Deep Learning for Internet of Things Infrastructure
- Length: 266 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-09-30
- ISBN-10: 0367457334
- ISBN-13: 9780367457334
- Sales Rank: #0 (See Top 100 Books)
This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users.
FEATURES
- Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures
- Addresses emerging trends and issues on IoT systems and services across various application domains
- Investigates the challenges posed by the implementation of deep learning on IoT networking models and services
- Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT
- Explores new functions and technologies to provide adaptive services and intelligent applications for different end users
Uttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia.
Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom.
Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.
Cover Half Title Title Page Copyright Page Dedication Table of Contents Acknowledgments Editors Contributors Chapter 1 Data Caching at Fog Nodes under IoT Networks: Review of Machine Learning Approaches 1.1 Introduction 1.1.1 Importance of Caching at Fog Nodes 1.2 Applications of Data Caching at Fog Nodes for IoT Devices 1.3 Life Cycle of Fog Data 1.4 Machine Learning for Data Caching and Replacement 1.5 Future Research Directions 1.6 Conclusion References Chapter 2 ECC-Based Privacy-Preserving Mechanisms Using Deep Learning for Industrial IoT: A State-of-the-Art Approaches 2.1 Introduction of Industrial IoT 2.2 Background and Related Works 2.2.1 Evolution of Industrial IoT 2.2.2 Literature Study on Authentication, Privacy, and Security in IIoT 2.3 Objectives and Mathematical Background 2.3.1 Objectives of the Proposed Work 2.3.2 Symbols Used and Its Description 2.3.3 Groundworks 2.4 Security Issues in Industrial IoT 2.5 Industrial Internet of Things System Architecture 2.5.1 Three Tier Architecture of IIoT 2.5.2 Security Issues 2.5.3 Security Framework in Industrial IoT 2.6 Proposed Scheme 2.6.1 ECC-Based Privacy-Preserving Deep Learning via Re-encryption (ECCRE) 2.6.2 ECC-Based Privacy-Preserving Deep Learning (ECCAL) 2.7 Security Analysis 2.7.1 Security Analysis of ECC Based Re-encryption 2.7.2 Security Analysis of ECC Base Encryption 2.8 Experimentation and Results 2.9 Conclusion References Chapter 3 Contemporary Developments and Technologies in Deep Learning–Based IoT 3.1 Introduction 3.2 DL Architecture 3.2.1 Neural Network 3.2.2 Multilayer Perceptron and Convolutional Neural Networks 3.2.3 Learning Methods 3.3 Internet of Things (IoT) 3.3.1 At the Intersection of DL and IoT 3.3.2 Recent Trends in DL-Based IoT 3.4 Popular Frameworks and Models 3.4.1 Models 3.4.2 Frameworks 3.4.3 Applications 3.5 Conclusion References Chapter 4 Deep Learning–Assisted Vehicle Counting for Intersection and Traffic Management in Smart Cities 4.1 Introduction 4.2 System Model 4.3 The Proposed Approach 4.3.1 Centroid Tracking Using Euclidean Distance 4.3.2 The Virtual Line Double Crossing Algorithm (VLDCA) 4.4 Performance Evaluation 4.5 Conclusion References Chapter 5 Toward Rapid Development and Deployment of Machine Learning Pipelines across Cloud-Edge 5.1 Introduction 5.1.1 Emerging Trends 5.1.2 Challenges and State-of-the-Art Solutions 5.1.3 Overview of Technical Contributions 5.1.4 Organization of the Chapter 5.2 Related Work 5.3 Problem Formulation 5.3.1 Motivating Case Study 5.3.2 ML Model Development 5.3.2.1 Challenges 5.3.2.2 Requirements 5.3.3 ML Pipeline Deployment 5.3.3.1 Challenges 5.3.3.2 Requirements 5.3.4 Infrastructure for Resource Management 5.3.4.1 Challenges 5.3.4.2 Requirements 5.4 Design and Implementation of Stratum 5.4.1 Addressing Requirement 1: Rapid AI/ML Model Prototyping Kit 5.4.1.1 Overview of the ML Model Development 5.4.1.2 Metamodel for ML Algorithms 5.4.1.3 Generative Capabilities 5.4.2 Addressing Requirement 2: Automated Deployment of Application Components on Heterogeneous Resources 5.4.2.1 Metamodel for Data Ingestion Frameworks 5.4.2.2 Metamodel for Data Analytics Applications 5.4.2.3 Metamodel for Heterogeneous Resources 5.4.2.4 Metamodel for Data Storage Services 5.4.3 Addressing Requirement 3: Framework for Performance Monitoring and Intelligent