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Data Summarization in the Node by Parameters (DSNP): Local Data Fusion in an IoT Environment.

Luis F C Maschi1, Alex S R Pinto2, Rodolfo I Meneguette3

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This study introduces data summarization, a local data fusion technique for the Internet of Things (IoT). This method significantly reduces data volume generated by sensor nodes, decreasing overall IoT message traffic.

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • The Internet of Things (IoT) involves billions of connected devices generating unprecedented data volumes.
  • Efficient data handling and transmission are critical challenges in large-scale IoT deployments.

Purpose of the Study:

  • To propose and evaluate a local data fusion technique called summarization for reducing data volume in IoT environments.
  • To ensure data quality at the sensor node level through application-defined parameters.

Main Methods:

  • Implementation of a data summarization algorithm for local data fusion at the sensor node.
  • Comparative analysis of sensor nodes performing data summarization versus those performing continuous data recording.
  • Testing with sensor nodes analyzing room luminosity and temperature.

Main Results:

  • A reduction of 97% in data volume was achieved for a sensor node analyzing room luminosity.
  • A reduction of 80% in data volume was achieved for a sensor node analyzing room temperature.
  • Demonstrated effectiveness of local data fusion in minimizing data generation.

Conclusions:

  • Local data fusion via summarization is an effective strategy for reducing data volume in IoT.
  • This approach leads to a consequential decrease in the number of messages transmitted in IoT networks.
  • The proposed method enhances the efficiency of IoT data processing and transmission.