Journal of Sensors

Volume 2016 (2016), Article ID 9152962, 9 pages

http://dx.doi.org/10.1155/2016/9152962

## Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

School of Electronic Information, Northwestern Polytechnical University, Xi’an 710072, China

Received 24 September 2014; Revised 9 May 2015; Accepted 20 May 2015

Academic Editor: Chi Chiu Chan

Copyright © 2016 Juan Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

#### 1. Introduction

Wireless Sensor Networks (WSNs) consist of a large amount of small, low-cost, and wirelessly connected sensor nodes deployed in an unattended natural environment. Since the sensor nodes are usually battery-powered and it is infeasible to replenish energy via replacing their battery after deployment, therefore optimization of energy consumption is essential in all aspects of WSN to prolong the network lifetime.

In WSNs, an important application of target tracking has received significant attention in recent years [1]. In this application, the sensor nodes collectively monitor the roaming path of moving objects in the area of deployment. Nevertheless, in target tracking, the user is only interested in the occurrence of a certain event, like movement of an intruder or enemy tanks in battle. Since sensor nodes are deployed densely in WSNs, a single target is recorded by many nodes while normally these records are spatially correlated. The degree of this correlation is inversely proportional to the distance among these nodes. In order to save energy, a smaller number of sensors in the event area rather than the whole number of the sensors are used to take the tracking tasks. Thus, dynamically choosing the best nodes for tracking task can balance the energy consumption of each sensor node and improve the lifetime of networks. Hence, it is crucial to select the optimization set of sensor nodes with the minimum cost and quality tracking performance. To solve the node selection problem, entropy-based information utility measurements were proposed, which are implemented with Bayesian Filter, such as [2]. Although they achieve good tracking accuracy, these methods are computationally expensive. In [3], the authors propose a weighted distance-based information utility measurement which needs less computation but can reach competitive tracking accuracy. Unfortunately, this method does not consider the spatial correlation of the sensed reports by different sensors. So far, the existing works can not meet all the goals of the target tracking WSNs.

With these motivations, we propose an efficient and adaptive node selection (EANS) strategy to dynamically choose the best set of sensor nodes for target tracking in WSNs. EANS combines the distance-based node selection strategy and particle filter prediction. The major objective of EANS is to keep reliable object tracking with minimum energy consumption. More precisely, the main contributions of this paper include the following:(i)This paper proposed a novel spatial-correlated node selection strategy, called EANS, which selects the node with more residual energy and considers the spatial correlation of the sensors located at the different positions within the sensing range. Thus, EANS can balance energy consumption and guarantee the tracking reliability with the optimal set of sensor nodes and minimize working nodes so as to decrease the energy consumption significantly.(ii)This paper proposed a joint distance weighted information utility measurement, in which the joint information utility can be presented as the overlap area of the sight lines of the possible sensors and the covariance-related ellipses. In this way, EANS evaluates the usefulness of a sensor node’s observation without the complex entropy calculation and the a posteriori distribution estimation. Therefore, EANS can reduce the computational complexity and save the computational cost.(iii)EANS considers not only the virtual range between sensors and target, but also the parallel degree of sensor’s sight line to the target. In other words, it also considers the effect of angular diversity of sight lines so that the sensing range of the nodes in EANS is more reliable.

The rest of this paper is organized as follows: Section 2 gives an overview of related work. Then, the proposed efficient and adaptive node selection approach is presented in detail in Section 3. The experimental results are shown in Section 4. Finally, we conclude the paper in Section 5.

#### 2. Related Works

Recently, the problem of selecting the best nodes for tracking a target in distributed WSNs has been attracting much research attention.

The simplest approach (such as [4]) is selecting the closest nodes which have the shortest distance to the target for tracking mission. This kind of methods has simple calculations but low tracking accuracy. To improve the tracking accuracy, a combination of the distance and utility function was proposed in [5], in which each node extracts a priority value based on its utility function, which is related to the distances of targets from that node. Nodes with less priority reduce their sensing range before their neighbors. Then, nodes that cannot cover any target or whose nearby targets are covered by neighbor nodes are not assigned the task and they are turned off. However, the approach required the location information of all the nodes.

In [6], the authors proposed a decentralized estimation method, which only needs to use local node information to achieve node selection. Furthermore, the number of active sensors is adaptively determined based on the absolute local innovations vector in [6]. In [7], posterior Cramer-Rao lower bound (PCRLB) was used as a criterion for sensor selection and exhaustive enumeration was adopted to search all possible combinations to seek for the minimum value of PCRLB. Unfortunately, the enumeration search would be a huge computational burden even when the density of a sensor network is just medium. In order to reduce complexity, the conditional posterior Cramer-Rao lower bound (C-PCRLB) in [8] was proposed as a sensor selection metric, which had a constraint on the total number of selected sensors to observe the target over a time window. These methods take the number of the selected nodes into account, but they did not consider the residual energy of each node and did not consider which nodes are more proper for the tracking mission. Moreover, there was also large computational burden.

