Manchester Metropolitan University's Research Repository

    Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning

    Qiao, Fuli, Wu, Jun, Li, Jianhua, Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522, Mumtaz, Shahid and Tariq, Usman (2021) Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems, 22 (7). pp. 4443-4456. ISSN 1524-9050

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    A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

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