Extended Binary Particle Swarm Optimization Approach for Disjoint Set Covers Problem in Wireless Sensor Networks

This paper proposes to use the binary particle swarm optimization (BPSO) approach to solve the disjoint set covers (DSC) problem in the wireless sensor networks (WSN). The DSC problem is to divide the sensor nodes into different disjoint sets and schedule them to work one by one in order to save energy while at the same time meets the surveillance requirement, e.g., the full coverage. The objective of DSC is to maximal the number of disjoint sets. As different disjoint sets form and work successively, only the sensors from the current set are responsible for monitoring the area, while nodes from other sets are sleeping to save energy. Therefore the DSC is a fundamental problem in the WSN and is significant for the network lifetime. In the literature, BPSO has been successfully applied to solve the optimal coverage problem (OCP) which is to find a subset of sensors with the minimal number of sensors to fully monitor the area. In this paper, we extend the BPSO approach to solve the DSC problem by solving the OCP again and again to find the disjoint subsets as many as possible. Once finding the minimal number of sensors for the OCP to fully monitor the area, we mark these sensors as unavailable and repeatedly find another subset of sensors in the remained WSN for the OCP. This way, BPSO can find disjoint subsets of the WSN as many as possible, which is the solution to the DSC problem. Simulations have been conducted to evaluate the performance of the proposed BPSO approach. The experimental results show that BPSO has very good performance in maximizing the disjoint sets number when compared with the traditional heuristic and the genetic algorithm approaches.