CAM: Clustering Algorithms for Multimodal WSN

Sensor nodes of Wireless Sensor Network (WSN) possess very limited power resources normally a battery and a solar cell could exist in some cases, which requires efficient usage of these resources to extend the network's lifetime. Accordingly, several research areas have been investigated to prolong the network longevity, Clustering was proposed to WSN as one of these areas that could help in decreasing the amount of consumed energy. A number of clustering algorithms were devised but to the authors' knowledge, this is the first work to consider clustering in Multimodal WSN, where a node can report more than one feature e.g. temperature and humidity. We compared the two general clustering algorithms K-Mean and Fuzzy C-Means to LEACH-C and LEACH, which are two clustering algorithms specially designed for WSN. Fuzzy C-Means and K-Means showed better performance using the techniques proposed in this work over LEACH-C and LEACH.