Clustering for Load Balancing and Energy Efficiency in IoT Applications
Shesha Sreenivasamurthy, Katia Obraczka · 2018
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 balanced 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 uniform or non-uniform data traffic. To this end, we also introduce a new clustering cost function that accounts not only for sensor node traffic load but also for the cost of communicating over physical distances. Through extensive simulations comparing the proposed algorithm to leading state-of-the-art clustering approaches, we show that our algorithm is able to improve both network lifetime as well as network coverage by keeping more sensor nodes alive for longer periods of time at lower computational cost.