Centralized Clustering Routing Based on Improved Sine Cosine Algorithm and Energy Balance in WSNs
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Centralized hierarchical routing protocols are often used to solve the problems of uneven energy consumptionand short network life in wireless sensor networks (WSNs). Clustering and cluster head election have becomethe focuses of WSNs. In this paper, an energy balanced clustering routing algorithm optimized by sine cosinealgorithm (SCA) is proposed. Firstly, optimal cluster head number per round is determined according tosurviving node, and the candidate cluster head set is formed by selecting high-energy node. Secondly, a randompopulation with a certain scale is constructed to represent a group of cluster head selection scheme, and fitnessfunction is designed according to inter-cluster distance. Thirdly, the SCA algorithm is improved by usingmonotone decreasing convex function, and then a certain number of iterations are carried out to select a groupof individuals with the minimum fitness function value. From simulation experiments, the process from thefirst death node to 80% only needs about 30 rounds. This improved algorithm balances the energy consumptionamong nodes and avoids premature death of some nodes. And it greatly improves the energy utilization andextends the effective life of the whole network.
Abstract
Centralized hierarchical routing protocols are often used to solve the problems of uneven energy consumptionand short network life in wireless sensor networks (WSNs). Clustering and cluster head election have becomethe focuses of WSNs. In this paper, an energy balanced clustering routing algorithm optimized by sine cosinealgorithm (SCA) is proposed. Firstly, optimal cluster head number per round is determined according tosurviving node, and the candidate cluster head set is formed by selecting high-energy node. Secondly, a randompopulation with a certain scale is constructed to represent a group of cluster head selection scheme, and fitnessfunction is designed according to inter-cluster distance. Thirdly, the SCA algorithm is improved by usingmonotone decreasing convex function, and then a certain number of iterations are carried out to select a groupof individuals with the minimum fitness function value. From simulation experiments, the process from thefirst death node to 80% only needs about 30 rounds. This improved algorithm balances the energy consumptionamong nodes and avoids premature death of some nodes. And it greatly improves the energy utilization andextends the effective life of the whole network.
참고문헌 (19)
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