Connectivity and Edge Computing in Iot: Customized Designs and Ai-Based Solutions

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

2.2.2 Related Standards

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

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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

3.2 Connectivity Requirements of UAV-assisted MEC for Rural

IoT

3.2.1 Network Constraints

3.2.2 State-of-the-Art Solutions

3.3 Multi-Resource Allocation for UAV-Assisted Edge Computing

3.3.1 Network Model

3.3.2 Communication Model

3.3.3 Computing Model

3.3.4 Energy Consumption Model

3.3.5 Problem Formulation

3.3.6 Optimization Algorithm for UAV-Assisted Edge

Computing

3.3.7 Proactive Trajectory Design based on Spatial

Distribution Estimation

3.4 Numerical Results

3.5 Summary

References

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

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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

5.2.1 Mobile VR Video Formats

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 Framewo