the RSSI node r receives not only from node t, but also from the target (the second transmitter), as shown in Figure 7. In this case, the RSSI measurement $R_{t, r, o}$ sent by node t and received by node r can be calculated as

\begin{matrix} R_{t, r, o} = P_{t} - 10 γ_{t} \log d_{t, r} + S_{t, r, o} + n, \end{matrix}

(9)

where

P_{t}

d_{t, r}

, and

γ_{t}

are the same as defined above, and

S_{s, r, o}

is the scattering gain defined in [22]:

\begin{matrix} S_{t, r, o} = 10 \log \frac{P_{t} G_{t} G_{r} λ^{2} σ}{{(4 π)}^{3} d_{1}^{2} d_{2}^{2}}, \end{matrix}

(10)

which is supplementary to

R_{t, r, o}

. The distance information can be calculated by (1) and (10) with the RSSI measurement. By combining with the results from the other influenced links and node locations, we are able to uniquely determine the target location.

Figure 7

The deployment scene when the target is off the link.

4.3. Symmetric Properties of Signal Strength

From the RSSI distribution shown in Figure 8, we observe that (i) when the target is on the link (i.e., the diffraction model) a certain RSSI value might be mapped to two positions, that is, actual position (AP) and virtual position (VP), and (ii) when the target is located off the link (i.e., the scattering model) there are even a larger number of VPs. The communication theory provides an explanation on the existence of VP [23]. We describe below the symmetric properties of signal strength in the diffraction and scattering models.

Figure 8

The link RSSI fluctuation degree with different communication distances.

4.3.1. Diffraction Model

In this model, the diffraction coefficient is given by

\begin{matrix} v = α \sqrt{\frac{2 d_{1} d_{2}}{λ (d_{1} + d_{2})}}, \end{matrix}

(11)

where

d_{1}

and

d_{2}

denote the distance from the target to the transmitter and the receiver, respectively. The distances

d_{1}

and

d_{2}

have symmetric effects on v: when a target is located at the symmetric positions with respect to the middle point of the link, the diffraction coefficient v would be the same. Therefore, in the case of

η > 0

, the AP and VP come in pairs, as shown in Figure 9(a).

Figure 9

Actual position and virtual position.

4.3.2. Scattering Model

In this model, the scattering gain is given by

\begin{matrix} S (dB) = γ 10 \log \frac{P_{t} G_{t} G_{r} λ^{2} σ}{{(4 π)}^{3} (d_{1}^{2} + d^{2}) (d_{2}^{2} + d^{2})}, \end{matrix}

(12)

where d denotes the perpendicular distance from the target to the influenced communication link,

G_{t}

denotes the transmit antenna gain, and

G_{r}

denotes the receive antenna gain. The parameters

d_{1}

and

d_{2}

denote the distances from the target to the transmitter and the receiver, respectively. Similar to the diffraction model, the distances

d_{1}

and

d_{2}

have a symmetric effect. Moreover, in this model, there are 3 VPs, one of which is caused by the perpendicular bisector of the communication link. Also, the symmetry exists in the other side of the communication link, as shown in Figure 9(b).

4.4. Target Localizability

According to the symmetric properties analyzed above, a single link is not sufficient to locate the target because of the existence of VPs. Therefore, we need two or more links for target localization. When there are two links available, we consider the following two cases.

4.4.1. Case I

The two links consist of three nodes including one common node shared by them, as shown in Figures 11 and 12.

In Figure 11, the target is on one link $(η > 0)$ . In this situation (Figures 11(a) and 11(b)), we can locate the target using (5) and (9). On the contrary, we are not able to locate the target in Figure 11(c), because both links can not be influenced by the target at the same time; that is, the target is located outside the first Fresnel zone of one of the links.

In Figure 12, although the target is off the link $(η < 0)$ , we are able to locate the target using (9) for different links in all of the three situations.

We summarize the localizability analysis results in Case I in Table 1.

Table 1

Localizability analysis results in Case I.

	(a)	(b)	(c)
$η > 0$	Localizable	Localizable	Un-localizable
$η < 0$	Localizable	Localizable	Localizable

4.4.2. Case II

The two links consist of four nodes, and there are three relationships between the two links, that is, intersecting, perpendicular, and parallel, as shown in Figures 13 and 14.

