Open Access

ARTICLE

Compliance-Integrated Data Integrity Framework for Large-Scale Sensor and Measurement Systems

Chirag Devendrakumar Parikh^*

Computer Engineering, California State University, Fullerton, CA, USA

* Corresponding Author: Chirag Devendrakumar Parikh. Email:

Journal on Big Data 2026, 8, 11-26. https://doi.org/10.32604/jbd.2026.077330

Received 07 December 2025; Accepted 01 June 2026; Issue published 07 September 2026

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.

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Keywords

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