This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the elds of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. . Speci es the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks

. Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks

. Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks

M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

Autorentext

M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011. His research interests include distributed machine learning and optimization algorithms, distributed intelligence, sensor networks, interdependent networks, and cyberphysical resilience. Application domains include energy systems, healthcare, device-free human sensing, and transportation networks. Prof. Amini is a life member of IEEE-Eta Kappa Nu (IEEE-HKN), the honor society of IEEE. He organized a panel on distributed learning and novel artificial intelligence algorithms, and their application to healthcare, robotics, energy cybersecurity, distributed sensing, and policy issues in 2019 workshop on artificial intelligence at FIU. He also served as President of Carnegie Mellon University Energy Science and Innovation Club; as technical program committee of several IEEE and ACM conferences; and as the lead editor for a book series on ''Sustainable Interdependent Networks'' since 2017. He has published more than 80 refereed journal and conference papers, and book chapters. He has co-authored two books, and edited three books on various aspects of optimization and machine learning for interdependent networks. He is the recipient of the best paper award of IEEE Conference on Computational Science & Computational Intelligence in 2019, best reviewer award from four IEEE Transactions, the best journal paper award in Journal of Modern Power Systems and Clean Energy, and the dean's honorary award from the President of Sharif University of Technology.

Inhalt

Introduction.- Interdependent Complex Networks: Tale of IoT-based Smart Cities.- Deep Learning Algorithms for Energy Systems.- Distributed Algorithms for Interdependent Networks.- Online Optimization Learning for Interdependent Complex Networks.- Deep Learning Algorithms for Ramp Rate Prediction in Unit Commitment.- Networked Control Systems: Case Study of Unmanned Aerial Vehicle.- Conclusion.