In the era of Industry 4.0, distributed storage systems face significant challenges in terms of reliability, data security, privacy, and maintenance. While traditional solutions like HDFS rely on centralized nodes that create single points of failure, existing blockchain-based alternatives often lack efficient mechanisms to dynamically evaluate node reliability in untrusted environments, leading to potential data unavailability.This study introduces a secure, blockchain-based distributed storage system using the Trusted Adaptive File Allocation (TAFA) Algorithm for efficient file distribution. The system uses smart contracts to automatically incentivize and monitor nodes, rewarding or penalizing them to boost long-term availability. Extensive simulation experiments demonstrate that this system achieves an optimal Load Balance Factor (LBF) of nearly 1.00 and maintains high storage reliability. It is noteworthy that, even in environments with up to 90% malicious nodes performing tampering or denial-of-service attacks, the file availability of this system remains at least 30%, which is up to 5.7 times higher than that of other distributed storage algorithms.

The remarkable success of deep learning (DL) in predicting battery health has prompted interest in its application in recent years. While state-of-the-art DL models have achieved high accuracy in battery health prediction, they have not been widely adopted in industrial workflows, primarily due to their lack of interpretability and security. To address this issue, we propose a blockchain-based interpretable prediction algorithm for battery health prediction in electric vehicles (EVs) within the Internet of Vehicles (IoV). Specifically, the proposed method includes a platform architecture for a blockchain-based DL system, ensuring secure storage of user data during the prediction process. Notably, we develop a novel battery life prediction algorithm called BLP-Transformer, which leverages short-term relationships between degraded data and explains the impact of feature extraction on predicted results through the contribution of aggregated features based on a feature focusing mechanism. Experimental results demonstrate that the system is feasible for security and can provide accurate battery life prediction. In addition, the comparison study further highlights the superiority of the proposed algorithm in terms of robustness, prediction accuracy, and model interpretability.