Integrating Edge Intelligence and Blockchain: What, Why, and How

117 Pages·2022·4.321 MB·English

by  Xiaofei Wang, Chao Qiu, Xiaoxu Ren, Zehui Xiong, Victor C. M. Leung, Dusit Niyato

Wireless Networks
SeriesEditor
XueminShermanShen,UniversityofWaterloo,Waterloo,ON,Canada
The purpose of Springer’s Wireless Networks book series is to establish the state
of the art and set the course for future research and development in wireless
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**Indexing:WirelessNetworksisindexedinEBSCOdatabasesandDPLB**
Xiaofei Wang (cid:129) Chao Qiu (cid:129) Xiaoxu Ren (cid:129)
Zehui Xiong (cid:129) Victor C. M. Leung (cid:129) Dusit Niyato
Integrating Edge Intelligence
and Blockchain
What, Why, and How
XiaofeiWang ChaoQiu
TianjinUniversity TianjinUniversity
Tianjin,China Tianjin,China
XiaoxuRen ZehuiXiong
TianjinUniversity SingaporeUniversityofTechnologyand
Tianjin,China Design
Singapore,Singapore
VictorC.M.Leung DusitNiyato
ShenzhenUniversity SchoolofComputerScienceand
Shenzhen,China Engineering
NanyangTechnologicalUniversity
Singapore,Singapore
ISSN2366-1186 ISSN2366-1445 (electronic)
WirelessNetworks
ISBN978-3-031-10185-4 ISBN978-3-031-10186-1 (eBook)
https://doi.org/10.1007/978-3-031-10186-1
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Preface
Alongwiththewaveofinformatizationtechnology,anunprecedentedboomingera
of artificial intelligence (AI) has emerged. Meanwhile, with the proliferation of
wireless communication, immense volumes of data are generated by mega-scale
terminals instead of traditional cloud datacenters. Driving by these trends, edge
intelligence(EI)haselicitedescalatingattention.Insteadofentirelyrelyingonthe
cloud,EImakesthemostofthewidespreadedgeresourcestogainAIinsight.Itis
yetfacingcriticalchallengesinitsdecentralizedmanagementandsecurity,limiting
itscapabilitiestosupportserviceswithnumerousrequirements.
In this monograph, the blockchain (BC) has been seen as a promising solution
to tackle the above issues, further support EI. Based on the number of citations
or the relevance of an emerging method, this monograph presents the results of
a literature survey on the integration of EI and BC. Accordingly, we conduct a
summarization of the recent research efforts on the existing works for EI and BC.
We then paint a comprehensive picture of the limitation of EI and why BC could
benefit EI. From there, we explore how BC benefits EI in terms of computing
powermanagement,dataadministration,andmodeloptimization.Tonarrowthegap
betweenimmatureBCandEI-amicableBC,wealsoprobeintohowtotailorBCto
EIfromfourperspectives,includingflexibleconsensusprotocol,effectiveincentive,
intellectualitysmartcontract,andscalability.Finally,someresearchchallengesand
futuredirectionsareaddressed.
Tianjin,China XiaofeiWang
Tianjin,China ChaoQiu
Tianjin,China XiaoxuRen
Singapore,Singapore ZehuiXiong
Shenzhen,China VictorC.M.Leung
Singapore,Singapore DusitNiyato
v
Acknowledgments
This work was supported in part by the National Key R & D Program of China
underGrant2019YFB2101901,inpartbytheNationalNaturalScienceFoundation
ofChinaunderGrant62072332,andGrant62002260,inpartbytheChinaPostdoc-
toralScienceFoundationunderGrant2020M670654,inpartbytheOpenResearch
Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy
(SZ) under Grant GML-KF-22-03, in part by the Guangdong Pearl River Talent
RecruitmentProgramunderGrant2019ZT08X603,inpartbytheGuangdongPearl
River Talent Plan under Grant 2019JC01X235, in part by the Shenzhen Science
and Technology Innovation Commission under Grant R2020A045, in part by
the National Research Foundation, Singapore and Infocomm Media Development
AuthorityunderitsFutureCommunicationsResearch&DevelopmentProgramme,
in part by the SUTD SRG-ISTD-2021-165, the SUTD-ZJU IDEA Grant (SUTD-
ZJU (VP) 202102), and the SUTD-ZJU IDEA Seed Grant (SUTD-ZJU (SD)
202101).
vii
Contents
1 Introduction .................................................................. 1
1.1 ABriefIntroductiontoEI .............................................. 1
1.2 ABriefIntroductiontoBC............................................. 2
1.3 OurFocus................................................................ 3
1.4 OurContribution........................................................ 4
References..................................................................... 6
2 OverviewofEdgeIntelligenceandBlockchain........................... 9
2.1 OverviewofEI.......................................................... 9
2.1.1 DefinitionofEI ................................................. 10
2.1.2 DivisionofEI................................................... 10
2.1.3 DeploymentofEI.............................................. 12
2.1.4 ApplicationsofEI.............................................. 14
2.2 OverviewofBC......................................................... 17
2.2.1 DefinitionofBC ................................................ 17
2.2.2 ArchitectureofBC.............................................. 19
2.2.3 CategoriesofBC................................................ 20
2.2.4 ApplicationofBC.............................................. 24
References..................................................................... 26
