Harness agentic and generative AI to build resilient urban infrastructure

AI for Sustainable Smart Cities: Generative and Agentic Innovations in Urban Systems and Climate Resilience offers a practice-oriented treatment of how autonomous AI agents and generative models can optimize urban infrastructure design, operation, and governance. Structured across 20 chapters, the book connects data science with civil, environmental, and systems engineering to address sustainability at the city scale.

AI for Sustainable Smart Cities delivers technical frameworks for integrating intelligent agents into energy systems, transportation networks, construction workflows, emergency response, and environmental monitoring. Case studies demonstrate AI-driven predictive maintenance, adaptive city management, and resource optimization. The book also addresses ethical, secure, and transparent deployment of AI within public and critical infrastructure systems.

Key topics include:

• Agentic AI architectures enabling autonomous, context-aware decision-making for real-time urban operations and infrastructure management across city systems
• Generative AI techniques for simulation, design optimization, and scenario planning in renewable energy and circular economy applications
• Cross-sectoral frameworks linking mobility, energy, and construction operations to scalable sustainability strategies for growing urban populations
• Methods for shifting from reactive to proactive infrastructure management using data-driven forecasting and intelligent resource allocation
• Guidance on responsible AI adoption covering transparency, security, and governance protocols for public-sector and critical infrastructure deployment
  Designed for civil and environmental engineers, AI engineers, urban planners, and infrastructure specialists, this book provides the technical depth needed to implement intelligent, adaptive urban systems. Graduate students and government professionals engaged in smart city planning and digital transformation will also find direct applicability to their work.

Autorentext

Pethuru Raj is Principal AI Architect at Infocion Inc., Bangalore, and former Vice President and Chief Architect of the AI Division at Reliance Jio Platforms Ltd. With over 25 years of industry experience spanning IBM, Wipro, and Robert Bosch, he has authored and edited over 75 books on AI, IoT, and cloud-edge integration. Satya Prakash Yadav is Associate Professor and Associate Dean R&D and PP of Research at Madan Mohan Malaviya University of Technology, Gorakhpur, India. With over 18 years of research experience, he has led applied AI projects in transportation and image processing, holds six patents, and has authored and edited over 50 books on AI, Federated Learning, and Digital Image Processing. Mudassir Khan is an accomplished academician, researcher, author, and consultant specializing in Computer Science and Data Analytics. He holds an MCA and a Ph.D. in Big Data Analytics using Deep Learning and is currently pursuing a postdoctoral fellowship at Multimedia University, Malaysia. With 13+ years of teaching and research experience, his expertise includes AI, ML, Deep Learning, Big Data, Data Science, IoT, and Medical Imaging. He has published 130+ research articles in SCI/SCIE/Scopus-indexed journals and has contributed to international conferences and reputed publishers. He has also edited numerous books and authored four books in Computer Science, contributing significantly to research and education. G. Jaspher Willsie Kathrine is Professor and Head of the Department of Computer Science and Engineering at Karunya Institute of Technology and Sciences, Coimbatore, India. She is a cybersecurity researcher with over 25 years of teaching and research experience, whose work spans across artificial intelligence, Machine Learning cloud computing, Quantum Computing, and Cyber-attacks. She has published more than 100 research papers in reputed Journals and Conferences.