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Deep Reinforcement Learning for Data Freshness in Blockchain-enabled Wireless Sensor Networks
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dc.contributor.advisor 이성진 -
dc.contributor.author Dongsun Kim -
dc.date.accessioned 2025-01-20T22:13:50Z -
dc.date.available 2025-01-20T22:13:50Z -
dc.date.issued 2024 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/57588 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000802743 -
dc.description Permissioned blockchain, multi-agent reinforcement learning, transaction generation control, latency-sensitive services, wireless sensor networks, age of information, missing data, imputation -
dc.description.tableofcontents I. INTRODUCTION 1
1.1 Backgrounds 1
1.2 Outline and Contributions 2
1.2.1 Chapter 2 2
1.2.2 Chapter 3 2
1.2.3 Chapter 4 3
1.2.4 Chapter 5 3
II. Intelligent Transaction Generation Control for Permissioned Blockchain-based Services 4
2.1 Introduction 4
2.2 Related Works 5
2.2.1 Performance Evaluation of Permissioned Blockchain 5
2.2.2 Performance Optimization in Blockchain 6
2.3 Blockchain-based Services Model 7
2.3.1 HLF Transaction Flow 7
2.3.2 Main Parameters Affecting HLF Performance 8
2.4 Intelligent Transaction Generation Control 9
2.4.1 Effective Throughput of Blockchain-based Services 9
2.4.2 Effective Throughput Maximization Problem 10
2.4.3 Dec-POMDP for intelligent transaction generation control (i-TGC) 12
2.4.4 i-TGC Algorithm 13
2.5 Experiment Results 16
2.5.1 Experiment Environments and Baseline 16
2.5.2 i-TGC vs p-TGC 18
2.5.3 Effect of Blockchain Parameters 22
2.5.4 Effect of Number of Clients 23
2.6 Conclusion 24
III. Reinforcement Learning-based Sensing Decision for Data Freshness in Blockchain-enabled Wireless Networks 25
3.1 Introduction 25
3.2 System Model and Problem Formulation 26
3.2.1 System Model 26
3.2.2 Transaction Flow at blockchain (BC) 27
3.2.3 Problem Formulation 28
3.3 Reinforcement Learning-based Sensing Decision 29
3.3.1 decentralized partially observable Markov decision process (dec-POMDP) Formulation 30
3.3.2 reinforcement learning-based sensing decision (RL-SD) Algorithm 31
3.4 Experiment Results 32
3.5 Conclusion 35
IV. IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data 36
4.1 Introduction 36
4.2 Related Works 37
4.3 Background 39
4.3.1 DDPG and MADDPG 39
4.3.2 IQL and QMIX 40
4.3.3 Imputation 41
4.4 Methods 41
4.4.1 Missing Training Data Problem and Imputation Method 41
4.4.2 IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning 43
4.5 Experimental Results 46
4.5.1 Environment and Hyperparameters 46
4.5.2 Performance of IA-MARL 49
4.5.3 Effect of Mask-based Update and Imputation Accuracy 51
4.5.4 Effect of Missing Training Data on MADDPG and QMIX 52
4.5.5 Computation Time for Training and Evaluation 53
4.6 Conclusion 54
V. CONCLUSIONS 56
References 58
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dc.format.extent 68 -
dc.language eng -
dc.publisher DGIST -
dc.title Deep Reinforcement Learning for Data Freshness in Blockchain-enabled Wireless Sensor Networks -
dc.type Thesis -
dc.identifier.doi 10.22677/THESIS.200000802743 -
dc.description.degree Doctor -
dc.contributor.department Department of Electrical Engineering and Computer Science -
dc.identifier.bibliographicCitation Dongsun Kim. (2024). Deep Reinforcement Learning for Data Freshness in Blockchain-enabled Wireless Sensor Networks. doi: 10.22677/THESIS.200000802743 -
dc.contributor.coadvisor Jemin Lee -
dc.date.awarded 2024-08-01 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.ID 김251 202408 -
dc.date.accepted 2024-07-24 -
dc.contributor.alternativeDepartment 전기전자컴퓨터공학과 -
dc.subject.keyword Permissioned blockchain, multi-agent reinforcement learning, transaction generation control, latency-sensitive services, wireless sensor networks, age of information, missing data, imputation -
dc.contributor.affiliatedAuthor Dongsun Kim -
dc.contributor.affiliatedAuthor Sungjin Lee -
dc.contributor.affiliatedAuthor Jemin Lee -
dc.contributor.alternativeName 김동선 -
dc.contributor.alternativeName Sungjin Lee -
dc.contributor.alternativeName 이제민 -
dc.rights.embargoReleaseDate 2026-02-28 -
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