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An Unequal Clustering-Based Topology Control Algorithm in Wireless Sensor Networks using Learning Automata

Clustering is an efficient method in saving nodes consumption of energy within wireless sensor networks. In the majority of clustering methods, the sizes of clusters are equal to each other. This feature will lead to a rise in consumption of energy in clusters which are close to the sink node. For this purpose, a new method has been presented based on the learning automata for the unequal clustering of nodes in wireless sensor network. In this method, upon reduction of the distance between the clusters and the sink hole, the size of clusters also shrinks. This feature in addition to optimization of consumption of energy in the network, will also be efficient in a rise in the number of packages delivered toward the sink hole. The effectiveness of the proposed method has been assessed based on its implementation in a simulated environment, while the corresponding results have been compared with previous approaches. The results show that the proposed method acts better than previous methods in reduction of consumption of energy and a rise in the number of delivered packages

Clustering is an efficient method in saving nodes consumption of energy within wireless sensor networks. In the majority of clustering methods, the sizes of clusters are equal to each other. This feature will lead to a rise in consumption of energy in clusters which are close to the sink node. For this purpose, a new method has been presented based on the learning automata for the unequal clustering of nodes in wireless sensor network. In this method, upon reduction of the distance between the clusters and the sink hole, the size of clusters also shrinks. This feature in addition to optimization of consumption of energy in the network, will also be efficient in a rise in the number of packages delivered toward the sink hole. The effectiveness of the proposed method has been assessed based on its implementation in a simulated environment, while the corresponding results have been compared with previous approaches. The results show that the proposed method acts better than previous methods in reduction of consumption of energy and a rise in the number of delivered packages