An Improved Lifetime Optimization Clustering using Kruskal’s MST and Batteries Aging for IoT Networks

Lifetime improvement is a major concern for energy constrained wireless networks. Clustering the network topology is widely utilized for managing and enhancing the system duration. With conventional clustering mechanism Cluster Heads (CHs) close to the Base Station (BS) utilize higher power resource for relaying data packets of the other network CHs. This scenario obstruct the network performance as nodes close to the BS attend an earlier death than their desired durability due to the overloaded routing task. This scenario unbalanced energy consumption and is designated as the hot spot problem. The interest in this work is to carry the intra clustering topology in a vast scale contexts to support the network rising and fairly power balance the energy consuming. In this context, we present an Improved Lifetime Optimization Clustering (ILCK) approach that uses the Kruskal minimal spanning tree heuristic (MST) and consider the state of health (SOH) of devices batteries for the network life maximization. ILCK appeal the Kruskal algorithm in a distributed trend to achieve a minimal MST tree inside wide cluster to consolidate the intra cluster routing topology and mitigate the energy allocated to wireless communications. To the best of our awareness, this is a primary solution that merge the Kruskal approach within an uneven clustering to prolong the objects battery endurance and ease the energy hot spot routing issues. The complexity proof of the proposed approach is provided and simulation results denote that ILCK can adequately scale down the power consumption and lengthen the execution time of the deployed network.