Energy-Efficient Routing Based on Reinforcement Learning
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Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions. However, IoT devices are memory and power-constrained and do not allow high computational applications, whereas the routing task is what makes an object to be part of an IoT network despite of being a high power-consuming task. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. The performance of the proposed protocol is compared with other existing energy-efficient routing protocols, and the results show that the proposed protocol performs better in terms of energy efficiency and network lifetime and scalability.
Vially Kazadi Mutombo, Seungyeon Lee, Jusuk Lee, Jiman Hong, "EER-RL: Energy-Efficient Routing Based on Reinforcement Learning", Mobile Information Systems, vol. 2021, Article ID 5589145, 12 pages, 2021. https://doi.org/10.1155/2021/5589145
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