Energy Efficient Reinforcement Learning Based Adaptive Resource Allocation for LoRa Networks
Suchitra N Shenoy
Department of Electronics and Communication Engineering, Canara Engineering College, Mangalore, Karnataka, India. Visvesvaraya Technological University, Belagavi, Karnataka, India.
Department of Electronics and Communication Engineering, Canara Engineering College, Mangalore, Karnataka, India, Visvesvaraya Technological University, Belagavi, Karnataka, India.
Department of Information Science and Engineering, Canara Engineering College, Mangalore, Karnataka, India, Visvesvaraya Technological University, Belagavi, Karnataka, India.
In the last few years, a surge in IoT applications has ramped up the need for effective and dependable data transmission in LoRa-based systems. Yet, traditional resource allocation methods in LoRa systems face major drawbacks such as higher packet loss, interference, excessive energy use, limited coverage, slow transmission speeds, and increased operational expenses. To tackle these issues, this study introduces a new hybrid optimisation framework that combines Hybrid Reinforcement Learning, named as Double Deep Q-Learning based Actor-Critic mechanism (Hy-DeoQ-AC), with a hybrid Levy Flight Assisted Rabbit optimisation algorithm (Hy-LevRBO). Hy-DeoQ-AC mechanism learns optimal network configurations dynamically by engaging with the environment, concentrating on key transmission parameters like spreading factor, transmission power, and channel selection to satisfy strict Quality of Service (QoS) requirements of IoT devices. Additionally, the hybrid optimisation gains from Hy-LevRBO, which fine-tunes chosen parameters and boosts capability to evade local optima. Thus, this combined strategy greatly enhances energy efficiency, maximises throughput, extends transmission range, and reduces latency in LoRa networks. The Comprehensive experimental analysis attains a throughput of 56.8471(bits/s), energy efficiency of 16.1364 (bits/J), which confirms the proposed model's superiority and achieves better performance across various metrics. This research offers an energy-efficient solution for IoT communications.
Keywords
Q Learning, Long-Range Network, Levy Flight, Rabbit Optimisation, Actor Critic Approach.
M. Jouhari, N. Saeed, M.-S. Alouini, and E. M. Amhoud, “A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges,” IEEE Communications Surveys & Tutorials, vol. 25, no. 3, pp. 1841–1876, 2023, doi: 10.1109/comst.2023.3274934.
I. Cheikh, R. Aouami, E. Sabir, M. Sadik, and S. Roy, “Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial,” IEEE Access, vol. 10, pp. 9198–9231, 2022, doi: 10.1109/access.2021.3140107.
A. Farhad and J.-Y. Pyun, “LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning,” Sensors, vol. 23, no. 15, p. 6851, Aug. 2023, doi: 10.3390/s23156851.
L. Acosta-Garcia, J. Aznar-Poveda, A. J. Garcia-Sanchez, J. Garcia-Haro, and T. Fahringer, “Dynamic transmission policy for enhancing LoRa network performance: A deep reinforcement learning approach,” Internet of Things, vol. 24, p. 100974, Dec. 2023, doi: 10.1016/j.iot.2023.100974.
M. Alkhayyal and A. Mostafa, “Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization,” Sensors, vol. 24, no. 14, p. 4482, Jul. 2024, doi: 10.3390/s24144482.
O. J. Pandey, T. Yuvaraj, J. K. Paul, H. H. Nguyen, K. Gundepudi, and M. K. Shukla, “Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 365–379, Mar. 2022, doi: 10.1109/tccn.2021.3114147.
M. Chen, L. Mokdad, J. Ben-Othman, and J.-M. Fourneau, “Dynamic Parameter Allocation with Reinforcement Learning for LoRaWAN,” IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10250–10265, Jun. 2023, doi: 10.1109/jiot.2023.3239301.