When the target is located on one link $(η > 0)$ , similar to the above analysis, it might be off the other link, and in each of the relationships (a), (b), and (c), the target is localizable in most cases based on (5) and (9). However, if the VPs of the two links overlap, we are not able to locate the target, as shown in Figure 10.

Figure 10

The VP of the two links overlap.

Figure 11

Two links consist of three nodes, and $η > 0$ .

Figure 12

Two links consist of three nodes, and $η < 0$ .

Figure 13

Two links consist of four nodes, and $η > 0$ .

Figure 14

Two links consist of four nodes, and $η < 0$ .

When the target is located off the links $(η < 0)$ , we are still able to locate the target unless the intersection is the midpoint of the two links in the relationships of (a) and (b) or the VPs overlap in the relationship of (c). If the VPs overlap, each link has four VPs, and we are not able to localize the target.

We summarize the localizability analysis results in Case II in Table 2.

Table 2

Localizability analysis results in Case II.

	(a)	(b)	(c)
$η > 0$	(Un)localizable	(Un)localizable	(Un)localizable
$η < 0$	(Un)localizable	(Un)localizable	(Un)localizable

4.5. Design of Localization Area Unit (LAU)

According to Section 4.4, we shall design a localization area unit to ensure that the localization result is unique. The hexagon deployment strategy can achieve a seamless coverage of the entire monitoring region according to the cellular coverage theory, as shown in Figure 15. Based on the localizability analysis results in Section 4.5, we need to deploy another node in the midpoint of each regular hexagon, which divides the regular hexagon into six separate equilateral triangles. We refer to each equilateral triangle as a localization area unit (LAU), as shown in Figure 16. Within an LAU, the distance from an unknown target to the sensor node with an interfered link determines the location of the target. Since each LAU has three nodes and three communication links, we first calculate the value of η separately and then select the two most affected links to localize the target.

Figure 15

Cellular coverage.

Figure 16

Localization area unit.

4.6. Target Position Estimation

Suppose that the location of an unknown target is $X_{k}$ and we are able to obtain the distance estimates $d_{i, k}$ to the ith reference node located at $C_{i}$ , $1 \leq i \leq 3$ . Let $d_{i, k}$ be the actual Euclidean distance to the ith reference node; that is,

\begin{matrix} d_{i, k} = \sqrt{{(X_{k} - C_{i})}^{T} (X_{k} - C_{i})} . \end{matrix}

(13)

The difference between the measured and the actual distances can be represented by $p i = d_{i, k} - d_{i, k}^{'}$ . Due to the measurement errors in $d_{i, k}^{'}$ , $p i$ is often nonzero in practice. We use the least squares method to determine $X_{k}$ that minimizes $\sum_{i = 1}^{3} ‍ ρ_{i}$ , which can be solved by a numerical solution to an overdetermined linear system.

For distance-based localization, we also need to consider how to measure the distance in the physical world. From the above analysis, we know that the interfered RSSI is a function of the distance from the target to two communication nodes. The distances from an unknown target to several reference nodes can be obtained by (10) and (11) within an LAU, and the target location can then be uniquely determined through trilateration.

4.7. LSA-POL Discussion

We summarize the advantages of LSA-POL in the following three aspects.

Data Type. LSA-POL does not require the target to carry any devices and only uses the location information of the nodes and the RSS values of the influenced link. These simple data types satisfy the requirements for POL.

Complexity. LSA-POL dose not require a learning phase and any priori knowledge before localization, so that the localization complexity is moderate. Furthermore, the LAU scheme together with an appropriate selection of links can further reduce the localization complexity.

Accuracy. LSA-POL determines the target location attributes, which confine the localization in a small area and therefore improve the localization accuracy to a certain degree.

The LSA-POL method does not require any training process; the demanding of bandwidth is lower and the monitoring area is larger. Combining the analysis on the data type, the complexity, and the accuracy, the proposed LSA-POL method is suitable for practical applications.

