The recent introduction of Artificial Intelligence (AI) into the Internet of Things (IoT) has heralded a new era where transformative changes are set to take different paths and pace from what we have experienced hitherto—with respect to altering dominant urban processes and practices. These are becoming highly responsive to a form of data-driven scientific urbanism that is the key mode of production for emerging sustainable smart cities.
The emergence of AIoT is instigating massive changes in the way urban systems can be managed and planned with regard to environmental sustainability. This can support the current global efforts being deployed and aimed at addressing the challenges of environmental degradation and climate crisis that require immediate, innovative, and long-term solutions.
As a cutting-edge innovation, AIoT is becoming essential to the operational functioning and planning of sustainable smart cities in the area of environmental sustainability. This entails integrating smart systems with sustainable systems as they have clear synergies in their operation. Accordingly, sustainable smart cities seek to develop and apply AI and AIoT technology solutions to maximize the performance of their smart systems as well as integrate them with sustainable systems to bolster environmental sustainability. The widespread acceptance, adoption, and penetration of AI have resulted in more AIoT applications to emerge, notably sustainable transportation, energy conservation and renewable energy, water resources conservation, sustainable waste management, biodiversity, and pollution control, among many others. These are mainly associated with urban management and urban planning in the realm of sustainable smart cities.
This Article Collection seeks to present original contributions reporting the latest advances in AI and AIoT technology solutions for sustainable smart cities. It solicits and fosters the dissemination of high-quality original research and review articles, which address the theories and practical applications for sustainable smart cities, supported by advanced AI and AIoT technologies and solutions. Potential topics of interest include but are not limited to the following:
- Machine Learning (ML) and Deep Learning (DL) models and algorithms for transportation, traffic, and mobility management
- Advanced AI and AIoT-empowered approaches to sustainable smart transport, energy, and waste systems
- Integrated Urban AIoT and Urban Digital Twins (UDT) models for smart grid, smart urban metabolism, road infrastructure, and traffic network.
- Advanced forms of simulation models and decision support systems for the operational management and short-term and strategic planning of sustainable smart cities
- Assessment methods for classifying and benchmarking sustainable smart cities regarding their environmental performance and efficiency based on the application of AI and AIoT technologies
- Conceptual frameworks integrating smart city data-driven technologies and sustainable city green technologies and their applied solutions for environmental sustainability
- AI and AIoT-enabled architectures and digital ecosystems for sustainable smart cities
About the Guest Advisor
Simon Elias Bibri is a Research Scientist at EPFL and an Honorary Associate Researcher at Paris-Sorbonne University. Prior to this, Simon had served as an Assistant Professor, IT Director, Software Engineer, IT Project Manager, and Green ICT and sustainability strategist. He holds a PhD in Computer Science from NTNU in the area of data-driven sustainable smart cities, as well as a number of Master’s degrees in computer science, systems science, innovation science, and environmental science, as well as the social sciences. He earned his Master’s degrees from different Swedish universities, namely Lund University, West University, Blekinge Institute of Technology, Malmö University, Stockholm University, and Mid–Sweden University.
Conflict of Interest Disclosure
The Guest Advisor has no conflict of interest to disclose.
