IoT and Big Data Analytics for Smart Cities: A Global Perspective

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
List of Contributors
Chapter 1: Era of Computational Big Data Analytics and IoT Techniques in Smart City Applications
1.1 Introduction
1.2 Work in Progress in the Background
1.2.1 Big Data Analytics
1.2.2 Internet of Things (IoT)
1.2.2.1 The Smart House
1.2.2.2 Smart Cities are Places Where People Live, Work and Play
1.2.2.3 Retailing in the 21st Century
1.2.2.4 The Smart Grid
1.2.2.5 Healthcare
1.2.2.6 Poultry and Agricultural Production
1.3 Challenges
1.3.1 Adding Value to the Customer’s Experience
1.3.2 Analytical Challenges
1.4 Smart City Applications
1.4.1 Challenges Faced by Smart City Applications
1.4.2 The Necessity of Integrating Big Data and IoT
1.4.3 To Use IoT with Big Data to Solve the Problems of Intelligent Cities
1.5 Case Study
1.5.1 Analyse the Historical Context (Prescriptive)
1.6 Final Dashboard
1.7 Conclusion
References
Chapter 2: Challenges and Roles of IoT and Big Data Analytics-Enabled Services in the Establishment of Smart Cities
2.1 Introduction
2.2 Related Work
2.2.1 Birth of IoT
2.2.2 The Internet of Things
2.2.3 The Cloud Computing
2.2.4 Big Data Analytics
2.2.5 Opportunities and Difficulties in Big Data Analytics
2.3 Efficacy in Relation to Time
2.4 Why Cloud of Things
2.5 Integration Involving Cloud and Internet Associated with Things
2.5.1 A Couple of Cloud-IoT Applications
2.6 The “QoS” on Integration of Cloud and IoT
2.7 The Benefits of Converging IoT-Cloud
2.8 One Cloud-Based IoT Architecture
2.9 Difficulties in Converging IoT-Cloud
2.10 Conclusion
References
Chapter 3: Security and Privacy Challenges and Solutions in IoT Data Analytics
3.1 Introduction
3.2 Security Challenges with Introduction of IoT and Big Data
3.3 IoT Challenges in Smart City Applications and Primary Variables
3.3.1 Storage and Management of Data
3.3.2 Issues Related to Data Visualization
3.3.3 Data Privacy and Confidentiality
3.3.4 Data Integrity
3.3.5 Security of the Devices
3.3.6 Issue of Power Supply
3.4 Solutions Offered for IoT Security Systems for Smart City Applications
3.4.1 Secure IoT System and Network
3.4.2 Using Authentication for IoT Systems
3.4.3 Using IoT Encryption Technology
3.4.4 IoT Analytics for Security
3.4.5 Testing Hardware
3.4.6 Developing Secure IoT Apps
3.4.7 Keeping Abreast with the Latest Security Threats
3.5 Conceptual Framework for IoT Data Analytics
3.6 Conclusion
References
Chapter 4: IOT-BDA Architecture for Smart Cities
4.1 Introduction
4.1.1 What Is IoT?
4.1.2 Prominent Advantages of IoT
4.1.3 Smart Cities
4.2 Architecture of Smart City
4.2.1 Level 1: Data Collection
4.2.2 Level 2: Transit
4.2.3 Level 3: Data Integration and Reasoning
4.2.4 Level 4: Device Control and Alerts
4.2.5 Smart Cities and Big Data Analytics
4.3 IoT in Smart Cities
4.3.1 Use of IoT in Developing a Smart City
4.3.2 Smart Lighting
4.3.3 Smart Parking
4.3.4 Smart Transit
4.3.5 Smart Waste Management
4.3.6 Smart Education
4.4 Applications of IoT in Smart Cities
4.4.1 Why Do We Need BDA?
4.4.2 Big Data Utilization in Information Management
4.4.3 Cloud Storage
4.4.4 Hybrid Data Storage
4.4.5 Security Framework in Smart Cities
4.5 Conclusion
4.6 Limitations and Future Research Extensions
References
Chapter 5: Intelligent Framework for Smart Traffic Management System: Case Study
5.1 Introduction
5.1.1 Internet of Things (IoT) in Intelligent Transport Systems
5.1.2 Big Data Analytics and Its Role in Traffic Management Systems
5.2 Technologies Used in Intelligent Traffic Management Systems
5.3 Basic Working of an ITMS
5.4 Case Studies on ITMS
5.4.1 ITMS in India
5.4.2 Exigency Ambulance Management System
5.4.3 Case Study-3
5.4.4 Case Study-4
5.4.4.1 Geographic Chart Information Handling
5.4.4.2 Automobile Recognition and Physical Size Assessment
5.4.4.3 Lane Occupancy and Increasing Queues
5.4.4.4 “Display” Alert Communications
5.5 Research and Applications of ITMS
5.6 Challenges in Smart City Applications
5.7 Summary
5.8 Conclusion and Future Scope
References
Chapter 6: IoT and Big Data Analytics-Based Intelligent Decision-Making Systems
6.1 Introduction
6.2 Literature Review
6.2.1 Necessity of Integrating IoT and BDA in Intelligent Decision Making
6.3 Proposed Work
6.3.1 Convergence of Big Data and IoT in Smart Urban Transportation System
6.3.1.1 Supervised Learning Algorithm
6.3.1.2 Unsupervised Learning
6.3.1.3 Semi-Supervised Learning
6.3.1.4 Reinforcement Learning
6.3.2 Decision-Making System Using Ensemble Learning (DMEL)
6.4 Results and Discussions
6.5 Conclusion
References
Chapter 7: Recent Advancement in Emergency Vehicle Communication System Using IoT
7.1 Introduction
7.2 Comparative Study
7.2.1 Traffic Monitoring
7.2.2 Patient Monitoring
7.2.3 Vehicle Monitoring
7.3 Challenges of Smart City Applications
7.4 Summary of the Survey
7.5 Proposed Methodology
7.6 Conclusion
References
Chapter 8: Pandemic Management Using Internet of Things and Big Data – A Security and Privacy Perspective
8.1 Introduction
8.2 Related Work
8.3 IoT in Pandemic Management
8.3.1 Role of Big Data in Pandemic Management
8.3.2 Role of Contact Tracing Applications in Pandemic Management
8.3.3 Role of Other IoT Devices in Pandemic Management
8.3.4 Role of IoT Empowered Smart Hospitals
8.4 Security and Privacy Requirements of IoT in Pandemic Management
8.5 Conclusion and Future Focus
References
Chapter 9: Sustainable Efficient Solutions for Smart Agriculture: Case study
9.1 Introduction
9.1.1 Challenges/Issues in Smart Agriculture
9.2 Literature Survey
9.3 Proposed Work
9.3.1 Framework
9.3.1.1 IoT Enabled Smart Farming
9.3.1.2 Smart Soil Selection
9.3.1.3 Smart Irrigation
9.3.2 IoT – BDA Architecture for Smart Farming
9.3.2.1 Processing and Data Architecture
9.3.2.2 Random Forest Algorithm
9.3.3 Smart and Sustainable Agriculture
9.4 Case Study
9.5 Conclusion
References
Index