Solving the Optimal Coverage Problem in Wireless Sensor Networks Using Evolutionary Computation Algorithms

This paper formulates the optimal coverage problem (OCP) in wireless sensor network (WSN) as a 0/1 programming problem and proposes to use evolutionary computation (EC) algorithms to solve the problem. The OCP is to determine to active as few nodes as possible to monitor the area in order to save energy while at the same time meets the surveillance requirement, e.g., the full coverage. This is a fundamental problem in the WSN which is significant for the network lifetime. Even though lots of models have been proposed for the problem and variants of approaches have been designed for the solution, they are still inefficient because of the local optima. In order to solve the problem effectively and efficiently, this paper makes the contributions to the following two aspects. First, the OCP is modeled as a 0/1 programming problem where 0 means the node is turned off whilst 1 means the node is active. This model has a very natural and intuitive map from the representation to the real network. Second, by considering that the EC algorithms have strong global optimization ability and are very suitable for solving the 0/1 programming problem, this paper proposes to use the genetic algorithm (GA) and the binary particle swarm optimization (BPSO) to solve the OCP, resulting in a direct application of the EC algorithms and an efficient solution to the OCP. Simulations have been conducted to evaluate the performance of the proposed approaches. The experimental results show that our proposed GA and BPSO approaches outperform the state-of-the-art approaches in minimizing the active nodes number.