Strategic Framework and Intelligent Solutions for Sustainable Cities and Communities

In developing economies, due to rising population and increasing migration, cities are rapidly growing. By 2050, two-thirds of all humanity (6.5 billion people) will be living in cities. Sustainable development cannot be achieved without significantly transforming how we build and manage our cities and develop communities. There is an urgent need to make our cities sustainable (i.e. creating more jobs, safe and affordable housing, and building resilient societies and economies). Technology has always been a crucial driver for economic development, and AI, IoT and Data Science should be leveraged to manage public transport, clean water and eco-friendly ways to manage waste, improve urban planning and create a sustainable environment for growth.

Strategic Framework and Intelligent Solutions for Sustainable Cities and Communities is a compilation of recent advancements in disruptive technologies such as AI, IoT, and Data Science, and ways to combat the challenges that are necessary for making our cities and communities sustainable.

Sommario

Preface xi
Chapter 1 Artificial Intelligence and Sustainable Development Goals: An Overview 1
Misha KAKKAR
1.1 Introduction 1
1.1.1 SDG 1 - no poverty 3
1.1.2 SDG 2 - zero hunger 5
1.1.3 SDG 3 - good health and well-being 7
1.1.4 SDG 4 - quality education 10
1.1.5 SDG 6 - clean water and sanitation 11
1.1.6 SDG 7 - affordable and clean energy 12
1.1.7 SDG 8 - decent work and economic growth 14
1.1.8 SDG 11 - sustainable cities and communities 15
1.1.9 SDG 13 - climate change 19
1.1.10 SDG 14 - life below water 21
1.1.11 SDG 15 - life on land 21
1.2 Conclusion 22
1.3 References 23
Chapter 2 Technologies and Frameworks for Sustainable Smart Cities 25
Depalle Sandhya RANI, Jagu Yeswanth Sai MAHESH, Swati SUCHARITA and Meerja Akhil JABBAR
2.1 Introduction: embracing smart cities 26
2.1.1 Technological infrastructure: a backbone of smart cities 27
2.1.2 The role of AI in smart cities 28
2.1.3 IoT and data connectivity 29
2.1.4 Case study: Singapore's smart nation 29
2.1.5 Environmental sustainability in smart cities 31
2.1.6 Challenges and opportunities 31
2.1.7 Future research and innovations 33
2.2 Technologies used in smart cities 33
2.2.1 AI in smart cities 33
2.2.2 AI-driven traffic management systems 34
2.2.3 AI in predictive infrastructure maintenance 34
2.2.4 Internet of Things: enabling connectivity 34
2.2.5 IoT in waste management 35
2.2.6 IoT in public utilities 35
2.2.7 Internet of Medical Things: enhancing public health 36
2.2.8 IoMT in disease monitoring and outbreak prediction 36
2.2.9 Cloud computing in smart cities 36
2.2.10 Edge computing: processing data locally 37
2.2.11 The role of 5G in enabling smart city technologies 37
2.2.12 Industrial Internet of Things in smart cities 37
2.2.13 IIoT in smart grids 38
2.2.14 Smart lighting systems 38
2.2.15 Smart water management systems: enhancing urban water efficiency and safety 38
2.2.16 Autonomous vehicles in smart cities: transforming urban travel 39
2.2.17 Smart parking solutions: reducing city congestion and simplifying parking 39
2.3 Smart buildings: making cities greener and more comfortable for everyday life 40
2.3.1 Urban air quality monitoring: safeguarding health through real-time data 40
2.3.2 AI in urban planning: shaping the future of livable cities 41
2.3.3 Cybersecurity in smart cities 41
2.3.4 Public safety and emergency response 42
2.3.5 Smart energy systems 42
2.3.6 Future of technology in smart cities 42
2.4 Case study: smart city implementation 43
2.4.1 Singapore: a model of smart city innovation 43
2.4.2 The role of AI in traffic management 43
2.4.3 Real-time traffic monitoring 43
2.4.4 Predictive analytics for traffic flow 44
2.4.5 Public transportation and AI integration 44
2.4.6 Autonomous vehicles: the future of Singapore's transport 44
2.4.7 IoT and environmental monitoring 45
2.4.8 AI in healthcare: revolutionizing public health 45
2.4.9 IoMT in patient monitoring 46
2.4.10 AI and predictive healthcare 46
2.4.11 Telemedicine and AI-driven diagnosis 46
2.4.12 AI in public health surveillance 47
2.4.13 Digital twins for city planning 47
2.4.14 Waste management through smart technology 47
2.4.15 Smart energy management in Singapore 48
2.4.16 Water management and AI-driven solutions 48
2.4.17 Public safety and AI surveillance systems 48
2.4.18 Smart buildings for energy efficiency 49
2.5 Challenges in adopting smart cities 51
2.5.1 High initial costs and investment hurdles 51
2.5.2 Data privacy and security concerns 51
2.5.3 Technological infrastructure gaps 51
2.5.4 Fragmented governance and policy issues 52
2.5.5 Integration with legacy systems 52
2.5.6 Lack of technical expertise 53
2.5.7 Social and cultural barriers 53
