Aug 2026· Applied Sciences· 0 citations· 95 references
Abstract
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.
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
Residential buildings are consistently reported to account for a large share of global electricity consumption, with lighting and heating, ventilation and air-conditioning (HVAC) systems constituting the dominant loads. This paper critically reviews the literature on Internet of Things (IoT)-enabled autonomous lighting and HVAC optimisation in smart homes, tracing the evolution of home energy management from manual and rule-based control to sensor-driven, edge-capable architectures. The review examines conceptual foundations of smart home energy management systems, IoT communication architectures, autonomous lighting and HVAC control strategies, hybrid manual–voice–autonomous control schemes, and empirical studies on occupancy sensing and load management. Recurring limitations identified across the literature include the fragmented treatment of lighting and HVAC as isolated subsystems, weak real-time occupancy adaptation, over-reliance on cloud connectivity, and shallow load-level energy visibility. These gaps motivate the development of an integrated, edge-resilient, and cost-effective IoT architecture capable of coordinating lighting, thermal comfort, precision load monitoring and renewable energy analytics within a single residential system.
LIVINGSTONE ADUKU, IKENNA CALISTUS DIALOKE, ABUBAKAR SURAJO IMAM· International Journal of Eng...· 0 citations
The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations.
A. Mimouni, Youssef Chahet, A. El Amrani et al.· Sustainability· 0 citations
Smart district heating systems require reliable data, scalable sensing infrastructure, and intelligent analytics to support energy efficient and low-carbon operation. However, many existing buildings and heating systems remain constrained by limited sensor coverage, heterogeneous infrastructure, poor data quality, fragmented building management systems, and insufficient digital readiness. This paper presents a practice-informed conceptual and deployment-oriented study of how long-life Internet of Things sensing platforms, local-first edge processing, and AI-supported analytics can support future smart district heating applications. Drawing on lessons from the LoLiPoP-IoT project, the paper proposes a layered architecture that connects energy-harvesting sensors, data acquisition, local preprocessing, AI-assisted interpretation, and human-centred decision support. The study does not claim a fully validated autonomous control system; instead, it identifies the technical and organisational conditions required before AI-enabled district heating optimisation can be reliably implemented. A key finding is that AI deployment in older buildings or buildings with multiple heating arrangements may be limited or delayed when reliable baseline data, interoperable interfaces, and validated datasets are unavailable. The paper concludes by outlining future research needs related to quantitative validation, digital twins, trustworthy AI, heating-and-cooling integration, cost-benefit assessment, and replication of the LoLiPoP-IoT architecture in operational energy environments.
G. Kalsi, Tomi Tuomaala, Ville Pitkäkangas et al.· Journal of Artificial Intell...· 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