Sensor Placement for Minimum Exposure in Distributed Active Sensing Networks

Abstract

Distributed active sensing is a new sensing paradigm, where active sensors and passive sensors are distributed in a field, and collaboratively detect and track the objects. "Exposure" of distributed active sensing networks (DASNs) quantifies the dimension limitations in detectability. It is important to deploy the sensors such that the exposure is minimized. Exposure minimization is shown to be NP-hard, and thus efficient heuristic algorithms are needed. In this paper, we propose a Genetic Algorithm (GA)-based solution that aims at achieving low exposure, scalability, and fast convergence. A novel flat binary chromosome encoding scheme and corresponding crossover and mutation operators are devised. Geometric knowledge is incorporated to significantly improve the convergence rate. Through extensive simulations, we demonstrate that the proposed algorithm outperforms a simple heuristic algorithm by up to 75%. The simulation results show that this algorithm is robust, self-adaptive and efficient under irregular boundary conditions.

Sensor Deployment Method Based on Faiw-DPSO in DASNs

“…In our previous work [16], the concept of ''exposure'' was introduced. It was designed to quantify the dimension limitations in detectability, i.e., the dimension of obstacles which cannot be detected by actuators and sensors arrangement.…”

“…It was designed to quantify the dimension limitations in detectability, i.e., the dimension of obstacles which cannot be detected by actuators and sensors arrangement. More specifically, given the deployment of sensors and actuators, exposure is the radius of the largest objects that cannot be detected [16]. It considers the indicators that affect the detectability of DASNs, including the deployment of visible actuators and sensors, as well as physical media and environmental layout.…”

“…In this paper, we consider the problem of the optimal deployment of active sensors for target detection with the objective to minimize exposure. However, the problem of minimizing exposure has been proven to be an NP-hard problem [16]. So heuristic solutions need to be devised.…”

“…So heuristic solutions need to be devised. In our previous work, we have applied other heuristic algorithms to solve sensor deployment, such as GA [16], Gibbs sampler [17]. We find that GA is relatively complex in calculation, weak in searching ability and poor in convergence speed, while Gibbs sampler for multi-dimensional problems, that is, when there are a large amount of sensors, the optimization effect is not obvious.…”

An Improved Artificial Bees Colony Algorithm to Solve Minimal Exposure Problem in Wireless Sensor Networks

“…In [15] introduced a bond-percolation theory based approach, which maps the problem of exposure path into a bond percolation model. In [16] suggested a Genetic Algorithm (GA)-based solution, which targets at attaining lesser exposure, scalability, and rapid convergence. A new flat binary chromosome encoding approach and the respective crossover and mutation operators are developed.…”