2.5.8 Environmental and sustainability concerns 54
2.5.9 Political and legal challenges 54
2.5.10 Ethical considerations in AI and surveillance 55
2.6 Research directions 56
2.6.1 AI for predictive urban analytics 56
2.6.2 Sustainability and smart energy grids 57
2.6.3 Public-private partnerships in smart city development 57
2.6.4 Ethical AI in smart cities 57
2.6.5 AI and public transportation optimization 58
2.6.6 Data-driven decision-making in urban governance 59
2.6.7 AI in emergency services and disaster response 59
2.6.8 Urban air quality management with IoT and AI 59
2.7 Conclusion 61
2.8 References 63
Chapter 3 IoT-Based Simulation for Emergency Vehicle Corridor for Smart Cities 65
Himanshu DIDEN, Hriday CHAWLA, Yashvi SIKKA, Misha KAKKAR and Deepti MEHROTRA
3.1 Introduction 65
3.2 Literature review 66
3.3 Methodology 70
3.3.1 Dataset used 70
3.3.2 Proposed solution 70
3.3.3 Detection phase 72
3.3.4 Regulation phase 75
3.4 Results and discussion 76
3.4.1 Phase 1: detection of ambulances 76
3.4.2 Phase 2: representation of traffic signals at a crossing 77
3.4.3 Regular traffic cycle 78
3.4.4 Traffic light during ambulance detection 78
3.5 Conclusion 79
3.6 References 79
Chapter 4 Smart Waste Collection System 83
Sumathi PAWAR, Ankitha KESHAV, Vandana BELENJI SANJEEVA and Rajermani THINAKARAN
4.1 Introduction 84
4.1.1 Key components 85
4.1.2 Advantages of smart garbage systems 87
4.1.3 Bottlenecks to adoption 88
4.1.4 Case studies 88
4.1.5 Applications 89
4.1.6 Challenges 89
4.2 Related work 89
4.3 Methodology 93
4.3.1 Architecture of the system 94
4.3.2 Components required 95
4.4 Results 96
4.5 Analysis 97
4.6 Conclusions and future work 99
4.7 References 99
Chapter 5 A Survey on AI Algorithms and Techniques for Developing Intelligent and Sustainable Cities 103
Elumalai RAJALAKSHMI and M. SHOBANA
5.1 Introduction 104
5.1.1 Understanding the need for sustainable and intelligent cities 104
5.1.2 Key AI techniques and algorithms 105
5.2 Artificial intelligence uses for sustainable urban development 107
5.2.1 Intelligent traffic management 107
5.2.2 Energy efficiency 108
5.2.3 Waste management 108
5.2.4 Public safety 108
5.2.5 Urban mobility and dynamics 109
5.3 Advanced analytical techniques 109
5.3.1 Leveraging AI for sustainable urban development: key research insights 110
5.3.2 Anomaly detection in energy consumption 112
5.3.3 Urban sustainability through air quality prediction 112
5.3.4 Crowdsourcing logistics optimization 112
5.3.5 Advanced anomaly detection for urban safety 113
5.3.6 Innovative techniques in image classification 113
5.3.7 Healthcare predictive modeling 113
5.3.8 Cybersecurity in urban environments 113
5.4 Advancements in intelligent solutions for sustainable urban development 114
5.4.1 Taxi passenger hot spot identification 114
5.4.2 Object contour recognition 115
5.4.3 Freshwater ecosystem optimization 115
5.4.4 Freeway incident duration prediction 115
5.5 Conclusion 119
5.6 Future directions 119
5.7 References 120
Chapter 6 Machine Learning-based Approaches for Energy Management and Optimization for Smart Cities 123
Anil SHARMA, Tushar PANT, Suresh KUMAR and Pawan KUMAR
6.1 Introduction 124
6.1.1 Smart energy management systems 125
6.1.2 Artificial intelligence and ML applications in SGs 127
6.1.3 Cloud computing and IoT integration for energy management 128
6.2 Literature review 130
6.2.1 Smart meters in energy management 131
6.2.2 Cloud computing 131
6.2.3 Artificial intelligence and machine learning 132
6.2.4 Internet of Things 134
6.2.5 Communication technologies (ZigBee, Wi-Fi, WiMAX) 135
6.2.6 Renewable energy integration 135
6.2.7 Smart homes and buildings 136
6.3 Discussion 137
6.4 Conclusion 139
6.5 Future work 140
6.5.1 Enhanced AI and machine learning models 140
6.5.2 Integration with renewable energy sources 141
6.5.3 Cybersecurity and privacy 141
6.5.4 Scalability and interoperability of IoT systems 142
6.5.5 Smart grids and decentralized energy management 142
6.5.6 Human-centric energy management 142
6.6 References 143
Chapter 7 Analyzing Accessible Learning Using Summarization for Communities 147
Misha KAKKAR, Anuranjana SHARMA and Jeteish Pratap SINGH
7.1 Introduction 147
7.2 Literature review 148
7.3 Proposed solution 151
7.4 Results and discussion 152
7.5 Conclusion 153
7.6 Future scope 153
7.7 References 154
Chapter 8 Ranking of Smart Cities in India: IF-MOORA Approach 157
Renuka NAGPAL, Deepti MEHROTRA and Rajni SEHGAL
8.1 Introduction 158
8.2 Literature review 160
8.3 IF-MOORA method 164
8.4 Results and discussion 168
8.4.1 Process of selecting smart cities 168
8.4.2 Process of selecting the criteria 170
8.4.3 Selection of DM 172
8.4.4 Perform process of application of IF-MOORA 172
8.5 Conclusion 178
8.6 References 179
List of Authors 183
Index 187

