The high number of nodes and dynamic and periodic topological changes, as well as constraints in the physical size of nodes, energy resources, and power of processing are some characteristics of sensor networks that make them different from other networks. One method to overcome these constraints is topology control with the aim of reducing energy consumption and increasing the network’s capacity, which has the most influence on the network’s efficiency, especially in terms of energy consumption and lifetime. In consideration of learning Automata’s abilities, such as low computational load and adaptability to changes via low environmental feedbacks, in this paper, neighbor-based topology control protocols based on learning Automata have been proposed somehow that all nodes are equipped with Automata. The nodes try to adapt their selected actions with required conditions for creating a connected and energy efficient network by selecting the best radio range for themselves. This approach finally forms a proper topology, and in this way it lowers the network’s energy consumption in its lifetime. The exclusive characteristic of these methods is the high number of transmission ranges that each node can select as transmission radius. In the first proposed protocol, a P-model environment is used for learning phase, but in the second proposed protocol, a Q-model environment is applied. Simulation results show favorite functionality of proposed protocols in comparison with some other similar protocols from topology control point of view, as well as high improvement of achieved results for the Q-model environment.
Topology Control for Energy-Efficient Localization in Mobile Underwater Sensor Networks Using Stackelberg Game
