Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 627479, 11 pages

http://dx.doi.org/10.1155/2015/627479

## Energy-Efficient Node Scheduling Method for Cooperative Target Tracking in Wireless Sensor Networks

School of Information Science and Engineering, Central South University, Changsha 410075, China

Received 25 July 2014; Revised 13 October 2014; Accepted 15 October 2014

Academic Editor: Wei Zhang

Copyright © 2015 Weirong Liu 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

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks.

#### 1. Introduction

Wireless sensor network (WSN), which consists of tiny low-cost, energy-limited, and sensing range-limited nodes, has received extensive research in recent years. The nodes in WSN, equipped with one or more sensors, can sense, measure, and gather information from vicinal area. By utilizing the wireless RF module, these nodes can transmit the gathered information from local region to remote base station through node’s multiple-hop relay. With the development of microelectronic technology, WSN has been deployed in various application scenarios to observe physical environmental change and detect events of interest [1].

In all kinds of practical scenarios, target tracking is one of the most important applications of WSN. Target tracking is a process of estimating or predicting the trajectories and velocities of some mobile targets by the sensor nodes in WSN collaboratively. The cooperation among sensor nodes could improve the accuracy of target’s location or velocity. The targets of tracking can be any mobile objects, such as animals, humans, and vehicles [2].

With the development of WSN, numerous target tracking applications have emerged in many practical projects. For instance, PinPtr [3] is a counter-sniper system applied in military field to detect and locate enemy shooters. Underwater monitoring system in [4] developed a submarine platform to monitor coral reefs and fisheries. CenWits [5] is a search-and-rescue system utilizing static or mobile sensor nodes to locate stranded person in wreckage. ZebraNet system [6] consists of sensor nodes installed in animals to track and research the migration of wildlife populations. These projects have been used intensively in environmental detecting, industrial monitoring, disaster alerting, and healthcare [2].

Differing from the traditional target tracking, the target tracking using WSN brings up many challenges: keeping the tracking accuracy under the node’s resources constraints. As most existing works have mentioned, the constraints of node resources, such as sensing range, communication bandwidth, and computation ability, are critical factors to keep accuracy and to save energy for target tracking in wireless sensor networks [7]. Recently, many researches have proposed algorithms to improve energy efficiency while keeping the target tracking accuracy.

Some researches focus on reducing the communication cost in target tracking. In [8], a heuristic algorithm to construct an efficient object tracking in wireless sensor networks was developed, which formulates the minimizing communication cost as 0/1 integer programming problem, and a Lagrangian relaxation based heuristic algorithm was proposed to solve this optimization problem. In [9], a publish-and-subscribe method and drain-and-balance policy were described, respectively, to optimize the structure of network communication and reduce communication consumption. Reference [10] was considered to adjust physical topology of the sensor network, so the total communication cost was reduced. Although these works were devoted to reducing the energy consumption, they have limitation on network energy saving by just minimizing the communication cost, and they may also not be scalable enough.

Recently much attention has been focusing on sensor node scheduling to reduce energy for target tracking. The node scheduling can be classified into 2 categories: the random selection method and adaptive selection method. In random selection method, the sensing nodes are randomly selected according to a certain degree of probability; in adaptive selection method, the sensing nodes are selected according to the critical factors such as node type, detecting ability, and residual energy.

Random selection method has compared low scheduling cost and it is easy to deploy in real WSN. In [11], a probabilistic scheduling of the duty cycle of the sensors was provided in a sensor network deployed in an area of interest based on a Poisson distribution. Its tracking algorithm exploits signal from multiple sensor nodes in several modalities, relying on prior statistical information about target models. In [12], the authors describe the key ideas behind the CSP algorithms for distributed sensor networks and present how the CSP algorithms interface with the networking/routing algorithms. An entropy-based sensor selection heuristic algorithm for location-to-location was proposed in [13], which needs (1) a prior probability distribution of the target location and (2) the locations and the sensing models of a set of candidate sensors for selection. These works concentrated on improving the energy conservation by randomly selecting tasking sensors.