5. Performance Evaluation through Simulations and Experiments

5.1. Simulation-Based Performance Evaluation

We conduct simulations in an equilateral triangular area with three different edge lengths (i.e., communication distance), namely, 3 m, 6 m, and 9 m. In each case, there are three anchor nodes located on the three vertices of the triangle and one target sensor node. We randomly choose three locations for the target sensor node and estimate the location using a genetic algorithm. The pseudocode of the simulation procedure is provided in Algorithm 1.

Algorithm 1: LSA simulation.

Input:

The locations of three anchor sensor nodes, a fixed triangle edge length.

A random location Target-true of the target senor node.

Output:

The estimated location Target-e of the target.

begin

(1) Compute the true RSSI values R-noise on each edge of the triangle using the true location Target-true of the target;

(2) Compute the estimated RSSI values R-e on each edge of the triangle. The locations of the target must be

the estimated target locations Target-e ;

(3) Construct the optimization function myfun(Target-e , R-noise) , which aims to minimize the sum

of differences between the true RSSI values R-noise and the estimated RSSI values R-e at each link;

(4) Solve the optimization function myfun(Target-e, R-noise) using a genetic algorithm;

(5) Obtain the target location Target-e and the sum of absolute differences fval between

the true RSSI values R-noise and the estimated RSSI values R-e at each link.

end

In Case I with the edge length of 3 m, the locations of the three anchor sensor nodes are $Sensor_1 (0,0)$ , $Sensor_2 (3,0)$ , and $Sensor_3 (3 / 2,3 \sqrt{3} / 2)$ . We change the location of the target three times, and we are able to successfully estimate the target location using the proposed LSA-POL method, as shown in Figure 17.

Figure 17

The LSA-POL localization results when the triangle edge length is 3 m.

In Case II with the edge length of 6 m, the locations of the three anchor sensor nodes are $Sensor_1 (0,0)$ , $Sensor_2 (6,0)$ , and $Sensor_3 (3,3 \sqrt{3})$ . The localization results using the proposed LSA-POL method are provided in Figure 18.

Figure 18

The LSA-POL localization results when the triangle edge length is 6 m.

In Case III with the edge length of 9 m, the locations of the three anchor sensor nodes are $Sensor_1 (0,0)$ , $Sensor_2 (9,0)$ , and $Sensor_3 (9 / 2,9 \sqrt{3} / 2)$ . The target location and localization results using the proposed LSA-POL method are shown in Figure 19.

Figure 19

The LSA-POL localization results when the triangle edge length is 9 m.

In these simulations, we are able to estimate the target location very close to its true location. The detailed localization results and the average errors are tabulated in Table 3, which shows that the localization error is within an acceptable range and the proposed LSA-POL method is effective in solving the POL problem.

Table 3

The target locations and localization results with average errors in the simulations.

The distance of communication	Times	Location	Localization result	Average error
3 m	1st	(1.03, 1.02)	(0.98, 0.96)	0.12 m
	2nd	(1.68, 1.91)	(1.71, 2.09)
	3rd	(1.90, 1.31)	(1.98, 1.28)

6 m	1st	(2.00, 1.00)	(1.79, 1.07)	0.38 m
	2nd	(3.00, 2.00)	(3.29, 2.37)
	3rd	(4.50, 0.98)	(4.91, 1.12)

9 m	1st	(4.50, 5.00)	(4.80, 4.31)	0.37 m
	2nd	(3.00, 4.20)	(3.09, 3.95)
	3rd	(1.68, 0.38)	(1.61, 0.43)

5.2. Experiment-Based Performance Evaluation

5.2.1. Experiment Setup

We conduct two experiments to illustrate the effectiveness of the proposed LSA-POL method in an open space. In the experiments, we use MICAZ sensor nodes [24] with CC2420 processing chip and 2.4 GHz wireless signal frequency at the data rate of 2 times/sec. The height of the obstacle (target) is 1.6 m, and the height of the transceiver node is 0.95 m. In order to reduce the interferences of external factors with the links, we run the experiments in the North Square on Chang'an Campus of Northwest University, China, as shown in Figure 20.

Figure 20

LSA-POL localization experiment in an open space.

