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Automated Secure Computing for Next-Generation Systems
| Automated Secure Computing for Next-Generation Systems |
| Autore | Tyagi Amit Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (468 pages) |
| ISBN |
1-394-21394-8
1-394-21392-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Part 1: Fundamentals -- Chapter 1 Digital Twin Technology: Necessity of the Future in Education and Beyond -- 1.1 Introduction -- 1.2 Digital Twins in Education -- 1.2.1 Virtual Reality for Immersive Learning -- 1.2.2 Delivery of Remote Education -- 1.2.3 Replication of Real-World Scenarios -- 1.2.4 Promote Intelligences and Personalization -- 1.3 Examples and Case Studies -- 1.3.1 Examples of DTT in Education -- 1.3.2 Digital Twin-Based Educational Systems -- 1.4 Discussion -- 1.5 Challenges and Limitations -- 1.5.1 Technical Challenges -- 1.5.2 Pedagogical Challenges -- 1.5.3 Ethical and Privacy Concerns -- 1.5.4 Future Research Directions -- 1.6 Conclusion -- References -- Chapter 2 An Intersection Between Machine Learning, Security, and Privacy -- 2.1 Introduction -- 2.2 Machine Learning -- 2.2.1 Overview of Machine Learning -- 2.2.2 Machine Learning Stages: Training and Inference -- 2.3 Threat Model -- 2.3.1 Attack Model of Machine Learning -- 2.3.2 Trust Model -- 2.3.3 Machine Learning Capabilities in a Differential Environment -- 2.3.4 Opposite Views of Machine Learning in Security -- 2.4 Training in a Differential Environment -- 2.4.1 Achieving Integrity -- 2.5 Inferring in Adversarial Attack -- 2.5.1 Combatants in the White Box Model -- 2.5.2 Insurgencies in the Black Box Model -- 2.6 Machine Learning Methods That Are Sustainable, Private, and Accountable -- 2.6.1 Robustness of Models to Distribution Drifts -- 2.6.2 Learning and Inferring With Privacy -- 2.6.3 Fairness and Accountability in Machine Learning -- 2.7 Conclusion -- References -- Chapter 3 Decentralized, Distributed Computing for Internet of Things-Based Cloud Applications -- 3.1 Introduction to Volunteer Edge Cloud for Internet of Things Utilising Blockchain.
3.2 Significance of Volunteer Edge Cloud Concept -- 3.3 Proposed System -- 3.3.1 Smart Contract -- 3.3.2 Order Task Method -- 3.3.3 KubeEdge -- 3.4 Implementation of Volunteer Edge Control -- 3.4.1 Formation of a Cloud Environment -- 3.5 Result Analysis of Volunteer Edge Cloud -- 3.6 Introducing Blockchain-Enabled Internet of Things Systems Using the Serverless Cloud Platform -- 3.7 Introducing Serverless Cloud Platforms -- 3.7.1 IoT Systems -- 3.7.2 JointCloud -- 3.7.3 Computing Without Servers -- 3.7.4 Oracle and Blockchain Technology -- 3.8 Serverless Cloud Platform System Design -- 3.8.1 Aim and Constraints -- 3.8.2 Goals and Challenges -- 3.8.3 HCloud Connections -- 3.8.4 Data Sharing Platform -- 3.8.5 Cloud Manager -- 3.8.6 The Agent -- 3.8.7 Client Library -- 3.8.8 Witness Blockchain -- 3.9 Evaluation of HCloud -- 3.9.1 CPU Utilization -- 3.9.2 Cost Analysis -- 3.10 HCloud-Related Works -- 3.10.1 Serverless -- 3.10.2 Efficiency -- 3.11 Conclusion -- References -- Chapter 4 Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications for Next-Generation Society -- 4.1 Introduction -- 4.2 Background Work -- 4.3 Motivation -- 4.4 Existing Innovations in the Current Society -- 4.5 Expected Innovations in the Next-Generation Society -- 4.6 An Environment with Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.7 Open Issues in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.8 Research Challenges in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.9 Legal Challenges in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.10 Future Research Opportunities Towards Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications. 