Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Ant Colony Optimization
Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
Wireless Sensor Networks: Technology, Protocols, and Applications
Wireless Sensor Networks: Technology, Protocols, and Applications
A cross-layer architecture of wireless sensor networks for target tracking
IEEE/ACM Transactions on Networking (TON)
Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks
ITNG '07 Proceedings of the International Conference on Information Technology
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Localization In Wireless Sensor Networks Based on Support Vector Machines
IEEE Transactions on Parallel and Distributed Systems
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
IEEE Transactions on Signal Processing
Evolutionary genetic algorithm for efficient clustering of wireless sensor networks
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks
IEEE Transactions on Wireless Communications
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In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi-objective two-nested genetic algorithm is presented. The top level algorithm is a multi-objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two-tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA-based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.