A Firefly-inspired Micro and Macro Clustering Approach for Wireless Sensor Networks

Bio-inspired algorithms have been widely used to solve Wireless Sensor Network (WSN) challenges. In several studies, they have demonstrated effective capabilities to fulfil the expected goals while adapting to contextual changes and using limited resources. In this paper, we propose a new firefly-based approach for WSN clustering. Our approach includes a micro clustering phase during which sensors self-organize into clusters. These clusters are polished during a macro-clustering phase where they compete to integrate small neighboring clusters. Our simulations show promising results where the number of clusters tend to stabilize independently from the density of the network and the various communication ranges of sensors.