Wireless sensor deployment for 3D coverage with constraints

Abstract

Abstract-We consider the problem of deploying wireless sensors in a three dimensional space to achieve a desired degree of coverage, while minimizing the number of sensors placed. Typical sensor deployment scenarios impose constraints on possible locations of the sensors, and on the desired coverage, but currently there is no unified way to handle these constraints in optimizing the number of sensors placed. We present a novel approach called discretization which allows us to cast the sensor deployment problem as a discrete optimization problem, and hence apply well-understood and flexible discrete optimization techniques for sensor deployment. Our results show that this approach yields solutions that nearly minimize the number of sensors used, while providing a high degree of coverage. Further, unlike typical approaches to sensor deployment, where 3D coverage is significantly more complex than 2D coverage, discretization is equally easy to apply for 2D as well as 3D coverage.

New variants of the covering location problem: Modeling and a two-stage genetic algorithm

“…In [7], the authors proposed a linear model to minimize the cost of sensors deployment in a three dimensions sensing field, while keeping a complete coverage area. Also, Anderson and Tirthapura [8] treat the total coverage in 3-dimensions area as a set-cover problem with multiplicity k.…”

“…Constraint (7) is a condition of connectivity. Constraint (8) requires that at most one station is connected to a client. Constraints (9) and (10) provide the link between the variables.…”

Optimizing Coverage in 3D Wireless Sensor Networks

“…Andersen et. Al (Andersen and Tirthapura 2009) presesnted a scheme to optimize sensor deployemnt in presence of constraints such as senor locations and non-uniform sensing regions for the 3D WSNs. The sensor deployemnt problem orginally modeled as continous optimzation was sloved using the discrete optimization method to minimize the number of sensor deployed in the target region.…”

Approximate Algorithms for Sensor Deployment with k-coverage in Constrained 3D Space

“…Andersen et al proposed a greedy algorithm for the sensor deployment problem with multiplicity k, which is modified from the standard greedy algorithm originally designed for the Set Cover problem [2]. We adopt this greedy algorithm for the DLDT problem as described in Algorithm 1.…”

“…Given a number of types of sensors with different sensing ranges and costs, the problem is to find a selection of sensors and a subset of points to place these sensors such that every point in R is covered by at least k sensors and the total cost of the sensors is minimized. In [2], Andersen et al considered the sensors to be deployed are identical, and the sensing field R is a continuous 3D space. They first discretized R into a number of grid points, and then deployed sensors to cover these grid points.…”