Resource Management 5.4.3.1 Performance Monitoring 5.4.3.2 Resource Management 5.4.4 Support for Collaboration and Versioning 5.4.5 Discussion and Current Limitations 5.5 Evaluation of Stratum 5.5.1 Evaluating the Rapid Model Development Framework 5.5.2 Evaluation of Rapid Application Prototyping Framework 5.5.3 Performance Monitoring on Heterogeneous Hardware 5.5.4 Resource Management 5.6 Conclusion Acknowledgments References Chapter 6 Category Identification Technique by a Semantic Feature Generation Algorithm 6.1 Introduction 6.2 Literature Review 6.3 Proposed Approach 6.3.1 Image Feature Generation 6.3.2 The MPEG-7 Visual Descriptors 6.3.3 Color Descriptors 6.3.3.1 Scalable Color Descriptor (SCD) 6.3.3.2 Color Layout Descriptor (CLD) 6.3.3.3 Color Structure Descriptor (CSD) 6.3.4 Texture Descriptors 6.3.5 Shape Descriptors 6.4 Understanding Machine Learning 6.4.1 Supervised Learning Model 6.4.1.1 Classification 6.4.1.2 Regression 6.4.2 Unsupervised Learning Model 6.4.3 Semi-Supervised Learning Model 6.4.4 Reinforcement Learning Model 6.5 Support Vector Machine (SVM) 6.5.1 Tuning Parameters: Kernel, Regularization, Gamma, and Margin 6.6 Experimental Results 6.7 Conclusion and Future Work References Chapter 7 Role of Deep Learning Algorithms in Securing Internet of Things Applications 7.1 Introduction 7.2 Literature Survey of Security Threats in IoT 7.3 ML Algorithms for Attack Detection and Mitigation 7.3.1 Linear Regression 7.3.2 Principal Component Analysis 7.3.3 Q-Learning 7.3.4 K-Means Clustering 7.4 DL Algorithms and IoT Devices 7.4.1 Multilayer Perceptron Neural Network (MLPNN) 7.4.2 Convolutional Neural Network (CNN) 7.4.3 Restricted Boltzmann Machine (RBM) 7.4.4 Deep Belief Network (DBN) 7.5 Requirements of Secured IoT System 7.6 Ideology 7.7 Summary References Chapter 8 Deep Learning and IoT in Ophthalmology 8.1 Introduction 8.1.1 Chapter Roadmap 8.2 Internet of Things 8.2.1 Applications in Healthcare 8.2.2 Role of Cloud Computing 8.3 Deep Learning 8.3.1 Applications in Healthcare 8.4 DL- and IoT-Based Infrastructure 8.4.1 Proposed Architecture 8.4.2 Components of the Architecture 8.4.3 Types of Ocular Disease Diagnoses 8.4.4 Business Operations Model 8.5 Privacy, Security, and Ethical Considerations 8.6 Research Challenges 8.7 Conclusions and Future Directions References Chapter 9 Deep Learning in IoT-Based Healthcare Applications 9.1 Introduction 9.2 Healthcare and Internet of Things 9.2.1 IoT Medical Devices 9.2.2 IoT-Aware Healthcare System 9.2.3 Emergence of IoMT in Healthcare 9.3 Prospects of Deep Learning 9.4 Challenges of Healthcare IoT Analytics 9.5 Deep Learning Techniques in Healthcare Applications 9.5.1 Health Monitoring 9.5.2 Human Activity Recognition 9.5.3 Disease Analysis 9.5.4 Security 9.6 Conclusion and Future Research References Chapter 10 Authentication and Access Control for IoT Devices and Its Applications 10.1 Introduction 10.2 Authentication in IoT 10.2.1 Authentication of IoT Devices 10.2.1.1 Security Issues in IoT Devices 10.2.1.2 Authentication Schemes in IoT 10.2.2 Authentication in IoT-Based Applications 10.2.2.1 Authentication in IoT-Based Smart Cities 10.2.2.2 Authentication in IoT-Based Smart Healthcare 10.2.2.3 Authentication for IIoT 10.2.3 Deep Learning–Based Authentication Techniques 10.2.3.1 Neural Networks for ECG-Based Authentication 10.2.3.2 Convolutional Neural Networks – Deep Face Biometric Authentication 10.3 Access Control in IoT 10.3.1 Blockchain-Based Access Control in IoT 10.3.2 Access Control for IoT Applications 10.3.2.1 Access Control for IoT-Based Healthcare Applications 10.3.2.2 Access Control for IoT-Based Smart Home 10.3.2.3 Access Control for IoT-Based Smart City 10.3.2.4 Access Control for IoT-Based Vehicle Tracking 10.4 Conclusion References Chapter 11 Deep Neural Network–Based Security Model for IoT Device Network 11.1 Introduction 11.2 Literature Review 11.3 Proposed Method 11.4 Dataset Description 11.4.1 IoT-Device-Network-Logs Dataset 11.4.2 OPCUA Dataset 11.4.2.1 Data Preprocessing 11.5 Experimental Configuration and Result Analysis 11.5.1 Performance Evaluation 11.5.2 Parameter Setting 11.5.3 Result Analysis 11.6 Conclusion References Index
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