Abstract
With the growing application of high-density wireless sensor network (HDWSN), coverage control technology has been a key and paramount problem in different scenarios of HDWSN. A regional coverage optimization algorithm is very significant to monitor the network, reduce the waste of resources and improve the lifetime of HDWSNs. Regional coverage control is a technique providing a method that avoids duplicate coverage in HDWSN. However, the regional coverage control is a nonlinear constrained problem whose complexity increases with a quantity of nodes. In this paper, an elite parallel cuckoo search algorithm (EPCSA), a randomized swarm optimization algorithm for regional coverage control in HDWSNs, motivated by elite selection and parallel theory is proposed. To assess the EPCSA's overall efficiency, the EPCSA flow of regional coverage optimization is designed. The model of the regional coverage control problem and the objective function is given. In the simulation, in order to further verify the effectiveness of EPCSA, particle swarm optimization (PSO) and ant colony optimization (ACO) are compared with EPCSA under the same conditions as EPCSA proposed in this paper. The performance of coverage optimization of three algorithms is compared and analyzed. Results show that the regional coverage rate and the deployment of sensor nodes of the network are optimized effectively by the EPCSA.
Published Version
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