Descriptor: Batteryless, Light- and Energy-Aware IoT Environmental Sensing Dataset (Li-IoT)

Towards Sustainable IoT: An AI-Driven Framework for Enhanced Energy Harvesting in Wireless Sensor Networks

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.

A novel IoT-aware wearable sensor device for healthcare data management framework using hybrid optimization algorithm.

Energy Harvesting-Based Intelligent Power Management Framework for Sustainable IOT Devices

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.

An Intelligent and Scalable IoT-Based Architecture for Residential Energy Consumption Forecasting Using Deep Learning

This paper presents a smart, scalable architecture that integrates Internet of Things (IoT) technologies and Deep Learning models to improve the accuracy and adaptability of energy consumption forecasting in residential environments. The proposed system is designed to support efficient data acquisition, storage, and analysis in dynamic home contexts, where consumption is influenced by multiple temporal, environmental, and behavioral variables. The system's foundation is a comprehensive IoT architecture developed for robust data collection. This infrastructure includes high-precision sensors to monitor power consumption across three phases, environmental sensors to capture weather variables like temperature and humidity, and occupancy detection mechanisms that infer human presence through smart device activity. Furthermore, a dedicated Android application facilitates the calibration of household appliance energy usage, enabling the identification of specific devices contributing to consumption fluctuations. Data is transmitted in real-time using the low-bandwidth MQTT (Message Queuing Telemetry Transport) protocol, managed via RESTful API services, and stored in JSON format within a highly scalable MongoDB NoSQL database, chosen for its big data capabilities. The predictive core of the system is a sophisticated neural network that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically employing LSTM/GRU blocks, to effectively extract spatiotemporal patterns and capture long-term dependencies in timeseries data. The model architecture consists of six hidden layers with 2048 fully connected neurons each and is trained using the Mean Absolute Error (MAE) as the loss function with an Adam optimizer. The model incorporates a wide range of contextual factors, such as time of day, day of the week, holidays, weather conditions, and occupancy. Critically, it also explores user specific behavioral indicators, such as the presence of specific individuals, to achieve a more granular and accurate prediction. Evaluation was conducted on a real-world dataset collected over a two-month period, split into 80% for training and 20% for testing, with five-fold cross validation to prevent overfitting. The model demonstrated remarkable accuracy, achieving a Mean Absolute Error below 5% under diverse conditions. The system is designed for scalability, making it adaptable for larger applications such as residential communities or smart grid energy management. Future work will focus on enhancing model generalization by incorporating larger datasets over extended time frames and exploring the conversion of energy consumption data into images to further leverage the pattern recognition capabilities of CNNs.

Energy Harvesting for IoT and Edge-Enabled Building Automation Systems: A Review of Technologies, Applications and Future Challenges

Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy harvesting (EH) offers a promising approach toward low-maintenance and partly autonomous sensing, but its practical value in building automation depends on more than the output of individual transducers. This article presents a structured review of EH for IoT/WSN and edge-enabled building automation, focusing on smart-building, Building Management System (BMS) and Building Automation and Control System (BACS) contexts. Light-based, thermoelectric, mechanical, RF/wireless-power-transfer and hybrid harvesting technologies are interpreted through a system-oriented chain linking energy sources, power management, storage, communication, adaptive operation, gateways, diagnostics and edge intelligence. The synthesis shows that EH is most promising for low-duty-cycle environmental monitoring, envelope and façade sensing, occupancy and human–building interaction, airflow-related sensing, technical monitoring and retrofit automation. The main challenges concern the transition from device autonomy to sensing-service autonomy, complete-node evaluation under real building conditions, interoperability with supervisory systems and diagnostic interpretation of intermittent operation. Further research is also needed on lifecycle value assessment and safe transferability toward remote, temporary, resilient and closed ecological infrastructure applications.

An Energy-Efficient IoT Sensor Network Framework Using Intelligent Energy Harvesting and Adaptive Routing

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.