Consortium Blockchain for Cooperative Location Privacy Preservation in 5G-Enabled Vehicular Fog Computing

Abstract

Privacy is a key requirement for connected vehicles. Cooperation between vehicles is mandatory for achieving location privacy preservation. However, non-cooperative vehicles can be a big issue to achieve this objective. To this end, we propose a novel monetary incentive scheme for cooperative location privacy preservation in 5G-enabled Vehicular Fog Computing. This scheme leverages a consortium blockchain-enabled fog layer and smart contracts to ensure a trusted and secure cooperative Pseudonym Changing Processes (PCPs). We also propose optimized smart contracts to reduce the monetary costs of vehicles while providing more location privacy preservation. Moreover, a resilient and lightweight Utility-based Delegated Byzantine Fault Tolerance (U-DBFT) consensus protocol is proposed to ensure fast and reliable block mining and validation. The performance analysis shows that our scheme has effective incentive techniques to stimulate non-cooperative vehicles and provides optimal monetary cost management and secure, private, fast validation of blocks.

Design and Optimization in MEC‐Based Intelligent Rail System by Integration of Distributed Multi‐Hop Communication and Blockchain

“…Blockchain Model. In this paper, the delegated Byzantine fault tolerance (dBFT) consensus mechanism is adopted in our blockchain system to increase the efciency of a consensus process without tampering [15]. Moreover, each relaying train in the routing path is regarded as a candidate for consensus nodes, and we consider the trust value of candidates to determine the nodes participating in the next round of consensus, which improves the throughput of blockchain, reduces the CPU cycles of transaction confrmation, and then efectively reduces the consensus latency [16].…”

“…. , H max do (5) Randomly choose a probability p; (6) if p < ϵ then (7) Randomly choose an action a(t) ≠ a * (t) based on ϵ-greedy policy; (8) else (9) a(t) � a * (t) � argmax a∈A Q(s ′ , a ′ ; ω ′ , θ ′ , ξ); (10) end if (11) Execute action a(t) and obtain the reward r(t), and proceed to the next observation s(t + 1); (12) Store the experience (s(t), a(t), r(t), s(t + 1)) into experience replay memory; (13) Randomly sample a mini-batch of (s(i), a(i), r(i), s(i + 1)) from experience replay memory D; (14) Obtain two parts of evaluated network, including V(s(t)) and A(a(t)), and merge them as Q(s(t), a(t); ω, θ, ξ) through equation ( 33); (15) Obtain target Q value in target network by Q target (s) � re(t) + c max a∈A Q(s ′ , a ′ ; ω ′ , θ ′ , ξ); (16) Train evaluated network to minimize loss function that total latency under all schemes increases with the increase of task data size. Te reason is that the increase in task size takes longer for end-to-end delivery and ofoading computation.…”

A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues

“…The proposed algorithm creates a completely trustworthy, low-latency communication network that allows prosumers to trade energy inside their neighborhood, based on the evaluation results. Boualouache et al [ 51 ] developed a monetary reward strategy for 5G-enabled FC-based vehicle location privacy preservation. This solution makes use of a consortium BC in the FC layer as well as smart contracts to assure pseudonym changing procedures and lower vehicle monetary expenses.…”

“…Bitcoin, for example, is changed when a large majority of network users agree that there is a need for updated code that sounds beneficial. Authors proposed several strategies to ensure trust support ([ 57 , 74 , 103 , 107 , 109 , 113 , 151 , 160 , 219 ]), to enable reliability ([ 114 , 156 , 190 , 206 , 220 ]), to enable transparency ([ 62 , 80 , 146 , 184 , 202 ]), to increase reputation ([ 63 , 72 , 95 , 170 ]), enhance QoS ([ 63 , 64 , 76 , 153 , 156 , 159 , 160 ]), and secure payment ([ 51 , 91 , 100 , 196 , 213 ]). The majority of the selected studies under this category reported that BC can enhance trust level, in general, in FC, followed by QoS purpose, and the least purpose mentioned was to achieve a high reputation.…”

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

“…These trained models need a huge source of vehicular information. For example, the authors of [ 12 ] proposed a Zero-X, a cutting-edge security framework designed for the IoV, addressing the mounting cybersecurity threats amplified by 0-day attacks. The evaluations on recent datasets validate the efficacy in detecting various attack types while maintaining a minimal false positive rate.…”