2026· Islamic University Journal of Applied Sciences· 0 citations
TL;DR
An AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems and achieves up to 300% improvement in network lifetime under low-energy harvesting conditions.
Abstract
Growing deployment of Internet of Things (IoT) ecosystems has intensified concerns regarding long-term energy sustainability and environmental impact. Large-scale deployment of wireless sensor networks (WSNs) demands intelligent energy management strategies beyond conventional battery-based solutions However reliance on traditional batteries faces challenges such as limited lifespan, high costs of replacement in remote areas, and environmental impact of battery disposal. This paper proposes an AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems. The proposed model employs a Deep Q-Network (DQN) to dynamically regulate sensing, transmission, and sleep operations based on system states. By modeling energy management as a Markov Decision Process (MDP), the framework enables adaptive decision-making under uncertain and fluctuating harvesting conditions. Experimental results show that the proposed framework achieves up to 300% improvement in network lifetime under low-energy harvesting conditions, with an average improvement of 168% and 41.5% higher energy utilization efficiency than static policies. This work presents a scalable framework for Green IoT networks with TinyML feasibility (~2,500 parameters), though hardware validation on microcontrollers remains future work.
The internet of things (IoT) has rapidly evolved into a ubiquitous communication paradigm for enabling the deployment of autonomous wireless networks across diverse application domains. However, the limited energy storage capacity and computational resources of IoT devices (IoTDs) pose a serious concern to their long-term sustainability and the expected quality of service delivery. Moreover, in the foreseeable era of the internet of everything, centralised network resource management is likely to constrain network scalability. To tackle these challenges in the current and next-generation communication networks, the adoption of adaptive and lightweight computational frameworks coupled with energy-efficient transmission strategies is essential. To demonstrate this, we exploit the concept of cooperative communication and radio frequency-based energy-harvesting to improve the network throughput while maintaining power supply to the IoTDs. Furthermore, to intelligently and autonomously perform resource allocation, we employ the reinforcement learning frameworks, particularly state–action–reward–state–action (SARSA) and Q-learning. Based on key performance evaluation metrics, we compare our findings with the baseline methods, including the equal, random, and greedy power level selection schemes, with SARSA exhibiting the most favourable performance trade-offs.
Olumide Alamu, T. Olwal, Emmanuel M. Migabo· Network· 0 citations
: Wireless Sensor Networks (WSNs) have gained significant attention due to their wide range of applications in environmental monitoring, smart agriculture, healthcare, industrial automation, and Internet of Things (IoT) systems. The current WSNs have limitations such as limited battery capacity, low network lifespan, spectrum scarcity, scalability problems, and poor communication quality. To address the constraint of limited energy, several technologies have been proposed for energy harvesting (EH) to ensure an operation of self-sustainable networks, which makes possible to tap into various sources of environmental energy, including sun, vibration, or radio frequency signals. Cognitive Radio (CR) technology has similarly proved to be a beneficial approach to spectrum utilization through dynamic spectrum access and intelligent channel selection. EH and CR technologies are combined to form Energy Harvesting Cognitive Wireless Sensor Networks (EH-CWSNs), which enable better energy efficiency, better spectrum utilization and longer network lifetime. However, the routing complexity, clustering efficiency, spectrum sensing, scalability, communication reliability and resource management are some of the problems that still remain with EH-CWSNs. This survey provides an exhaustive analysis of the existing routing protocols, clustering, optimization and energy management techniques in WSNs and their benefits and drawbacks, with emphasis on EH-WSNs, Cognitive WSNs, and EH-CWSNs. In addition, some current challenges and future research directions are outlined for the development of efficient and intelligent wireless sensor networks.
A. V Anuja, B. Meena Preethi· International Journal of Sci...· 0 citations
This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.
Dr. Deepa, M. Mehfooza, Padmavathy Thiruppathi Raj· Microsystem Technologies· 0 citations
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management. The framework effectively harvests energy from solar, thermal, radio frequency (RF), vibration, and wind sources while optimizing power utilization through adaptive energy prediction and intelligent task scheduling. Experimental evaluation demonstrates that the proposed system achieves higher energy utilization, lower power consumption, improved communication reliability, and extended operational lifetime compared with conventional battery-powered IoT systems. Furthermore, the integration of cloud and edge computing enables real-time monitoring, predictive analytics, and scalable deployment across diverse IoT applications. Overall, the proposed framework offers a reliable, cost-effective, and environmentally sustainable solution for smart cities, healthcare, industrial automation, environmental monitoring, and precision agriculture, while providing a strong foundation for future research on AI-driven energy optimization and next-generation wireless-enabled self-powered IoT networks.
B. Vaishnavi, Kalasani Siddhartha, Dasarinki Ramprasad· International Journal of Cre...· 0 citations
An Energy-Efficient IoT Sensor Network Framework that integrates intelligent energy harvesting techniques, adaptive sleep scheduling, edge computing, and Artificial Intelligence (AI)-based routing algorithms to optimize power consumption and extend network longevity is proposed.
Dabbeta Ganapathi Dabbeta Ganapathi, Halavath Vijaya Halavath Vijaya, P. K. P Kavitha· International Journal of Sci...· 0 citations
Research on low-power Internet of Things (IoT) systems has gained significant momentum within the broader context of green and sustainable IoT. In this setting, batteryless (BL) IoT has emerged as a promising solution to reduce maintenance costs and environmental impact by eliminating the need for battery replacements. As large-scale IoT deployments for monitoring and sensing applications continue to expand, important challenges arise in the design and operation of sustainable BL IoT networks, including long-term reliability, performance evaluation, and the analysis of energy-harvesting behavior under real-world conditions. To help address these challenges, this article presents a comprehensive dataset capturing the behavior of BL IoT devices deployed in an indoor environmental sensing network. The dataset comprises 100 days of measurements collected from sensors installed throughout an office building and includes data from two types of IoT devices: 1) plugged-in (PI) sensors with continuous power supply; and 2) BL sensors powered exclusively by energy harvested from indoor light. This dual-device deployment enables direct comparison of sensing performance, reliability, and energy dynamics between powered and energy-harvesting systems. The final dataset contains over three million samples collected from 28 sensors (14 PI and 14 BL) deployed across approximately 300 m<inline-formula><tex-math notation="LaTeX">${}^{2}$</tex-math></inline-formula> of office space spanning nine rooms. The dataset provides a valuable resource for the systematic investigation of BL IoT systems, enabling rigorous analysis of deployment strategies, spatial–temporal sensing dynamics, energy-harvesting behaviors, system performance, and long-term sustainability considerations in next-generation self-powered IoT sensing networks.</p> <p><bold>IEEE SOCIETY/COUNCIL</bold> Communications Society (COMSOC)</p> <p><bold>DATA TYPE/LOCATION</bold> Comma Separated Values (CSV); KU Leuven, Belgium</p> <p><bold>DATA DOI/PID</bold> <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.21227/TF0N-SB66">10.21227/TF0N-SB66</ext-link>
Jimmy Fernandez Landivar, Ihsane Gryech, A. Colpaert et al.· IEEE Data Descriptions· 0 citations