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Smoothed RSSI-Based Distance Estimation Using Deep Neural Network

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2023, 9(2), pp.71-76
  • DOI : 10.20465/KIOTS.2023.9.2.071
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : February 16, 2023
  • Accepted : March 17, 2023
  • Published : April 30, 2023

Hyeokdon Kwon 1 SOLBEE LEE 1 Kwon Jung-Hyok 1 EUIJIK KIM 1

1한림대학교

Accredited

ABSTRACT

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.