Surveillance with wireless sensor networks in obstruction: Breach paths as watershed contours

Abstract

For surveillance applications of wireless sensor networks, analysis of sensing coverage and quality of sensing is crucial. For rough terrains where obstacles block the sensing capability, region-based approaches must be employed to determine the sensing quality. In this paper, we present a method to determine the breach paths and the deployment quality defined as the minimum of the maximum detection probabilities on the breach paths in the presence of obstacles. We propose the utilization of watershed segmentation on the iso-sensing map that reveals the equally-sensed regions of the field-of-interest in a surveillance application. Probabilistic sensor models are utilized to produce the iso-sensing map considering the sensing coverage degree and reliability level as the design criteria. The watershed segmentation algorithm is applied on the iso-sensing map to identify the possible breach paths. An algorithm is proposed to convert the watershed segmentation to an auxiliary graph which is then employed to determine the deployment quality measure (DQM). The effects of the sensor count and coverage degree on the DQM are analyzed.

Introduction

Suppose that a rough terrain is to be monitored to detect unauthorized entries as shown in Fig. 1a. This task can be risky for humans. Instead of deploying a wired surveillance system, a wireless sensor network (WSN) is an easy alternative where the sensors can be dropped by an aircraft. As soon as the sensors configure themselves, they can start sensing the environment and communicate the target presence decisions to a sink.

Measures must be defined to analyze the sensing quality that signifies how well a surveillance wireless sensor network (SWSN) covers a region and senses the phenomena of interest such as an intrusion. The sensing quality depends on the characteristics of the sensor, target and environment, as well as the number and the deployment scheme of the sensors. The variety of sensor technologies makes the coverage analysis difficult because the underlying signal processing and detection procedures depend on the physics of the devices. If the target detection probability is well-defined, a quality of sensing measure can be established.

The sensing quality can be defined through how well the breach path is covered. Finding breach paths is referred to as the weakest breach path problem [1], or the best coverage problem [2]. Megerian et al. define the worst-and the best-case coverage of a WSN for homogeneous sensors [3]. The target wants to avoid the sensors. Thus, passing far from the sensor is good from the target’s viewpoint. The worst case coverage (maximal breach path) is defined with the quality of sensing measure as the closest distance to the sensors while the target crosses the field [4]. A similar problem derived from the worst case coverage definition is to find the farthest distance to the sensors when the target wants to stay as close as possible to the sensors. This problem is referred to as the best case coverage (maximal support path) problem. Such worst case measures can be employed in applications with high security requirements such as the surveillance of a mission-critical place. Clouqueur et al. consider the unauthorized traversal problem where the likelihood of detecting the target (referred as path exposure) is studied [5]. The field is modelled as a grid in [5] and the grid size is equal to the product of the target speed and the sensor sampling period. If the sampling periods of sensors are not synchronized, this approach weakens the accuracy. Moreover, signal characteristics and environmental conditions are not taken into account in [3], [5].

The main research question addressed in this paper is: How can the sensing quality be measured in the presence of obstacles in a SWSN? We propose a deployment (sensing) quality measure in terms of the detection probability of a target crossing the field on breach paths. This deployment quality measure (DQM) can be used to design the network to provide a required sensing quality and to answer typical questions such as how many sensors or how the sensors should be deployed. If the field of interest is known a priori, and deterministic deployment is possible, the designer may utilize the near optimal sensor placement algorithm proposed by Lin and Chiu [6] to provide a complete sensing coverage. For random deployment, it is concluded that the optimal sensor placement problem is NP-complete [6]. Younis and Akkaya present an extensive survey on the issues for node placement in WSNs in [7]. Therefore, it is practically important to have an accurate DQM because the main functionality of a SWSN is to secure a field-of-interest.