A Self-Organising Clustering Algorithm for Wireless Sensor Networks

This paper describes an application level, data-centric algorithm that creates clusters in a sensor network based on the changes of the signal being observed by the sensor nodes without any predetermination by the user. The algorithm was developed for the Self-Organising Collegiate Sensor Network (SECOAS) project and its design was influenced by biological examples of emergence in complex systems. Specifically, this paper refers to the processes Quorum Sensing (QS) and Local Activation and Lateral Inhibition (LALI). The former shows how bacterial cells get into clusters while the latter allows the sensors to recognise patterns in the environment that influences how the clusters are made. Testing the algorithm involved looking at the scalability and the tolerance of the algorithm with a simplified model of the signals to be monitored in SECOAS.