4.11 An Open Discussion -- 4.12 Conclusion -- References -- Chapter 5 Artificial Intelligence for Cyber Security: Current Trends and Future Challenges -- 5.1 Introduction: Security and Its Types -- 5.1.1 Human Aspects of Information Security -- 5.2 Network and Information Security for Industry 4.0 and Society 5.0 -- 5.2.1 Industry 4.0 vs Society 5.0 -- 5.2.2 Industry 4.0 to Society 5.0 -- 5.3 Internet Monitoring, Espionage, and Surveillance -- 5.4 Cyber Forensics with Artificial Intelligence and without Artificial Intelligence -- 5.5 Intrusion Detection and Prevention Systems Using Artificial Intelligence -- 5.6 Homomorphic Encryption and Cryptographic Obfuscation -- 5.7 Artificial Intelligence Security as Adversarial Machine Learning -- 5.8 Post-Quantum Cryptography -- 5.9 Security and Privacy in Online Social Networks and Other Sectors -- 5.10 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications -- 5.11 Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0 -- 5.11.1 Implementation on the Internet of Things and Protecting Data in IoT Connected Devices -- 5.12 Digital Trust and Reputation Using Artificial Intelligence -- 5.13 Human-Centric Cyber Security Solutions -- 5.14 Artificial Intelligence-Based Cyber Security Technologies and Solutions -- 5.15 Open Issues, Challenges, and New Horizons Towards Artificial Intelligence and Cyber Security -- 5.15.1 An Overview of Cyber-Security -- 5.15.2 The Role of Artificial Intelligence in Cyber Security -- 5.15.3 AI Is Continually Made Smarter -- 5.15.4 AI Never Misses a Day of Work -- 5.15.5 AI Swiftly Spots the Threats -- 5.15.6 Impact of AI on Cyber Security -- 5.15.7 AI in Cyber Security Case Study -- 5.16 Future Research with Artificial Intelligence and Cyber Security -- 5.17 Conclusion -- References. Part 2: Methods and Techniques -- Chapter 6 An Automatic Artificial Intelligence System for Malware Detection -- 6.1 Introduction -- 6.2 Malware Types -- 6.3 Structure Format of Binary Executable Files -- 6.4 Malware Analysis and Detection -- 6.5 Malware Techniques to Evade Analysis and Detection -- 6.6 Malware Detection With Applying AI -- 6.7 Open Issues and Challenges -- 6.8 Discussion and Conclusion -- References -- Chapter 7 Early Detection of Darknet Traffic in Internet of Things Applications -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Proposed Work -- 7.3.1 Drawback -- 7.4 Analysis of the Work -- 7.5 Future Work -- 7.6 Conclusion -- References -- Chapter 8 A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Implementation and Analysis -- 8.3.1 Dataset Description -- 8.3.2 Methodology -- 8.3.3 Checking for Missing Values -- 8.3.4 Exploratory Data Analysis -- 8.4 Conclusion -- 8.4.1 Future Work -- 8.4.2 Limitations -- References -- Chapter 9 Automated Secure Computing for Fraud Detection in Financial Transactions -- 9.1 Introduction -- 9.2 Historical Perspective -- 9.3 Previous Models for Fraud Detection in Financial Transactions -- 9.3.1 CatBoost -- 9.3.2 XGBoost -- 9.3.3 LightGBM -- 9.4 Proposed Model Based on Automated Secure Computing -- 9.5 Discussion -- 9.6 Conclusion -- References -- Additional Readings -- Chapter 10 Data Anonymization on Biometric Security Using Iris Recognition Technology -- 10.1 Introduction -- 10.2 Problems Faced in Facial Recognition -- 10.3 Face Recognition -- 10.4 The Important Aspects of Facial Recognition -- 10.5 Proposed Methodology -- 10.6 Results and Discussion -- 10.7 Conclusion -- References -- Chapter 11 Analysis of Data Anonymization Techniques in Biometric Authentication System. 