Info autore

Misha Kakkar is Associate Professor and Head of Accreditation, Ranking and Quality Assurance at the Amity School of Engineering and Technology, Amity University Uttar Pradesh, India.
Nitasha Hasteer is Professor, Head of the Information Technology Department and Deputy Director (Academics) at the Amity School of Engineering and Technology, Amity University Uttar Pradesh, India.
Rahul Sindhwani is Assistant Professor at IIM Sambalpur, India.
Marita Turpin is Full Professor in Informatics at the University of Pretoria, South Africa.
J. Paulo Davim is Full Professor at the University of Aveiro, Portugal, and a Fellow (FIET) of the Institution of Engineering and Technology (UK).

Dettagli sul prodotto

• Autori: Misha Kakkar
• Con la collaborazione di: J Paulo Davim (Editore), J. Paulo (University of Aveiro Davim (Editore), Nitasha Hasteer (Editore), Nitasha (Amity University Hasteer (Editore), Misha Kakkar (Editore), Misha (Amity University Kakkar (Editore), Rahul Sindhwani (Editore), Rahul (Indian Institute of Management Sindhwani (Editore), Marita Turpin (Editore), Marita (University of Pretoria Turpin (Editore)
• Editore: Wiley
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 11.09.2025
• EAN: 9781836690641
• ISBN: 978-1-83669-064-1
• Pagine: 224
• Dimensioni: 156 mm x 234 mm x 13 mm
• Peso: 476 g
• Serie: ISTE Invoiced
• Categoria: Scienze umane, arte, musica > Arte > Architettura