SELF-ORGANIZING CLUSTERING METHODS FOR ENERGY-EFFICIENT DATA GATHERING IN SENSOR NETWORKS

Abstract

By deploying wireless sensor nodes and composing a sensor network. one can remotely obtain information about the behaviour, conditions. and positions of entities in a region. Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable to prolong the lifetime of a sensor network as long as possible. A sensor node consumes energy in observing its surroundings, transmitting data, and receiving data. Especially, energy consumption in data transmission scales proportionally to the n-th power of the radius of the radio signal. Therefore, cluster-based data gathering mechanisms effectively save energy. In cluster-based data gathering, since each node can save transmission power and the number of collisions is also reduced. sensor networks can live for longer period. In clustering, however, we need to consider that a cluster-head consumes more energy than the other nodes in receiving data from cluster members, fusing data to reduce the size, and sending the aggregated data to a base station. This dissertation synthesises existing clustering algorithms in WSNs and compare them in terms of their stable operation period (SOP) and highlights the challenges in clustering.