EEHCHR: Energy Efficient Hybrid Clustering and Hierarchical Routing for Wireless Sensor Networks

In cluster-based Wireless Sensor Network (WSN), the Cluster Heads (CHs) consume massive amount of energy due to uneven processing and routing of the information of their Cluster Members (CMs) to the Base Station (BS), and results in lower network lifetime e.g., REHR (Residual Energy based Hybrid Routing). Also, the centralized clustering algorithms introduce unnecessary consumption of energy e.g., FFTHR (Fitness Function based Two-Hop Routing) and UCRA-GSO (Uneven Clustering Routing Algorithm using Glowworm Swarm Optimization). Thus, for extending the network lifetime, we introduce a new clustering algorithm named as Energy Efficient Hybrid Clustering and Hierarchical Routing (EEHCHR) in WSN. Here, a new scheme of adaptive and hybrid clustering has been proposed for minimum usage of the node's energy using the Euclidean distance parameter, Fuzzy C-Means (FCM) technique, location of BS, and residual energy of the nodes. Here, the clustering is performed only in a few rounds, and this results in reduction of energy consumption of the network. All the CHs are selected using the energy efficient fitness function, which works in an adaptive way with the residual energy of the nodes to improve the CH selection process. For efficient usage of the network's energy, we have also given a hierarchical packet routing strategy by introducing the concept of DCH (Direct Cluster Head) and CCH (Central Cluster Head), which are selected by different fitness functions, and work as a relay for few other CHs. The simulation results of EEHCHR proved that it extends the network lifetime, coverage, and saves the network energy as compared to similar existing algorithms e.g., FCM, REHR, FFTHR, UCRA-GSO and CCA-GWO.