SELARP: Scalable and Energy-Aware Learning Automata-Based Routing Protocols for Wireless Sensor Networks

This paper presents an energy-aware routing protocol, which uses learning automata concepts for wireless sensor network that is an approach to increase the network lifetime. Moreover, this protocol is scalable and can be used in various places. This approach has the advantage of decreasing energy consumption in order to increase network lifetime and manage routing discovery procedure to find best route in energy consumption by irregular cellular learning automata concept. We will first introduce a model of cellular learning automata in which learning automata is used to adjust the state transition probabilities of cellular automata, and then we will use irregular cellular learning automata for routing problem in a network which is based on Voronoi diagrams. Finally, we will simulate and evaluate our proposed routing protocol with other learning automata- based routing protocols by Matlab and Glomosim simulator. According to the results of simulations, we will show that the proposed protocol operates better than similar routing protocols in energy consumption and overhead of sensor networks.