A dynamic K-means-based clustering algorithm using fuzzy logic for CH selection and data transmission based on machine learning

Clustering is effective method to increase network lifetime, energy efficiency, and connectivity of sensor nodes in wireless sensor network. An energy efficient clustering algorithm has been proposed in this paper. Sensor nodes are clustered using K-means algorithm which dynamically forms number of clusters in accordance with number of alive nodes. Selection of suitable CH is done by fuzzy inference system by choosing three fuzzy input variable such as residual energy of Sensor node, its distance from cluster center and base station. Amount of data transmitted by member nodes to CH is reduced by machine learning that classify similar data at regular interval. The simulation results show that proposed algorithm (DKFM) outperforms other cluster-based algorithms in terms of data received by base station, number of alive node per round, time of first node, middle node and last node to die for various density of sensor nodes and scalable conditions.