Aug 2026· Journal of Artificial Intelligence and Data Analytics· Vol 1, pp. 1-8· 0 citations· 27 references
TL;DR
This paper identifies the technical and organisational conditions required before AI-enabled district heating optimisation can be reliably implemented and proposes a layered architecture that connects energy-harvesting sensors, data acquisition, local preprocessing, AI-assisted interpretation, and human-centred decision support.
Abstract
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.
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.
The study examines how IoT and AI technologies support resource management, trash reduction, and energy efficiency—three important urban sustainability objectives, and indicates that IoT and AI will play a significant role in creating sustainable cities of the future with careful deployment and regulatory support.
R. K R, B. V· International Journal of Inn...· 0 citations
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.
Livingstone Aduku, Ikenna Calistus Dialoke, Abubakar Surajo Imam· International Journal of Eng...· 0 citations
Interrelated environments are also becoming the driving force behind how intricate physical, social, and cyber-physical systems are observed and handled. The key in this development is the incorporation of Internet of Things (IoT) sensing infrastructures and smart systems that have data analytics, learning, and decision support capabilities. The review is a well-organized account of the development of recent connected environments with a specific emphasis on the interactions of IoT sensors and intelligent systems to improve monitoring and decision-making. First, the article discusses IoT sensing infrastructures, which comprise architectural foundations, sensor technologies, communication mechanisms, and deployment considerations that facilitate the acquisition of large volumes of data. It then looks at intelligent systems data processing and analytics, with the contribution of machine learning, artificial intelligence, and edge-cloud design to the conversion of raw sensor information into insights. Based on these premises, the review examines frameworks and strategies of fusion that combine heterogeneous sensor data and intelligent analytics across multiple layers of systems. The application domains, such as smart cities, healthcare, industrial automation, and environmental monitoring, are mentioned as examples of the representative areas where the concept of intelligent connected environments has practical implications and issues. The Major technical, organizational, and societal issues—scalability, data heterogeneity, security, privacy, and trust—are discussed critically. Lastly, the article also describes the emerging trends and future research directions, such as distributed intelligence, privacy-preserving learning, and autonomous decision systems. With its holistic view of connected environments, this review is expected to help researchers and practitioners in developing robust, scalable, and trustworthy next-generation monitoring and decision support in connected environments.
Jun Zhan, Wei Yu· Journal of Environmental &am...· 0 citations
: Smart city systems increasingly depend on data analytics and visualization to support informed and timely decision making in complex urban environments. However, existing middleware solutions predominantly focus on data acquisition and communication, while analytical processing and visualization are typically delegated to external applications, resulting in increased development complexity, reduced reusability, and fragmented system architectures. This study presents analytics and visualization-centric middleware named “Service-Oriented Middleware for Smart City Applications” (SOMSCA), in which these capabilities are embedded directly within the middleware layer. SOMSCA adopts a service-oriented approach, exposing analytics and visualization functionalities as reusable platform services, and incorporates a data-type-oriented visualization strategy along with a template-assisted dash-boarding mechanism to enable dynamic and flexible application development. To validate the proposed approach, a prototype implementation is developed using React, FastAPI, MySQL, and TimescaleDB and evaluated using air-quality data collected from the London Air Quality Network, comprising more than one million observations across multiple pollutants and monitoring locations. The implementation supports real-time, historical, and aggregated analytical services together with dynamic dashboard generation. The results demonstrate the practical feasibility of middleware-integrated analytics and visualization through reusable service creation, flexible dashboard configuration, and interactive environmental monitoring capabilities. These findings highlight the potential of middleware-level intelligence to simplify application development and support data-driven decision making in smart city environment.
Zulfiqar Ali, A. Mahmood, S. Khatoon et al.· Computers, Materials & C...· 0 citations
Rapid urbanization has intensified pressure on energy systems, transportation networks, water resources, waste-management infrastructure, public health services, and the urban environment. Conventional city-management models, which often rely on fragmented information and reactive decision-making, are increasingly inadequate to address these interconnected challenges. Artificial Intelligence (AI) and the Internet of Things (IoT) provide a technological foundation for a transition from conventional urban administration toward intelligent, adaptive, and sustainability-oriented city management. IoT infrastructures enable continuous sensing and communication across physical urban environments, whereas AI converts large volumes of heterogeneous sensor data into predictions, classifications, recommendations, and automated decisions. This paper examines the integrated role of AI and IoT in sustainable smart city development through a conceptual and interdisciplinary review of research on smart urban systems, data analytics, edge computing, intelligent transportation, energy management, environmental monitoring, waste management, water conservation, public safety, and urban governance. The paper proposes an AI–IoT Closed-Loop Sustainable Urban Intelligence Framework consisting of sensing, connectivity, edge/cloud processing, artificial intelligence, decision-making and actuation, and sustainability evaluation layers. The analysis demonstrates that the value of AI–IoT integration lies not simply in increasing technological sophistication but in enabling cities to minimize resource consumption, anticipate infrastructure failure, reduce emissions, improve service responsiveness, and make urban systems increasingly adaptive. At the same time, cybersecurity vulnerabilities, privacy risks, algorithmic bias, interoperability problems, digital inequality, high infrastructure costs, and the environmental footprint of computing can weaken sustainability outcomes. The study therefore argues for a human-centered, secure, interoperable, transparent, and sustainability-measured model of smart city development. Future smart cities should be evaluated not by the quantity of connected devices deployed but by measurable improvements in environmental quality, resource efficiency, social inclusion, resilience, and quality of urban life.
P. S., Shaik Rahamtula, S. J et al.· Stanzaleaf International Jou...· 0 citations
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