## 5 Convergence of ML and Blockchain in IoV
### Prospects of ML and BC Convergence for IoV System
#### 5.1.6 Secure Energy-Trading System  
Electric vehicles (EVs) in the IoV system require charging to be operational. During their charging regime, they establish a vehicular network (VN) that connects them to charging stations, roadside units (RSU), and other EVs. Participants in this VN share, among other things, road and weather information, nearby charging EVs stations, and other important information in RSUs. This predisposes them to issues such as privacy, trust, or the inefficient use of shared information. To address all these, researchers are exploring different approaches for privacy-preserving solutions. [PERSON] et al. [165] proposed a blockchain-based secure energy-trading system that integrates machine learning for optimal energy trading scheme. In this study, the k-NN algorithm was used to calculate the shortest distance between a charging station and an EV, while the blockchain provided a solution to the transaction redundancy problem through a hashing algorithm. The authors report that the proposed mechanism enhanced evaluation of the energy request by an EV and suggested the amount of energy and the closet available at the charging station. A summary of all the different IoV applications where technical solutions is provided by the integrated approach involving blockchain and machine learning towards an improved ITS is provided in Table 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Application** & **BC Type/Consensus Mechanism** & **ML Adopted** & **Key Findings** \\ \hline [PERSON] et al. [144] & Secure traffic information collection & Proof-of-Work (PoW) & TE-LSTM & The proposed scheme was able to protect users’ identities and travel traces, and defend against Byzantine attacks and Sybil attacks. \\ \hline [PERSON] et al. [143] & Secure traffic data management & Consortium Blockchain (PBFT) & FL & The proposed scheme achieved improvement in the runtime overhead and model accuracy. \\ \hline [PERSON] et al. [145] & Secured vehicular data collection system & Proof of Computing (PoC) & DRL & The proposed approach achieved a good tradeoff between latency and safety, and greatly lowered the uploading delay. \\ \hline [PERSON] and [PERSON] [105] & Efficient traffic incident reporting & Ethereum blockchain & LSTM & The proposed scheme achieved improvement in the overall efficiency. \\ \hline [PERSON] et al. [140] & Effective traffic flow prediction & dBF-based consortium blockchain & FL & The proposed schemes effectively enabled data poisoning attacks defense and achieved improved privacy protection for traffic flow prediction. \\ \hline [PERSON] et al. [166] & Accurate credit evaluation & Hyperledger & LSTM & The proposed approach was effective in the supervision and management of the food supply chain. \\ \hline [PERSON] and [PERSON] [152] & Efficient vehicular communication networking & Proof-of-Work (PoW) & FL & The proposed method minimized system delay. \\ \hline [PERSON] et al. [155] & Enabled vehicle data sharing & DRoS & DDL & The proposed method \\ \hline [PERSON] and [PERSON] [156] & Efficient knowledge sharing and collective learning & Public ledger & RL & The proposed approach acallized synchronous car learning and knowledge sharing. \\ \hline [PERSON] et al. [157] & Efficient knowledge sharing & Proof-of-Knowledge (PoK) & hierarchical FL & The proposed scheme enhanced the sharing efficiency and increased learning accuracy and optimal utility during the sharing process. \\ \hline [PERSON] et al. [151] & Efficient data sharing for CAVs & Proof of Federated Learning (PoFL) & FL & The proposed model showed better performance in terms of privacy preserving, secure storage, incentive mechanism, robustness, gradient encryption, and distribution of the system. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of applications of ML in blockchain-based IoV.