Leveraging Blockchain Technology With Enhanced MDSVA for Robust Meteorological Sensor Data Validation - Md Abdullah Al Mamun; Mei Li; Bijon Kumar Pramanik; Faisal Hussain; A. Z. M. Shakilur Rahman (IEEE Access)
With the proliferation of meteorological sensor networks, ensuring data quality and reliability has become increasingly challenging. Traditional validation methods often fail to handle complex sensor behaviors, environmental variations, and real-time data verification requirements. This paper presents an innovative approach that leverages blockchain technology with a custom Multidimensional Sensor Validation Algorithm (MDSVA) to comprehensively address these challenges. Our enhanced MDSVA introduces a sophisticated five-component validation function that simultaneously addresses drift and noise compensation, environmental adaptation, periodic variations, dynamic thresholds, and transient adjustments. These components work multiplicatively, ensuring that significant issues in any single aspect substantially affect the overall validation result, while minor variations across multiple components have a more moderate combined effect. The mathematical model employs weighted exponential decay for noise reduction, temperature-based environmental scaling, sinusoidal periodic compensation, logistic threshold transitions, and temporal adaptation functions. This holistic approach significantly improves traditional validation methods by providing more nuanced and context-sensitive data validation. Integration with Hyperledger Fabric blockchain technology is achieved through smart contracts that implement this advanced validation algorithm, ensuring immutable record keeping and distributed consensus on data quality. The experimental results show that our implementation achieved a precision of 94.7% in sensor drift detection, with consistent performance (±1.2% variation) in diverse environments over 24 weeks. The system maintained 99.99% uptime with 4.2 second recovery time and demonstrated robust resilience by handling up to 30% simultaneous node failures while keeping resource utilization below 85%. This research contributes to the field of meteorological data quality assurance by providing a robust, mathematically sound, and technologically advanced solution that meets the growing demands of modern weather monitoring systems.