Details

Subject(s)

• Smart cities [Browse]

Editor

• Chakraborty, Chinmay, 1984- [Browse]
• Lin, Jerry Chun-Wei [Browse]
• Alazab, Mamoun, 1980- [Browse]

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.

Show 197 more Contents items

ISBN

3-030-72139-6

OCLC

1249472659

Statement on language in description

Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...