State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative models. These advancements are reshaping data-driven planning strategies, practices, and approaches, thereby facilitating the achievement of environmental sustainability goals. This transformative wave signals a fundamental shift - marked by the synergistic operation of artificial intelligence (AI), artificial intelligence of things (AIoT), and urban digital twin (UDT) technologies. While previous research has largely explored urban AI, urban AIoT, and UDT in isolation, a significant knowledge gap exists regarding their synergistic interplay, collaborative integration, and collective impact on data-driven environmental planning in the dynamic context of sustainable smart cities. To address this gap, this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies, models, and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart cities. Central to this study are four guiding research questions: 1. What theoretical and practical foundations underpin the convergence of AI, AIoT, UDT, data-driven planning, and environmental sustainability in sustainable smart cities, and how can these components be synthesized into a novel comprehensive framework? 2. How does integrating AI and AIoT reshape the landscape of data-driven planning to improve the environmental performance of sustainable smart cities? 3. How can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities? 4. What challenges and barriers arise in integrating and implementing AI, AIoT, and UDT in data-driven environmental urban planning, and what strategies can be devised to surmount or mitigate them? Methodologically, this study involves a rigorous analysis and synthesis of studies published between January 2019 and December 2023, comprising an extensive body of literature totaling 185 studies. The findings of this study surpass mere interdisciplinary theoretical enrichment, offering valuable insights into the transformative potential of integrating AI, AIoT, and UDT technologies to advance sustainable urban development practices. By enhancing data-driven environmental planning processes, these integrated technologies and models offer innovative solutions to address complex environmental challenges. However, this endeavor is fraught with formidable challenges and complexities that require careful navigation and mitigation to achieve desired outcomes. This study serves as a comprehensive reference guide, spurring groundbreaking research endeavors, stimulating practical implementations, informing strategic initiatives, and shaping policy formulations in sustainable urban development. These insights have profound implications for researchers, practitioners, and policymakers, providing a roadmap for fostering resiliently designed, technologically advanced, and environmentally conscious urban environments.

Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities

The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow

Standing at the cusp of an augmented age facilitates a glance into the future of a cybernetic world aligned with planetary wellbeing. The era of exponential technological revolutions has brought with it a plethora of opportunities expanding in a multi-faceted dimension with an added emphasis towards nurturing a mutual synergy of nature with a daily dose of digitalization. The paper is written with an intent to lay out an accumulated comprehensive review of different literary works which lay the grounds for how different Smart Earth Technologies aid in monitoring and tackling the degradation of air and water resources. If an intertwined state-of-the-art centralized research source could be created, it would become a boon for seasoned researchers and neophytes succeeding portion of the article expands itself to a wide variety of research literature complimented with real-time models, case, and empirical studies which help heighten the previous limit to the research done on these Technologies tinkering the present monitoring systems. The primary aim of this work is to fuel the need of theoretical, practical, and empirical evolution in the ways the intelligent technologies help blossom a pollution-free environment. The secondary intention was to ensure that in-depth study of Smart Environmental Pollution the Monitoring Systems provisioned a multitude of prospects for upgrading one's knowledge on environmental management through current world technologies. By looking at these trends of the past, the enthusiast of technology could collaborate with the researchers of Environmental Pollution to assist in proliferation of diverse 'smart' solutions creating a Smarter, Greener, and Brighter future for research and developments in Sustainable Technologies devising a pollution-free environment.