AI-Driven Energy Harvesting Communication Framework for Battery-less IoT Devices in 6G Networks
DOI:
https://doi.org/10.47134/jtsi.v3i1.5488Keywords:
Battery-less IoT, Energy Harvesting, 6G Networks, TinyML, Reinforcement Learning, Sustainable Communications, Self-Powered DevicesAbstract
Batteryless IoT devices will be key in sixth-generation (6G) networks in the future because they allow massive, maintenance-free implementation of smart cities, agriculture, healthcare, and industrial monitoring. Even though the use of ambient energy sources of solar, thermal, vibration, and radio-frequency (RF) does not require using batteries, the randomized and unpredictable characteristics of harvested energy considerably decrease the communication reliability and performance. Current communication schemes on energy harvesting are mostly fixed and do not adjust themselves to the varying energy situations. The paper will suggest an AI-based, energy-conscious communication architecture of the battery-free IoT devices in 6G settings. The construction is a lightweight model using TinyML to predict the short-term forecasted energy, which was collected by using real-time and historical environmental data. Based on these predictions, a reinforcement learning (RL)-based scheduler would then trade-off spectral energy consumption and communication throughput by dynamically optimizing transmission power and data rate and duty cycle. The proposed method allows reliable and autonomous communication within severe and strict energy constraints through combined energy prediction and adaptive scheduling. Evaluation Simulation allows us to conclude that the given framework is much more effective in terms of throughput stability, packet delivery reliability, latency, as well as using energy in an efficient manner, when compared to traditional fixed-energy-harvesting-based communication approaches. The publication offers a long-lasting and smart background on self-enhanced IoT communications in the next-generation 6G networks.
References
A. Sabovic, M. Aernouts, D. Subotic, J. Fontaine, E. De Poorter, and J. Famaey, "Towards energy-aware tinyML on battery-less IoT devices," Internet of Things, vol. 22, p. 100736, 2023.
B. Zheng, T. Ma, C. You, J. Tang, R. Schober, and R. Zhang, "Rotatable antenna enabled wireless communication and sensing: Opportunities and challenges," arXiv preprint arXiv:2505.16828, 2025.
C. R. Banbury, V. J. Reddi, M. Lam, W. Fu, A. Fazel, J. Holleman, et al., "Benchmarking tinyml systems: Challenges and direction," arXiv preprint arXiv:2003.04821, 2020.
Enahoro, S. (2025). Sustainable THz SWIPT via RIS-Enabled Sensing and Adaptive Power Focusing: Toward Green 6G IoT. Sensors, 25(15), ISSN 1424-8220, https://doi.org/10.3390/s25154549
H. Nguyen and Y. M. Jang, "Survey of Next-generation Optical Wireless Communication Technologies for 6G and Beyond 6G," ICT Express, 2025.
H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjöland, and F. Tufvesson, "6G wireless systems: Vision, requirements, challenges, insights, and opportunities," Proceedings of the IEEE, vol. 109, pp. 1166–1199, 2021.
Hora, J. A. (2025). Low Voltage Ultra-Low Power PVT Independent and High PSRR Voltage Reference for Zero-Energy 6G Application. Lecture Notes in Electrical Engineering, 1439, 145–156, ISSN 1876-1100, https://doi.org/10.1007/978-981-96-8093-1_12
J. W. Rogers and C. Plett, Radio Frequency Integrated Circuit Design. Artech House, 2010.
K. B. Letaief, Y. Shi, J. Lu, and J. Lu, "Edge artificial intelligence for 6G: Vision, enabling technologies, and applications," IEEE Journal on Selected Areas in Communications, vol. 40, pp. 5–36, 2021.
Khalifa, O. (2024). Energy Conservative Data Aggregation for IoT Devices: An Aerial Wake-Up Radio Approach. IEEE Internet of Things Journal, 11(2), 2006–2026, ISSN 2327-4662, https://doi.org/10.1109/JIOT.2023.3292305
M. R. Sarker, A. Riaz, M. H. Lipu, M. H. M. Saad, M. N. Ahmad, R. A. Kadir, et al., "Micro energy harvesting for IoT platform: Review analysis toward future research opportunities," Heliyon, vol. 10, 2024.
O. López, R. K. Singh, D.-T. Phan-Huy, E. Katranaras, N. Mazloum, K. Ruttik, et al., "Zero-energy devices for 6G: Technical enablers at a glance," IEEE Internet of Things Magazine, vol. 8, pp. 14–22, 2025.
P. Rajeswari, M. Nitheeswari, and G. Sneha, "AI-Driven Energy-Efficient 6G IoT Network with Blockchain Security and Digital Twin Optimization," in International Conference on Computer Science and Communication Engineering (ICCSCE 2025), 2025, pp. 2112–2124.
Pihtili, M. E. (2024). Index Modulation-Based Information Harvesting for Far-Field RF Power Transfer. IEEE Transactions on Wireless Communications, 23(10), 14895–14909, ISSN 1536-1276, https://doi.org/10.1109/TWC.2024.3420073
S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y. Zomaya, "Edge intelligence: The confluence of edge computing and artificial intelligence," IEEE Internet of Things Journal, vol. 7, pp. 7457–7469, 2020.
Singhal, C. (2023). Sustainable Application Support in Battery-Less IoT Sensing Network System. 2023 IEEE International Conference on Communications Workshops Sustainable Communications for Renaissance (ICC Workshops 2023), 1277–1282, https://doi.org/10.1109/ICCWorkshops57953.2023.10283554
V. Tsoukas, A. Gkogkidis, E. Boumpa, and A. Kakarountas, "A Review on the emerging technology of TinyML," ACM Computing Surveys, vol. 56, pp. 1–37, 2024.
Wenman, T. (2024). Micro Renewable Power System for Lightweight Smart Home Devices in the Net-Zero IoT. 2024 IEEE International Conference on Communications Control and Computing Technologies for Smart Grids (SmartGridComm 2024), 465–470, https://doi.org/10.1109/SmartGridComm60555.2024.10738040
Y. Kim, Y. S. Park, H. G. Lee, and D. Baek, "Designing Cost-Effective Battery-less Energy Harvesting for Intermittent Wireless Communication," IEEE Access, 2025.
Downloads
Published
How to Cite
Issue
Section
