Cost-efficient sensor deployment in indoor space with obstacles

Abstract

In this paper, we tackle the problem to achieve kcoverage of a target indoor space with obstacles, that is, any point in the target monitoring area has a line-of-sight to and is located in the sensing range rs of at least k sensors. We propose heuristic algorithms for computing a deployment pattern achieving the kcoverage in an arbitrary 3D target space with stationary and mobile obstacles, while minimizing the overall deployment cost. For the case with only stationary obstacles, we propose a greedy algorithm that puts sensor nodes one by one on a grid point of the deployable area in the descending order of the cost-performance value (i.e., how many monitoring points are covered by putting a sensor at the deployment point per unit deployment cost). In order to extend the algorithm for the case with a mobile obstacle, we define mobile k-coverage that guarantees k-coverage of a target space for arbitrary position of a mobile obstacle, then provide a sufficient condition for the mobile k-coverage: the halfsphere of radius rs centered at a monitoring point bounded by the vertical plane containing the point includes at least k sensor nodes. Based on this condition, we propose a heuristic algorithm that puts at least one sensor node in each π k+1 spherical wedge for each monitoring point. With the proposed algorithms, we have computed the sensor nodes position in an existing indoor environment and confirmed that the obtained WSN deployment in the real space accurately achieves k-coverage.

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments

“…For instance, in Konda and Conci’s [ 6 ], a set of cameras were deployed using the Genetic Algorithm as a global optimization algorithm to achieve the maximum coverage with high-quality image output by considering the light resource and obstacles in the indoor environment. Furthermore, a greedy sensor deployment optimization on the ceilings of a building (in a simulation case) was conducted for monitoring the indoor area [ 27 ]. In this study, some virtual obstacles were considered in the simulation and the floor surfaces were chosen as the coverage area.…”

A Deterministic Sensor Deployment Method for Target Coverage

“…The deployment of wireless sensor networks (WSN) can be mainly divided into two types according to the type of sensors, the application background, and the environment condition: deterministic deployment method and stochastic deployment method. We mainly adopt the deterministic deployment method in the cases where locations of sensors have a great impact on the operation of the WSN, such as deployment of sensor nodes on the pipe [2,3], deployment of image/video sensors indoors [4][5][6], deployment of underwater sensors [7], and deployment of seismic sensors for volcano monitoring [8]. However, we adopt the stochastic deployment method in some special occasions [9,10], especially when the gas leakage happened in the chemical industrial parks.…”