Intelligent user identity authentication in vehicle security system based on wireless signals

Abstract

Intelligent identity authentication in vehicle security systems, as a vital component in anti-theft system and safety driving assist system, has received wide attention. Current vehicle security systems, however, focus the car security on the car keys security, ignore the owner of car keys. Anyone who owns the car keys can operate the car. This paper introduces an intelligent identity authentication method for vehicle security system based on wireless signals. Unlike past work, our approach combines car security with car owners and car keys. The intuition underlying our design is that when a user walks towards the car, the user’s gait information can be leveraged to identify the user. We capture the user’s gait information using wireless devices which can be deployed in the car, and then extract features from the received wireless signals using convolution kernel and apply artificial neural network to identify the user. We built a prototype and experimental results show that our approach can achieve high accuracy and strong robustness.

A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network

“…Our design assumes no human activity in the current environment when detecting the drinking category, which is the assumption of most current wireless-signal-based detection methods. When the environment is noisy or there is human activity, the CSI measurements received at the receiver are mixed signals [ 24 , 27 ] of the target signals and environmental noise and they are difficult to separate. However, we believe that by combining the method of Wang [ 49 ] and Venkatnarayan [ 34 ], the noisy signals can be separated to improve the detection performance, which is our work in the future.…”

“…Compared with RSS, CSI can obtain more fine-grained information and higher accuracy [ 27 ]; so, our design chooses the drinking category detection method based on CSI.…”