LiMCA: an optimal clustering algorithm for lifetime maximization of internet of things

Abstract

The idea of Internet of Things (IoT) is that many of the live objects (e.g., appliances) in the network are accessible, sensed, and interconnected. However, energy-constrained IoT nodes limit the performance of the IoT network. Hence, preserving energy in IoT network requires utmost attention. Unequal clustering is commonly considered as one of the efficient energy saving technique. Here, the traffic load is evenly distributed among the nodes using variable size clusters across the network. However, none of the existing solutions considered (1) realistic factors like fading model, routing protocol etc., or (2) optimization of cluster radius while devising clustering structure. The contribution of this paper is two-fold. First, we analyze the maximization of network lifetime by balancing the energy consumption among Cluster Heads (CHs). We found that cluster radius of each level has significant role in maximization of network lifetime. Second, to meet the requirement of maximization of network lifetime, this paper proposes a novel Lifetime Maximizing optimal Clustering Algorithm (LiMCA) for battery-powered IoT devices. Particularly, LiMCA includes a novel stochastic deployment scheme for Member Nodes (MNs) and CHs and a training protocol to train CHs and MNs about their coarse-grain location. Extensive simulation study shows that our algorithm improves the network lifetime by more than 30%, compared to other existing approaches.