Secure and Efficient Data Sharing Among Vehicles Based on Consortium Blockchain

Cui, Jie; Ouyang, Fenqiang; Ying, Zuobin; Wei, Lu

doi:10.1109/tits.2021.3086976

Cited by 104 publications

(43 citation statements)

References 42 publications

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“…6, the confirmation delay is elaborated investigated. We adopt four existing blockchains with different consensus algorithms as the comparison groups: Standard DAG with MCMC algorithm, Standard PoW, Proof-of-Driving (PoD) with a simplified PoW difficulty [26] and Proof-of-Storage (PoS) [27] that is essentially a Delegated Proof-of-Stake algorithm [28]. The driving style indicators of incoming sites are set as the uniform distribution between (0.3, 0.35).…”

Section: B Numerical Results and Discussionmentioning

confidence: 99%

Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

Chai

Leng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms.

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Section: B Numerical Results and Discussionmentioning

confidence: 99%

Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

Chai

Leng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…In [25], the authors employ contract theory to balance the stakes and efforts in blockchain IoT towards the maximum profit while tackling the practical scenarios with hidden information and hidden action. In [26], the authors apply the consortium blockchain with delegated PoS consensus to achieve traceable and anonymous vehicular IoT. In [27], the authors propose a drone-based delegated PoS for IoT to enhance decentralized security with reduced latency.…”

Section: Related Workmentioning

confidence: 99%

Incentivizing Proof-of-Stake Blockchain for Secured Data Collection in UAV-Assisted IoT: A Multi-Agent Reinforcement Learning Approach

Tang¹,

Lan²,

Li³

et al. 2022

Preprint

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The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchainbased security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated is the performance superiority of our proposal as compared with the baselines.

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“…• CA: The CA is responsible for the management of identity and legitimacy, issuing certificates, and distributing public and private keys through secure channels based on public key infrastructure (PKI). It records the pseudoidentity and manages the dynamic joining and exiting of vehicles by employing a blacklist to improve the flexibility of the network [18]. • LNs: LNs correspond to CAVs, which provide native sensory data to FNs for the cognitive procedure and do not have to keep the ledger information of the whole network which are located at the perception layer for uploading sensory data.…”

Section: ) Components and Functionsmentioning

confidence: 99%

“…In the proposed online system, the communication capability will be greatly improved assisted by 5G. Meanwhile, combining consortium blockchain with mobile edge computing (MEC) can well meet the low-latency requirements of vehicular networking [18]. However, the conventional algorithm cannot perform efficient data cognition as well as the attacks from unscreened CNs will seriously slow down the consensus time.…”

Section: Theoretical Analysis 1) Communication Overheadmentioning

confidence: 99%

“…Generally, the highly dynamic and latency-sensitive vehicular networks require a faster and more secure consensus process [16]. However, due to the high cost and unauthorized nature of the public blockchain, the conventional consensus mechanisms cannot be suitable for the large-scale CIoVs as well as unauthenticated nodes will launch attacks to prolong consensus latency and even consensus bias [17], [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blockchain-Enabled Online Traffic Congestion Duration Prediction in Cognitive Internet of Vehicles

Chang

Liu

Sheng³

2022

IEEE Internet Things J.

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Secure and Efficient Data Sharing Among Vehicles Based on Consortium Blockchain

Cited by 104 publications

References 42 publications

Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

Incentivizing Proof-of-Stake Blockchain for Secured Data Collection in UAV-Assisted IoT: A Multi-Agent Reinforcement Learning Approach

Blockchain-Enabled Online Traffic Congestion Duration Prediction in Cognitive Internet of Vehicles

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