Introduction

Optimization belongs to a mathematical field that has been generalized and applied to many problems and fields in engineering. The intelligent optimization algorithm is an optimization algorithm based on population iteration. Data-driven optimization combines data and intelligent optimization algorithms to solve a wide variety of problems. Data-driven optimization algorithms have been used in a variety of fields for decades, but their application to smart and sustainable cities is a relatively new area of research. The increasing availability of data from sensors, devices, and databases has made it possible to develop more sophisticated data-driven optimization algorithms that can be used to improve the efficiency and sustainability of urban systems. With the help of emerging control, management, and sensing technologies, as well as recent advances in data analytics and urban experimentation, data-driven optimization promises to enhance our understanding of smart city system design, operation, and planning. Data-driven optimization algorithms typically use a variety of techniques, such as machine learning, artificial intelligence, and mathematical programming, to solve complex optimization problems. These algorithms can be used to identify optimal solutions to problems with a wide range of constraints, such as budget, time, and environmental impact.

At the heart of data-driven urbanism is a computational understanding of urban systems and domains, which reduces urban life to algorithms and computational procedures. Urban science is a field of practicing big data science and analytics that is increasingly making smart and sustainable cities more sustainable and resilient by making them more measurable, understandable, and tractable, efficient, fair and livable.

This special issue aims to collect the latest findings related to data-driven optimization and its application in smart cities, to provide a platform to advance sustainable development of sustainable cities and smart cities, and more importantly, to integrate their strategies and solutions into the framework of smart sustainable cities based on data-driven technologies and solutions.

Recommended topics (but not limited to)

• Big data collection for smart cities
• Data-driven smart city planning and design
• Visual analysis algorithm for smart city monitoring
• Data-driven optimization of transportation and logistics systems
• Data driven design in civil and structural engineering
• Innovative algorithms, software solutions and methodologies for data collection and analysis of big data
• Data-driven modeling techniques
• Data-driven approaches for digital twins
• Smart, sustainable and green supply chain
• Data mining and machine learning for smart cities
• Data-driven safety performance assessment and improvement

Important dates

Submission deadline: 15 June, 2024

Authors notification: 15 October, 2024

Revision submission: 31 December, 2024

Completion of Special Issue: March, 2024

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Prof. Zhengyi Chai , Tiangong University, China, email: zhengyichai.tiangong@gmail.com

Zhengyi Chai is currently a professor at the School of Computer Science and Technology, Tiangong University, Tianjin, China. He received his Ph.D. degree in computer science in China in 2012. He was a visiting scholar with the department of computer science, University of Nottingham, UK. He is selected as an Innovation Leading Talent of Tianjin University Discipline. He has published his own academic monograph and over 50 papers in international journals and conferences. He has contributed to the development of 20 copyrighted software systems and invented over 6 patents. His research interests include the internet of things and artificial intelligence (especially in machine learning and intelligent computing).

Dr. Hung Cao, University of New Brunswick, Canada, email: hcao3@unb.ca

Dr. Hung Cao is affiliated with the University of New Brunswick as an Assistant Professor of Computer Science. Dr. Cao received his Ph.D. in Geomatics Engineering (specializing in Data Science) and Diploma in University Teaching from the University of New Brunswick in 2020 and 2018 respectively. Dr. Cao has been a member of 15 Technical Communities (e.g. IEEE Smart City, Internet of Everything, Big Data, Cloud Computing, Intelligent Informatics, Wearable and Ubiquitous Computing, etc.). He has served as a TPC member, conference session chair, and scientific reviewer invited by many conferences and journals. He is currently a Topic Editor appointed by Electronics Journal. His research portfolio bridges the area of Smart Cities, Cyber-Physical Systems (CPS), Internet of Things (IoT), Embedded AI, Edge/Fog/Cloud Computing, Machine Learning, Data Science, Decision Intelligence, and Real-time System.

Prof. Anand Nayyar, Duy Tan University, Vietnam, email: anandnayyar@duytan.edu.vn

Dr. Anand Nayyar is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. Published more than 175+ Research Papers in various High-Quality Journals. He is acting as Associate Editor for Wireless Networks, Computer Communications, International Journal of Sensor Networks (IJSNET), Frontiers in Computer Science, PeerJ Computer Science, Human Centric Computing and Information Sciences (HCIS) and so on. He is currently researching in the area of Wireless Sensor Networks, Internet of Things, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Drones, Blockchain, Cyber Security, Healthcare Informatics, Big Data and Wireless Communications.