A Distributed Energy-Efficient Unequal Clustering Based Kruskal Heuristic for IoT Networks

Energy efficiency is a major concern and a critical issue for energy constrained wireless networks. In this context, clustering is commonly used for topology management and maximizing the network lifetime. Clustering approaches typically use a multi-hopping mechanism where Cluster Heads (CHs) near the Base Station (BS) consume higher energy since they relay data of farther CHs. Therefore, nodes close to the BS are strangled with an overloaded routing task and tend to die earlier than their intended lifetime, which affects the network performance. This situation is known as the hot spot problem that induces unbalanced energy consumption among CHs. The concern in this work is to address the intra-clustering structure in large scale environments to tolerate the network scaling and reasonably balance the energy consumption among CHs. In this regard, we propose a new Unequal Clustering algorithm based on Kruskal heuristic (UCKA) to optimize the network lifetime. UCKA applies the Kruskal heuristic in a distributed fashion to perform a minimum spanning tree within large cluster which strengthen the intracluster routing structure and reduce the energy devoted to wireless communications. To the best of our knowledge, this is the first solution that combines the Kruskal heuristic and the unequal clustering to extend the devices durability and alleviate the hot spot problem. Simulation results indicate that UCKA can effectively reduce the energy consumption and lengthen the network lifetime.