Data-driven mining, learning and analytics for secured smart cities : trends and advances / Chinmay Chakraborty, Jerry Chun-Wei Lin, Mamoun Alazab, editors.

Format
Book
Language
English
Published/​Created
  • Cham, Switzerland : Springer, [2021]
  • ©2021
Description
1 online resource (390 pages)

Details

Subject(s)
Editor
Series
Advanced Sciences and Technologies for Security Applications Ser. [More in this series]
Source of description
Description based on print version record.
Contents
  • Intro
  • 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
  • 1.1 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
  • Smart Cities Ecosystem in the Modern Digital Age: An 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
  • A Reliable Cloud Assisted IoT Application in Smart Cities
  • 2 Literature Survey
  • 3 Previous Work
  • 4 Proposed Architecture
  • 5 Analysis of the Contribution
  • 6 Future Work
  • 7 Conclusion
  • 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
  • Blockchain Integrated Framework for Resolving Privacy Issues in Smart City
  • 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
  • Field Programmable Gate Array (FPGA) Based IoT for Smart City Applications
  • 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
  • Modified Transaction Against Double-Spending Attack Using Blockchain to Secure Smart Cities
  • 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
  • Smart City Ecosystem Opportunities: Perspectives and Challenges
  • 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
  • Data-Driven Generative Design Integrated with Hybrid Additive Subtractive Manufacturing (HASM) for Smart Cities
  • 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
  • End-to-End Learning for Autonomous Driving in Secured Smart Cities
  • 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
  • Smart City Technologies for Next Generation Healthcare
  • 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
  • An Investigation on Personalized Point-of-Interest Recommender System for Location-Based Social Networks in Smart Cities
  • 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.
ISBN
3-030-72139-6
OCLC
1249472659
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