Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 8th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Topology control for wireless sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Clustering strategies for improving the lifetime of two-tiered sensor networks
Computer Communications
DIN: An Ad-Hoc Algorithm to Estimate Distances in Wireless Sensor Networks
ADHOC-NOW '08 Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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There are several techniques for routing in wireless sensor network WSN. Using minimum transmission energy model and minimum hop routing model techniques it may happen that the same path is used for more times and nodes on this route are drained of energy. This leads to network partition and thus, reduction in network lifetime which makes the routing algorithm unsuccessful and ineffective. Energy conservation in the WSN is of paramount importance. In this paper, we present energy-efficient routing techniques for two-tiered WSN using Genetic Algorithm, Particle Swarm Optimisation and A-Star algorithm based approach to enhance lifetime of the network. Result analysis shows that A-star algorithm based approach extends lifetime of sensor network comparatively more. But after network lifetime is over, PSO and GA based approach preserves more stronger nodes which signifies that selection/rotation of cluster head strategy can improve performance of network.