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Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things.

Jehan Esheh¹, Sofiene Affes¹

¹EMT Centre (Energy, Materials and Telecommunications), INRS (Institut National de la Recherche Scientifique), Université du Québec, Montréal, QC H5A 1K6, Canada.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This study introduces a Deep Learning (DL) approach to enhance localization accuracy in Wireless Sensor Networks (WSNs). By using a Data Augmentation Strategy (DAS), the method improves positioning for the Internet of Things (IoT).

Keywords:

data augmentation neural networks range-free localization wireless sensor networks

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Area of Science:

Computer Science
Electrical Engineering
Networking

Background:

Wireless Sensor Networks (WSNs) are crucial for IoT applications, but accurate node localization remains a challenge.
The Dv-hop algorithm offers a simple, range-free localization method for WSNs, yet it has limitations in accuracy.
Deep Learning (DL) shows promise for improving localization but requires substantial training data.

Purpose of the Study:

To develop an accurate, Deep Learning (DL)-based range-free localization technique for WSNs in IoT environments.
To address the data scarcity issue in DL for WSN localization.
To improve the localization accuracy of the Dv-hop algorithm.

Main Methods:

A Deep Neural Network (DNN) was employed to refine distance estimations between unknown and anchor nodes.
A Data Augmentation Strategy (DAS) was proposed, creating virtual anchors to generate more training data.
The enhanced Dv-hop algorithm integrated DNN correction for improved localization.

Main Results:

The proposed Data Augmentation Strategy (DAS) effectively increases the training dataset size for DNNs.
The integration of DNN correction significantly enhances the localization accuracy compared to the standard Dv-hop algorithm.
The DL-based approach provides a feasible solution for low-cost, accurate localization in WSNs and IoT.

Conclusions:

Deep Learning, augmented with a Data Augmentation Strategy, offers a powerful method to overcome the accuracy limitations of range-free localization algorithms like Dv-hop in WSNs.
This approach makes advanced DL-aided localization more practical and cost-effective for Internet of Things (IoT) deployments.
The developed technique demonstrates superior performance, paving the way for more precise location awareness in wireless sensor networks.