Connectivity and Edge Computing in IoT: Customized Designs and AI-based Solutions , 1st ed. 2021 Wireless Networks Series

This book covers connectivity and edge computing solutions for representative Internet of Things (IoT) use cases, including industrial IoT, rural IoT, Internet of Vehicles (IoV), and mobile virtual reality (VR). Based on their unique characteristics and requirements, customized solutions are designed with targets such as supporting massive connections or seamless mobility and achieving low latency or high energy efficiency. Meanwhile, the book highlights the role of artificial intelligence (AI) in future IoT networks and showcases AI-based connectivity and edge computing solutions.

The solutions presented in this book serve the overall purpose of facilitating an increasingly connected and intelligent world. The potential benefits of the solutions include increased productivity in factories, improved connectivity in rural areas, enhanced safety for vehicles, and enriched entertainment experiences for mobile users. Featuring state-of-the-art research in the IoT field, this book can help answer the question of how to connect billions of diverse devices and enable seamless data collection and processing in future IoT. The content also provides insights regarding the significance of customizing use case-specific solutions as well as approaches of using various AI methods to empower IoT. 

This book targets researchers and graduate students working in the areas of electrical engineering, computing engineering, and computer science as a secondary textbook or reference. Professionals in industry who work in the field of IoT will also find this book useful.

Introduction

1.1 The Era of Internet of Things

1.2 Connectivity in IoT

1.3 Edge Computing in IoT

1.4 AI in IoT

1.5 Scope and Organization of This Book

References

2 Industrial Internet of Things: Smart Factory

2.1 Industrial IoT Networks

2.2 Connectivity Requirements of Smart Factory

2.2.1 Application-Specific Requirements

2.2.3 Potential Non-Link-Layer Solutions

2.2.4 Link-Layer Solutions: Recent Research Efforts

2.3 Protocol Design for Smart Factory

2.3.1 Networking Scenario

2.3.2 Mini-Slot based Carrier Sensing (MsCS)

2.3.3 Synchronization Sensing (SyncCS)

2.3.4 Di_erentiated Assignment Cycles

2.3.5 Superimposed Mini-slot Assignment (SMsA)

2.3.6 Downlink Control

2.4 Performance Analysis

2.4.1 Delay Performance with No Buaer

2.4.2 Delay Performance with Buaer

2.4.3 Slot Idle Probability

2.4.4 Impact of SyncCS

2.4.5 Impact of SMsA

2.5 Scheduling and AI-Assisted Protocol Parameter Selection

2.5.1 Background

2.5.2 The Considered Scheduling Problem

ix

x Contents

2.5.3 Device Assignment

2.5.4 AI-Assisted Protocol Parameter Selection

2.6 Numerical Results

2.6.1 Mini-Slot Delay with MsCS, SyncCS, and SMsA

2.6.2 Performance of the Device Assignment Algorithms

2.6.3 DNN-Assisted Scheduling

2.7 Summary

References

3 UAV-Assisted Edge Computing: Rural IoT Applications

3.1 Background on UAV-Assisted Edge Computing

IoT

3.2.1 Network Constraints

3.2.2 State-of-the-Art Solutions

3.3.1 Network Model

3.3.2 Communication Model

3.3.3 Computing Model

3.3.5 Problem Formulation

3.3.6 Optimization Algorithm for UAV-Assisted Edge

Computing

Distribution Estimation

3.4 Numerical Results

3.5 Summary

4 Collaborative Computing for Internet of Vehicles

4.1 Background on Internet of Vehicles

4.2 Connectivity Challenges for MEC

4.2.1 Server Selection for Computing Offoading

4.2.2 Service Migration

4.2.3 Cooperative Computing

4.3 Computing Task Partition and Scheduling for Edge Computing

4.3.1 Collaborative Edge Computing Framework

4.3.2 Service Delay

4.3.3 Service Failure Penalty

4.3.4 Problem Formulation

4.3.5 Task Partition and Scheduling

4.4 AI-Assisted Collaborative Computing Approach

4.5 Performance Evaluation

4.5.1 Task Partition and Scheduling Algorithm

4.5.2 AI-based Collaborative Computing Approach

Contents xi

4.6 Summary

References

5 Edge-assisted Mobile VR

5.1 Background on Mobile Virtual Reality

5.2 Caching and Computing Requirements of Mobile VR

5.2.2 Edge Caching for Mobile VR

5.2.3 Edge Computing for Mobile VR

5.3 Mobile VR Video Caching and Delivery Model

5.3.1 Network Model

5.3.2 Content Distribution Model

5.3.3 Content Popularity Model

5.3.4 Research Objective

5.4 Content Caching for Mobile VR

5.4.1 Adaptive Field-of-View Video Chunks

5.4.2 Content Placement on an Edge Cache

5.4.3 Placement Scheme for Video Chunks in a VS

5.4.4 Placement Scheme for Video Chunks of Multiple VSs

5.4.5 Numerical Results

5.5 AI-assisted Mobile VR Video Delivery

5.5.1 Content Distribution

5.5.2 Intelligent Content Distribution Framework

5.5.3 WI-based Delivery Scheduling

5.5.4 Reinforcement Learning Assisted Content Distribution

5.5.6 Numerical Results

5.6 Summary

References

6.1 Summary of the Research

6.2 Discussion of Future Directions

Index