Extending Wireless Sensor Networks’ Lifetimes Using Deep Reinforcement Learning in a Software-Defined Network Architecture

Authors : Zainab ABBOOD, Mahmoud SHUKER, Çağatay AYDIN, Doğu Çağdaş ATİLLA

Pages : 39-46

Doi:10.21541/apjes.687496

View : 10 | Download : 6

Publication Date : 2021-01-29

Article Type : Research Paper

Abstract :Routing packets in a Wireless Sensor Network insert ignore into journalissuearticles values(WSN); is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning insert ignore into journalissuearticles values(ML); techniques in a Software-Defined Network insert ignore into journalissuearticles values(SDN); topology has shown a good potential toward solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning insert ignore into journalissuearticles values(DRL); approach. By emphasizing on the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased but the time required for the first node to exhaust its energy has been significantly increased.
Keywords : Software Defined Network, Internet of Things, Wireless Sensor Network, Reinforcement Learning