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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold.

Handuo Zhang¹, Jun Na², Bin Zhang²

¹School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This study introduces an adaptive threshold method for Internet of Things (IoT) data reduction. It dynamically balances data reduction rates and reconstruction accuracy, improving efficiency in intelligent IoT applications.

Keywords:

Internet of Things Kalman filtering concept drift detection data reduction

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Area of Science:

Computer Science
Data Science
Internet of Things (IoT)

Background:

Intelligent IoT applications generate vast sensor data, necessitating data reduction for bandwidth and energy efficiency.
Current data reduction methods use fixed thresholds, failing to adapt to dynamic IoT environments and compromising reconstruction accuracy.
The trade-off between reduction rate and reconstruction accuracy is critical but difficult to optimize with static parameters.

Purpose of the Study:

To propose an autonomous IoT data reduction method using an adaptive threshold.
To dynamically balance data reduction rate and reconstruction accuracy in changing IoT conditions.
To improve the efficiency and effectiveness of data handling in intelligent IoT systems.

Main Methods:

Implemented an adaptive threshold for IoT data reduction, adjusting dynamically based on data characteristics.
Incorporated concept drift detection to identify IoT system changes and trigger threshold adjustments.
Enhanced data reconstruction by adding data trend information to improve accuracy.

Main Results:

The proposed adaptive threshold method demonstrated significant improvements over static methods.
Achieved an average of 11.7% improvement in accuracy for the same reduction rate.
Showcased a 17.3% improvement in reduction rate for the same level of accuracy.

Conclusions:

The adaptive threshold method effectively balances data reduction and reconstruction accuracy in dynamic IoT environments.
Concept drift detection and data trend incorporation are key to optimizing IoT data management.
The proposed method offers a superior alternative to traditional algorithms like Kalman filtering, LMS, and PIP for IoT data processing.