Green Internet of Things (Iot): Energy Efficiency Perspective

ISBN-13: 9783030640569 / Angielski / Miękka / 2022 / 200 str.

Zhenyu Zhou; Zheng Chang; Haijun Liao

Spis treści

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 Smart

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

5.3.1 Dynamic Queue Model

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

6.1.2 Problem Formulation

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  

9.1.1 System Model

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  .    . . .  

O autorze

Zhenyu Zhou received his M.E. and Ph.D degree from Waseda University, Tokyo, Japan in 2008 and 2011 respectively. From April 2012 to March 2013, he was the chief researcher at Department of Technology, KDDI, Tokyo, Japan. From September 2012 to April 2019, he was an Associate Professor at School of Electrical and Electronic Engineering, North China Electric Power University, China. Since April 2019, he has been a full professor at the same university.  He served as an Associate Editor for IEEE Internet of Things Journal, IEEE Access, EURASIP Journal on Wireless Communications and Networking and a Guest Editor for IEEE Communications Magazine, IEEE Transactions on Industrial Informatics, and Transactions on Emerging Telecommunications Technologies. He served as the Tutorial Chair for the 2020 IEEE 91st Vehicular Technology Conference (VTC), Workshop Co-Chair for the 2020 IEEE/CIC International Conference on Communications in China (ICCC 2020), Workshop Co-Chair for IEEE Globecom 2018 International Workshop on Ultra-high Speed, Low Latency and Massive Connectivity Communications for 5G/B5G, and IEEE ISADS 2015 International Workshop on Smart Grid Communications and Networking. He also served as the TPC Member for numerous international conferences such as IEEE Globecom, IEEE ICC, IEEE CCNC, IEEE APCC, IEEE VTC, IEEE PIMRC, etc. He is a voting member of IEEE Standard Association P1932.1 Working Group.  He was the recipient of the Ministry of Education of the People’s Republic of China Science and Technology Second Prize, the IEEE Communications Society Asia-Pacific Board Outstanding Young Researcher 2019, 2018 Industry-University-Research Institute Collaboration Innovation Award of China, the “Beijing Outstanding Young Talent” Award in 2016, the IET Premium Award in 2017, the IEEE ComSoc Green Communications and Computing Technical Committee 2017 Best Paper Award, the IEEE Globecom 2018 Best Paper Award, the IEEE ComSoc Green Communications and Computing Technical Committee 2018 Best Paper Award, IEEE ComSoc Communications Systems Integration and Modeling (CSIM) Technical Committee 2019 Best Paper Award, and IEEE International Wireless Communications and Mobile Computing Conference (IWCMC) 2019 Best Paper Award, IEEE Vehicular Technology Society "Young Researcher Encouragement Award" in 2009. His research interests include green communications, vehicular communications, and smart grid communications.

Zheng Chang received the B.Eng. degree from Jilin University, Changchun, China in 2007, M.Sc. (Tech.) degree from Helsinki University of Technology (Now Aalto University), Espoo, Finland in 2009 and Ph.D degree from the University of Jyväskylä, Jyväskylä, Finland in 2013. Since 2008, he has held various research positions at Helsinki University of Technology, University of Jyväskylä and Magister Solutions Ltd in Finland. During June to August in 2013, he was a visiting researcher at Tsinghua University, China, and during April to May in 2015, he was a visiting researcher at University of Houston, TX. He has been awarded by the Ulla Tuominen Foundation, the Nokia Foundation, and the Riitta and Jorma J. Takanen Foundation for his research excellence. He has been awarded as 2018 IEEE Communications Society Best Young Researcher for Europe, Middle East, and Africa Region. He has received Best Conference Paper awards from IEEE Technical Committee on Green Communications \& Computing (TCGCC) and 23rd Asia-Pacific Conference on Communications (APCC) in 2017. He serves as editor of IEEE Access, Springer Wireless Networks and International Journal of Distributed Sensor Networks, and guest editor for IEEE Wireless Communications, IEEE Communications Magazine, IEEE Network, IEEE Internet of Things Journal, EURASIP Journal on Wireless Communications and Networking, Physical Communications, and Wireless Communications and Mobile Computing. He also served as TPC member for many IEEE major conferences. He was the exemplary reviewer of IEEE Wireless Communications Letter in 2018. He has participated in organizing workshop and special session in Globecom’19, WCNC’18–20, SPAWC’19, and ISWCS’18. He was also a TPC member for many IEEE major conferences, such as INFOCOM, ICC, and Globecom.

Haijun Liao is working toward the Ph.D degree at School of Electrical and Electronic Engineering, North China Electric Power University, China. She has participated in several national-level projects and provincial and ministerial level projects such as the National Natural Science Foundation of China. She was the recipient of the IEEE IWCMC 2019 Best Paper Award and the IEEE VTC-2020 Spring Best Student Paper Award. Her research interests include green communications and smart grid communications.

Blurb

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.

This book provides readers with a comprehensive overview of the state-of-the-art key technologies, frameworks, related optimization algorithms, and corresponding integrated designs on green IoT.  It also presents an easy-to-understand style in a professional manner, making the book suitable for a wider range of readers from students to professionals interested in the green IoT.