TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Akrasi-Mensah, Nana Kwadwo
A1  - Agbemenu, Andrew Selasi
A1  - Nunoo-Mensah, Henry
A1  - Tchao, Eric Tutu
A1  - Sikora, Axel
A1  - Welte, Dominik
A1  - Ahmed, Abdul-Rahman
A1  - Keelson, Eliel
A1  - Kponyo, Jerry John
T1  - Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning
JF  - IEEE Access
N2  - 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 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.
KW  - Blockchains
KW  - Cloud computing
KW  - Heuristic algorithms
KW  - Reinforcement learning
KW  - Deep learning
KW  - Scalability
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:ofb1-opus4-66254
UR  - https://ieeexplore.ieee.org/document/10004557
SN  - 2169-3536
SS  - 2169-3536
U6  - https://doi.org/10.1109/ACCESS.2022.3233474
DO  - https://doi.org/10.1109/ACCESS.2022.3233474
VL  - 11
SP  - 1372
EP  - 1385
PB  - IEEE
ER  -