RF signal Strength based clustering protocols for a self-organizing cognitive radio network

This paper presents two novel distributed clustering algorithms that exploit cognitive radio based principles in that they have the ability to learn from received signal strength indicator (RSSI) beacons, to form clusters which reduce the average distance between nodes and cluster head, as well as reducing the level of overlap between clusters. One proposed method is based on a multiple summation of RSSI values, while the other is based on a multiple of the last sensed RSSI value. Nodes effectively compete to become a cluster head, with the winning nodes being those that are located in an area of locally high node density. It is shown that the two learning based approaches both have similar performance, and are significantly better than LEACH (Low-Energy Adaptive Clustering Hierarchy) and a no learning algorithm.