Resource Management for Energy and Spectrum Harvesting Sensor Networks

87 Pages·2017·2.796 MB·English

by  Deyu Zhang, Zhigang Chen, Haibo Zhou, Xuemin (Sherman) Shen (auth.)

SPRINGER BRIEFS IN
ELECTRICAL AND COMPUTER ENGINEERING
Deyu Zhang
Zhigang Chen
Haibo Zhou
Xuemin (Sherman) Shen
Resource
Management
for Energy
and Spectrum
Harvesting Sensor
Networks
123
SpringerBriefs in Electrical and Computer
Engineering
More information about this series at http://www.springer.com/series/10059
Deyu Zhang Zhigang Chen
(cid:129)
Haibo Zhou Xuemin (Sherman) Shen
(cid:129)
Resource Management
for Energy and Spectrum
Harvesting Sensor Networks
123
Deyu Zhang HaiboZhou
Schoolof Software Department ofElectrical andComputer
Central SouthUniversity Engineering
Changsha,Hunan University of Waterloo
China Waterloo, ON
Canada
Zhigang Chen
Schoolof Software Xuemin(Sherman) Shen
Central SouthUniversity Department ofElectrical andComputer
Changsha,Hunan Engineering
China University of Waterloo
Waterloo, ON
Canada
ISSN 2191-8112 ISSN 2191-8120 (electronic)
SpringerBriefs inElectrical andComputer Engineering
ISBN978-3-319-53770-2 ISBN978-3-319-53771-9 (eBook)
DOI 10.1007/978-3-319-53771-9
LibraryofCongressControlNumber:2017931571
©TheAuthor(s)2017
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or
dissimilarmethodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a specific statement, that such names are exempt
fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor
for any errors or omissions that may have been made. The publisher remains neutral with regard to
jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringerInternationalPublishingAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Preface
To alleviate the energy and spectrum constraints in wireless sensor networks
(WSNs), WSNs necessitate energy and spectrum harvesting (ESH) capabilities to
scavenge energy from renewable energy sources, and opportunistically access the
underutilized licensed spectrum; hence, give rise to the energy and spectrum har-
vesting sensor networks (ESHSNs). In spite of the energy and spectrum efficiency
brought by ESHSNs, their resource management faces new challenges. First,
energy harvesting (EH) process is dynamic, which makes balancing energy con-
sumption and energy replenishmentchallenging. Depleting thesensorsbattery ata
rate slower or faster than the energy replenishment rate leads to either energy
underutilization or sensor failure, respectively. Second, the spectrum utilization by
sensorsinESHSNshastoadapttothedynamicactivityofprimaryusers(PUs)over
licensed spectrum.
In this monograph, we investigate the resource management and allocation, to
facilitate energy- and spectrum-efficient sensed data collection in ESHSNs. In
Chapter1,wediscussthemotivationtointegrateESHcapabilitiesinWSNs,aswell
as the network architecture, typical application scenarios, and challenges of
ESHSNs. Chapter 2 surveys the related state-of-the-art research literature. In
Chapter 3, an EH-powered licensed spectrum sensing scheme is proposed to
schedulethespectrumsensorswhicharededicatedlydeployedforspectrumsensing
to periodically estimate the licensed spectrum availability. Accordingly, an access
time and power management of battery-powered data sensors is presented as well,
which has been verified an effective solution to minimize the energy consumption
ofdatatransmissionovertheavailablelicensedspectrum.InChapter4,wepropose
an online algorithm which jointly manages the available licensed spectrum and
harvestedenergy tooptimizethenetwork utility whichcapturesthedatacollection
efficiencyofESHSNs.Theproposedalgorithmdynamicallyschedulessensors’data
sensingandspectrumaccessbyconsideringthestochasticnatureofEHprocess,PU
activities, and channel conditions. Finally, Chapter 5 concludes the monograph by
outlining some open issues, pointing out new research directions for resource
management in ESHSNs.
v
vi Preface
TheauthorswouldliketothankNingZhangoftheBroadBandCommunications
Research (BBCR) group at University of Waterloo, Prof. Mohamad Khattar Awad
of Kuwait University, and Prof. Ju Ren of Central South University for their
contribution to the presented research works, and Prof. Shibo He of Zhejiang
Universityforhisvaluablesuggestionsonthemonographdraft.Wealsowouldlike
to thank all the members of BBCR group for their valuable comments and sug-
gestions. Special thanks are due to the staff at Springer Science+Business Media:
SusanLagerstrom-FifeandJenniferMalat,fortheirhelpthroughoutthepublication
preparation process.
Changsha, China Deyu Zhang
Changsha, China Zhigang Chen
Waterloo, ON, Canada Haibo Zhou
Waterloo, ON, Canada Xuemin (Sherman) Shen
Contents
1 Introduction.... .... .... ..... .... .... .... .... .... ..... .... 1
