Energy-Efficient Transformative Technologies for Data-Driven Smart Cities delves into innovative solutions and groundbreaking methodologies essential for constructing smart cities that prioritize energy efficiency and security. With the modern urban landscape rapidly evolving, this book serves as a vital resource for policymakers, engineers, and researchers striving to harness data-driven technologies effectively. From sustainable energy systems to advanced data management frameworks, this comprehensive guide explores pivotal tools and strategies that address the challenges of urbanization and environmental impact.
Beyond energy efficiency, the book emphasizes the importance of robust cybersecurity measures, seamless integration of IoT devices, and intelligent urban planning. It offers actionable insights for achieving smart city infrastructures that are both resilient and adaptive.
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Table of Contents
1. An Introduction to the Data-driven Super-smart Cities of the Future2. Energy Efficiency in Wireless Sensor Networks
3. Security Considerations in an Energy-efficient Super-smart City
4. Energy Harvesting Technologies in a Super-smart City
5. Energy Challenges in Transformative Technologies-based Super-smart City Implementation
6. Optimization Techniques for Energy Efficiency in the Super-smart City
7. Communication Protocols for Low-Energy Devices
8. Cross-layer Optimization for Energy Efficiency in Super-smart Cities of the Future
9. Energy-aware Routing Protocols in Smart Cities
10. Energy-efficient Management Policies in Smart Cities
11. Machine Learning for Energy Prediction in Super Smart Cities
12. Edge Computing for an Energy-efficient Super-smart City
Authors
Hamed Nozari University of National and World Economy.Hamed Nozari is a distinguished researcher at University of National and World Economy (UNWE), Sofia, Bulgaria, and innovator in industrial engineering, supply chain optimization, and artificial intelligence applications. Holding a Ph.D. in Industrial Engineering, he has made significant contributions to the fields of multi-objective decision-making, smart supply chains, and digital twin technologies. His research spans various interdisciplinary areas, including predictive maintenance in green supply chains, AI-driven marketing optimization, cybersecurity in smart economies, Digital twin, and autonomous AI for sustainable last-mile delivery.
Reza Tavakkoli-Moghaddam Professor of Industrial Engineering, College of Engineering, University of Tehran, Iran.Reza Tavakkoli-Moghaddam is a Professor of Industrial Engineering at the College of Engineering in the University of Tehran, Iran. He received the Order of Academic Palms Award for a distinguished educator and scholar and the insignia of Chevalier dans l'Ordre des Palmes Academiques from the Ministry of National Education, France in 2019.
