Minimal and maximal exposure path algorithms for wireless embedded sensor networks

Abstract

Sensor networks not only have the potential to change the way we use, interact with, and view computers, but also the way we use, interact with, and view the world around us. In order to maximize the effectiveness of sensor networks, one has to identify, examine, understand, and provide solutions for the fundamental problems related to wireless embedded sensor networks. We believe that one of such problems is to determine how well the sensor network monitors the instrumented area. These problems are usually classified as coverage problems. There already exist several methods that have been proposed to evaluate a sensor network's coverage.We start from one of such method and provide a new approach to complement it. The method of using the minimal exposure path to quantify coverage has been optimally solved using a numerical approximation approach. The minimal exposure path can be thought of as the worst-case coverage of a sensor network. Our first goal is to develop an efficient localized algorithm that enables a sensor network to determine its minimal exposure path. The theoretical highlight of this paper is the closed-form solution for minimal exposure in the presence of a single sensor. This solution is the basis for the new and significantly faster localized approximation algorithm that reduces the theoretical complexity of the previous algorithm. On the other hand, we introduce a new coverage problem -the maximal exposure path -which is in a sense the best-case coverage path for a sensor network. We prove that the maximal exposure path problem is NP-hard, and thus, we provide heuristics to generate approximate solutions.In addition, we demonstrate the effectiveness of our algorithms through several simulations. In the case of the minimal singlesource minimal exposure path, we use variational calculus to determine exact solutions. For the case of maximal exposure, we use networks with varying numbers of sensors and exposure models.

Sacrificing a little coverage can substantially increase network lifetime

“…The quality of surveillance in a partially covered sensor field has been studied in [7,[14][15][16]20]. In [7], Gui and Mohapatra define the quality of surveillance metric using the expected traveled distance of a target before it is first detected by any sensor.…”

“…present a mathematical model to analyze the object detection quality under probabilistic coverage. This work is complementary to our work in that the main focus in [7,[14][15][16]20] is to define the quality of partial coverage.…”

Partially controlled deployment strategies for wireless sensors

“…In the paper, the authors analyze the exposure of several regular and random node deployments. The problem of computing the exposure provided by an arbitrary WSN has also been addressed [10,17]. The problem of optimal sensor placement for target location in a grid-like sensor field has been investigated in [2].…”

“…For instance, it is clear that sensing granularity and exposure (see [17]) are related measures: the finer the granularity, the easier it is for the WSN to detect a target moving within the monitored region. An in-depth investigation of the relation between these two important QoS measures is the subject of ongoing studies.…”

Local Voronoi Diagram based Heuristic Anti-Monitoring Method for Moving Objects Traversing through Sensory Fields

“…In reference [8], the path's exposure degree is defined as the possibility that the mobile object will be detected by the sensor network when it moves along the path to its destination location. In reference [9], the exposure degree is defined as the signal strength that the sensor network received from the mobile object, and the candidate points set of next hop location is obtained by the voronoi tessellation partition. In reference [10], a new concept of local voronoi tessellation is proposed to confirm the candidate points set of next location.…”