Machine Biometrics -- Towards Identifying Machines in a Smart City Environment

TL;DR

This paper introduces machine biometrics for identifying machines like cars in smart cities, using engine sound features and machine learning, achieving up to 98% accuracy in car manufacturer identification.

Contribution

First to propose machine biometrics for machine identification in smart city environments, combining sound features with machine learning classifiers.

Findings

Engine sound features effectively distinguish car manufacturers.

MLP neural network achieves 98% accuracy.

Machine biometrics can authenticate machines in smart environments.

Abstract

This paper deals with the identification of machines in a smart city environment. The concept of machine biometrics is proposed in this work for the first time, as a way to authenticate machine identities interacting with humans in everyday life. This definition is imposed in modern years where autonomous vehicles, social robots, etc. are considered active members of contemporary societies. In this context, the case of car identification from the engine behavioral biometrics is examined. For this purpose, 22 sound features were extracted and their discrimination capabilities were tested in combination with 9 different machine learning classifiers, towards identifying 5 car manufacturers. The experimental results revealed the ability of the proposed biometrics to identify cars with high accuracy up to 98% for the case of the Multilayer Perceptron (MLP) neural network model.