Aug 2026· International Journal of Engineering Innovation and Technology Research· 0 citations
TL;DR
The literature on Internet of Things (IoT)-enabled autonomous lighting and HVAC optimisation in smart homes is critically reviewed, tracing the evolution of home energy management from manual and rule-based control to sensor-driven, edge-capable architectures.
Abstract
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.
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 study conducted a systematic review of IoT-enabled demand-response frameworks for integrated energy management in smart buildings. Adhering to PRISMA 2020 guidelines, the review synthesises 51 peer-reviewed studies published between 2011 and 2026. The study examined IoT-driven improvements in demand-response efficiency, evaluated solar-aligned HVAC and EV scheduling to reduce fossil-fuel use, identified adoption barriers to IoT demand-response technologies, and compared dynamic IoT controls with static scheduling for peak loads. The literature indicates that automated, artificial intelligence- and machine learning-driven control strategies achieve total energy consumption reductions of 13–30%, peak-load reductions of 10–55%, and operational cost savings of up to 58%. These intelligent systems replace slow, manual interventions with closed-loop controls, significantly enhancing energy efficiency and grid stability. However, scaling these technologies faces critical barriers, including high upfront capital costs, infrastructure heterogeneity, cybersecurity vulnerabilities, and resistance to user behaviour. Furthermore, the review exposes a pronounced methodological gap: roughly two-thirds of the existing research corpus relies on theoretical models and simulations rather than large-scale, real-world empirical validation. To bridge the divide between simulated potential and field-proven performance, future investigations must prioritise an integrated research agenda encompassing multi-building district coordination, edge intelligence, digital twins, blockchain-enabled energy trading, and harmonised regulatory frameworks. Therefore, optimised IoT architectures are vital for advancing sustainable smart buildings, mitigating grid congestion, and achieving resilient, net-zero urban futures.
Unknown authors· Journal of Systematic, Evalu...· 0 citations
A Smart HVAC system that integrates Artificial Intelligence (AI), Internet of Things (IoT) sensors, cloud-based analytics, and machine learning to enhance energy efficiency, thermal comfort, and reliability is presented.
Suresh Babu Reddy· International Journal of Mod...· 0 citations
Traditional energy management at universities is characterised by manual monitoring, static control systems, and lack of real-time data, resulting in excessive energy consumption and high operational costs. This study presents the design, implementation, and evaluation of a Smart Energy Management System (SEMS) at the University of Calabar, Cross River State, Nigeria, with the objective of reducing energy consumption and operational costs. The SEMS integrates Internet of Things (IoT) sensors, real-time data analytics, and automated control mechanisms to monitor and manage energy consumption dynamically. Key hardware components include motion sensors, smart meters, and environmental monitors, all connected to a centralised dashboard that provides actionable insights for energy optimisation. A six-month pilot deployment using a three-tier IoT architecture demonstrated energy savings of 30–40%, improved operational efficiency, with automated response times of 2–4 seconds and system uptime exceeding 95%. Comparative analysis confirmed that the SEMS outperforms traditional energy management in responsiveness (automated response time of 2.5–3.5 seconds versus delayed manual response), cost-effectiveness (30–40% reduction in energy expenditure with low long-term operational costs), and data visibility (real-time, room-level consumption data versus monthly, incomplete utility bills). The study provides a scalable framework for implementing smart energy solutions in university settings.
Ofem Ajah Ofem, Iniobong Ime, Osowomuabe Njama-Abang et al.· Global Journal of Pure and A...· 0 citations
The monitoring dataset from central Sweden (covering 2019-2022) is analyzed in this paper to understand how intelligent energy optimization can be implemented using sensor-driven ventilation and machine control HVAC systems. The dataset includes measurements of hourly electricity, ventilation airflow, temperature, relative humidity, CO2, and PM with time intervals of 10 mins or less. During the study period, three approaches to ventilation control were implemented: demand-controlled exhaust ventilation, flat-plate MVHR, and rotating-wheel MVHR. Since this paper performs a secondary empirical analysis on published outcomes of measured systems and does not reprocess raw sensor data, no p values have been fabricated. Out of the measures examined, demand-controlled exhaust ventilation achieved a 47 % airflow reduction factor, as compared to a 19 % guideline reference. In contrast to flat-plate constant-flow MVHR, rotating-wheel MVHR required substantially less heating power at -10 °C and 0 °C, respectively, with a higher fan power rating. During the pandemic, average daily unoccupied time reduced from 6.5 hours to 5.25 hours. Intelligent HVAC management will consider optimization of total system energy, and balanced peak and mean occupancy/IAQ response to minimize energy consumption. An IoT (Internet of Things) architecture is proposed to implement the findings.
Unknown authors· International journal of com...· 0 citations
A Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization for scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making is proposed.
Mahabala H. N.· International Journal of Mod...· 0 citations
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