Data-Driven Mining, Learning and Analytics for Secured Smart Cities: Trends and Advances (Advanced Sciences and Technologies for Security Applications)

Preface
Contents
About the Editors
Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments
	1 Introduction
	2 Review of Related Works
		2.1 High Utility Itemset Mining (HUIM)
		2.2 High Average-Utility Itemset Mining
		2.3 Multi-threshold Pattern Mining Works
	3 Background of HAUIM and Problem Statement
	4 Designed Model and Pruning Stratrgies
		4.1 Developed Closure Property
		4.2 Proposed Multi-HAUIM Model
		4.3 Designed Strategy 1
		4.4 Designed Strategy 2
	5 Experimental Evaluation
		5.1 Runtime Evaluation
		5.2 Evaluation of Candidate Size
		5.3 Evaluation of the Used Memory
		5.4 Evaluation of Scalability
	6 Conclusion and Future Work
	References
Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities
	1 Introduction
		1.1 Smart City Applications
		1.2 Technologies Used in Smart Cities and Integrated Technology in the Smart City-Edge/Cloud
		1.3 Security Loophole in Smart Cities
		1.4 AI/ML Based Counter Measures
		1.5 Open Issues, Challenges and Recommendation
		1.6 Conclusion and Future Scope
	References
Smart Cities Ecosystem in the Modern Digital Age: An Introduction
	1 Introduction
	2 Smart Cities Concepts
	3 Smart Cities Applications
	4 Importance of Big Data for Smart Cities
	5 Blockchain for Smart Cities
	6 Machine Learning for Smart Cities
	7 Discussion
		7.1 Challenges on the Implementation of Smart City
	8 Trends and Future Directions
	9 Conclusions
	References
A Reliable Cloud Assisted IoT Application in Smart Cities
	1 Introduction
	2 Literature Survey
	3 Previous Work
	4 Proposed Architecture
	5 Analysis of the Contribution
	6 Future Work
	7 Conclusion
	References
Lightweight Security Protocols for Securing IoT Devices in Smart Cities
	1 Introduction to Smart City Initiatives
	2 Case Study: Smart Singapore
	3 Smart City Backbone: Internet-of-Things (IoT)
	4 The Requirement of a Lightweight Security Solution
	5 Lightweight Block Ciphers
	6 Lightweight Stream Ciphers and Hash Functions
	7 Opportunities and Challenges
	8 Conclusion and Future Scope
	References
Blockchain Integrated Framework for Resolving Privacy Issues in Smart City
	1 Introduction
	2 Overview of Blockchain
		2.1 Types of Blockchain
		2.2 Working Steps of Blockchain
		2.3 Protocols
	3 Smart City: An Overview
	4 Security and Privacy Issues in IoT
	5 Blockchain Usage in Smart City
		5.1 Applications of Blockchain
		5.2 Problem Domains in Blockchain
	6 Proposed Architecture
	7 Challenges and Future Research Directions
	8 Conclusion
	References
Field Programmable Gate Array (FPGA) Based IoT for Smart City Applications
	1 Introduction
	2 Artificial Intelligence (AI) and Internet of Things (IoT) for Smart Cities
	3 FPGA for Deep Learning
		3.1 AI and Deep Learning Applications on FPGAs
	4 What Exactly is Field Programmable Gate Array (FPGA)?
		4.1 Benefits of FPGAs
		4.2 FPGAs and Artificial Intelligence
	5 FPGA Based IoT Architecture and Applications for Secured Smart Cities
		5.1 FPGA Based IoT for Smart Homes
		5.2 FPGA Based IoT for Data Encryption, Storage, and Security
		5.3 FPGA Based IoT for Safety and Surveillance Applications
	6 FPGA Based IoT Architecture and Applications for Healthcare Analytics
		6.1 Advantages of Programmable Logic
		6.2 Medical Applications for Programmable Logic
	7 IoT Architecture and Its Applications for Urban Planning Based on FPGA
		7.1 FPGA Based IoT for 5G and Beyond
		7.2 FPGA Based IoT for Energy Management
	8 Further Applications of FPGA Based IoT for Smart Cities
		8.1 FPGA Based Neuroscience and Its IoT Applications
		8.2 FPGA Implementation of Automatic Monitoring Systems for Industrial Applications
		8.3 Reconfigurable Embedded Web Services Based on FPGA
		8.4 Smart Sensor Based on SoCs for Incorporation in Industrial Internet of Things
		8.5 FPGA Based Health Monitoring System