E. Sisinni et al., “Can adaptive strategies sustain bidirectional LoRaWAN traffic?,” 2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0& IoT), pp. 66–71, Jun. 2023, doi: 10.1109/metroind4.0iot57462.2023.10180144.
A. Farhad, D.-H. Kim, B.-H. Kim, A. F. Y. Mohammed, and J.-Y. Pyun, “Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks,” IEEE Access, vol. 8, pp. 186111–186124, 2020, doi: 10.1109/access.2020.3029575.
B. Dai, J. Niu, T. Ren, and M. Atiquzzaman, “Toward Mobility-Aware Computation Offloading and Resource Allocation in End–Edge–Cloud Orchestrated Computing,” IEEE Internet of Things Journal, vol. 9, no. 19, pp. 19450–19462, Oct. 2022, doi: 10.1109/jiot.2022.3168036.
K. Lasri, Y. Ben Maissa, L. Echabbi, O. Iova, and F. Valois, “Probabilistic and distributed traffic control in LPWANs,” Ad Hoc Networks, vol. 143, p. 103121, Apr. 2023, doi: 10.1016/j.adhoc.2023.103121.
S. K. Suman, S. Pandey, V. K. Kumar, S. Balamurugan, M. Pingili, and P. Tanna, “Optimizing Point of Care Interaction with Speech Recognition to Preserve Learnability and Intelligibility,” 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 1–5, Apr. 2025, doi: 10.1109/icdcece65353.2025.11034855.
F. Yao, Y. Ding, S. Hong, and S.-H. Yang, “A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications,” International Journal of Network Dynamics and Intelligence, pp. 4–19, Dec. 2022, doi: 10.53941/ijndi0101002.
C. Anitha, S. S. Priscila, M. R, and B. Balakrishnan, “An Efficient Secure Routing and Hierarchical Approach to Elliptic Curve Cryptography Combined with Distributed Group Key Management,” 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 2208–2212, Mar. 2023, doi: 10.1109/icaccs57279.2023.10113088.
W. Wu, H. Wang, and Z. Cheng, “ReLoRaWAN: Reliable data delivery in LoRaWAN networks with multiple gateways,” Ad Hoc Networks, vol. 147, p. 103203, Aug. 2023, doi: 10.1016/j.adhoc.2023.103203.
F. Azizi, B. Teymuri, R. Aslani, M. Rasti, J. Tolvaneny, and P. H. J. Nardelli, “MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN,” 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), pp. 1–6, Jun. 2022, doi: 10.1109/vtc2022-spring54318.2022.9860807.
M. R. Rao and S. Sundar, “An Efficient Method for Optimal Allocation of Resources in LPWAN Using Hybrid Coati-Energy Valley Optimization Algorithm Based on Reinforcement Learning,” IEEE Access, vol. 11, pp. 116169–116182, 2023, doi: 10.1109/access.2023.3325724.
S. U. Minhaj et al., “Intelligent Resource Allocation in LoRaWAN Using Machine Learning Techniques,” IEEE Access, vol. 11, pp. 10092–10106, 2023, doi: 10.1109/access.2023.3240308.
M. A. Gava, H. R. O. Rocha, M. J. Faber, M. E. V. Segatto, H. Wörtche, and J. A. L. Silva, “Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN,” Sensors, vol. 23, no. 3, p. 1239, Jan. 2023, doi: 10.3390/s23031239.
H. Poddar, V. Roy, M. Bhoyar, A. Gangrade, K. N. Venkata Kowshik, and P. Rathod, “Leveraging Context-Aware Fogging for Advanced Remote Patient Monitoring and Healthcare in Modern Life,” 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, pp. 1–6, Apr. 2025, doi: 10.1109/otcon65728.2025.11070467.
et al., “Real-Time Classroom Emotion Analysis Using Machine and Deep Learning for Enhanced Student Learning,” Journal of Intelligent Systems and Internet of Things, vol. 16, no. 2, pp. 82–101, 2025, doi: 10.54216/jisiot.160207.