3 MotivationsforIntegratingEdgeIntelligencewithBlockchain........ 33
3.1 TheLimitationofEI.................................................... 33
3.2 TheBenefitofBC....................................................... 35
3.2.1 Computing-PowerManagement ............................... 36
3.2.2 DataAdministration............................................ 37
3.2.3 ModelOptimization ............................................ 37
References..................................................................... 39
4 BlockchainDrivenEdgeIntelligence ...................................... 43
4.1 BC-DrivenComputing-PowerManagementinEI..................... 43
4.1.1 Value-DrivenComputing-PowerSharing...................... 44
4.1.2 Performance-DrivenComputing-PowerAllocation........... 50
ix
x Contents
4.1.3 Service-DrivenComputingOffloading ........................ 52
4.1.4 BCImplementationTutorialforComputing-Power
Management .................................................... 55
4.2 BC-DrivenDataAdministrationinEI ................................. 57
4.2.1 IncentiveDataTrading ......................................... 58
4.2.2 DataCachingStrategy.......................................... 59
4.2.3 ReliableDataCollaboration................................... 61
4.2.4 BCImplementationTutorialforDataAdministration........ 65
4.3 BC-DrivenModelOptimizationinEI.................................. 66
4.3.1 HighEfficiencyTraining....................................... 66
4.3.2 CredibleInference.............................................. 72
4.3.3 BCImplementationTutorialforModelOptimization ........ 73
References..................................................................... 75
5 TailoringBlockchaintoEdgeIntelligence................................. 79
5.1 FlexibleConsensusProtocolTailoringToEI.......................... 79
5.1.1 Multi-FunctionalDesign ....................................... 80
5.1.2 CompatibilityEnhancement.................................... 82
5.1.3 AttackDefense.................................................. 85
5.1.4 BCImplementationTutorialforConsensusProtocol
Tailoring......................................................... 86
5.2 EffectiveIncentiveMechanismTailoringtoEI........................ 86
5.2.1 MiningStrategyOptimization ................................. 88
5.2.2 RiskPreventionAgainstCryptocurrency...................... 89
5.2.3 BCImplementationTutorialforIncentiveTailoring ......... 90
5.3 IntellectualitySmartContractTailoringtoEI ......................... 90
5.3.1 PerformanceImprovementforSmartContract................ 93
5.3.2 ThreatDetectionAgainstSmartContract ..................... 94
5.3.3 BCImplementationTutorialforSmartContractTailoring... 95
5.4 ScalabilityTailoringtoEI .............................................. 96
5.4.1 ScalabilityImprovement ....................................... 98
5.4.2 BCImplementationTutorialforScalabilityTailoring........ 101
References..................................................................... 101
6 ResearchChallengesandFutureDirections.............................. 105
6.1 ComprehensiveArchitecture ........................................... 105
6.2 QuantizationIntelligence............................................... 106
6.3 TradingIntelligence..................................................... 107
References..................................................................... 108
Conclusions ....................................................................... 109
Index............................................................................... 111
Acronyms
2Ts-DRL Two-timescaledeepreinforcementlearning
ACK Acknowledgecharacter
ADMM Alternatedirectionmethodofmultipliers
AI Artificialintelligence
AIE Artificialintelligenceonedge
API Applicationprogramminginterface
BaaS Blockchain-as-a-service
BC Blockchain
CD Computing-powerdevice
CLI CommandLineInterface
CNN Convolutionalneuralnetwork
CNN Convolutionalneuralnetwork
CNN Proofofwork
CSP Computingserviceprovider
DAO Decentralizedautomaticorganization
DApp Decentralizedapplications
DL Deeplearning
DLT Distributedledgertechnology
DNN Deepneuralnetworks
DQL DeepQ-learning
DQN DeepQ-network
DRGO Deepreinforcementlearningcombinedwithgeneticalgorithm
DRL Deepreinforcementlearning
DSIC Dominant-strategyincentivecompatibility
EC Edgecomputing
ECSP Edgecomputingserviceprovider
EI Edgeintelligence
ePoW Enhancedproofofwork
FL Federatedlearning
HRL Hierarchicalreinforcementlearning
IC Incentivecompatibility
xi
xii Acronyms
IDC Internationaldatacorporation
IIoT Industrialinternetofthings
IoT Internetofthings
IoV Internetofvehicles
IR Individualrationality
ITS Intelligenttelehealthsystem
LM Learningmarkets
LSTM Longshort-termmemory
MARL Multi-agentreinforcementlearning
MDP Markovdecisionprocess
ML Machinelearning
NFV-MANO Networkfunctionvirtualizationmanagementandorchestration
P2P Peer-to-peer
PBFT Practicalbyzantinefaulttolerance
PoDL Proofofdeeplearning
PoET Proofofelapsedtime
PoK Proofofknowledge
POMDP Partiallyobservablemarkovdecisionprocess
PoQ Proofoftrainingquality
PoS Proofofstake
PoT Proofoftrading
PoW Proofofutility
QoS Qualityofservice
RBFT Redundantbyzantinefaulttolerance
UDEC Ultra-denseedgecomputing
VANET Vehicleadhocnetwork
WAN Wideareanetwork
ZB zettabytes