5.2.2. Experimental Results

(a) Experiment A. The target is located on the communication link. In this experiment, the distance of communication is set to be 4 m, 5 m, 6 m, 8 m, and 10 m, respectively. An obstacle (i.e., target) with the height of 1.6 m is placed at five different positions of each link (the relative position $L_{R}$ to the transmitter is 1/4, 1/3, 1/2, 2/3, and 3/4) and stands for 90 seconds. The diffraction gains $G_{d} (dB)$ of the five points are shown in Table 4. The localization results $L_{E}$ are shown in Table 5.

Table 4

The target locations and diffraction gains in Experiment A.

The distance of communication	1/4		1/3		1/2		2/3		3/4
The distance of communication	$L_{R}$	$G_{d}$ (dB)	$L_{R}$	$G_{d}$ (dB)	$L_{R}$	$G_{d}$ (dB)	$L_{R}$	$G_{d}$ (dB)	$L_{R}$	$G_{d}$ (dB)
4 m	1	−21.986	1.33	−21.613	2	−20.719	2.67	−21.893	3	−22.013
5 m	1.25	−22.341	1.67	−20.371	2.5	−20.263	3.33	−20.372	3.75	−21.349
6 m	1.5	−20.993	2	−19.809	3	−19.501	4	−19.901	4.5	−20.013
8 m	2	−19.381	2.67	−18.901	4	−18.339	5.33	−23.587	6	−19.503
10 m	2.5	−19.401	3.33	−18.987	5	−17.617	6.67	−17.790	7.5	−18.929

Note: two or three digits after the decimal point are retained.

Table 5

The target locations and localization results in Experiment A.

The distance of communication	1/4		1/3		1/2		2/3		3/4
The distance of communication	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$
4 m	1	0.88	1.33	1.19	2	2.15	2.67	2.81	3	2.89
5 m	1.25	0.64	1.67	1.90	2.5	2.31	3.33	2.98	3.75	4.07
6 m	1.5	1.17	2	2.29	3	3.13	4	4.17	4.5	4.29
8 m	2	1.87	2.67	2.35	4	4.09	5.33	7.13	6	5.98
10 m	2.5	1.79	3.33	2.73	5	4.68	6.67	6.17	7.5	8.11

Note: two digits after the decimal point are retained.

(b) Experiment B. The target is located off the communication link. In this experiment, the distance of communication is set to be 3 m, 4 m, 6 m, and 8 m, respectively. An obstacle with the height of 1.6 m is placed at four random positions $L_{R}$ and again stands for 90 seconds. The localization results $L_{E}$ produced by the proposed LSA-POL method are in Table 6.

Table 6

The target locations and the localization results in Experiment B.

The distance of communication	$P_{1}$		$P_{2}$		$P_{3}$		$P_{4}$
The distance of communication	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$	$L_{R}$	$L_{E}$
3 m	(0.8, 0.9)	(0.6, 0.8)	(1.0, 1.3)	(1.2, 1.1)	(1.6, 2.0)	(1.5, 2.2)	(2.0, 1.0)	(2.4, 0.7)
4 m	(1.1, 0.7)	(1.0, 1.2)	(1.8, 1.3)	(2.1, 1.7)	(2.2, 1.8)	(1.9, 2.0)	(3.2, 0.9)	(3.6, 0.4)
6 m	(1.5, 1.3)	(1.4, 1.5)	(2.3, 1.6)	(2.7, 2.2)	(3.0, 3.1)	(2.8, 3.4)	(4.5, 1.9)	(4.6, 1.7)
8 m	(2.0, 2.1)	(1.8, 1.7)	(2.7, 2.3)	(2.9, 2.7)	(4.5, 4.0)	(4.8, 4.2)	(6.8, 1.3)	(7.2, 1.0)

In these experiments, we are able to localize the target. The results of the localization are close to its actual location. The detailed localization results are tabulated in Tables 5 and 6. The LSA-POL method proposed in this paper is effective in solving the POL problem in real world.

