@Article{jbd.2026.077330,
AUTHOR = {Chirag Devendrakumar Parikh},
TITLE = {Compliance-Integrated Data Integrity Framework for Large-Scale Sensor and Measurement Systems},
JOURNAL = {Journal on Big Data},
VOLUME = {8},
YEAR = {2026},
NUMBER = {1},
PAGES = {11--26},
URL = {http://www.techscience.com/jbd/v8n1/68715},
ISSN = {2579-0056},
ABSTRACT = {Sensors and large-scale measurement systems generate continuous data streams used in industrial monitoring, IoT analytics, and decision-making. However, sensor drift, undocumented maintenance, environmental stress, and component variability often degrade the integrity and reliability of collected measurements. This study proposes a compliance-integrated data integrity framework that combines hardware qualification records, traceability documentation, lifecycle validation checkpoints, and automated anomaly detection methods. The framework introduces five integrity layers that link sensor characterization with statistical filtering and compliance-driven verification. To validate feasibility, a proof-of-concept simulation was conducted using drift-injected sensor datasets. Results show that integrating compliance metadata improve anomaly detection accuracy compared to conventional statistical validation methods. The proposed approach provides a reproducible architecture for improving long-term consistency, authenticity, and trustworthiness of sensor data in distributed measurement environments.},
DOI = {10.32604/jbd.2026.077330}
}