1.1 Resource Constraints in Wireless Sensor Networks ... ..... .... 1
1.2 Enabling Techniques for Energy and Spectrum Harvesting .. .... 2
1.2.1 Energy Harvesting... .... .... .... .... .... ..... .... 2
1.2.2 Spectrum Harvesting. .... .... .... .... .... ..... .... 2
1.3 Energy and Spectrum Harvesting Sensor Networks ... ..... .... 3
1.3.1 Network Architecture .... .... .... .... .... ..... .... 3
1.3.2 Applications of ESHSNs.. .... .... .... .... ..... .... 5
1.3.3 Challenges for ESHSNs .. .... .... .... .... ..... .... 6
1.4 Aim of the Monograph..... .... .... .... .... .... ..... .... 7
References.. .... .... .... ..... .... .... .... .... .... ..... .... 8
2 Energy and Spectrum Harvesting in Sensor Networks... ..... .... 9
2.1 Energy Harvesting ... ..... .... .... .... .... .... ..... .... 9
2.1.1 EH Process Modeling.... .... .... .... .... ..... .... 9
2.1.2 Energy Allocation... .... .... .... .... .... ..... .... 11
2.2 Spectrum Harvesting.. ..... .... .... .... .... .... ..... .... 14
2.2.1 Spectrum Sensing ... .... .... .... .... .... ..... .... 14
2.2.2 Resource Allocation in Spectrum Harvesting Sensor
Networks. .... ..... .... .... .... .... .... ..... .... 17
2.3 Joint Energy and Spectrum Harvesting in Wireless Networks .... 19
2.3.1 Green Energy-Powered SH Networks.... .... ..... .... 19
2.3.2 RF-Powered SH Networks .... .... .... .... ..... .... 20
2.4 Conclusion. .... .... ..... .... .... .... .... .... ..... .... 20
References.. .... .... .... ..... .... .... .... .... .... ..... .... 21
vii
viii Contents
3 Spectrum Sensing and Access in Heterogeneous SHSNs . ..... .... 25
3.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 25
3.2 System Model... .... ..... .... .... .... .... .... ..... .... 26
3.2.1 Network Architecture .... .... .... .... .... ..... .... 26
3.2.2 EH-Powered Spectrum Sensing. .... .... .... ..... .... 28
3.3 Problem Statement and Proposed Solution.. .... .... ..... .... 29
3.3.1 Spectrum-Sensing Scheduling.. .... .... .... ..... .... 30
3.3.2 Data Sensor Resource Allocation ... .... .... ..... .... 34
3.4 Performance Evaluation .... .... .... .... .... .... ..... .... 38
3.4.1 Detected Channel Available Time... .... .... ..... .... 39
3.4.2 Energy Consumption of Data Transmission ... ..... .... 44
3.5 Summary .. .... .... ..... .... .... .... .... .... ..... .... 46
References.. .... .... .... ..... .... .... .... .... .... ..... .... 46
4 Joint Energy and Spectrum Management in ESHSNs ... ..... .... 49
4.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 49
4.2 System Model and Problem Formulation ... .... .... ..... .... 50
4.2.1 Channel Allocation and Collision Control Model .... .... 51
4.2.2 Energy Supply and Consumption Model.. .... ..... .... 54
4.2.3 Data Sensing and Transmission Model... .... ..... .... 55
4.2.4 Problem Formulation. .... .... .... .... .... ..... .... 56
4.3 Network Utility Optimization Framework... .... .... ..... .... 57
4.3.1 Problem Decomposition .. .... .... .... .... ..... .... 57
4.3.2 Utility-Optimal Resource Management Algorithm.... .... 63
4.4 System Performance Analysis.... .... .... .... .... ..... .... 63
4.4.1 Upper Bounds on Queues. .... .... .... .... ..... .... 64
4.4.2 Required Battery Capacity .... .... .... .... ..... .... 65
4.4.3 Optimality of the Proposed Algorithm ... .... ..... .... 67
4.5 Performance Evaluation .... .... .... .... .... .... ..... .... 68
4.5.1 Network Utility and Queue Dynamics.... .... ..... .... 69
4.5.2 Impact of Parameter Variation.. .... .... .... ..... .... 71
4.6 Summary .. .... .... ..... .... .... .... .... .... ..... .... 73
References.. .... .... .... ..... .... .... .... .... .... ..... .... 74
5 Conclusion and Future Research Directions... .... .... ..... .... 77
5.1 Concluding Remarks . ..... .... .... .... .... .... ..... .... 77
5.2 Future Research Directions.. .... .... .... .... .... ..... .... 78
5.2.1 Real Data-Driven EH Process and PU Activities
Modeling. .... ..... .... .... .... .... .... ..... .... 78
5.2.2 Joint Spectrum Detection and Access.... .... ..... .... 79
5.2.3 Resource Allocation in Multi-hop ESHSNs.... ..... .... 79
Acronyms
AoI Area of Interest
CR Cognitive Radio
CSMA/CA Carrier Sense Multiple Access with Collision Avoidance
EH Energy Harvesting
EMI ElectroMagnetic Interference
ESHSN Energy and Spectrum Harvesting Sensor Network
ESI Energy State Information
HSHSN Heterogeneous Spectrum Harvesting Sensor Network
ISM Industrial, Scientific, and Medical
MAC Media Access Control
MDP Markov Decision Process
PU Primary User
QoS Quality of Service
RF Radio Frequency
SH Spectrum Harvesting
SHSN Spectrum Harvesting Sensor Network
SNR Signal-to-Noise Ratio
SoC State of Charge
SP Spectrum Provider
SU Secondary User
TDMA Time Division Multiple Access
TPS Third-Party System
WSN Wireless Sensor Network
ix