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Existing Survey -- 11.3.1 Biometrics Technology -- 11.3.2 Palm Vein Authentication -- 11.3.3 Methods of Palm Vein Authentication -- 11.3.4 Limitations of the Existing System -- 11.4 Proposed System -- 11.4.1 Biometric System -- 11.4.2 Data Processing Technique -- 11.4.3 Data-Preserving Approach -- 11.4.3.1 Generalization -- 11.4.3.2 Suppression -- 11.4.3.3 Swapping -- 11.4.3.4 Masking -- 11.5 Implementation of AI -- 11.6 Limitations and Future Works -- 11.7 Conclusion -- References -- Part 3: Applications -- Chapter 12 Detection of Bank Fraud Using Machine Learning Techniques -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Problem Description -- 12.4 Implementation and Analysis -- 12.4.1 Workflow -- 12.4.2 Dataset -- 12.4.3 Methodology -- 12.5 Results -- 12.6 Conclusion -- 12.7 Future Works -- References -- Chapter 13 An Internet of Things-Integrated Home Automation with Smart Security System -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Methodology and Working Procedure with Diagrams -- 13.4 Research Analysis -- 13.5 Establishment of the Prototype -- 13.6 Results and Discussions -- 13.7 Conclusions -- Acknowledgment -- References -- Chapter 14 An Automated Home Security System Using Secure Message Queue Telemetry Transport Protocol -- 14.1 Introduction -- 14.2 Related Works -- 14.2.1 PIR Home Security Solutions -- 14.2.2 Solutions for MQTT Security -- 14.2.3 Solutions for Home Automation -- 14.3 Proposed Solution -- 14.3.1 Technological Decisions -- 14.3.2 Hardware Decision -- 14.3.3 Module Overview -- 14.4 Implementation -- 14.5 Results -- 14.6 Conclusion and Future Work -- References -- Chapter 15 Machine Learning-Based Solutions for Internet of Things-Based Applications -- 15.1 Introduction -- 15.2 IoT Ecosystem -- 15.2.1 IoT Devices -- 15.2.2 IoT Gateways -- 15.2.3 IoT Platforms. 15.2.4 IoT Applications. |
| Record Nr. | UNINA-9910830154103321 |
Tyagi Amit Kumar
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Automated Secure Computing for Next-Generation Systems
| Automated Secure Computing for Next-Generation Systems |
| Autore | Tyagi Amit Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (468 pages) |
| ISBN |
1-394-21394-8
1-394-21392-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Part 1: Fundamentals -- Chapter 1 Digital Twin Technology: Necessity of the Future in Education and Beyond -- 1.1 Introduction -- 1.2 Digital Twins in Education -- 1.2.1 Virtual Reality for Immersive Learning -- 1.2.2 Delivery of Remote Education -- 1.2.3 Replication of Real-World Scenarios -- 1.2.4 Promote Intelligences and Personalization -- 1.3 Examples and Case Studies -- 1.3.1 Examples of DTT in Education -- 1.3.2 Digital Twin-Based Educational Systems -- 1.4 Discussion -- 1.5 Challenges and Limitations -- 1.5.1 Technical Challenges -- 1.5.2 Pedagogical Challenges -- 1.5.3 Ethical and Privacy Concerns -- 1.5.4 Future Research Directions -- 1.6 Conclusion -- References -- Chapter 2 An Intersection Between Machine Learning, Security, and Privacy -- 2.1 Introduction -- 2.2 Machine Learning -- 2.2.1 Overview of Machine Learning -- 2.2.2 Machine Learning Stages: Training and Inference -- 2.3 Threat Model -- 2.3.1 Attack Model of Machine Learning -- 2.3.2 Trust Model -- 2.3.3 Machine Learning Capabilities in a Differential Environment -- 2.3.4 Opposite Views of Machine Learning in Security -- 2.4 Training in a Differential Environment -- 2.4.1 Achieving Integrity -- 2.5 Inferring in Adversarial Attack -- 2.5.1 Combatants in the White Box Model -- 2.5.2 Insurgencies in the Black Box Model -- 2.6 Machine Learning Methods That Are Sustainable, Private, and Accountable -- 2.6.1 Robustness of Models to Distribution Drifts -- 2.6.2 Learning and Inferring With Privacy -- 2.6.3 Fairness and Accountability in Machine Learning -- 2.7 Conclusion -- References -- Chapter 3 Decentralized, Distributed Computing for Internet of Things-Based Cloud Applications -- 3.1 Introduction to Volunteer Edge Cloud for Internet of Things Utilising Blockchain.
