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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.
4 Software Defined Machine-to-Machine Communication for SmartEnergy 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
5.3.1 Dynamic Queue Model5.3.2 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
6.1.2 Problem Formulation6.2 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
9.1.1 System Model9.1.2 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 . . . .
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