Environment Aware Localization with BLE Fingerprinting for the Next Generation PEPS system

Bae, Han Jun; Choi, Lynn

doi:10.1109/wcnc.2019.8886078

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Keyless ignition systems improve a lot in these scenarios. Wireless network technologies, such as NFC (Near-Field Communication), UWB (Ultra-Wide Band), and BLE (Bluetooth Low Energy), have been adopted in the past few years [1][2][3][4]. They have the advantage of low power consumption.…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things

Chang,

Lin,

Lin

2024

Electronics

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With the development of sensor and communication technologies, the 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 emerging. For example, a driver with the smart key in their pocket can push the start button to start a car. At the same time, security issues in the push-to-start scenario are pervasive, such as smart key forgery. In this study, we propose a vehicle Passive Entry Passive Start (PEPS) system that adopts deep learning algorithms to recognize the driver using the electrocardiogram (ECG) signals measured on the driver’s smart watch. ECG signals are used for personal identification. Smart watches, serving as new smart keys of the PEPS system, can improve convenience and security. In the experiment, we consider commercial smart watches capable of sensing ECG signals. The sample rate and precision are typically lower than those of a 12-lead ECG used in hospitals. The experimental results show that Long Short-Term Memory (LSTM) models achieve the best accuracy score for identity recognition (91%) when a single ECG cycle is used. However, it takes at least 30 min for training. The training of a personalized Auto Encoder model takes only 5 min for each subject. When 15 continuous ECG cycles are sensed and used, this can achieve 100% identity accuracy. As the personalized Auto Encoder model is an unsupervised learning one-class recognizer, it can be trained using only the driver’s ECG signal. This will simplify the management of ECG recordings extremely, as well as the integration of the proposed technology into PEPS vehicles. A FIDO (Fast Identify Online)-like environment for the proposed PEPS system is discussed. Public key cryptography is adopted for communication between the smart watch and the PEPS car. The driver is first verified on the smart watch via local ECG biometric authentication, and then identified by the PEPS car. Phishing attacks, MITM (man in the middle) attacks, and replay attacks can be effectively prevented.

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Section: Introductionmentioning

confidence: 99%

A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things

Chang,

Lin,

Lin

2024

Electronics

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“…Keyless ignition systems improve a lot in these scenarios. Wireless network technologies, such as NFC (Near Field Communication), UWB (Ultra-Wide Band), BLE (Bluetooth Low Energy) have been adopted in the past few years [1][2][3][4]. They have the advantages of low power consumption.…”

Section: Introductionmentioning

confidence: 99%

Vehicle PEPS System with Intelligent Internet of Things

Chang,

Lin,

Lin

2023

Preprint

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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.

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“…et.al., (2019, April) Implements Passive Entry and Passive Start (PEPS) systems which is considered as next generation approche along with the fingerprinting algorithm. To calculate the exact position of an object or a person using fingerprinting algorithm added with the last recorded location based on the signal availability in the smart phones are proposed [4]. By examining, analyzing and comparing all the literature works, the new approch is proposed to calculate the exact location using BLE (Bluetooth Low Energy) beacons along with LoRa (Long Range) Gateway (Hybrid Technology) to findout exact locations of an Object or a Person in indoor Environments and send the information over a long distance with the help of LoRa Transceiver modules and moreover this entire information will be updated in the "Ubidots Cloud" platforms using LoRaWAN Central Gateway and The Things Network (TTN) to trace out the Indoor Location of an Objects, garments, Machine parts, goods or a person any where in the world.…”

Section: Introductionmentioning

confidence: 99%

IoT Enabled BLE and LoRa based Indoor Localization without GPS

Et. al.

2021

TURCOMAT

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The common problem that people face these days to find out the Indoor location of a particular Object or a Person exactly. GPS-based location tracking is one of the very important services nowadays. To use GPS tracking to find a path to our destination and also track the position of our goods using GPS Tracking. It is also possible to track the exact location in absence of GPS with the help of proposed implementation. In some cases like indoor location, the GPS tracker cannot locate exact position of a person or an object. GPS Tracker generally requires an open space with no roof on it. Bluetooth Low Energy (BLE) iBeacons that have to understand basically where this technology can be used or where it is useful. Basically, it is for an especially indoor location tracking of something movable or mobile without the use of a GPS Receiver. This proposed article is going to discuss the idea of making an Indoor location tracker using BLE and LoRa Technology along with IoT Dashboard through “The Things Network” (TTN) to know the position of the object or a person anywhere in the world

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Environment Aware Localization with BLE Fingerprinting for the Next Generation PEPS system

Cited by 4 publications

References 2 publications

A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things

A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things

Vehicle PEPS System with Intelligent Internet of Things

IoT Enabled BLE and LoRa based Indoor Localization without GPS

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