Advanced Energy Harvesting Approaches for Energy-Efficient IoT Systems

Abstract

The rapid expansion of the Internet of Things (IoT) has significantly increased the demand for sustainable and energy-efficient power solutions for smart devices. Heterogeneous Energy Harvesting (HEH), which integrates multiple renewable energy sources such as solar, thermal, kinetic, and radio frequency (RF), has emerged as a promising approach for enabling self-powered IoT systems. This review paper presents a systematic analysis of recent advancements in AI-driven and Edge-enabled HEH technologies published between 2018 and 2025. The study examines different energy harvesting techniques, Artificial Intelligence and Machine Learning methods for adaptive energy management, and the contribution of Edge Computing to real-time optimization. Furthermore, key challenges including scalability, prediction accuracy, security, and lack of standardization are discussed.

Introduction

The text reviews the growing need for sustainable power solutions in IoT systems due to the rapid expansion of connected devices in areas like smart homes, healthcare, agriculture, and industry. Traditional battery-powered systems face limitations such as short lifespan, high maintenance, and environmental concerns. To address this, the concept of Heterogeneous Energy Harvesting (HEH) is introduced, which combines multiple energy sources including solar, thermal, kinetic/piezoelectric, and RF energy to ensure continuous and reliable power supply.

Recent research highlights the integration of Artificial Intelligence (AI) and Edge Computing to improve the efficiency of HEH systems. AI techniques such as Reinforcement Learning, Decision Trees, Random Forests, and Neural Networks are used for energy prediction, scheduling, and optimization, while Edge Computing enables local processing to reduce latency, improve privacy, and minimize dependence on cloud systems. Literature shows that hybrid approaches significantly improve device uptime, energy savings, and system reliability, although challenges remain in scalability, cost, security, and lack of standardized datasets.

The proposed methodology describes a complete framework where multiple energy sources are harvested and stored in batteries or supercapacitors, continuously monitored, and managed using AI-based decision models. Energy allocation is dynamically optimized based on real-time sensor data, environmental conditions, and device workload. Overall, the system aims to create a self-sustaining, intelligent IoT energy management solution that improves efficiency, reliability, and long-term sustainability.

Conclusion

This project presents an efficient vibration energy harvesting system for IoT applications using piezoelectric technology The proposed system successfully converts mechanical vibrations generated by a DC motor into electrical energy through piezoelectric plates. The harvested energy is regulated using an energy harvester module and stored in a lithium-ion battery for future use. The integration of the ESP32 microcontroller enables continuous monitoring of generated voltage, battery level, and charging percentage. An automatic switching mechanism was implemented to optimize energy utilization. When the battery level is below 50%, the system operates using direct piezo/input power, and when the battery level exceeds 50%, the motor automatically switches to battery power. This improves energy efficiency and reduces dependency on external power sources. The collected system data is displayed on an LCD screen and transmitted to a cloud dashboard for real-time monitoring, making the system suitable for smart IoT-based applications. Overall, the proposed system demonstrates that vibration energy harvesting is a practical and sustainable solution for powering low-energy IoT devices. The project contributes toward the development of self-powered electronic systems and supports future green energy solutions.

References

[1] M. Li, Y. Chen, and R. Kumar, “Multi-Source Energy Harvesting for Self-Powered IoT Devices,” IEEE Trans. Ind. Electron., 2023. [2] S. Wang, J. Park, and A. Gupta, “Edge-AI Driven Energy Optimization Using Heterogeneous Energy Harvesting,” IEEE Access, 2024. [3] K. Zhang, L. Rodriguez, and E. N. Smith, “5G-Enabled Smart Homes: RF Energy Harvesting and LoRa Backscatter,” IEEE Internet Things J., 2025. [4] T. M. Murugan and A. Jeyam, “IoT-Enabled Condition Monitoring via Energy Harvesting,” Int. J. Data Sci. Artif. Intell., 2023. [5] A. Madavarapu et al., “Heterogeneous Energy Harvesting Techniques for Smart Home IoT Acceleration,” IEEE Smart Syst. Conf., 2024. [6] R. She et al., “Energy-efficiency optimization for heterogeneous computing-assisted non-orthogonal multiple access mobile edge . . . ” (2025) — Investigates EE optimisation in heterogeneous computing + NOMA + energy harvesting settings [7] A. Shabbir, S. Rizvi, M.M. Alam, F. Shirazi, M.M. Su’ud, “Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS frame- work.” (2024) [8] S. Rajoria, “Energy efficiency optimization for MM-NOMA heteroge- neous network with wireless backhauling and energy harvesting.” (2023) [9] Prof.Archana Ugale, Rutik Ramesh Sadaphal, Rushikesh Navnath Phad, Shrutika Punjahari Shelke, Asmita Samadhan Adke, \"A Comparative Review on-: Optimizing Energy Efficiency Through Heterogeneous Energy Harvesting,\" International Journal of Scientific Research in Engineering and Management, vol. 10, no. 3, Mar 2026. https://doi.org/10.55041/IJSREM58348

Copyright

Copyright © 2026 Prof. Archana Ugale, Rutik Ramesh Sadaphal, Rushikesh Navnath Phad , Shrutika Punjahari Shelke , Asmita Samadhan Adke . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.