Enhanced Traffic Carbon Emissions Data Sharing and Modeling via Blockchain and Personalized Federated Learning in IIoT Ecosystem

The energy transition is essential for achieving sustainable development, and in this context, sharing traffic carbon emissions data within the IIoT ecosystem plays a pivotal role in incentivizing the reduction of greenhouse gas emissions and promoting cleaner transportation solutions. By leveraging interconnected IoT devices and data analytics, we can enhance transparency and collaboration, driving more effective strategies for sustainable transportation. However, managing traffic carbon emissions and facilitating the complex sharing of emission data across various entities present significant challenges. To address these issues, we propose a blockchain-based traffic carbon data sharing and modeling (BCSM) method that provides a secure, efficient, and reliable platform for carbon emissions trading. Our approach introduces a blockchain-based framework designed to ensure data integrity and user privacy in carbon markets, thereby enhancing transparency and trust among participants. We further develop a hierarchical aggregated carbon trading data modeling architecture that integrates hypernetworks with attention mechanisms, enabling the creation of high-quality, personalized models for federated learning clients. This architecture allows clients to benefit from collective insights while preserving data confidentiality. Additionally, we implement a blockchain-editable access control mechanism, facilitating fine-grained permissions management and maintaining a comprehensive, auditable trail of system interactions. The experimental results demonstrate that our BCSM approach not only enhances data security but also significantly improves model performance, offering a robust solution to the challenges in traffic carbon trading and contributing to the broader energy transition objectives.