2005 7th International Conference on Information Fusion (FUSION)
Sensor Deployment Optimization for Detecting Maneuvering Targets Shijian Li
Congfu Xu
Weike Pan
Yunhe Pan
Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Zheda Road 38#, 310027, Hangzhou, P.R.China (Congfu Xu is the corresponding author.)
[email protected],
[email protected],
[email protected], Pyh@ sun. z j u. edu. cn
Abstract - Sensor deployment is a fundamental issue in sensor networks. Studies on the deployment problem are concentrated on deploying sensors to cover assigned areas or set of points efficiently. In this paper, we discuss the sensor deployment problem in the conte-xt of target tracking. Firstly, we propose a sensor deployment optimization strategy based on Target Involved Virtual Force Algorithm (TIVFA). The TIVFA algorithm can dynamically adjust sensor networks configuration according to terrains, intelligence and those detected maneuvering targets in order to improve coverage and detection probability. Secondly, we present an improved sensor-ranking algorithm as well as a sensor protecting strategy with targets' importance sequence in consideration. The 7TVFA algorithm, together with the sensor protecting strategy, can produce an efficient and robust sensor network. Experiment results sufficiently demonstrate the effectiveness of the proposed approach. Keywords: Sensor networks, sensor deployment, coverage, virtual force, sensor protecting.
1
Introduction
More and more researchers give their attention to sensor networks as an effective method of target detecting and tracking. Sensor networks aim to observe region of interest (ROI) efficiently through sensor deployment strategy especially in battlefield. In this paper, we discuss the sensor deployment strategy in terms of improving coverage and detection probability in target tracking applications. Since the environment of sensor networks is often unknown or hostile, sensor deployment usually can not be performed by hand. Scattering sensors by aircraft or by cast is one available approach, but the actual landing positions can not be controlled due to wind, trees, and terrains, etc. All of the sensors mentioned in this paper are homogenous mobile ones. There have been some research efforts on deploying mobile sensors similar to this paper, such as virtual force based method [7] and force field based method [14]. They usually can deploy sensors to achieve high coverage without considering targets' appearance. However in the context of target tracking, they can not adjust sensor
0-7803-9286-8105/$20.00 ©2005 IEEE
networks configuration efficiently to achieve high detection probability. We introduce the novel idea of targets' forces on sensors, and propose the Target Involved Virtual Force Algorithm (TIVFA). Sensor deployment is a fundamental issue in sensor networks [8]. Howard [14] presented a virtual force field based sensor deployment strategy originated from Khatib [4], who first proposed the potential field based techniques in the field of robotic obstacle-avoidance. And Zou [7] described the virtual force based method inspired from Howard [14]. Virtual force based strategy in sensor networks has recently emerged as one of main approaches for sensor self-deployment. Coverage is an essential standard to evaluate the effectiveness of sensor deployment [23]. Gage [2] gave the definition of three types of coverage. Yan [16] proposed an adaptable sensing coverage mechanism with the degree of coverage a , and this mechanism achieved both energy efficiency and differentiated degree of sensing coverage. However, Wu [18] proved that the algorithm in [16] did not guarantee a > 2. Target detecting and tracking is one of the most important applications in sensor networks [12]. Chakrabarty [9] presented a systematic framework that combined the sensor deployment with target observation and localization. This method [9] tried to deploy a unique subset of sensors to cover every grid point of ROI, but it is almost impossible in mobile sensor networks for limited energy and computation capability. Vanheeghe [20] showed that sensor management should take targets danger or importance level into consideration. In this paper, we assume that targets' importance level can be obtained through the proposed methods [6,13,21] when detected by sensors in ROI. Synthesizing the above ideas, we bring forward the TIVFA algorithm based sensor deployment strategy, which can dynamically adjust sensor networks configuration according to positions, intelligence and importance sequence of those detected targets. Additionally, sensor protection is important especially for key nodes, but employing many redundant sensors will result in the problems of communication interference and
1629
management burden [11, 17]. We present
an improved sensor-ranking algorithm inspired by Lu [3] with points' importance rank in consideration. And based on the sensor importance sequence, we introduce a sensor protecting strategy implemented by adding or waking up redundant sensors in the region of key sensors. The organization of this paper is as follows. Section 2 discusses details of the TIVFA algorithm in the context of target involved sensor deployment. Section 3 presents an improved sensor-ranking algorithm and some heuristic rules for sensor protecting. Section 4 gives experiment results to demonstrate the proposed approach in several typical scenes. Finally, we conclude this paper and outline directions for future work.
2.2 Sensor Detection Model There are several types of sensor detection model at present, such as binary detection model [9] and probability model [7]. The former is not practical and there is jump
transition of detection probability in the latter. In this section, we present an improved probability model, ensuring continuity of detection probability. Furthermore, considering the factor of electromagnetism and white noises in real situations, we add normally distributed disturbing effect. We assume that each sensor has a detection range r and a detection uncertainty range r,(r. < r). The detection probability of sensor i on pointj is given as below. if r+r, .1D ROI. Hotspot areas attract more attention like headquarter in battlefield. Obstacles represent complex terrains that sensors need to keep away from. And static target areas are modeled as circle regions from intelligence where 10. targets may appear. Maneuvering targets are divided into $-.3 N, several importance levels, and targets of higher \.\1 importance attract more attention. Through targets' detection probability, we can estimate the confidence range from Eq. (4). For simpleness, we assume the Fig. 2. Sensor detection model. homogeneous mobile sensors are initially placed randomly in ROI, formed into a cluster-based network. 2.3 TIVFA Model Each sensor has the ability of self-localization. Fig.1 describes forces exerted on a single sensor and detailed In this section we describe the TIVFA algorithm in detail discussion of each force model is in the following with the preliminary knowledge related to target involved sensor deployment and sensor detection model. And we subsections. We propose a target-involved sensor deployment suppose that the threshold of communication distance strategy based on TIVFA, which is executed on the head between each two sensors is ce. node, and aims of which are: 1) Improving overall coverage of ROI while ensuring sensor networks 2.3.1 Force Model ofSensor on Sensor connectivity; 2) Adjusting sensor networks configuration In our TIVFA model, we assume that sensor exerts j dynamically and detecting targets of higher importance attractive force on sensor i FT when ( ) the distance more precisely; 3) Ensuring obstacle-avoidance and high coverage of hotspot areas. between them (d, ) is larger than the predefined threshold Du but smaller than C., and repulsive force when d, < D,. ROI D. can be calculated from user defmed sensor density, and Hotspot Twget I......s usually f3-r < D 2r. And the force calculation is given as below. a2
l5r2
I
-.'