5.3. Discussion of Performance Evaluation

The location attributes help reduce the localization area and therefore improve the localization accuracy. We run LSA-POL simulations using the parameters in Table 7 and compare with Zhang's work [14]. The accuracy measurements of three localization methods (i.e., LSA-POL, Midpoint, and Intersection) in the same simulation environment are plotted in Figure 21, which shows that LSA-POL performs significantly better than the other two methods in comparison. The location estimation error of LSA-POL is about 2 m, and that of Midpoint and Intersection is about 9 m and 5 m, respectively. The performance superiority of LSA-POL is mainly brought by the determined location attributes, which improve the localization accuracy.

Table 7

Parameter setting.

Parameter	Value
Density	0.03 (nodes/m²)
Area	100 × 100 (m²)
Communication distance	20 (m)
Target height	0.55 (m)

Figure 21

The cumulative density function (CDF) of the target localization errors in LSA, midpoint, and intersection.

6. Conclusion and Future Work

In this paper, we proposed LSA for passive object localization. LSA-POL determines the location attributes according to the fluctuation η and then uses different models to calculate the target-node distance. We analyzed the localizability of a target and designed a localization area unit scheme. The simulation and experimental results showed that our approach has a superior performance in terms of target localization accuracy over existing methods.

We are interested in the localization problem in the presence of multiple targets which would result in more complex RSSI values of the influenced links and hence pose a greater challenge.

Footnotes

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This research was supported in part by China NSFC Grants (61170218 and 61272461), the National Key Technology R&D Program (2013BAK01B02), Department of Education research project of Shaanxi Province, China (2013JK1126, 2013JK1127), and the Natural Science Foundation of Northwest University (12NW05).

References

Seifeldin

Saeed

Kosba

A. E.

El-Keyi

Youssef

Nuzzer: a large-scale device-free passive localization system for wireless environments

IEEE Transactions on Mobile Computing 2013 12 7 1321 1334

10.1109/TMC.2012.106

2-s2.0-84878159171

Jun-geun

Dorothy

Teller

Jonathan

Implications of device diversity for organic localization

Proceedings of the 30th IEEE International Conference on Computer Communications (IEEE INFOCOM '11)

2011

3182 3190

Al-Ali

A. R.

Aloul

F. A.

Aji

N. R.

Al-Zarouni

A. A.

Fakhro

N. H.

Mobile RFID tracking system

Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA '08)

April 2008

IEEE

1 4

10.1109/ICTTA.2008.4530117

2-s2.0-49149131836

Sigg

Scholz

Shi

Beigl

Rf-sensing of activities from non-cooperative subjects in devicefree recognition systems using ambient and local signals

IEEE Transactions on Mobile Computing 2014 13 4 907 920

Wilson

Patwari

See-through walls: motion tracking using variance-based radio tomography networks

IEEE Transactions on Mobile Computing 2011 10 5 612 621

10.1109/TMC.2010.175

2-s2.0-79952925499

Firner

Zhang

Howard

Lin

Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods

Proceedings of the 11th ACM/IEEE Conference on Information Processing in Sensing Networks (IPSN '12)

April 2012

Beijing, China

209 220

10.1145/2185677.2185734

2-s2.0-84860528113

Wang

Fang

Chen

Yang

Xing

Cai

LCS: compressive sensing based device-free localization for multiple targets in sensor networks

Proceedings of the 32nd IEEE Conference on Computer Communications (IEEE INFOCOM '13)

April 2013

145 149

10.1109/INFCOM.2013.6566752

2-s2.0-84883117300

Liu

Fang

Guo

Chen

Xing

Wang

Demo: rhinopithecus roxellana monitoring and identification using wireless sensor networks

Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys '11)

November 2011

New York, NY, USA

ACM

427 428

10.1145/2070942.2071022

2-s2.0-83455226848

Yang

Liu

Locating in fingerprint space: Wireless indoor localization with little human intervention

Proceedings of the 18th Annual International Conference on Mobile Computing and Networking

August 2012

ACM

269 280

10.1145/2348543.2348578

2-s2.0-84866633671

10.

Lionel

M. N.

Liu

Lau

Y. C.

Patil

A. P.

LANDMARC: indoor location sensing using active RFID

Wireless Networks 2004 10 6 701 710

10.1023/B:WINE.0000044029.06344.dd

2-s2.0-5544326540

11.

