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Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

  • Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storageBlockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.show moreshow less

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Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/6625
Bibliografische Angaben
Title (English):Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning
Author:Nana Kwadwo Akrasi-Mensah, Andrew Selasi Agbemenu, Henry Nunoo-Mensah, Eric Tutu Tchao, Axel SikoraStaff MemberORCiDGND, Dominik WelteStaff MemberORCiD, Abdul-Rahman Ahmed, Eliel Keelson, Jerry John Kponyo
Year of Publication:2023
Date of first Publication:2022/12/30
Publisher:IEEE
First Page:1372
Last Page:1385
Parent Title (English):IEEE Access
Volume:11
ISSN:2169-3536
DOI:https://doi.org/10.1109/ACCESS.2022.3233474
URL:https://ieeexplore.ieee.org/document/10004557
URN:https://urn:nbn:de:bsz:ofb1-opus4-66254
Language:English
Inhaltliche Informationen
Institutes:Forschung / ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik
Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Tag:Blockchains; Cloud computing; Deep learning; Heuristic algorithms; Reinforcement learning; Scalability
Funded by (selection):Bundesministerium für Bildung und Forschung
Funded by (textarea):Deutscher Akademischer Austauschdienst
Formale Angaben
Relevance:Wiss. Zeitschriftenartikel reviewed: Listung in Master Journal List
Open Access: Open Access 
 Gold 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International