Green Internet of Things (IoT): Energy Efficiency Perspective

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 Energ