3.2 Significance of Volunteer Edge Cloud Concept -- 3.3 Proposed System -- 3.3.1 Smart Contract -- 3.3.2 Order Task Method -- 3.3.3 KubeEdge -- 3.4 Implementation of Volunteer Edge Control -- 3.4.1 Formation of a Cloud Environment -- 3.5 Result Analysis of Volunteer Edge Cloud -- 3.6 Introducing Blockchain-Enabled Internet of Things Systems Using the Serverless Cloud Platform -- 3.7 Introducing Serverless Cloud Platforms -- 3.7.1 IoT Systems -- 3.7.2 JointCloud -- 3.7.3 Computing Without Servers -- 3.7.4 Oracle and Blockchain Technology -- 3.8 Serverless Cloud Platform System Design -- 3.8.1 Aim and Constraints -- 3.8.2 Goals and Challenges -- 3.8.3 HCloud Connections -- 3.8.4 Data Sharing Platform -- 3.8.5 Cloud Manager -- 3.8.6 The Agent -- 3.8.7 Client Library -- 3.8.8 Witness Blockchain -- 3.9 Evaluation of HCloud -- 3.9.1 CPU Utilization -- 3.9.2 Cost Analysis -- 3.10 HCloud-Related Works -- 3.10.1 Serverless -- 3.10.2 Efficiency -- 3.11 Conclusion -- References -- Chapter 4 Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications for Next-Generation Society -- 4.1 Introduction -- 4.2 Background Work -- 4.3 Motivation -- 4.4 Existing Innovations in the Current Society -- 4.5 Expected Innovations in the Next-Generation Society -- 4.6 An Environment with Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.7 Open Issues in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.8 Research Challenges in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.9 Legal Challenges in Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications -- 4.10 Future Research Opportunities Towards Artificial Intelligence-Blockchain-Enabled-Internet of Things-Based Cloud Applications. 4.11 An Open Discussion -- 4.12 Conclusion -- References -- Chapter 5 Artificial Intelligence for Cyber Security: Current Trends and Future Challenges -- 5.1 Introduction: Security and Its Types -- 5.1.1 Human Aspects of Information Security -- 5.2 Network and Information Security for Industry 4.0 and Society 5.0 -- 5.2.1 Industry 4.0 vs Society 5.0 -- 5.2.2 Industry 4.0 to Society 5.0 -- 5.3 Internet Monitoring, Espionage, and Surveillance -- 5.4 Cyber Forensics with Artificial Intelligence and without Artificial Intelligence -- 5.5 Intrusion Detection and Prevention Systems Using Artificial Intelligence -- 5.6 Homomorphic Encryption and Cryptographic Obfuscation -- 5.7 Artificial Intelligence Security as Adversarial Machine Learning -- 5.8 Post-Quantum Cryptography -- 5.9 Security and Privacy in Online Social Networks and Other Sectors -- 5.10 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications -- 5.11 Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0 -- 5.11.1 Implementation on the Internet of Things and Protecting Data in IoT Connected Devices -- 5.12 Digital Trust and Reputation Using Artificial Intelligence -- 5.13 Human-Centric Cyber Security Solutions -- 5.14 Artificial Intelligence-Based Cyber Security Technologies and Solutions -- 5.15 Open Issues, Challenges, and New Horizons Towards Artificial Intelligence and Cyber Security -- 5.15.1 An Overview of Cyber-Security -- 5.15.2 The Role of Artificial Intelligence in Cyber Security -- 5.15.3 AI Is Continually Made Smarter -- 5.15.4 AI Never Misses a Day of Work -- 5.15.5 AI Swiftly Spots the Threats -- 5.15.6 Impact of AI on Cyber Security -- 5.15.7 AI in Cyber Security Case Study -- 5.16 