Multi-objective Optimization (MOO) approach for sensor node placement in WSN

Abstract

It is desirable to position sensor nodes in a Wireless Sensor Network (WSN) to be able to provide maximum coverage with minimum energy consumption. However, these two aspects are contradicting and quite impossible to solve the placement problem with a single optimal decision. Thus, a Multi-objective Optimization (MOO) approach is needed to facilitate this. This paper studies the performance of a WSN sensor node placement problem solved with a new biologically inspired optimization technique that imitates the behavior of territorial predators in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The simulation study is done for a single objective and multi-objective approaches. The MOO approach of TPSMA (MOTPSMA) deployed in this paper uses the minimum energy consumption and maximum coverage as the objective functions while the single objective approach TPSMA only considers maximum coverage. The performance of both approaches is then compared in terms of coverage ratio and total energy consumption. Simulation results show that the WSN deployed with the MOTPSMA is able to reduce the energy consumption although the coverage ratio is slightly lower than single approach TPSMA which only focuses on maximizing the coverage.