Clustering at the Edge: Load balancing and energy efficiency for the IoT

This paper explores clustering as a technique to improve energy efficiency for a variety of current and emerging IoT application scenarios. We introduce a novel load balancing clustering algorithm based on Simulated Annealing whose main goal is to increase network lifetime while maintaining adequate sensing coverage in scenarios where sensor nodes produce non-uniform data traffic. Through simulations on the Cooja/Contiki simulation-emulation platform, we compare the proposed algorithm to leading state-of-the-art clustering approaches and show that our algorithm is able to improve both time until first clusterhead failure as well as network coverage by keeping more sensor nodes alive for longer periods of time. We also explore different clustering criteria applied to our simulated annealing clustering framework and investigated different network lifetime metrics. We show that accounting for both workload and communication cost improves network lifetime by prolonging clusterhead lifetime and that clustering solely on the basis of physical distance improves network coverage.