Green Internet of Things (IoT): Energy Efficiency Perspective, 1st ed. 2021 Wireless Networks Series

Energy efficiency issues for green internet of things (IoT) are investigated in this book, from the perspectives of device-to-device (D2D) communications, machine-to-machine (M2M) communications, and air-ground networks. Specifically, critical green IoT techniques from D2D communications in the cellular network to M2M communications in industrial IoT (IIoT), (from single physical-layer optimization to cross-layer optimization, and from single-layer ground networks to stereoscopic air-ground networks) are discussed in both theoretical problem formulation and simulation result analysis in this book.

Internet of Things (IoT) offers a platform that enables sensors and devices to connect seamlessly in an intelligent environment, thus providing intelligence services including monitoring systems, industrial automation, and ultimately smart cities. However, the huge potentials of IoT are constrained by high energy consumption, limited battery capacity, and the slow progress of battery technology. The high energy consumption of IoT device causes communication interruption, information loss, and short network lifetime. Moreover, once deployed, the batteries inside IoT devices cannot be replaced in time. Therefore, energy efficient resource allocation is urgent to be investigated to improve the energy efficiency of IoT, facilitate green IoT, and extend the network lifetime.

Preface

1 Introduction

2 Energy-Efficient Resource Allocationin for D2D Enabled Cellular

Networks

2.1 Energy-Efficient Resource Allocation Problem

2.1.1 System Model  

2.1.2 Problem Formulation

2.2 Energy-Efficient Stable Matching for D2D Communications

2.2.1 Preference Establishment

2.2.2 Energy-Efficient Stable Matching

2.3 Performance Results and Discussions

3 Energy Harvesting Enabled Energy Efficient Cognitive

Machine-to-Machine Communications    

3.1 Framework of Energy-Efficient Resource Allocation for

EH-Based CM2M

3.1.1 Data Transmission Model   

3.1.2 Energy Harvesting and Energy Consumption Model 

3.1.3 Energy Efficient Resource Allocation Problem

Formulation

3.2 Energy Efficient Joint Channel Selection, Peer Discovery, Power

Control and Time Allocation for EH-CM2M Communications

3.2.1 Matching Based Problem Transformation

3.2.2 First-Stage Joint Power Control and Time Allocation

Optimization

3.2.3 Preference List Construction    

1

2 Contents

3.2.4 Second-Stage Joint Channel Selection and Peer

Discovery Based on Matching    

3.3 Performance Results and Discussions   

3.3.1 Improve Average Energy Efficiency of M2M-TXs

3.3.2 Improve Average Energy Efficiency of M2M Pairs.   

Energy Management in Power Grids

4.1 Framework of Energy-Efficient SD-M2M for Smart Energy

Management

4.1.1 Architecture Overview   

4.1.2 The Benefits of the SD-M2M    

4.2 Software-Defined M2M Communication for Smart Energy

Management Applications

4.3 Case Study and Analysis    

4.3.1 Improve Spectral Efficiency       

4.3.2 Reduce the Total Energy Generation Cost.   

5 Energy-Efficient M2M Communications in for Industrial

Automation  

5.1 Framework of Energy-Efficient M2M Communications

5.2 Contract-Based Incentive Mechanism Design for Access Control

5.2.1 MTC Type Modeling

5.2.2 Contract Formulation

5.2.3 Contract Optimization

5.3 Resource Allocation Base on Lyapunov Optimization and

Matching Theory

5.3.2 Problem Formulation and Transformation

5.3.3 Joint Rate Control, Power Allocation and Channel

Selection  

5.4 Performance Results and Discussions

5.4.1 Feasibility and Efficiency of Access Control Mechanism

5.4.2 Feasibility and Efficiency of Resource Allocation Scheme   

6 Energy-Efficient Context-Aware Resource Allocation for

Edge-Computing-Empowered Industrial IoT

6.1 Framework of Energy-Efficient Edge-Computing-Empowered IIoT

6.1.1 System Model

6.2 Learning-Based Context-Aware Channel Selection for the

Single-MTD Scenario

6.2.1 Lyapunov Based Problem Transformation

Contents

6.2.2 SEB-GSI Algorithm for the Ideal Case    

6.2.3 SEB-UCB Algorithm for the Nonideal Case

6.3 Learing–Based Context-Aware Channel Selcetion for the

Multi-MTD Scenario  

6.3.1 SEB-MGSI Algorithm for the Ideal Case      

6.3.2 SEBC-MUCB Algorithm for the Nonideal Case

6.4 Performance Results and Discussions   

6.4.1 Performance under the Single-MTD Scenario    

6.4.2 Performance under the Multi-MTD Scenario    

7 Licensed and Unlicensed Spectrum Management for Energy Efficient Cognitive M2M  . .   

7.1 Framework of CM2M Network     

7.1.1 System Model

7.1.2 Problem Formulation    

7.2 Context-Aware Learning-Based Channel Selection for CM2  

7.2.1 Problem Transformation        

7.2.2 C2

-GSI for Channel Selection with GSI

7.2.3 C2

-EXP3 for Channel Selection with Local Information .  

7.3 Performance Results and Discussions

8 Energy-Efficient Task Assignment and Route Planning for UAV

8.1 Framework of UAV-Aided MCS Systems

8.1.1 The Utility Function of the MCS Carrier     

8.1.2 The Utility Function of UAVs  

8.1.3 UAV-Aided MCS Systems Problem Formulation  

8.2 Energy-Efficient Joint Task Assignment and Route Planning

8.2.1 Problem Transformation        

8.2.2 The Route Planning

8.2.3 Preference List Construction  

8.2.4 GS Based Second-Stage Task Assignment  

8.3 Performance Results and Discussions  

9 Energy-Efficient and Secure Resource Allocation for MultipleAntenna NOMA with Wireless Power Transfer

9.1 Framework of Energy-Efficient and Secure Resource Allocation

for Multiple-Antenna NOMA with Wireless Power Transfer  

9.1.2 Problem Formulation    

9.2 The Energy-Efficient and Secure Resource Allocation Scheme  

9.2.1 Transformation of the Optimization Problem

9.2.2 Proposed Algorithmic Solution

9.3 Performance Evaluation

9.3.1 Improve Secure Data Rate    

9.3.2 Improve the Energy Efficiency    

10 Dynamic Computation Offloading Scheme for Fog Computing

System with Energy Harvesting Devices    

10.1 Framework of Socially Aware Dynamic Computation Offloading

for Fog Computing System with EH Devices     

10.1.1 System Movel

10.1.2 Problem Formulation    

10.2 Proposed Solution  

10.3 Performance Evaluation  .  

11 Energy-Efficient Resource Allocation for Wireless Powered Massive

MIMO System with Imperfect CSI        

11.1 Framework of Resource Allocation for Wireless Powered

Massive MIMO System with Imperfect CSI    . . . . .  

11.1.1 System Model  .  

11.1.2 Throughput Analysis   .  

11.1.3 Problem Formulation    

11.2 Proposed Antenna Selection and Resource Allocation Scheme . . .  

11.2.1 Proposed Antenna Selection Algorithm    .  

11.2.2 Power and Time Allocation Schemes    . . .  

11.3 Performance Evaluation   

12 Summary      

References  .    . . .  

Provides a comprehensive reference on the green IoT from theory to implementation

Introduces an in-depth review of state-of-the-art key technologies and the related applications in green IoT

Covers practical instructions for future theoretical research in architectures and frameworks of the green IoT

Presents a comprehensive summary of the challenges and open issues on the current technologies, architectures