Version 1
: Received: 1 December 2023 / Approved: 4 December 2023 / Online: 4 December 2023 (04:26:06 CET)
How to cite:
Chang, R.; Lin, T.; Lin, J. Vehicle PEPS System with Intelligent Internet of Things. Preprints2023, 2023120113. https://doi.org/10.20944/preprints202312.0113.v1
Chang, R.; Lin, T.; Lin, J. Vehicle PEPS System with Intelligent Internet of Things. Preprints 2023, 2023120113. https://doi.org/10.20944/preprints202312.0113.v1
Chang, R.; Lin, T.; Lin, J. Vehicle PEPS System with Intelligent Internet of Things. Preprints2023, 2023120113. https://doi.org/10.20944/preprints202312.0113.v1
APA Style
Chang, R., Lin, T., & Lin, J. (2023). Vehicle PEPS System with Intelligent Internet of Things. Preprints. https://doi.org/10.20944/preprints202312.0113.v1
Chicago/Turabian Style
Chang, R., Tzu-Chieh Lin and Jeng-Wei Lin. 2023 "Vehicle PEPS System with Intelligent Internet of Things" Preprints. https://doi.org/10.20944/preprints202312.0113.v1
Abstract
With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.
Keywords
Passive Entry Passive Start; Smart Watch; Electrocardiogram; Long Short-Term Memory; Auto Encoder; Collective Decision
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
