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Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing

Abdusomad Nur1,2, Yonas Muanenda2

  • 1Addis Ababa Institute of Technology, Addis Ababa University, King George VI St, Addis Ababa 1000, Ethiopia.

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|September 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient cloud-based pipeline for managing large Distributed Acoustic Sensing (DAS) data using Amazon Web Services (AWS) DynamoDB, optimizing real-time monitoring and data processing. Performance evaluation using CloudSim demonstrates scalable cloud computing for DAS, highlighting resource optimization for efficient data analysis.

Keywords:
cloud computingdistributed acoustic sensingdistributive sensingfiberremote sensingsensors

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Area of Science:

  • Geophysics
  • Data Science
  • Cloud Computing

Background:

  • Managing vast datasets from Distributed Acoustic Sensing (DAS) poses significant storage and processing challenges.
  • Efficient cloud-based solutions are needed for real-time analysis of long-range DAS data.

Purpose of the Study:

  • To develop and evaluate an efficient cloud-based data management pipeline for DAS.
  • To assess the performance of cloud computing systems for DAS data processing using the CloudSim framework.

Main Methods:

  • Implemented a pipeline system to efficiently transfer large DAS data volumes to Amazon Web Services (AWS) DynamoDB.
  • Utilized the CloudSim framework to evaluate the performance of various virtual machine (VM) configurations for DAS data computations.

Main Results:

  • The DynamoDB pipeline achieved low latency (40 ms per batch) and demonstrated scalability for DAS data storage.
  • CloudSim analysis revealed that VM performance, processing elements, and MIPS significantly impact processing time, with optimal resource allocation crucial for efficiency.
  • Increased fiber length and data volume showed improved processing efficiency, suitable for real-time monitoring.

Conclusions:

  • The proposed DynamoDB-based pipeline offers a scalable and low-latency solution for cloud-based DAS data management.
  • Cloud computing resources can be effectively optimized for DAS data processing, ensuring efficient real-time monitoring and feature extraction.
  • Further research into resource optimization can enhance the performance and cost-effectiveness of cloud-based DAS systems.