However, sensors’ random sleep with a probability may not keep the target tracking accuracy because some sensors close to a target may be in sleep mode. Even in the target sensing region, there are not active nodes. But it is also sufficiently important to keep the performance of the target tracking. From this point of view, node selection along with the trajectory of moving target has aroused much interest. Some practical distributed sensor node selection algorithms have been proposed to improve energy efficiency with reliable tracking [14–18].

In [14], an energy-efficient selection of cooperative nodes was presented. According to the information utility and the remaining energy of sensor nodes, the authors in [10] constructed an objective function and proposed a dynamic node selection scheme based on genetic algorithms. Although the simulation results have shown its good effect, the node selection scheme based on genetic algorithms is difficult to apply in real applications and may not be suitable for the real-time requirement. The authors in [15] proposed an energy-efficient distributed adaptive multisensor scheduling for target tracking. The number of tasking sensors and the sampling time interval are taken into consideration. To select the tasking node, the leader needs to know its target detection probability which can be deduced from the target state equations. But this process may be somehow complex and requires implementing the Monte Carlo method. An adaptive sensor scheduling is formulated in [16], which contains two tracking modes: the fast tracking mode and the tracking maintenance mode. The energy conservation was achieved by adaptively adjusting the sampling time interval. But it is only applied to selected single tasking sensor at each sample interval. In [17, 18], the variable sampling interval was also adopted, but it cannot realize the joint optimization to energy.

Summarizing the above works, the main factors that influence position accuracy and energy efficiency of target tracking include the network communication topology, the sampling time interval, and the number of tasking sensors. The number of tasking sensors is directly related to the total energy consumption in tracking process. However, the current adaptive node selection method could not permit large candidate node set because of their high complexity.

Comprehending these factors, this paper aims to propose a novel node scheduling method with cooperative Kalman-consensus filter to reduce the energy consumption while keeping tracking accuracy. The Kalman-consensus filter is used to obtain the target state estimation and predict the next step position. The node selection problem is transformed into a convex optimization problem, which is decomposed, and a Lagrangian function is used to solve it.

The main contributions of this paper include (1) extending the classic Kalman filter to cooperative form, which can combine the local nodes’ information to improve the tracking precision; (2) proposing a joint sample interval and node selection optimization scheme, which can realize the energy consumption minimum while keeping the tracking accuracy; (3) addressing the NP-hard joint optimization problem, adopting a map method to map the selecting factor to real domain; and utilizing gradient information to get the solution rapidly.

The rest of this paper is organized as follows. The problem formulation, dynamic model, and energy model are analyzed in Section 2. In Section 3, the novel node selection method is presented. Simulation results are proposed in Section 4. Finally, conclusion and future work are given in Section 5.

#### 2. Problem Formulation

##### 2.1. Target Model and Kalman-Consensus Filter (KCF)

Considering that wireless sensor network is constructed by deploying sensor nodes and a moving target, all cognitive sensors have the same sensing range and can jointly capture the target trajectory. The target model is taken as general linear model, like in [17]. A moving target with the system disturbance is described by the differential equationwhere denotes the state of the target at time ; and are the coordinate and velocity of the target, respectively, in two-dimensional coordinate system; and is the zero-mean Gaussian white noise with variance , which is the process noise.

The state matrix is depicted aswhere is the step size and does not depend on the sampling time interval, which means that the target’s motion is independent of the sensor’s sampling frequency. is defined by

The matrix is defined as where are the parameters of a PD controller and “” denotes the Kronecker product of matrix.

The measurement model is given by where is the state of the target, is the observation model, and is the measurement zero-mean Gaussian white noise of the sensor with covariance .

The Kalman-consensus filter (KCF) algorithm used in this paper is mainly referred to in [19] and operates as shown in Algorithm 1.