Replacing or recharging batteries in the sensor nodes of a wireless sensor network(WSN)is a significant challenge.Therefore,efficient power utilization by sensors is a critical requirement,and it is closely related to...Replacing or recharging batteries in the sensor nodes of a wireless sensor network(WSN)is a significant challenge.Therefore,efficient power utilization by sensors is a critical requirement,and it is closely related to the life span of the network.Once a sensor node consumes all its energy,it will no longer function properly.Therefore,various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network operation.Recently,clustering algorithms combined with artificial intelligence have been proposed for this purpose.In particular,various protocols employ the K-means clustering algorithm,which is a machine learning method.The number of clustering configurations required by the K-means clustering algorithm is greater than that required by the hierarchical algorithm.Further,the selection of the cluster heads considers only the residual energy of the nodes without accounting for the transmission distance to the base station.In terms of energy consumption,the residual energy of each node,the transmission distance,the cluster head location,and the central relative position within the cluster should be considered simultaneously.In this paper,we propose the KOCED(K-means with Optimal clustering for WSN considering Centrality,Energy,and Distance)protocol,which considers the residual energy of nodes as well as the distances to the central point of the cluster and the base station.A performance comparison shows that the KOCED protocol outperforms the LEACH protocol by 259%(223 rounds)for first node dead(FND)and 164%(280 rounds)with 80%alive nodes.展开更多
文摘Replacing or recharging batteries in the sensor nodes of a wireless sensor network(WSN)is a significant challenge.Therefore,efficient power utilization by sensors is a critical requirement,and it is closely related to the life span of the network.Once a sensor node consumes all its energy,it will no longer function properly.Therefore,various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network operation.Recently,clustering algorithms combined with artificial intelligence have been proposed for this purpose.In particular,various protocols employ the K-means clustering algorithm,which is a machine learning method.The number of clustering configurations required by the K-means clustering algorithm is greater than that required by the hierarchical algorithm.Further,the selection of the cluster heads considers only the residual energy of the nodes without accounting for the transmission distance to the base station.In terms of energy consumption,the residual energy of each node,the transmission distance,the cluster head location,and the central relative position within the cluster should be considered simultaneously.In this paper,we propose the KOCED(K-means with Optimal clustering for WSN considering Centrality,Energy,and Distance)protocol,which considers the residual energy of nodes as well as the distances to the central point of the cluster and the base station.A performance comparison shows that the KOCED protocol outperforms the LEACH protocol by 259%(223 rounds)for first node dead(FND)and 164%(280 rounds)with 80%alive nodes.
