Intelligent Task Offloading and Energy Management for Battery-Less 6G Industrial IoT Using Multi-Agent Deep Reinforcement Learning

Volume 12 ,Issue 3 ,August 2026 ,Pages 254-267

Authors

Ghufran Farhan Marzoog 1 ; Ammar Awad Kazm 2 ; Mustafa Kamil Ati 3

1 Department of Electrical Engineering, college of Engineering, University of Wasit, Kut, Iraq

2 Department of computer science, College of Education for Pure Sciences, University of Wasit, Kut, Iraq.

3 College of Engineering, University of Maisan, Iraq

DOI logo 10.17656/sjes.10227

Keywords

Abstract

Battery-less Industrial Internet of Things (IIoT) devices that are energized by energy harvesting (EH) must both finish latency-critical tasks and avoid capacitor brownouts simultaneously, but prior multi-agent learning approaches only optimize average performance, penalizing energy safety as a soft constraint and thus providing no assurances for energy-neutral operation. In this paper, a constrained, risk-sensitive multi-agent reinforcement learning framework is presented for joint task offloading and EH scheduling in battery-less 6G industrial networks. Learned dual variables that constrain brownout and energy-neutrality as hard budgets augment a centralized-training, decentralized-execution MAPPO backbone, a conditional-value-at-risk (CVaR) distributional critic is used to down-weight worst-case brownout, and a 6G ambient-backscatter transmission mode is used to enable communication in energy-scarce regimes. The method, L-MAPPO-EH, has the highest return and task completion, and reduces the brownout rate by a factor of three, on average, over the best learning baseline, and backscatter gains improve completion by an additional twenty-six percentage points under rare EH regimes, yielding a Pareto-non-dominated, statistically validated policy. 

References

  1. Ma, Y., Zhao, Y., Hu, Y., He, X., & Feng, S. (2025). Multi-agent deep reinforcement learning for joint task offloading and resource allocation in IIoT with dynamic priorities. Sensors, 25(19), 6160. https://doi.org/10.3390/s25196160
  2. Yu, Z., Zhang, Z., & Zeadally, S. (2025). Energy-efficient task offloading in the Industrial Internet of Things: A Lyapunov-guided multi-agent deep reinforcement learning approach. Journal of Industrial Information Integration, 47, 101037. https://doi.org/10.1016/j.jii.2025.101037
  3. Zhao, D., Ding, R., & Song, B. (2025). Satellite-assisted 6G wide-area edge intelligence: Dynamics-aware task offloading and resource allocation for remote IoT services. Science China Information Sciences, 68(1), 122303. https://doi.org/10.1007/s11432-024-4258-x
  4. Almuseelem, W. (2025). Deep reinforcement learning-enabled computation offloading: A novel framework to energy optimization and security-aware in vehicular edge-cloud computing networks. Sensors, 25(7), 2039. https://doi.org/10.3390/s25072039
  5. Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019). Learning-based computation offloading for IoT devices with energy harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930–1941. https://doi.org/10.1109/TVT.2018.2890685
  6. Basharat, S., Hassan, S. A., Pervaiz, H., Mahmood, A., Ding, Z., & Gidlund, M. (2021). Reconfigurable intelligent surfaces: Potentials, applications, and challenges for 6G wireless networks. arXiv. https://arxiv.org/abs/2107.05460
  7. Hassouna, S., Jamshed, M. A., Rains, J., Kazim, J. U. R., Ur Rehman, M., Abualhayja'a, M., Mohjazi, L., Cui, T. J., Imran, M. A., & Abbasi, Q. H. (2023). A survey on reconfigurable intelligent surfaces: Wireless communication perspective. IET Communications, 17(5), 497–537. https://doi.org/10.1049/cmu2.12571
  8. Worka, C. E., Khan, F. A., Ahmed, Q. Z., Sureephong, P., & Alade, T. (2024). Reconfigurable intelligent surface (RIS)-assisted non-terrestrial network (NTN)-based 6G communications: A contemporary survey. Sensors, 24(21), 6958. https://doi.org/10.3390/s24216958
  9. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv. https://arxiv.org/abs/1707.06347
  10. Yu, C., Velu, A., Vinitsky, E., Wang, Y., Bayen, A., & Wu, Y. (2022). The surprising effectiveness of PPO in cooperative multi-agent games. arXiv. https://arxiv.org/abs/2103.01955
  11. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. arXiv. https://arxiv.org/abs/1706.02275
  12. Mi, X., He, H., & Shen, H. (2024). A multi-agent RL algorithm for dynamic task offloading in D2D-MEC network with energy harvesting. Sensors, 24(9), 2779. https://doi.org/10.3390/s24092779
  13. Xu, S., Liu, Q., Gong, C., & Wen, X. (2025). Energy-efficient multi-agent deep reinforcement learning task offloading and resource allocation for UAV edge computing. Sensors, 25(11), 3403. https://doi.org/10.3390/s25113403
  14. Liu, Y., Li, H., Vasilakos, X., Hussain, R., & Simeonidou, D. (2025). Cooperative task offloading through asynchronous deep reinforcement learning in mobile edge computing for future networks. arXiv. https://arxiv.org/abs/2504.17526
  15. Yin, P., Liang, W., Wen, J., Kang, J., Chen, J., & Niyato, D. (2025). Multi-agent DRL for multi-objective twin migration routing with workload prediction in 6G-enabled IoV. arXiv. https://arxiv.org/abs/2505.07290
  16. Adu, E., Lee, Y., Moon, J., Jang, S., Bang, I., & Kim, T. (2026). Decentralized computation offloading strategy via multi-agent deep reinforcement learning for multi-access edge computing systems. Sensors, 26(3), 914. https://doi.org/10.3390/s26030914
  17. Park, J., & Chung, K. (2023). Distributed DRL-based computation offloading scheme for improving QoE in edge computing environments. Sensors, 23(8), 4166. https://doi.org/10.3390/s23084166
  18. He, H., Yang, X., Mi, X., Shen, H., & Liao, X. (2024). Multi-agent deep reinforcement learning based dynamic task offloading in a device-to-device mobile-edge computing network to minimize average task delay with deadline constraints. Sensors, 24(16), 5141. https://doi.org/10.3390/s24165141
  19. He, Z., Xiang, S., Ding, B., & Xu, R. (2025). Joint optimization of dependent task offloading and resource allocation in Internet of Vehicles. Transportation Research Record. Advance online publication. https://doi.org/10.1177/03611981251324193
  20. Fox, A., De Pellegrini, F., & Altman, E. (2025). Multi-agent reinforcement learning for task offloading in wireless edge networks. arXiv. https://arxiv.org/abs/2509.01257
  21. Achiam, J., Held, D., Tamar, A., & Abbeel, P. (2017). Constrained policy optimization. arXiv. https://arxiv.org/abs/1705.10528
  22. Dabney, W., Rowland, M., Bellemare, M. G., & Munos, R. (2018). Distributional reinforcement learning with quantile regression. arXiv. https://arxiv.org/abs/1710.10044
  23. Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443–1471. https://doi.org/10.1016/S0378-4266(02)00271-6
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  • First online10 August 2026
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