Future Research with Artificial Intelligence and Cyber Security -- 5.17 Conclusion -- References. Part 2: Methods and Techniques -- Chapter 6 An Automatic Artificial Intelligence System for Malware Detection -- 6.1 Introduction -- 6.2 Malware Types -- 6.3 Structure Format of Binary Executable Files -- 6.4 Malware Analysis and Detection -- 6.5 Malware Techniques to Evade Analysis and Detection -- 6.6 Malware Detection With Applying AI -- 6.7 Open Issues and Challenges -- 6.8 Discussion and Conclusion -- References -- Chapter 7 Early Detection of Darknet Traffic in Internet of Things Applications -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Proposed Work -- 7.3.1 Drawback -- 7.4 Analysis of the Work -- 7.5 Future Work -- 7.6 Conclusion -- References -- Chapter 8 A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Implementation and Analysis -- 8.3.1 Dataset Description -- 8.3.2 Methodology -- 8.3.3 Checking for Missing Values -- 8.3.4 Exploratory Data Analysis -- 8.4 Conclusion -- 8.4.1 Future Work -- 8.4.2 Limitations -- References -- Chapter 9 Automated Secure Computing for Fraud Detection in Financial Transactions -- 9.1 Introduction -- 9.2 Historical Perspective -- 9.3 Previous Models for Fraud Detection in Financial Transactions -- 9.3.1 CatBoost -- 9.3.2 XGBoost -- 9.3.3 LightGBM -- 9.4 Proposed Model Based on Automated Secure Computing -- 9.5 Discussion -- 9.6 Conclusion -- References -- Additional Readings -- Chapter 10 Data Anonymization on Biometric Security Using Iris Recognition Technology -- 10.1 Introduction -- 10.2 Problems Faced in Facial Recognition -- 10.3 Face Recognition -- 10.4 The Important Aspects of Facial Recognition -- 10.5 Proposed Methodology -- 10.6 Results and Discussion -- 10.7 Conclusion -- References -- Chapter 11 Analysis of Data Anonymization Techniques in Biometric Authentication System. 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Existing Survey -- 11.3.1 Biometrics Technology -- 11.3.2 Palm Vein Authentication -- 11.3.3 Methods of Palm Vein Authentication -- 11.3.4 Limitations of the Existing System -- 11.4 Proposed System -- 11.4.1 Biometric System -- 11.4.2 Data Processing Technique -- 11.4.3 Data-Preserving Approach -- 11.4.3.1 Generalization -- 11.4.3.2 Suppression -- 11.4.3.3 Swapping -- 11.4.3.4 Masking -- 11.5 Implementation of AI -- 11.6 Limitations and Future Works -- 11.7 Conclusion -- References -- Part 3: Applications -- Chapter 12 Detection of Bank Fraud Using Machine Learning Techniques -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Problem Description -- 12.4 Implementation and Analysis -- 12.4.1 Workflow -- 12.4.2 Dataset -- 12.4.3 Methodology -- 12.5 Results -- 12.6 Conclusion -- 12.7 Future Works -- References -- Chapter 13 An Internet of Things-Integrated Home Automation with Smart Security System -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Methodology and Working Procedure with Diagrams -- 13.4 Research Analysis -- 13.5 Establishment of the Prototype -- 13.6 Results and Discussions -- 13.7 Conclusions -- Acknowledgment -- References -- Chapter 14 An Automated Home Security System Using Secure Message Queue Telemetry Transport Protocol -- 14.1 Introduction -- 14.2 Related Works -- 14.2.1 PIR Home Security Solutions -- 14.2.2 Solutions for MQTT Security -- 14.2.3 Solutions for Home Automation -- 14.3 Proposed Solution -- 14.3.1 Technological Decisions -- 14.3.2 Hardware Decision -- 14.3.3 Module Overview -- 14.4 Implementation -- 14.5 Results -- 14.6 Conclusion and Future Work -- References -- Chapter 15 Machine Learning-Based Solutions for Internet of Things-Based Applications -- 15.1 Introduction -- 15.2 IoT Ecosystem -- 15.2.1 IoT Devices -- 15.2.2 IoT Gateways -- 15.2.3 IoT Platforms. 15.2.4 IoT Applications. |