	9 Futuristic Applications and Challenges of FPGA Based IoT for Smart Cities
	10 Conclusion
	References
Modified Transaction Against Double-Spending Attack Using Blockchain to Secure Smart Cities
	1 Introduction
		1.1 Work Contribution
	2 Proof of Work Classes
		2.1 Challenge-Response
		2.2 Solution—Verification
	3 Distribution and Cryptographic Attacks
		3.1 Characteristics of Uniform Distribution
		3.2 Cryptographic Attacks
	4 Blockchain Overview
		4.1 Bitcoin
		4.2 Public Ledger
		4.3 BlockChain Mechanism
		4.4 Consensus Algorithm
		4.5 PoW (Proof of Work)
		4.6 PoS (Proof of Stake)
	5 Basic Blockchain Design
	6 Modes of Operation
		6.1 Electronic Code Book (ECB)
		6.2 Cipher Block Chaining (CBC)
		6.3 Cipher Feedback (CFB)
		6.4 Output Feedback (OFB)
		6.5 Counter (CTR)
	7 Modified Blockchain Design
	8 Performance Analysis
	9 Conclusion
	References
Smart City Ecosystem Opportunities: Perspectives and Challenges
	1 Introduction
	2 Smart City Layers
	3 Smart City Value Creators
	4 Related Works
	5 Role of Big Data in Smart City
		5.1 Big Data Layers in Smart City Ecosystem
		5.2 Issues in Smart City Big Data
	6 Role of Internet of Things (IOT) in Smart City Ecosystem
		6.1 IOT Open Issues in Smart City
		6.2 Communication Vulnerabilities
		6.3 Physical Security Issues and Remedies in IOT
	7 Role of Artificial Intelligence (AI) in Smart City Ecosystem
		7.1 Applications of Artificial Intelligence (AI) in Smart City Ecosystem
		7.2 Application of Artificial Intelligence for Smart Citizens or Individuals
		7.3 Artificial Intelligence (AI) Challenges in Building the Smart City
	8 Role of Crowdsourcing in Smart Cities
	9 Conclusion
	References
Data-Driven Generative Design Integrated with Hybrid Additive Subtractive Manufacturing (HASM) for Smart Cities
	1 Introduction
	2 Generative Design Approach
	3 Generative Design Applications
	4 Hybrid Additive Subtractive Manufacturing and Generative Design for Smart Cities
	5 Generative Design Integrated with Hybrid Additive Subtractive Manufacturing
	6 Case Study: Generate Design of a Chassis for a Drone
	7 Conclusion and Future Scope
	References
End-to-End Learning for Autonomous Driving in Secured Smart Cities
	1 Introduction
	2 Background and Related Works
		2.1 End-To-End Learning Paradigm
		2.2 Modular Pipeline Paradigm
		2.3 Adversarial Attacks and Defenses
		2.4 Building upon and Contrasting with Related Works
	3 Proposed Model: Temporal Conditional Imitation Learning (TCIL)
	4 Experiment and Results
		4.1 Dataset
		4.2 Training
		4.3 Evaluation of System Performance
		4.4 Comparison with the State-Of-Art
		4.5 Ongoing Work: Evaluation of Defense Against Adversarial Attacks
	5 Conclusion and Future Research Direction
	6 Future Research Directions
		6.1 Improving Dataset and Learning Method
		6.2 Improving Defense Against Adversarial Attacks
	References
Smart City Technologies for Next Generation Healthcare
	1 Introduction
	2 Smart City–An Overview
		2.1 Smart People
		2.2 Smart Infrastructure
		2.3 Smart Economy
		2.4 Smart Mobility
		2.5 Smart Environment
		2.6 Smart Healthcare
		2.7 Smart Education
		2.8 Smart Governance
	3 Layers of Smart City Ecosystem
	4 Smart City Ecosystem- Layer-Wise Protocols
	5 Next Generation Healthcare and Internet of Healthcare Things (IoHT)
		5.1 Device Connectivity
		5.2 Data Processing
		5.3 Cloud Computing
		5.4 Edge Computing
		5.5 Security and Privacy of Healthcare Data
	6 Integration of Smart Healthcare with Other Smart City Components
		6.1 Infrastructural Collaboration
		6.2 Smart Education
		6.3 Medical Waste Management
		6.4 Anytime, Anywhere Services
	7 Open Issues, Challenges and Recommendations
	8 Conclusion
	References
An Investigation on Personalized Point-of-Interest Recommender System for Location-Based Social Networks in Smart Cities
	1 Introduction
	2 POI Based Recommendation Systems Based on Topographical Features
		2.1 Mining Topographical Impact for Collaborative POI Recommendation