Lorincz

Welsh

Motetrack: a robust, decentralized approach to rf -based location tracking

Location-and Context-Awareness 2005

New York, NY, USA

Springer

63 82

12.

Bahl

Padmanabhan

V. N.

RADAR: an in-building RF-based user location and tracking system

Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00)

March 2000

775 784

2-s2.0-0033872896

13.

Kaltiokallio

Bocca

Eriksson

L. M.

Distributed RSSI processing for intrusion detection in indoor environments

Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN '10)

April 2010

ACM

404 405

10.1145/1791212.1791276

2-s2.0-77954531317

14.

Zhang

Liu

L. M.

RASS: a real-time, accurate and scalable system for tracking transceiver-free objects

Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom ’11)

March 2011

Seattle, Wash, USA

197 204

10.1109/PERCOM.2011.5767585

2-s2.0-79957940105

15.

Zhang

Yang

Cheng

Liu

L. M.

COCKTAIL: an RF-based hybrid approach for indoor localization

Proceeding of the IEEE International Conference on Communications (ICC ’10)

May 2010

Cape Town, South Africa

1 5

10.1109/ICC.2010.5502137

2-s2.0-77955398306

16.

Zhang

L. M.

Dynamic clustering for tracking multiple transceiver-free objects

Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom '09)

March 2009

IEEE

1 8

10.1109/PERCOM.2009.4912777

2-s2.0-70349319713

17.

Liu

Zhao

Chen

Pei

Han

Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays

IEEE Transactions on Parallel and Distributed Systems 2012 23 11 2138 2149

10.1109/TPDS.2011.307

2-s2.0-84867305900

18.

Liu

Shen

Dai

Noninteractive localization of wireless camera sensors with mobile beacon

IEEE Transactions on Mobile Computing 2013 12 2 333 345

10.1109/TMC.2011.278

2-s2.0-84871740781

19.

Yang

Liu

WILL: wireless indoor localization without site survey

IEEE Transactions on Parallel and Distributed Systems 2013 24 4 839 848

10.1109/TPDS.2012.179

2-s2.0-84874980752

20.

Longley

A. G.

Rice

P. L.

Prediction of tropospheric radio transmission loss over irregular terrain. A computer method—1968

Technical Report, DTIC Document 1968

21.

Okumura

Eiji

Kawano

Fukuda

Field strength and its variability in vhf and uhf land-mobile radio service

Review of the Electrical Communication Laboratory 1968 16 9 825 873

22.

Dadson

C. E.

Durkin

Martin

R. E.

Computer prediction of field strength in the planning of radio systems

IEEE Transactions on Vehicular Technology 1975 24 1 1 8

23.

Durkin

Computer prediction of service areas for vhf and uhf land mobile radio services

IEEE Transactions on Vehicular Technology 1977 26 4 323 327

2-s2.0-0017558469

24.

Krämer

Geraldy

Energy measurements for micaz node. 5

GI/ITG KuVS Fachgespräch ? Drahtlose Sensornetze, 2006

Link Sensing-Adaptive Passive Object Localization in Wireless Sensor Networks

Abstract

1. Introduction

2. Related Work

3. Passive Object Localization Problem

4. Link Sensing-Adaptive POL

4.1. Target Location Attributes

4.2. Target-Node Distance Calculation

4.2.1. The Target is on the Link ( η > 0 )

4.2.2. Target is off the Link ( η < 0 )

4.3. Symmetric Properties of Signal Strength

4.3.1. Diffraction Model

4.3.2. Scattering Model

4.4. Target Localizability

4.4.1. Case I

4.4.2. Case II

4.5. Design of Localization Area Unit (LAU)

4.6. Target Position Estimation

4.7. LSA-POL Discussion

5. Performance Evaluation through Simulations and Experiments

5.1. Simulation-Based Performance Evaluation

Algorithm 1: LSA simulation.

5.2. Experiment-Based Performance Evaluation

5.2.1. Experiment Setup

5.2.2. Experimental Results

5.3. Discussion of Performance Evaluation

6. Conclusion and Future Work

Footnotes

Conflict of Interests

Acknowledgments

References

4.2.1. The Target is on the Link ( $η > 0$ )

4.2.2. Target is off the Link ( $η < 0$ )