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Blockchain-based vehicular edge computing networks: the communication perspective

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Abstract

Vehicular edge computing (VEC) networks can satisfy the increasing demands of data processing by offloading the computation tasks to multiple distributed edge computing nodes assisted by servers. These edge servers generally combine with roadside units (RSUs). However, RSUs with edge servers cannot be fully trusted, possibly leading to serious security and privacy problems. Combining blockchain with VEC networks may establish a trusted and decentralized vehicular environment, but the coexistence of multiple communication modes comprising vehicle-to-vehicle (V2V), vehicle-to-RSU (V2R), and RSU-to-RSU (R2R) makes the block propagation patterns have different effects on the block consensus process. In this paper, we study the block propagation patterns between vehicles and RSUs with edge servers under three communication modes. We first give the closed-form expressions for the single-block and multiblock propagation times in two specific conditions to explore how the priority of a block generated from a specific node affects the block propagation process, in which multiblock propagation embodies competitive propagation due to blockchain forking. Then, an innovative consensus mechanism that fully invokes the communication capability of the nodes is proposed, and the block propagation time and frequency can be substantially reduced under this mechanism. In addition, an important finding is that under the conventional and proposed consensus mechanisms, an RSU or a vehicle that creates a new block plays a decisive role in the block propagation pattern.

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AcknowledgementsThis work was supported in part by National Natural Science Foundation of China (Grant No. 62271073), Beijing Natural Science Foundation (Grant No. L212003), and 111 Project of China (Grant No. B16006).

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Correspondence to Xuefei Zhang.

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He, L., Li, F., Xu, H. et al. Blockchain-based vehicular edge computing networks: the communication perspective. Sci. China Inf. Sci. 66, 172301 (2023). https://doi.org/10.1007/s11432-022-3658-7

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  • DOI: https://doi.org/10.1007/s11432-022-3658-7

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