		2.2 Exploring Geographical Inclinations for POI Recommendation
		2.3 Integrating Matrix Factorization with Joint Geographical Modeling (GeoMF) Method for POI Recommender System
		2.4 A Ranking Based Geographical Factorization (Rank-GeoMF) Approach for POI Recommender System
		2.5 Integration of Geographical Impact with POI Recommender Systems
		2.6 General Topographical Probabilistic Based Factor Approach for Point of Interest Recommendation
		2.7 Exploiting Geographical Neighborhood Characteristics for POI Recommender System
	3 POI Based Recommendation Systems Based on Temporal Features
		3.1 Time-Aware POI Recommendation
		3.2 A Probabilistic Framework to Exploit Correlation of Temporal Impact in a Time-Aware Locale Recommender System
	4 POI Based Recommendation Systems Based on User Behavior
		4.1 Exploiting Sequential Influence for Location Recommendation (LORE)
		4.2 Joint Modeling Behavior Based on Check in Approach
		4.3 Exploiting User Check-in Data for Location Recommendation in LSBN
		4.4 Extraction of User Check-in Behavior with Random Walk for Urban POI Recommender Systems
	5 POI Based Recommendation Systems Based on Integration of Various Features
		5.1 Graph-Based Approach with Spatial and Temporal Impacts for POI Recommender Systems
		5.2 Adaptive Approach for POI Recommender System Based on Temporal Features and Check-in Features
		5.3 Experimental Examination of POI in LSBNs
	6 Proposed Work
		6.1 Preprocessing of Data
		6.2 Experimental Results
	7 Conclusion and Future Scope
	References
Privacy Issues of Smart Cities: Legal Outlook
	1 Introduction
	2 Understanding Privacy Rights
	3 Collective Legal Position on Privacy and Data Protection
		3.1 Collective Initiative—European Union
	4 Realization and Recognition—Debate
	5 Smart Cities
	6 Impact of Smart Cities
		6.1 Overemphasis on Technical Solutions
		6.2 Top-Down Implementation and Technocratic Governance
		6.3 Corporatization and Privatization
		6.4 Reinforcing Divides and Inequities
		6.5 Surveillance and Privacy Violations
		6.6 Security Concerns
		6.7 How Privacy and Security Are Two Distinctive Concerns?
		6.8 Consent in the Digital World! Is It Informed Consent?
		6.9 Internet of Things and Data Privacy
	7 Challenges and Future Direction
	8 Conclusion
	References
Artificial Intelligence and Financial Markets in Smart Cities
	1 Introduction
	2 Methods for Analysis of Financial Markets
		2.1 Statistical Methods
		2.2 Artificial Intelligence Methods
	3 Applications of Machine Learning Algorithms in Financial Markets
		3.1 Applications of Supervised Learning Algorithms
		3.2 Unsupervised Learning and Its Application in Stock Markets
	4 Deep Learning and Its Application in Stock Markets
		4.1 Deep Neural Generative Model
		4.2 LSTM
		4.3 The Convolutional Neural Network
		4.4 Other Deep Learning Algorithms
	5 Application of Reinforcement Learning Algorithms in Stock Market
		5.1 Q-Learning
		5.2 Deep Q-Learning
	6 Conclusion
	References
Cybercrime Issues in Smart Cities Networks and Prevention Using Ethical Hacking
	1 Introduction
	2 Literature Review
	3 Internet of Things (IoT)
	4 Ethical Hacking
	5 Breach Testing
	6 Vulnerability Assessment
	7 Financial Losses and Cybercrime Cases
	8 Ethical Hacking and Breach Testing Using Kali
	9 Social Engineering Tool Kit (Set) Discussion
	10 Browser Exploitation Framework (BeEF) Discussion
	11 SQL Analysis Discussion
	12 NMAP
	13 Policy Base Solutions-Information Security Model and Frameworks
	14 Attacks Mitigation Solutions
	15 Conclusion
	References
A Look at Machine Learning in the Modern Age of Sustainable Future Secured Smart Cities
	1 Introduction
	2 Artificial Intelligence Concepts
		2.1 The Advantages of AI
		2.2 Smart Cities Using Artificial Intelligence Technology
	3 Machine Learning Concepts
		3.1 Deep Learning
		3.2 Natural Language Processing (PLN)
	4 Discussion
		4.1 Role of ML for Secured Smart City
		4.2 Challenges of Using AI in Smart City
	5 Trends
	6 Conclusions
	References