• Vineeth N
• Dr. VENKATESAN S

Wireless Sensor Networks (WSN) is the key resource of insight and is widely used trends now a days. Sensor nodes are powered by batteries and proper energy conservation is needed to prolong the lifetime of the sensors. The determination of scheduling and routing algorithm is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this work, a self-adaptive sleep/wake-up scheduling and routing approach is proposed. Unlike most existing studies that use the Duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not use duty cycling, avoids such a tradeoff. The proposed approach, based on the Reinforcement learning technique, enables each node to autonomously decide its own operation mode (sleep, listen, or transmission) in each time slot in a decentralized manner. The effectiveness of the network energy consumption and it has the longest network lifetime which can be achieved.

WSN, Duty Cycling, Reinforcement learning technique

"Optimizing the Life Time of Sensor Network Nodes using Scheduling and Routing Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.589-594, May-2019, Available :http://www.jetir.org/papers/JETIR1905980.pdf

"Optimizing the Life Time of Sensor Network Nodes using Scheduling and Routing Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp589-594, May-2019, Available at : http://www.jetir.org/papers/JETIR1905980.pdf