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Resource Management for Energy and Spectrum Harvesting Sensor Networks 1st ed. 2017 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 79 pages, aukštis x plotis: 235x155 mm, weight: 1533 g, 25 Illustrations, black and white; IX, 79 p. 25 illus., 1 Paperback / softback
  • Serija: SpringerBriefs in Electrical and Computer Engineering
  • Išleidimo metai: 17-Mar-2017
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319537709
  • ISBN-13: 9783319537702
Kitos knygos pagal šią temą:
Resource Management for Energy and Spectrum Harvesting Sensor Networks 1st ed. 2017Didesnis paveikslėlis
  • Formatas: Paperback / softback, 79 pages, aukštis x plotis: 235x155 mm, weight: 1533 g, 25 Illustrations, black and white; IX, 79 p. 25 illus., 1 Paperback / softback
  • Serija: SpringerBriefs in Electrical and Computer Engineering
  • Išleidimo metai: 17-Mar-2017
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319537709
  • ISBN-13: 9783319537702
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319537702.html
Raktažodžiai:
This SpringerBrief offers a comprehensive review and in-depth discussion of the current research on resource management. The authors explain how to best utilize harvested energy and temporally available licensed spectrum. Throughout the brief, the primary focus is energy and spectrum harvesting sensor networks (ESHNs) including energy harvesting (EH)-powered spectrum sensing and dynamic spectrum access.

To efficiently collect data through the available licensed spectrum, this brief examines the joint management of energy and spectrum. An EH-powered spectrum sensing and management scheme for Heterogeneous Spectrum Harvesting Sensor Networks (HSHSNs) is presented in this brief. The scheme dynamically schedules the data sensing and spectrum access of sensors in ESHSNs to optimize the network utility, while considering the stochastic nature of EH process, PU activities and channel conditions.

This brief also provides useful insights for the practical resource management scheme design for ESHSNs and motivates a new line of thinking for future sensor networking. Professionals, researchers, and advanced-level students in electrical or computer engineering will find the content valuable.
1 Introduction
1(8)
1.1 Resource Constraints in Wireless Sensor Networks
1(1)
1.2 Enabling Techniques for Energy and Spectrum Harvesting
2(1)
1.2.1 Energy Harvesting
2(1)
1.2.2 Spectrum Harvesting
2(1)
1.3 Energy and Spectrum Harvesting Sensor Networks
3(4)
1.3.1 Network Architecture
3(2)
1.3.2 Applications of ESHSNs
5(1)
1.3.3 Challenges for ESHSNs
6(1)
1.4 Aim of the Monograph
7(2)
References
8(1)
2 Energy and Spectrum Harvesting in Sensor Networks
9(16)
2.1 Energy Harvesting
9(5)
2.1.1 EH Process Modeling
9(2)
2.1.2 Energy Allocation
11(3)
2.2 Spectrum Harvesting
14(5)
2.2.1 Spectrum Sensing
14(3)
2.2.2 Resource Allocation in Spectrum Harvesting Sensor Networks
17(2)
2.3 Joint Energy and Spectrum Harvesting in Wireless Networks
19(1)
2.3.1 Green Energy-Powered SH Networks
19(1)
2.3.2 RF-Powered SH Networks
20(1)
2.4 Conclusion
20(5)
References
21(4)
3 Spectrum Sensing and Access in Heterogeneous SHSNs
25(24)
3.1 Introduction
25(1)
3.2 System Model
26(3)
3.2.1 Network Architecture
26(2)
3.2.2 EH-Powered Spectrum Sensing
28(1)
3.3 Problem Statement and Proposed Solution
29(9)
3.3.1 Spectrum-Sensing Scheduling
30(4)
3.3.2 Data Sensor Resource Allocation
34(4)
3.4 Performance Evaluation
38(8)
3.4.1 Detected Channel Available Time
39(5)
3.4.2 Energy Consumption of Data Transmission
44(2)
3.5 Summary
46(3)
References
46(3)
4 Joint Energy and Spectrum Management in ESHSNs
49(28)
4.1 Introduction
49(1)
4.2 System Model and Problem Formulation
50(7)
4.2.1 Channel Allocation and Collision Control Model
51(3)
4.2.2 Energy Supply and Consumption Model
54(1)
4.2.3 Data Sensing and Transmission Model
55(1)
4.2.4 Problem Formulation
56(1)
4.3 Network Utility Optimization Framework
57(6)
4.3.1 Problem Decomposition
57(6)
4.3.2 Utility-Optimal Resource Management Algorithm
63(1)
4.4 System Performance Analysis
63(5)
4.4.1 Upper Bounds on Queues
64(1)
4.4.2 Required Battery Capacity
65(2)
4.4.3 Optimality of the Proposed Algorithm
67(1)
4.5 Performance Evaluation
68(5)
4.5.1 Network Utility and Queue Dynamics
69(2)
4.5.2 Impact of Parameter Variation
71(2)
4.6 Summary
73(4)
References
74(3)
5 Conclusion and Future Research Directions
77
5.1 Concluding Remarks
77(1)
5.2 Future Research Directions
78
5.2.1 Real Data-Driven EH Process and PU Activities Modeling
78(1)
5.2.2 Joint Spectrum Detection and Access
79(1)
5.2.3 Resource Allocation in Multi-hop ESHSNs
79