Mr. Abdusomad Nur Profile

Abdusomad Nur

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Proceedings Article | 22 May 2023 Poster + Paper

Design of a real-time big data analytics scheme for continuous monitoring with a distributed acoustic sensor

Abdusomad Nur, Fabrizio Di Pasquale, Yonas Muanenda

Proceedings Volume 12327, 1232721 (2023) https://doi.org/10.1117/12.2644955

KEYWORDS: Data storage, Acoustics, Sensors, Environmental monitoring, Fiber optics sensors, Databases, Analytics, Spatial resolution, Design and modelling, Vibration

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We propose a method to address the issue of handling the large amount of data involved in Distributed Acoustic Sensing (DAS) by designing and implementing a data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS) which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1km-110 km. The results show latencies of 40 msec per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing sample size paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes such as remote environmental & railways infrastructure monitoring and precision agriculture.

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