| 000 | 07448nam a22005053i 4500 | ||
|---|---|---|---|
| 001 | EBC6480204 | ||
| 003 | MiAaPQ | ||
| 005 | 20250821091653.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 250807s2021 xx o ||||0 eng d | ||
| 020 |
_a9783030640545 _q(electronic bk.) |
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| 020 | _z9783030640538 | ||
| 035 | _a(MiAaPQ)EBC6480204 | ||
| 035 | _a(Au-PeEL)EBL6480204 | ||
| 035 | _a(OCoLC)1237868615 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
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| 050 | 4 | _aTK5105.5-5105.9 | |
| 082 | 0 | _a004.678 | |
| 100 | 1 | _aZhou, Zhenyu. | |
| 245 | 1 | 0 |
_aGreen Internet of Things (IoT) : _bEnergy Efficiency Perspective. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing AG, _c2021. |
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| 264 | 4 | _c�2021. | |
| 300 | _a1 online resource (194 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aWireless Networks Series | |
| 505 | 0 | _aIntro -- Preface -- Contents -- List of Acronyms -- 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 -- 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. | |
| 505 | 8 | _a5.2 Contract-Based Incentive Mechanism Design for AccessControl -- 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 -- Rate Control -- Joint Power Allocation and Channel Selection -- 5.4 Performance Results and Discussions -- 5.4.1 Feasibility and Efficiency of Access ControlMechanism -- 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 -- Task Transmission Model -- Energy Consumption Model -- Delay Model -- Service Reliability Requirement 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 -- 6.2.2 SEB-GSI Algorithm for the Ideal Case -- 6.2.3 SEB-UCB Algorithm for the Nonideal Case -- 6.3 Learning-Based Context-Aware Channel Selection 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 CM2M -- 7.2.1 Problem Transformation -- 7.2.2 C2-GSI for Channel Selection with GSI -- 7.2.3 C2-EXP3 for Channel Selection with LocalInformation. | |
| 505 | 8 | _a7.3 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 Multiple-Antenna 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. | |
| 505 | 8 | _a12 Summary -- References. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aComputer networks. | |
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aChang, Zheng. | |
| 700 | 1 | _aLiao, Haijun. | |
| 776 | 0 | 8 |
_iPrint version: _aZhou, Zhenyu _tGreen Internet of Things (IoT): Energy Efficiency Perspective _dCham : Springer International Publishing AG,c2021 _z9783030640538 |
| 797 | 2 | _aProQuest (Firm) | |
| 830 | 0 | _aWireless Networks Series | |
| 856 | 4 | 0 |
_uhttps://ebookcentral-proquest-com.mlisicats.remotexs.co/lib/ppks/detail.action?docID=6480204&query=9783030640545 _zClick to View |
| 942 |
_2lcc _cEB |
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