Strategic Framework and Intelligent Solutions for Sustainable Cities and Communities
Description
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.
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Persons
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).
Content
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
1
Artificial Intelligence and Sustainable Development Goals: An Overview
Sustainable development goals (SDGs) were established by the United Nations in 2015 to foster global collaboration among nations to end poverty, protect the planet and ensure peace and prosperity by 2030. Disruptive technologies such as artificial intelligence (AI) along with data science play an important role in preparing a road map on how these SDGs can be attained. This chapter highlights how AI is crucial and can help attain 11 out of 17 SDGs. Various real-life case studies and examples are discussed on how AI can be beneficial and is used by various countries as well as organizations.
1.1. Introduction
The SDGs are a collection of 17 global goals established by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. They aim to address the world's most pressing challenges: poverty, inequality, climate change and peace. The SDGs provide a shared blueprint for peace and prosperity for all people and the planet. They are universal, applying to every developed or developing country, ensuring no one is left behind. The SDGs, adopted by the United Nations in 2015, provide a framework for addressing the world's most pressing issues, such as poverty, inequality, climate change and access to education. These 17 goals are a roadmap for creating a sustainable and equitable future by 2030 (Ahmad et al. 2021). The SDGs were established to address the following questions:
1) Addressing global challenges: the world faces complex, interrelated challenges such as poverty, inequality, environmental degradation and climate change. The SDGs provide a roadmap for systematically tackling these issues.
2) Building on the millennium development goals (MDGs): the SDGs were designed to build on the progress and lessons learned from the MDGs, which focused primarily on reducing extreme poverty from 2000 to 2015. While the MDGs made significant strides, they had gaps (e.g. inequality and environmental concerns) that the SDGs aim to fill.
3) Promoting sustainable development: the SDGs emphasize balancing economic growth, social inclusion and environmental protection - essential components of sustainable development.
4) Global collaboration and accountability: the SDGs encourage countries, organizations and individuals to work together toward shared goals, fostering global cooperation. They also provide measurable targets and indicators to track progress and ensure accountability.
The SDGs are built on five key themes:
- people: eradicating poverty and hunger while ensuring dignity and equality;
- planet: protecting natural resources and combating climate change;
- prosperity: promoting sustainable economic growth and innovation;
- peace: fostering just, inclusive societies;
- partnerships: encouraging global cooperation to achieve the goals.
A total of 17 SDGs are designed on these themes having 169 targets. AI has a direct impact on 11 of the SDGs. These goals are as follows:
1) SDG 1 - no poverty;
2) SDG 2 - zero hunger;
3) SDG 3 - good health and well-being;
4) SDG 4 - quality education;
5) SDG 6 - clean water and sanitation;
6) SDG 7 - affordable and clean energy;
7) SDG 8 - decent work and economic growth;
8) SDG 11 - sustainable cities and communities;
9) SDG 13 - climate change;
10) SDG 14 - life below water;
11) SDG 15 - life on land.
These SDGs are a universal call to action to end poverty, protect the planet and ensure peace and prosperity by 2030 (Wang et al. 2019; Ahmad et al. 2021). AI, with its ability to analyze data, predict outcomes and optimize processes, offers unique solutions to many of these goals. For example, AI can identify poverty hotspots, predict climate impacts, or enhance education systems, contributing significantly to global progress.
1.1.1. SDG 1 - no poverty
Poverty elimination remains a cornerstone of sustainable development. AI helps by analyzing satellite images and socioeconomic data to map poverty in real time. Organizations like the World Bank use these insights to better allocate resources. Additionally, AI can also enhance access to microloans through credit scoring systems for unbanked populations, promoting financial inclusion. To effectively tackle poverty, it is crucial to grasp its multidimensional nature. Poverty is not just the lack of income but encompasses the inability to access opportunities, resources and basic services.
Types of poverty are as follows:
1) Absolute poverty: is defined by living below a certain income threshold (e.g. $2.15/day, according to the World Bank). This type of poverty is common in low-income countries and characterized by lack of food, shelter and healthcare.
2) Relative poverty: is based on income levels relative to the median of a specific society. This type of poverty is found even in developed nations, manifesting as inequality in living standards.
3) Multidimensional poverty: includes factors like lack of education, poor health outcomes and inadequate housing. It is measured by the multidimensional poverty index (MPI), which looks beyond income.
Poverty mapping refers to the process of identifying and visualizing poverty-stricken regions using data to guide interventions. AI is transformative in making poverty mapping more accurate, efficient and actionable. By combining satellite imagery, socioeconomic data and machine learning algorithms, AI-driven poverty mapping offers more profound insights into poverty's distribution and root causes. AI can be used in the following ways for poverty mapping:
1) Satellite imagery analysis: AI models, particularly those involving computer vision, analyze high-resolution satellite images to detect indicators of poverty, such as:
- infrastructure quality: roads, housing materials and urban density;
- agricultural patterns: crop health, land use and deforestation;
- energy access: the presence of electricity grids and light emissions at night.
2) Integrating multidimensional data: AI integrates diverse datasets, such as:
- census information: population density, literacy rates and income levels;
- mobile phone data: communication patterns and financial transactions indicating economic activity;
- climate data: environmental stressors impacting livelihoods, like droughts or floods.
By combining these datasets, AI creates multidimensional poverty indices tailored to specific regions.
3) Identifying hidden patterns: AI uncovers correlations and trends that might not be evident through traditional data analysis. For instance:
- linking educational outcomes to infrastructure quality;
- highlighting regions where environmental factors exacerbate poverty.
4) Real-time and dynamic updates: AI allows for dynamic, real-time poverty mapping by continuously analyzing incoming data, such as:
- changes in urbanization or migration patterns;
- impacts of natural disasters or economic shocks.
This capability enables timely responses to emerging crises.
5) Cost-effective and scalable solutions: Traditional poverty assessments require extensive field surveys, which are expensive and time-consuming. AI dramatically reduces costs by automating data collection and analysis, making large-scale poverty mapping feasible even in resource-limited settings (Khan et al. 2020).
Figure 1.1. Case study.
1.1.2. SDG 2 - zero hunger
Sustainable development goal 2 aims to create a hunger-free world by 2030. The global issue of hunger and food insecurity has increased alarmingly since 2015, exacerbated by a combination of factors, including the pandemic, conflict, climate change and deepening inequalities. By 2022, around 9.2% of the world's population was in a state of chronic hunger, a staggering rise compared to 2019. These data underscore the severity of the situation, revealing a growing crisis. Around 2.4 billion people are estimated to face moderate to severe food insecurity in 2022. This classification signifies their lack of access to sufficient nourishment. This number escalated by an alarming 391 million people compared to 2019. We can harness AI for sustainable food security (Liu et al. 2021). Food security, defined as the availability, accessibility and affordability of nutritious food, is one of the most pressing global challenges. Achieving it is central to sustainable development goal 2 (zero hunger), especially in the face of population growth, climate change and resource constraints. AI is a powerful tool for addressing these challenges, offering innovative solutions to ensure food security while promoting sustainability. Various key applications of AI in agriculture are as follows:
1) Precision agriculture: AI applications can be used to optimize resource use and boost productivity by providing data-driven insights for farming practices. AI models assess soil health, nutrient content and moisture levels, recommending appropriate fertilizers and irrigation schedules. Drones equipped with AI-powered cameras detect crop health issues, such as pest infestations or nutrient deficiencies. Machine learning algorithms analyze historical and real-time data to...
