Heap-based Optimization with Corporate Rank Hierarchy for Enhanced Cluster Head Selection in IoT-Enabled Wireless Sensor Networks

Therefore, appropriate selection of cluster heads is eminent in achieving the best performance especially for large-scale WSNs. Therefore, in this paper, we put forward a new kind of optimized cluster head selection method for WSN, we call it Heap-Based Optimizer based Cluster head selection (HBO-CH/WSN adopting a new idea from the corporate hierarchy. In HBO-CH selection of cluster head is done using heap based optimization through energy levels residual energy and node connectivity of WSN. For this purpose, HBO-CH mimics the corporate structure by grouping the sensor nodes into clusters with considerable focus placed on the choice of the heads of clusters. The algorithm tries to give a fair chance of loading new routes when network conditions change, while at the same time, ensuring that it makes optimum use of the available resources to provide the best throughput and to increase overall network's life cycle. The scalability, adaptibility and ch selection efficiency of the proposed HBO-CH protocol are also assessed by means of complex simulations and comparisons with a number of already existing approaches. The results prove the feasibility of our proposed HBO-CH algorithm and show it as a robust solution for designing large scale WSNs with efficient cluster head selection.