Jul 2026· International Journal of Innovative Science and Research Technology· 0 citations· 16 references
TL;DR
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.
Abstract
The study examines how technology, particularly the Internet of Things (IoT) and artificial intelligence (AI),
might support sustainable urban development and real estate. Cities' effects on the environment, including energy use,
garbage output, and resource demand, present serious problems as they grow. As a result, sustainable urban development
has become a vital tactic to lessen these effects, with the goal of minimising ecological footprints while improving the
quality of life for locals. By enabling data-driven decision-making, real-time monitoring, and automated controls, IoT and
AI technologies present intriguing solutions that support the shift to more sustainable urban environments.
The study examines how IoT and AI technologies support resource management, trash reduction, and energy
efficiency—three important urban sustainability objectives. For example, precise energy monitoring and consumption
optimisation are made possible by smart grids and IoT-enabled sensors, and AI-driven algorithms help increase building
energy efficiency. IoT sensors that monitor garbage levels, optimise collection routes, and lower emissions are beneficial to
waste management. Real-time monitoring of air and water quality also improves resource management by facilitating
prompt responses and more sustainable resource utilisation.
To comprehend the potential of IoT and AI in improving urban sustainability, the research takes a conceptual
approach, combining current literature, theoretical frameworks, and case studies from smart city projects. The analysis
emphasizes these technologies' advantages as well as their drawbacks, including ethical issues with data privacy and
environmental effects. This study highlights the revolutionary potential of smart technology in building durable, effective,
and sustainable urban developments, despite its limitations due to its reliance on secondary data. The results indicate that
IoT and AI will play a significant role in creating sustainable cities of the future with careful deployment and regulatory
support.
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 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.
P. Kumaresan, Hayel Khafajeh, R. Latha et al.· International Conference on...· 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
In recent years, the development of smart cities has gained considerable attention due to the growing need for sustainable and efficient resource management. As urbanization accelerates, cities are facing increasing challenges in managing resources such as energy, water, waste, and transportation. This paper explores the integration of Internet of Things (IoT) technologies and data analytics in improving resource management within smart cities. By leveraging IoT devices, real-time data collection, and advanced data analytics techniques, cities can optimize resource allocation, reduce waste, and enhance citizen engagement. Through various case studies, the paper highlights the impact of IoT-driven solutions on urban services, including smart grids, water conservation, waste management, and traffic control. While the integration of IoT and data analytics presents significant opportunities, the paper also addresses key challenges, such as data privacy, security, and system integration. Finally, the paper discusses future trends and opportunities for creating smarter, more sustainable cities through the convergence of IoT and data analytics.
S. Verma, Priya Kapoor· International Journal of Eme...· 0 citations
Smart cities are a combination of technology and urban development and are intended to enhance the quality of life of people in a city, as well as ensuring better management of the available resources. In this paper, the author explores the new technological trends that are defining the future of smart cities, which have been Internet of Things (IoT), Artificial Intelligence (AI), 5G communication networks, blockchain, and sustainable energy solutions. The research is a synthesis of existing literature, a discussion of the methodological options of integrating smart technologies in urban infrastructures and the effects that it could have on governance, transportation, energy conservation, security of the populace and environmental sustainability. Through the review of case studies and the use of data-driven analytics, the study shows the practical and theoretical consequences of implementing smart city technologies. The results show that the implementation of multi-layered technological systems can contribute to the increased resilience, better interaction with citizens, and efficiency of urban operations greatly. The paper ends by discussing future research directions, policy implications and dilemmas like cybersecurity, privacy concerns and other research and policy dilemmas like the digital divide.
Isabella Franklin, Sathya Narayanan K· International Journal of Mod...· 0 citations
The Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT), where intelligent data processing complements large-scale connectivity across applications ranging from smart homes to industrial automation. However, its rapid expansion and the increasing adoption of AI have led to growing environmental concerns, particularly increased energy consumption and electronic waste. These issues highlight the importance of Green AIoT practices, which extend Green IoT by combining energy-efficient communication, computing, and intelligent resource management to achieve energy-efficient and sustainable AIoT operation. This paper presents a comprehensive survey of techniques aimed at improving the energy efficiency and sustainability of Green AIoT systems. The focus is placed on networking aspects, particularly machine-to-machine (M2M) communications and wireless sensor networks (WSNs), alongside the roles of computing infrastructures, data centers, and energy-efficient processor architectures. The survey further examines how AI-assisted techniques, including TinyML, edge AI, and intelligent computation offloading, complement traditional Green IoT mechanisms to reduce energy consumption. Key approaches such as low-power communication protocols, energy-efficient data processing, data compression, smart energy management, and energy harvesting are reviewed and compared. Furthermore, the paper summarizes representative state-of-the-art solutions with quantitative insights and discusses open challenges and future research directions toward environmentally sustainable AIoT systems.
Mislav Has, Fehmi Ben Abdesslem, Mario Kušek et al.· Applied Sciences· 0 citations
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