Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A coverage-preserving node scheduling scheme for large wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Computational Intelligence in Wireless Sensor Networks: A Survey
IEEE Communications Surveys & Tutorials
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
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Wireless sensor networks (WSNs) have become a hot area of research in recent years due to the realization of their ability in myriad applications including military surveillance, facility monitoring, target detection, and health care applications. However, many WSN design problems involve tradeoffs between multiple conflicting optimization objectives such as coverage preservation and energy conservation. Many of the existing sensor network design approaches, however, generally focus on a single optimization objective. For example, while both energy conservation in a cluster-based WSNs and coverage-maintenance protocols have been extensively studied in the past, these have not been integrated in a multi-objective optimization manner. This paper employs a recently developed multi-objective optimization algorithm, the so-called multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve simultaneously the coverage preservation and energy conservation design problems in cluster-based WSNs. The performance of the proposed approach, in terms of coverage and network lifetime is compared with a state-of-the-art evolutionary approach called NSGA II. Under the same environments, simulation results on different network topologies reveal that MOEA/D provides a feasible approach for extending the network lifetime while preserving more coverage area.