Computationally Effective and Practically Aware Pareto-Based Multi-Objective Evolutionary Approach for Wireless Sensor Network Deployment

摘要

Wireless Sensor Network Deployment (WSND) is an active research topic. Different approaches have been effectively developed for WSND. Multi-Objective Evolutionary Algorithms (MOEAs) are regarded as powerful deployment methods because of their adaptive flexibility in effectively searching and providing numerous deployment options for the user. In this study, a computationally effective and practically aware Pareto-based multi-objective evolutionary approach was developed for WSND. On the one hand, the initialization of the population and crossover operation were modified to obtain solutions that meet the connectivity constraints and improve the computational aspect for producing the solutions. On the other hand, a constraint of the dead zone was added to make the deployment practically aware in presence of restricted areas in the Region of Interest (ROI). The approach of the current study was compared with that of Khalesian and Delavar by generating the values of the lifetime and coverage as the conflicting objectives of the deployment. Results showed that the developed approach outperforms the previous approach with respect to these objectives.