| Record Nr. | UNINA-9910841889103321 |
|
Tyagi Amit Kumar
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent Interactive Multimedia Systems for e-Healthcare Applications
| Intelligent Interactive Multimedia Systems for e-Healthcare Applications |
| Autore | Tyagi Amit Kumar |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2022 |
| Descrizione fisica | 1 online resource (451 pages) |
| Altri autori (Persone) |
AbrahamAjith
KaklauskasArturas |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9789811665424
9789811665417 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910510547803321 |
|
Tyagi Amit Kumar
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent transportation systems : theory and practice / / Amit Kumar Tyagi and Niladhuri Sreenath
| Intelligent transportation systems : theory and practice / / Amit Kumar Tyagi and Niladhuri Sreenath |
| Autore | Tyagi Amit Kumar |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (407 pages) |
| Disciplina | 929.374 |
| Collana | Disruptive Technologies and Digital Transformations for Society 5. 0 |
| Soggetto topico | Engineering |
| ISBN | 981-19-7622-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- About the Authors -- 1 Introduction to Intelligent Transportation System -- 1.1 Introduction -- 1.2 Background/Literature Review -- 1.3 Intelligent Transportation Technologies, Evolution and History -- 1.4 Future of Intelligent Transportation Systems in Smart Cities -- 1.4.1 Analyzing Public Attitudes and Perceptions from Cyber Sources -- 1.4.2 CSP Traffic Network Modeling -- 1.5 Cooperative System Over the Road Network -- 1.6 Smart Transportation System -- 1.7 Current Trends in Intelligent Transportation Systems (ITSs) -- 1.8 Intelligent Cities and Related Artificial Intelligence Techniques Reviews -- 1.9 Critical Issues and Challenges in Intelligent Transportation System -- 1.10 Current Issues and Challenges in Wired, Wireless, and Vehicular Technology -- 1.11 Conclusion -- References -- 2 Intelligent Transportation System: Past, Present, and Future -- 2.1 Introduction -- 2.2 Background/Literature Review -- 2.3 History of Intelligent Transportation System -- 2.3.1 Early History -- 2.3.2 The 1980s -- 2.3.3 The 1990s -- 2.3.4 The 2000s -- 2.3.5 The 2010s -- 2.3.6 2015-2020 -- 2.3.7 2021-The Future -- 2.4 Existing Intelligent Transportation System (ITS) -- 2.4.1 Traffic Management Center (TMC) -- 2.4.2 Dynamic Message Sign (DMS) -- 2.4.3 Lane Control and Variable Speed Limit Signs (LCS and VSL) -- 2.4.4 Overheight Vehicle Detection Systems (OVDS) -- 2.5 Fundamental Problems in Indian Transportation System -- 2.5.1 Faculty Planning of Transport System -- 2.5.2 Lack of Rail Road Coordination -- 2.5.3 Worn Out and Obsolete Assets -- 2.5.4 Improved Technology -- 2.6 Solutions for the Problems in Indian Transportation System -- 2.7 Future of Intelligent Transportation System -- 2.7.1 Traffic Management Center (TMC) -- 2.7.2 Automatic Incident Detection (AID) Systems -- 2.7.3 Impact on Road Safety.