In [9], the authors proposed a node selection scheme within the framework of particle filter, which uses clustering network architecture for collaborative tracking. This work considered the remaining energy of sensor node and achieved energy saving efficiently as well as required tracking reliability. However, the cluster head always consumes extra energy because of controlling the tracking. And the node selection also neglects the angular diversity of nodes. In [10], an energy-efficient node selection algorithm for bearings-only sensors was proposed. The residual energy of a node was incorporated into the objective function to make a new criterion for node selection. However, all nodes in the paper needed time synchronized, which was difficult to implement in practice. In [11], a user selection scheme is proposed to minimize the overhead energy consumed by cooperative spectrum sensing. This method can conserve energy and achieve reasonably sensing accuracy, but it only focuses on the sensor node with a cognitive radio.

In [2], an entropy-based sensor selection heuristic approach is proposed. It is implemented with Bayesian Filter. The main idea of entropy-based approaches is to optimize an information utility function using the defined metrics. In [12], a mutual-information based sensor selection (MISS) algorithm was implemented for involvement in the mixture procedure. MISS allowed the sensor nodes with the highest mutual information about the target state to transfer data so that the energy ingestion was reduced while the preferred target position estimation accuracy was met. In [13], the authors proposed a light-weight sensing node selection scheme, which solved the sensing node selection problem by adopting a composite function in which information utility measure and energy consumption measure carry different weights. Although these approaches achieve good tracking accuracy, these methods all take the information utility brought by nodes as the objective function and have high computational complexity and expensive cost on computation.

In [14], the node selection is formulated as a subset selection problem which is shown to have a complexity that is NP-hard. At each step of the tracking task, the active nodes are selected from all sensors within the sensing range to minimize residue energy variations. In [15], the authors proposed a Fixed-Tree Relaxation-Based Algorithm (FTRA) and a very efficient Iterative Distributed Algorithm (IDA) to jointly optimize both sensor selection and routing structure and obtain the best possible estimation performance at a given querying node, for a given total power budget. However, these methods selected one sensor node for the tasks at each step and did not consider the locations correlations of the sensing nodes. Because the different combinations of the nodes within the sensing range can obtain the different tracking performance and different residue energy variations, the node selection problem should be considered completely. Besides, in [3], the authors proposed a weighted distance-based node selection method for bearings-only sensors in WSN. The sensor with the minimum weighted distance is activated for tracking mission. Although weighted distance method needs less computation comparing with entropy-based sensor selection method, it chooses only one sensor to track the target each time and does not consider the spatial correlations of sensor nodes.

#### 3. Efficient and Adaptive Node Selection

##### 3.1. Network Model

In this paper, we consider a static WSN which is composed of one sink and randomly distributed sensor nodes , in a two-dimensional sensing field, where is the number of the deployed nodes. Our sensor network model has the following properties and assumptions:(i)The sink is fixed and has an infinite power supply. And it gathers the information sensed by sensor nodes.(ii)The distribution of sensor nodes is mutually independent. Every node is homogeneous and energy constrained.(iii)Each node knows its position by using GPS or any localization algorithm. Let be the location of node .

Sensor nodes have three states, that is, active, sleep, and idle state. They remain in the sleep state most of the time and switch to the active state at specified time slots scheduled by the sink. In the active time slots, sensor nodes receive the assignment messages from the sink and check if there are sensing or relaying tasks in the next time instant. If there are tasks, they will keep active; otherwise they will sleep in next sensing instant. Afterward, the sink node predicts the next position of target by received data using particle filter algorithm. Then, the sink chooses the best nodes for the next task according to the joint distance weighted information utility measurement.

The goal of our work is to select the best subset of nodes for the next tracking task. Moreover, we decide which subset of nodes is the best according to two factors: (1) a joint distance weighted information utility and (2) the successful detection probability and residual energy of the candidate nodes. Therefore, given any time , when a target’s location , the target can be reliably detected with less and balanced energy consumption.

##### 3.2. Prediction Target Position Based on Particle Filter

In target tracking research field, particle filter (PF) has become a very effective algorithm because of its potential of coping with difficult nonlinear or non-Gaussian problems. PF with parallel structure is based on Monte Carlo simulation and Bayesian sampling estimation theories [16]. And it is a sequential importance sampling method which is flexible and easy to be implemented.

As the sink node can obtain the collaborative sensing result of target positions, the PF algorithm is performed on the sink node to predict the target position in the next sensing instant. The schematic diagram of PF algorithm is shown in Figure 1.