Energy Efficient Cluster Head Selection Using Drawer Algorithm for Wireless Sensor Networks

One of the major effective methods for reducing the energy consumption of Wireless Sensor Networks (WSNs) has been demonstrated to be clustering.Yet, Cluster Heads (CHs) in a hierarchical cluster oriented WSN use more energy as a result of an increased workload while collecting information from their member Sensor Nodes (SNs), combining it, and then transferring it to the Base Station (BS).Hence, choosing the right CHs is crucial for preserving the energy linked with the SNs and extending the lifespan of WSNs.In this study, a novel Drawer Algorithm-based Energy-Efficient Cluster Head Selection (DA-EECHS) technique is proposed.The key idea behind the proposed DA-EECHS is its unique methodology to CH selection, integrating sink distance, intra-cluster distance, and sensor node residual energy.The algorithm's object encoding, and fitness function approach are both highly effective.A number of factors are taken into account, including sink distance, intra-cluster distance, and SN residual energy, to determine how energy-efficient the suggested DA technique is.The cluster formation is also demonstrated, where SNs that are not CHs join their CHs on the basis of weight functions that are deduced.The method is thoroughly evaluated using a variety of WSN situations, SNs with different densities, and CHs.To show the suggested method's superiority, the findings are contrasted with those from a few other current methods.The proposed DA-EECHS for the WSNs model is 68.18%, 63.64%, 75%, and 50% better than the other methods in terms of received packet, network lifetime, energy consumption, and delay respectively.