EcoIntegrity: AI-Augmented Blockchain Framework for Carbon Footprint Tracking and Incentives in IoT

The accelerating demands for environmental sustainability necessitate the development of robust systems capable of meticulous carbon footprint tracking. This paper introduces “EcoIntegrity,” an AI-augmented blockchain framework tailored for IoT environments to ensure transparent and accurate carbon footprint monitoring. Our comprehensive approach involves a three-phase solution that combines advanced AI algorithms, immutable blockchain technology, and an equitable incentive mechanism. The first phase harnesses IoT devices for data acquisition, emphasizing the need for precise and reliable data collection. This sets the foundation for the second phase, where AI, particularly recurrent neural networks and Local Outlier Factor algorithms, come into play. These algorithms are adept at predicting anomalous activities and emissions, thereby bolstering data integrity. The subsequent phase leverages the blockchain’s secure ledger system to store the verified data, thereby fortifying the framework against potential breaches and ensuring data immutability. Furthermore, recognizing the collaborative nature of IoT networks in environmental monitoring, the framework integrates the Shapley value from cooperative game theory. This ensures a fair distribution of incentives among the IoT devices, encouraging accurate data reporting and collaboration. Our results demonstrate that “EcoIntegrity” not only streamlines carbon tracking but also significantly contributes to sustainable environmental management. Our proposed framework pioneers an intelligent framework for IoT-based carbon monitoring advances AI applications for integrity assurance, promotes blockchain for secure data storage, and fosters fairness through strategic reward distribution. The practical implications of our work extend to improved environmental reporting, regulatory adherence, and the promotion of sustainable practices within IoT networks.