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Green Internet of Things (IoT) = ene...

Zhou, Zhenyu.

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Green Internet of Things (IoT) = energy efficiency perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Green Internet of Things (IoT)/ by Zhenyu Zhou, Zheng Chang, Haijun Liao.
Reminder of title:	energy efficiency perspective /
Author:	Zhou, Zhenyu.
other author:	Chang, Zheng.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xii, 185 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Preface- 1 -- Introduction -- 2 Energy-Efficient Resource Allocationin for D2D Enabled Cellular Networks -- 3 Energy Harvesting Enabled Energy Efficient Cognitive Machine-to-Machine Communications -- 4 Software Defined Machine-to-Machine Communication for Smart Energy Management in Power Grids -- 5 Energy-Efficient M2M Communicationsin for Industrial Automation -- 6 Energy-Efficient Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT -- 7 Licensed and Unlicensed Spectrum Management for EnergyEfficient Cognitive M2M -- 8 Energy-Efficient Task Assignment and Route Planning for UAV -- 9 Energy-Efficient and Secure Resource Allocation for MultipleAntenna NOMA with Wireless Power Transfer.
Contained By:	Springer Nature eBook
Subject:	Computer networks. -
Online resource:	https://doi.org/10.1007/978-3-030-64054-5
ISBN:	9783030640545

Green Internet of Things (IoT) = energy efficiency perspective /
Zhou, Zhenyu.

Green Internet of Things (IoT)energy efficiency perspective /[electronic resource] :by Zhenyu Zhou, Zheng Chang, Haijun Liao. - Cham :Springer International Publishing :2021. - xii, 185 p. :ill. (some col.), digital ;24 cm. - Wireless networks,2366-1186. - Wireless networks..

Preface- 1 -- Introduction -- 2 Energy-Efficient Resource Allocationin for D2D Enabled Cellular Networks -- 3 Energy Harvesting Enabled Energy Efficient Cognitive Machine-to-Machine Communications -- 4 Software Defined Machine-to-Machine Communication for Smart Energy Management in Power Grids -- 5 Energy-Efficient M2M Communicationsin for Industrial Automation -- 6 Energy-Efficient Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT -- 7 Licensed and Unlicensed Spectrum Management for EnergyEfficient Cognitive M2M -- 8 Energy-Efficient Task Assignment and Route Planning for UAV -- 9 Energy-Efficient and Secure Resource Allocation for MultipleAntenna NOMA with Wireless Power Transfer.

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.

ISBN: 9783030640545

Standard No.: 10.1007/978-3-030-64054-5doiSubjects--Topical Terms:

539554
Computer networks.

LC Class. No.: TK5105.7 / .Z46 2021

Dewey Class. No.: 610.285

Green Internet of Things (IoT) = energy efficiency perspective /
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520 $a 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.
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