Blockchain-Backed Secure Transmission of GPS and EEG Sensor Data for ML Models

Enhancing Machine Learning Model Reliability in Intelligent Systems

Abstract

The integration of Global Positioning System (GPS) and Electroencephalogram (EEG) sensors has become critical in diverse applications ranging from intelligent transportation systems to healthcare monitoring and cognitive analysis. However, the continuous transmission of such sensitive data to machine learning (ML) models raises significant concerns about integrity, privacy, and security. Traditional communication frameworks are often vulnerable to interception, tampering, and unauthorized access, which can compromise both model performance and user trust. This study proposes a blockchain-backed framework for the secure transmission of GPS and EEG sensor data. By leveraging distributed ledger technology, the system ensures immutability, traceability, and resilience against cyber intrusions, while reducing reliance on centralized intermediaries. The architecture integrates lightweight cryptographic protocols and consensus mechanisms optimized for resource-constrained environments, enabling real-time data sharing without excessive energy overhead. Experimental evaluation demonstrates that blockchain-enhanced transmission not only safeguards data authenticity but also maintains compatibility with ML pipelines for predictive analytics and decision-making tasks. The proposed approach provides a scalable and trustworthy solution for future sensor-driven ecosystems, where secure data exchange is essential for both accuracy and ethical deployment of AI technologies.