Parag Verma
Department of Computer Science and Engineering, Uttaranchal University, Dehradun, Uttarakhand, India.

Ankur Dumka
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarkahnd, India.

Dhawal Vyas
Department of Computer Science and Engineering, Government Engineering College, Bharatpur, Rajasthan, India.

Anuj Bhardwaj
Department of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, India.

DOI https://doi.org/10.33889/IJMEMS.2020.5.4.057

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Abstract

A wireless sensor network is a collection of small sensor nodes that have limited energy and are usually not rechargeable. Because of this, the lifetime of wireless sensor networks has always been a challenging area. One of the basic problems of the network has been the ability of the nodes to effectively schedule the sleep and wake-up time to overcome this problem. The motivation behind node sleep or wake-up time scheduling is to take care of nodes in sleep mode for as long as possible (without losing data packet transfer efficiency) and thus extend their useful life. This research going to propose scheduling of nodes sleeps and wake-up time through reinforcement learning. This research is not based on the nodes' duty cycle strategy (which creates a compromise between data packet delivery and nodes energy saving delay) like other existing researches. It is based on the research of reinforcement learning which gives independence to each node to choose its own activity from the transmission of packets, tuning or sleep node in each time band which works in a decentralized way. The simulation results show the qualified performance of the proposed algorithm under different conditions.

Keywords- Sleep or wake-up scheduling, Wireless sensor network, Sensor node energy.

Citation

Verma, P., Dumka, A., Vyas, D., & Bhardwaj, A. (2020). Reinforcement Learning based Node Sleep or Wake-up Time Scheduling Algorithm for Wireless Sensor Network. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 707-731. https://doi.org/10.33889/IJMEMS.2020.5.4.057.