Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D

An energy-efficient Wireless Sensor Network (WSN) design often requires the decision of optimal locations (deployment) and power assignments of the sensors to be densely deployed in an area of interest. In the literature, no attempts have beenmade on optimizing both decision variables formaximizing the network coverage and lifetime objectives,whilemaintaining the connectivity constraint, at the same time. In this paper, theDenseDeployment andPowerAssignment Problem (d-DPAP) inWireless SensorNetworks (WSNs) is defined, and a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) hybridized with a problem-specific Generalized Subproblem-dependent Heuristic (GSH), is proposed. In our method, the d-DPAP is decomposed into a number of scalar subproblems. The subproblems are optiense deployment ulti-objective optimization volutionary algorithms ecomposition euristics mized in parallel, by using neighbourhood information and problem-specific knowledge. The proposed GSH probabilistically alternates between six d-DPAP specific strategies, which are designed based on various WSN concepts and according to the subproblems objective preferences. Simulation results have shown that the proposed hybrid problem-specific MOEA/D performs better than the general-purpose MOEA/D and NSGA-II in severalWSN instances, providing a diverse set of high-quality near-optimal netthe d roblem-specific knowledge work designs to facilitate space is also discussed.