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Energy consumption is one of the major challenges in wireless sensor networks, thus necessitating an approach for its minimization and for load balancing data. The network lifetime ends with the death of one of its nodes, which, in turn, causes energy depletion in and partition of the network. Furthermore, the total energy consumption of nodes depends on their location; that is, because of the loaded data, energy discharge in the nodes close to the base station occurs faster than other nodes, the model presented here, through using learning automata, selects the path appropriate for data transferring; the selected path is rewarded or penalized taking the reaction of surrounding paths into account. We have used learning automata for energy management in finding the path; the routing protocol was simulated by NS2 simulator; the lifetime, energy consumption and balance in an event-driven network in our proposed method were compared with other algorithms.

EEULA: AN ENERGY-AWARE EVENT-DRIVEN UNICAST ALGORITHM FOR WIRELESS SENSOR NETWORK BY LEARNING AUTOMATA