Fuzzy K-means clustering (FKmC) to maximize the Energy Efficiency in Sensor-Enabled Internet of Things

With recent growth of smart devices, wireless green communication is promising solution to improve the network performance, stability and robustness in the Internet of Things (IoT) networks. Clustering is one the best candidate for network partition which enables energy efficient mechanism for data gathering and transmission from sensor enabled IoT nodes, which also improve the quality of service underlying heterogeneous systems in IoT networks. Mostly, researchers proposed clustering approach in the regard of belongings of nodes to cluster or not. In real scenario, nodes has fuzzy relationship with clsuters, in this regard, we present Fuzzy K-means clustering (FKmC) algorithm to form balanced clustering, which divide the network into disjoint clusters focuses on efficient energy consumption in the IoT network. The work proposed soft clustering (FKmC) for better clusters using the membership parameter of sensor nodes and residual energy of sensor nodes. Simulation work is done in three phases; first phase show the effective formation of cluster between proposed FKmc and state-of-art algorithms, Second and third phase of the simulation are performed and found that FKmC outperforms in terms of network lifetime and energy consumption.