ABSTRACT
Recent advancements in automotive systems such as sensor networks, electronic systems, cloud computing, connected vehicles, and cloud are increasing the functionalities and features of vehicles toward intelligent data-driven control systems for an increasingly smooth journey. The objective of this chapter is to introduce a methodology to help draw inferences from the enormous data generated by a vehicular system. Researchers have been proposing multiple approaches for achieving the respective goals of safe, secure and zero emission travel. With increasing electronics and software, the risk of vulnerability is also increasing raising a steep demand for advances in cybersecurity in the Internet of Things (IOT) network of vehicles. The framework presented in the chapter is applicable to the current classic vehicles with diagnostics modules as well as advanced and connected vehicles with an integrated connected diagnostic module. The framework details the various considerations and results, including security and standards for connected applications in the automotive domain. The proposed framework utilizes an On-Board Diagnostic-II (OBD II) module to collect the vehicle data along with a smartphone with multiple inbuilt sensors and connectivity with the cloud. Different data cleaning, characterization, machine learning and analytics models are used to present and analyze the results. The framework has been tested across multiple vehicles and in different locations for the purposes of validation. This chapter also focuses on emphasizing the importance of cybersecurity in implementation of IOT in automotive applications.