2.8 A Connected Environment for Smart Mobility -- 2.9 Intelligent Transportation System Applications -- 2.9.1 Road Safety Application -- 2.9.2 Traffic Management Applications -- 2.9.3 Autonomous Driving Applications -- 2.9.4 Infotainment and Comfort Applications -- 2.9.5 Emergency Vehicle Notification Systems -- 2.10 Intelligent Transportation System Early Findings -- 2.10.1 Traffic Signal Control -- 2.10.2 Traffic Management and Surveillance -- 2.10.3 Incident Management -- 2.10.4 Electronic Toll Collection (ETC) -- 2.10.5 Transit Management -- 2.10.6 Commercial Vehicle Operations (CVO) -- 2.10.7 Vehicle Control Technologies -- 2.10.8 Human Factors -- 2.11 Conclusion -- References -- 3 Applications of Vehicles and Its Related Technology in Previous and the Next Decade -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Applications of 4G-Based Versus 5G-Based Vehicles -- 3.4 Next-Generation (NG)-Based Future Vehicles -- 3.4.1 Next-Generation Vehicle Concepts -- 3.4.2 Next Generation-Energy Management Technologies -- 3.4.3 Next Generation-Future Powertrain Technologies -- 3.4.4 Next Generation-Mechatronic Chassis Technologies -- 3.4.5 Next Generation-Vehicle Intelligence -- 3.4.6 Next Generation-Body Development and Technologies -- 3.5 Hyperloop Transportation System -- 3.5.1 Basic Principle, Construction, and Working of Hyperloop Transportation System -- 3.6 Electric Vehicles -- 3.7 Hybrid Vehicles -- 3.7.1 Cost -- 3.7.2 Fuel Economy -- 3.7.3 Infrastructure Availability -- 3.7.4 Emissions -- 3.7.5 Batteries -- 3.8 Connected Vehicles -- 3.8.1 Why Connected Vehicle Technologies are Needed -- 3.8.2 How Connected Vehicles Work -- 3.8.3 How Connected Vehicles Will Improve Safety -- 3.8.4 How Connected Vehicles Will Keep People Moving -- 3.9 Vehicle Data Collection, Emerging Vehicle, and Intelligent Transport Technology -- 3.9.1 Vehicle Data Collection. 3.9.2 Emergency Vehicle Notification System -- 3.9.3 Intelligent Transportation Technology -- 3.9.4 Video Vehicle Detection -- 3.9.5 Audio Detection -- 3.9.6 Sensing -- 3.9.7 Inductive Loop Detection -- 3.10 Other Vehicles -- 3.10.1 Self-driving Cars -- 3.10.2 Autonomous Motorbikes -- 3.10.3 Applications of Artificial Intelligence in Transport -- 3.10.4 Autonomous Vehicles -- 3.11 Applications of Vehicles Connected Through Intelligent Transportation System -- 3.12 Conclusion -- References -- 4 Autonomous Vehicles and Intelligent Transportation Systems-A Framework of Intelligent Vehicles -- 4.1 Introduction -- 4.1.1 Six Levels of Autonomous Vehicles -- 4.1.2 Autonomous Vehicle Advantages -- 4.2 Related Work -- 4.3 Autonomous Intelligent Vehicles -- 4.3.1 Objectives of Autonomous Vehicles -- 4.3.2 Autonomous Intelligent Vehicles (AIV) Mobile Robots -- 4.4 Autonomous Vehicles -- 4.4.1 Early Advances in Autonomous Vehicles -- 4.4.2 Obstacles to Adoption of Autonomous Cars Technology -- 4.5 A Framework of Intelligent Vehicles/ITS -- 4.5.1 Region Correspondence of Driving Brain and Human Brain Functions -- 4.5.2 Framework Based on Driving Brain -- 4.6 Issues and Challenges in Autonomous Intelligent Vehicles -- 4.6.1 Improved Safety -- 4.6.2 Privacy Protection Towards Autonomous Intelligent Vehicles -- 4.6.3 Business Opportunities and Increasing Revenue -- 4.6.4 Ease of Use and Convenience -- 4.6.5 Improving Traffic Conditions -- 4.6.6 Autonomous Parking -- 4.6.7 Byer-Driven Methodology -- 4.6.8 Others Services -- 4.7 Research Opportunities in Autonomous Intelligent Vehicles -- 4.7.1 Implementation of Computer Vision in Autonomous Intelligent Vehicles -- 4.7.2 Implementation of Machine and Deep Learning Techniques -- 4.7.3 Sensors, Communications, and Control in Autonomous Intelligent Vehicles -- 4.7.4 Decision-Making for Autonomous Intelligent Vehicles. 