HCM: a hierarchical clustering framework with MOORA based cluster head selection approach for energy efficient wireless sensor networks

Energy-efficient operation is one of the prime goals of Wireless Sensor Networks (WSNs) because of the limited battery capacity and the harshness of the environment they are deployed in. Clustering techniques with multihop communication is one such technique that is suitable to achieve this goal. These approaches suffer from load balancing and hotspot issues because of the uneven energy consumption between cluster head (CH) nodes and member nodes (MNs). The Hierarchical Layer Balanced Clustering (HLBC) approach is a clustering framework that is highly effective in comparison to the current state of the art in addressing such issues. This work aims to optimize the process of CH selection and reduce intra-cluster communication distance (IACD) in the context of HLBC. For this purpose, Multi-Objective Optimization based on Ratio Analysis (MOORA) has been employed by considering three critical attributes, viz., residual energy, node centrality, and distance to relay, along with a Shannon entropy-based attribute weighting scheme for CH selection. The modified Dijkstra-based minimum spanning tree formation technique based on energy left, load, and distance to the relay node has been presented to reduce IACD and to distribute the load on MNs evenly. The proposed HCM scheme has been analyzed corresponding to two network scenarios, each for a homogeneous and heterogeneous network, based on three performance measures: node death rate, energy consumption, and network lifetime. The proposed framework has outperformed other state-of-the-art techniques for both homogeneous and heterogeneous cases in all considered scenarios. The First Node Death (FND) of EMUC, HLBC, and the proposed HCM approach are at rounds 201, 341, and 417 for scenario 1 and 254, 309, and 382 for scenario 2, respectively, for homogeneous cases. The proposed HCM protocol has achieved a percentage increase of [22–24]% in terms of FND corresponding to homogeneous network scenarios and a [23–27]% improvement in FND corresponding to heterogeneous network scenarios.