Skip to Main Content
Among some of sensor network properties which make it different from other networks, can refer to very high number of nodes, dynamic, and probably periodic topological changes and also some constraints in physical size of nodes, energy resource and power of processing. According to these restrictions, giving solutions and self-configurable protocols that do global tasks without requiring a central controller or manager are necessary. Topology control and node scheduling that constitute a part of the maintenance phase of self-organization protocols, are providing the main goal of this phase which is increasing network lifetime and also maintaining the infrastructure support for the network. In consideration of learning Automata's abilities such as low computational load, the ability of being used in distributed environments, with no precise information, the adaptability to changes via low environmental feedbacks and etc. and also its functionality that has some correspondence with essential methods which are used in self-organization systems, such as positive and negative feedbacks, interacting of special nodes with each other and with the environment, and probabilistic methods, results in the fact that using them is proper for improving the performance of sensor networks. So, in this paper a neighbour based topology control protocol has been proposed, in which an irregular cellular learning automaton is mapped to network, and with it nodes which are equipped with Automata, try to adapt their selected actions with required conditions for creating a connected, energy efficient network through selecting the best radio transmission range for themselves. This approach finally forms a proper topology which causes to lower network's energy consumption in its lifetime. The exclusive characteristic of this method is, the high number of transmission ranges that each node can select as transmission radius. Simulation's results show favorite functionality of the proposed pr- tocol in comparison with some others from the above point of view.