4.7.5 Real-World Tests of Autonomous Intelligent Vehicles -- 4.8 Problems with IPv4, IPv6, and Future with IPv9 of Vehicles -- 4.8.1 Problems with IPv4 -- 4.8.2 Problems with IPv6 -- 4.8.3 Future with IPv9 -- 4.9 A Survey on Autonomous Vehicles in Highway Scenarios -- 4.10 Conclusion -- References -- 5 Vehicle Localization and Navigation -- 5.1 Introduction -- 5.1.1 Sensors and Instrumentation -- 5.1.2 Localization System Designs for Autonomous Driving -- 5.1.3 Relative Localization -- 5.2 Related Work -- 5.3 Message Passing in the Internet of Things (IoT)-Based Cloud Vehicles -- 5.3.1 Perception Layer -- 5.3.2 Network Layer -- 5.3.3 Application Layer -- 5.4 Vehicle Localization and Navigation -- 5.4.1 Dead Reckoning and Inertial Navigation -- 5.4.2 Acoustic Navigation -- 5.4.3 Geophysical Navigation -- 5.4.4 Optical Navigation -- 5.4.5 Simultaneous Location and Mapping (SLAM) -- 5.4.6 Sensor Fusion -- 5.5 Road Detection and Tracking -- 5.6 Integrated Global Positioning System (GPS)-Enabled Vehicles -- 5.7 Multiple Sensor-Based Multiple-object Tracking -- 5.8 Vehicle Navigation and Tracking in the Internet of Things (IoT)-Based Cloud Vehicles -- 5.9 Issues and Challenges in Vehicle Localization and Navigation -- 5.10 Conclusion and Future of Localization in Autonomous Driving -- References -- 6 Environmental Sustainability for Intelligent Transportation System -- 6.1 Introduction -- 6.1.1 Mobile Infrastructure -- 6.1.2 Static Infrastructure -- 6.2 Background/Literature Review -- 6.3 Mobile Elements of Intelligent Transportation System/Intelligent Vehicles -- 6.3.1 Vehicle Operation -- 6.3.2 Navigation -- 6.3.3 Driver -- 6.4 Types of Intelligent Transport Systems (ITS) -- 6.5 Environment Sustainability in Intelligent Transport Systems -- 6.6 Techniques for Environmental Sustainability in Intelligent Transport Systems. 6.7 Challenges in Environmental Sustainability in Intelligent Transportation System -- 6.7.1 Reducing Costs -- 6.7.2 Responding to Stakeholder Influences -- 6.7.3 Achieving Competitive Advantage -- 6.7.4 Strategic Position -- 6.7.5 Environmental Impact Assessment -- 6.7.6 Priorities and Policies -- 6.7.7 Budgets -- 6.8 Environmental Management Systems for Intelligent Transportation System -- 6.8.1 Information and Data Capture -- 6.8.2 Life-Cycle Assessment (LCA) -- 6.8.3 Carbon Footprinting -- 6.8.4 Water Footprinting -- 6.9 Future Development of Intelligent Transportation System -- 6.9.1 Wireless Communication and Data Aggregation -- 6.9.2 Low-Tech Solutions -- 6.10 Conclusion -- References -- 7 Fog and Edge Computing in Navigation of Intelligent Transportation System -- 7.1 Introduction-Fog and Edge Computing -- 7.2 Related Work -- 7.3 Routing in the Internet of Things-Based Cloud Vehicles -- 7.4 Tracking and Navigating Intelligent Vehicles -- 7.4.1 Global Positioning System (GPS) -- 7.4.2 Radio Frequency Identification (RFID) -- 7.5 Security-Based Architecture Using Cloud Computing -- 7.5.1 Importance of Cloud Security Architecture -- 7.5.2 Cloud Security Core Capabilities -- 7.5.3 IaaS Cloud Computing Security Architecture -- 7.5.4 PaaS Cloud Computing Security Architecture -- 7.5.5 SaaS Cloud Computing Security Architecture -- 7.6 Secure, Privacy Preserved Architecture for Future Vehicles Using Fog Computing -- 7.6.1 Network Model -- 7.6.2 Security and Privacy-Preserving Approach -- 7.7 Secure, Privacy Preserved Architecture for Future Vehicles Using Edge Computing -- 7.7.1 Cloud Layer -- 7.7.2 Edge Cloud Layer -- 7.7.3 Smart Vehicular Layer -- 7.7.4 SDN-Supported Components -- 7.8 Recent Trends in Intelligent Transportation System -- 7.9 Future Research Opportunities for Edge Computing-Based Vehicles -- 7.9.1 Computation Offloading. 7.9.2 Edge Computing and 5G. |
| Record Nr. | UNINA-9910632471703321 |
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Tyagi Amit Kumar
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| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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