Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1223-1229· 0 citations· 22 references
Abstract
The fast pace of urbanization has made smart and sustainable infrastructure management more important than ever. Because of their inherent silos, traditional urban management systems are unable to adapt in real-time to shifting demands in areas such as water distribution, public safety, energy consumption, traffic flow, and energy consumption. This study found that smart cities may use AI and the internet of things to adapt and manage their infrastructure using data. Sensors throughout the city’s infrastructure for transportation, power, buildings, and the environment provide data into Internet of Things devices. Analytics systems powered by AI can automate decision-making, enhance resource allocation, discover anomalies, and forecast demand using massive amounts of data. For predictive maintenance and real-time monitoring, the framework places an emphasis on interoperability, scalability, cybersecurity, and sustainability. By replacing reactive systems with proactive ones, adaptive algorithms and machine learning models can increase dependability, save costs, and revolutionize urban planning. Topics covered in the research include data privacy, infrastructure integration, and data governance. The convergence of AI with the Internet of Things (IoT) creates robust, efficient, citizen-centric urban ecosystems, as shown by comprehensive design and performance evaluation metrics. Smart cities that can adjust to changes in the environment, population, and economy are made possible by these discoveries.
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
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 high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Grace Ndlovu· International Journal of Mod...· 0 citations
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
The need for sustainable urban management systems for transport, energy, and waste has grown as urbanization has increased. Traditional management systems do not dynamically adjust and manage systems in real time, leading to waste and negative environmental impacts. This paper proposes an AI optimization framework, using IoT and edge computing, combined with hybrid machine learning models, to enhance urban infrastructure management systems. This approach employs Long Short-Term Memory (LSTM) networks for time-series forecasting and Reinforcement Learning (RL) for real-time, dynamic, adaptive, iterative model-based decision-making. IoT edge computing also enhances rapid processing and control by reducing latency and enabling responses to changes in managed systems. Testing was conducted on a framework built on datasets from simulated smart city environments (50,000 records for urban traffic, energy consumption, and waste). The framework reduced traditional energy management systems by 25 % and traffic management by 30-33%, and waste management systems by 20%. AI has the potential to enhance the effective management systems and the efficiency of urban centers. The research determined that incorporating AI into smart systems makes urban management scalable, flexible, and data-driven. Furthermore, this framework helps integrate smart systems and cities to achieve SDGs and enhance urban dwellers' quality of life.
Lincy Roy, Abhishek Sharma· 2026 4th International Confe...· 0 citations
This study explores the role of artificial intelligence driven smart energy management system as a tool for
addressing rising urban energy demand and promoting sustainable development in developing countries, with specific
focus on Nigeria. The study revealed that rapid urbanization has placed significant strain on conventional power
infrastructure, resulting in inefficiencies, high operational costs, frequent outages, and increased carbon emissions. To
respond to these challenges, the paper examined the design and application of an AI-powered platform that integrates
data from IoT sensors, smart meters, and weather stations to support real time energy monitoring and decision making.
The study analyzed existing utility operations and technical frameworks to identify key system requirements and data
workflows that inform the development of an intelligent energy management solution. Using Objection-Oriented Analysis
and Design Methodology (OOADM) and Unified Modeling Language (UML), the study proposed modular and scalable
system architecture capable of demand forecasting, grid anomaly detection, predictive maintenance, and optimized
integration of renewable energy sources such as solar and wind. The findings indicated that the adoption of AI-driven
energy management systems can substantially reduce peak energy load, minimize unplanned outages, lower maintenance
costs, and cut carbon emissions, while providing grid operators and policymakers with accurate, real time insights. The
paper concludes that intelligent energy management solutions are critical for improving efficiency, strengthening energy
security, and supporting sustainable national development in Nigeria and similar developing economies.
Stella Ebere Edeh, C. Ituma, Maduabuchi Ignatius Edeh et al.· International Journal of Inn...· 0 citations
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