Increasing Network Lifetime in WSNs with Use of Learning Automata in Clustering Method

Increasing miniaturization and sensor communication abilities make them invisible and expand the availability everywhere and in any time. Sensor network applications, increase the challenging issues related to design network protocols have emerged. One of them is increasing energy efficiency and lifetime in the network. Sensor nodes with limited energy reserves are deployed, so the network must operate with minimal energy overhead. This article focuses on improving the network lifetime by using energy efficient arrangement of nodes in a state primary goal to reduce energy waste with using energy balance. Therefore, learning automata capabilities - to solve issues in sensor networks is appropriate - this tool is used. For the purpose mentioned above, energy balanced clustering technique based on learning automata is proposed that learning automata residing in the cluster head, for balance, the best node according to the amount of energy remaining as the new cluster head is selected. Proposed technique with the NS2 simulator to simulate the behavior is evaluated. Results show that the calculated energy balance improves the life time of sensor network substantially

Keywords:

Sensor networks , Energy balance , Learning Automata , Clustering , Nodes classification , Increase lifetime