LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks

Rami Hamdi, Emna Baccour, Aiman Erbad, Marwa Qaraqe, Mounir Hamdi

Research output: Contribution to journalArticlepeer-review


LoRa wireless networks are considered as a key enabling technology for next-generation Internet of Things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this article, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This article proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system’s quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two subproblems: 1) channel and SF assignment problem and 2) energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on reinforcement learning (RL) are developed. Simulation results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.
Original languageEnglish
Pages (from-to)6458 - 6476
Number of pages19
JournalIEEE Internet of Things Journal
Issue number9
Early online date25 Apr 2022
Publication statusPublished - 1 May 2022

Bibliographical note

10.13039/100008982-NPRP-Standard (NPRP-S) Thirteen (13th) Cycle Grant from the Qatar National Research Fund (QNRF) (a member of Qatar Foundation) (Grant Number: NPRP13S-0205-200265)
10.13039/100008982-TÜBITAK—QNRF Joint Funding Program Grant from the Scientific and Technological Research Council of Turkey and QNRF (Grant Number: AICC03-0324-200005)


  • Energy harvesting
  • LoRa
  • Reinforcement learning (RL)
  • Resource Management


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