期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 24, 期 2, 页码 2568-2577出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3102957
关键词
Task analysis; Real-time systems; Security; Servers; Internet of Things; Computational modeling; Blockchains; IoT; maritime transport networks; service provisioning; blockchain; smart contract; vessel tracking
This paper presents a blockchain-enabled edge-centric framework for real-time data analysis in maritime transportation systems, addressing security and privacy issues. By introducing blockchain and smart contracts, the transactions of each block can be validated, and different classification models can be used to predict malicious vessels.
With the exponential growth of the Internet of Things (IoT) devices in Maritime Transportation Systems (MTS), the centralized cloud-centric framework can hardly meet the requirements of the applications in terms of low latency and power consumption. By inventing the distributed edge-centric framework, real-time IoT applications can meet the requirements of the MTS by analyzing the tasks at the edge of the networks. However, one of the critical challenges of the edge-centric MTS is to provide security and privacy between local IoT devices and distributed edge nodes. Motivated by that, in this paper, we design a blockchain-enabled edge-centric framework for analyzing the real-time data at the edge of the networks with minimum latency and power consumption while meeting the security and privacy issue of MTS. The introduction of blockchain and smart contract in the edge-centric MTS frameworks help to validate the transactions of each block at edge nodes by estimating the lifetime, belief, and trustfulness, and mitigate various types of security threats. Further, we introduce different classification models to predict the malicious vessels over the real-time maritime dataset at a secured edge-centric MTS framework. Extensive simulation results demonstrate that the superiority of the proposed strategy with baseline approaches under various performance metrics.
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