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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Towards an AI-Driven Data Reduction Framework for Smart City Applications.

Laercio Pioli¹, Douglas D J de Macedo^1,2, Daniel G Costa³

¹INE, Computer Science Department, Federal University of Santa Catarina, Florianopolis 88040-370, Brazil.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This summary is machine-generated.

Internet of Things (IoT) data reduction is crucial for smart cities. This study proposes an AI framework using a machine learning model to select optimal data reduction techniques, with Huffman algorithm showing superior performance for time-series data.

Keywords:

Internet of Things artificial intelligence edge intelligence machine learning urban sensing

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Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

The Internet of Things (IoT) generates massive heterogeneous data, posing challenges for storage, bandwidth, and computation.
Smart city environments with multiple data sources exacerbate these data management issues.
Artificial intelligence-driven data reduction techniques offer a promising solution to mitigate these challenges.

Purpose of the Study:

To propose a novel framework for heterogeneous data reduction in IoT applications.
To develop a machine learning model for predicting distortion and reduction rates to guide technique selection.
To incorporate data producer context into the selection of appropriate reduction algorithms.

Main Methods:

Development of a machine learning model to predict data distortion and reduction rates.
Integration of data producer context to constrain algorithm selection.
Evaluation of various data reduction techniques, including the Huffman algorithm.

Main Results:

The proposed framework effectively reduces heterogeneous data volume.
The machine learning model accurately predicts distortion and reduction rates.
The Huffman algorithm demonstrated superior performance for time-series data reduction.

Conclusions:

The developed AI framework provides an effective approach to data reduction in IoT systems.
The Huffman algorithm is a highly effective method for reducing time-series data in smart city contexts.
This research offers significant potential for optimizing resource utilization in smart city IoT deployments.