RLBEEP: Reinforcement-learning-based energy efficient control and routing protocol for wireless sensor networks

Ali Forghani Elah Abadi, Seyyed Amir Asghari, Mohammadreza Binesh Marvasti, Golnoush Abaei, Morteza Nabavi, Yvon Savaria

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

Original languageEnglish
Pages (from-to)44123-44135
Number of pages13
JournalIEEE Access
Publication statusPublished - 2022


  • Energy consumption
  • Energy efficiency
  • Energy Management
  • Licenses
  • Machine Learning
  • Network Lifetime
  • Reinforcement Learning
  • Routing
  • Routing Policy
  • Routing protocols
  • Sensors
  • Wireless Sensor Network
  • Wireless sensor networks

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