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