An Integrated Optimization Enabled Sensor Deployment Model in Wireless Sensor Network

Abstract

ABSTRACT Target coverage (TCOV) and network connectivity (NCON) are the most basic problems affecting robust data communication and environmental sensing in a wireless sensor network (WSN) application. This article proposes an intelligent Context Aware Sensor Network (CASN) for the process of sensor deployment in WSNs. Accordingly, the process is sub-divided into two phases. In the initial phase, optimal TCOV is performed; whereas, in the second phase, the proposed algorithm establishes NCON among the sensors. The objective model that meets both TCOV and NCON is evaluated as the minimization problem. This problem is solved by a new method that hybridizes the Artificial Bee Colony (ABC) algorithm and the Whale Optimization Algorithm (WOA) together, which is known as the Onlooker Probability-based WOA (OP-WOA) for the determination of optimal sensor locations. In addition, the adopted OP-WOA model is compared with the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the ABC algorithm, Differential Evolution (DE), FireFly (FF), the WOA, and the Evolutionary Algorithm (EA)-based TCOV and NCON models. Finally, the results attained from the execution demonstrate the enhanced performance of the implemented OP-WOA technique.