Smart Cities: A System-of-Systems Perspective explores System-of-Systems Engineering (SoSE) in smart cities. Sections address how analytics and innovative technologies can transform urban environments, equipping readers with the knowledge to optimize city operations, enhance public services, and promote sustainable urban growth. The book begins with the foundational concepts of SoSE and key principles of smart cities, exploring emergent behavior and interoperability. It then examines the integration of SoSE within urban infrastructure, highlighting the roles of IoT, AI, and machine learning. Subsequent sections focus on core technologies and analytical tools, including data analytics, predictive modeling, and decision support systems.

Readers will also discover innovative solutions to pressing urban challenges, such as sustainability, cybersecurity, and governance, alongside detailed case studies showcasing successful smart city initiatives from around the globe. This book is invaluable for academics and researchers in urban studies, engineering, analytics, IoT, and AI, as well as data scientists and analytics professionals working with urban datasets. Graduate and postgraduate students in engineering, analytics, computer science, or urban studies will also find it a crucial resource.

Covers both theoretical foundations and practical methodologies
Uses real-world examples to make the concepts accessible and actionable
Provides innovative strategies and tools to address the challenges faced by urban planners and system engineers, such as interoperability between systems, scalability, sustainability, and emergent behavior management
Includes detailed, practical case studies of successful smart city projects worldwide, showcasing best practices, challenges, and lessons learned and offering valuable insights for replicating and adapting smart city solutions
Explores cutting-edge technologies such as AI, IoT, 5G, and renewable energy integration, providing readers with an understanding of how these innovations are transforming urban systems and highlighting future trends and opportunities in smart city development
Offers decision-making and policy support frameworks, optimization techniques, and governance models to help readers navigate the complexities of multi-stakeholder collaboration

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About the Author

Dr Iman Rahimi is an experienced Machine Learning Engineer and Data Scientist with over a decade in AI, optimisation, and high-performance computing. He specialises in developing, deploying, and managing advanced ML models, including LLMs and NLP systems, using TensorFlow, PyTorch, and scikit-learn. His expertise extends to GPU-accelerated computing, cloud data platforms (AWS, Azure), and scalable MLOps practices with FastAPI, Flask, and Django. He has led research in energy systems, optimisation, and delivering high-impact solutions for industry and academia. Iman is widely published, with numerous high-impact journal articles and edited books in AI, operations research, and data analytics.

Ali Emrouznejad is a Professor and Chair in Business Analytics at Surrey Business School, UK, and director of the Centre for Business Analytics in Practice. Recognized among the top 2% of influential scientists globally, he has extensive expertise in performance measurement, AI, and big data. A prolific author with over 250 publications and editor of Springer’s Business Analytics in Practice series, he collaborates on high-impact research projects funded by esteemed organizations worldwide.

Mo Jamshidi, Lutcher Brown Endowed Chair Professor at the University of Texas at San Antonio, is a globally renowned expert in systems engineering and control. A Fellow of IEEE, ASME, AAAS, and multiple academies, he has authored over 640 technical publications, including 63 books, and has supervised 40 PhD and 70 MS graduates worldwide. As a pioneer in systems of systems engineering, he has held distinguished roles with NASA, NATO, and the IEEE and continues to lead international initiatives and research in autonomous control and robotics.

Amir H. Gandomi, PhD, is a leading researcher in global optimization and big data analytics, currently serving as a Professor of Data Science and an ARC DECRA Fellow at the University of Technology Sydney (UTS). With over 450 journal publications and 60,000 citations, he is among the most cited researchers worldwide. Dr. Gandomi has authored 14 books and received numerous accolades, including the IEEE TCSC Award and the Achenbach Medal. His editorial roles span several prestigious journals, and he is a sought-after keynote speaker in the fields of artificial intelligence and genetic programming. Previously, he held academic positions at the Stevens Institute of Technology and Michigan State University, where he contributed significantly to advancing knowledge in machine learning and evolutionary computation.

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