S. Jain, P. K. Sholapurapu, B. Sharma, M. Nagar, N. Bhatt, and N. Swaroopa, “Hybrid Encryption Approach for Securing Educational Data Using Attribute-Based Methods,” 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, pp. 1–6, Apr. 2025, doi: 10.1109/otcon65728.2025.11070667.
A. Chaturvedi, N. Pathak, N. Sharma, and R. Mahaveerakannan, “Deep Learning in the Context of Artificial Intelligence: Advancements and Applications,” Innovative Computing and Communications, pp. 23–44, Oct. 2024, doi: 10.1007/978-981-97-4152-6_3.
W. Xu, G. Cai, Y. Fang, S. Mumtaz, and G. Chen, “Performance Analysis and Resource Allocation for a Relaying LoRa System Considering Random Nodal Distances,” IEEE Transactions on Communications, vol. 70, no. 3, pp. 1638–1652, Mar. 2022, doi: 10.1109/tcomm.2022.3146289.
Hamdi, R., Baccour, E., Erbad, A., Qaraqe, M., & Hamdi, M. (2021). LoRa-RL: Deep reinforcement learning for resource management in hybrid energy LoRa wireless networks. IEEE Internet of Things Journal, 9(9), 6458–6476.
X. Ji, C. Lu, B. Xie, H. Guo, and B. Zheng, “Combination of a Rabbit Optimization Algorithm and a Deep-Learning-Based Convolutional Neural Network–Long Short-Term Memory–Attention Model for Arc Sag Prediction of Transmission Lines,” Electronics, vol. 13, no. 23, p. 4593, Nov. 2024, doi: 10.3390/electronics13234593.
B. M and M. R, “Urban Object Detection in UAV Imagery for Healthcare Applications: Leveraging the Internet of Things (IoT) in Smart Cities for Enhanced Urban Healthcare Monitoring and Management,” 2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), pp. 607–612, Sep. 2023, doi: 10.1109/icseiet58677.2023.10303381.
A. K. Thomas, H. Shekhar, L. Bhagyalakshmi, S. K. Suman, R. Sreelakshmy, and A. Bhasha, “IoT Enabled Smart Street Light and Air Quality Control,” Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 1, pp. 843–853, Oct. 2024, doi: 10.1007/978-981-97-8031-0_90.
H. P. Bhupathi, S. Chinta, S. K. Yeditha, V. Biradar, L. Bhagyalakshmi, and S. K. Suman, “Enhanced SOC Estimation Performance Through Noise Covariance Analysis in Nonlinear Kalman Filters,” 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, pp. 1–6, Apr. 2025, doi: 10.1109/otcon65728.2025.11070919.
S. Chinta, H. P. Bhupathi, S. K. Yeditha, V. Biradar, S. K. Suman, and L. Bhagyalakshmi, “Optimized Battery Modeling Using Adaptive Forgetting Factor in Recursive Least Squares Algorithm,” 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, pp. 1–6, Apr. 2025, doi: 10.1109/otcon65728.2025.11070505.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Suchitra N Shenoy, Ganesh V Bhat, Manoj H Gadiyar T;
Writing- Original Draft Preparation: Suchitra N Shenoy;
Validation: Ganesh V Bhat, Manoj H Gadiyar T;
Supervision: Manoj H Gadiyar T; All authors reviewed the results and approved the final version of the manuscript.
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Suchitra N Shenoy
Department of Electronics and Communication Engineering, Canara Engineering College, Mangalore, Karnataka, India, Visvesvaraya Technological University, Belagavi, Karnataka, India.
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Cite this article
Suchitra N Shenoy, Ganesh V Bhat and Manoj H Gadiyar T, “Energy Efficient Reinforcement Learning Based Adaptive Resource Allocation for LoRa Networks”, Journal of Machine and Computing, vol.5, no.4, pp. 2405-2421, October 2025, doi: 10.53759/7669/jmc202505186.
