A Crowd-Based Intelligence Approach for Measurable Security, Privacy, and Dependability in Internet of Automated Vehicles with Vehicular Fog

With the advent of Internet of things (IoT) and cloud computing technologies, we are in the era of automation, device-to-device (D2D) and machine-to-machine (M2M) communications. Automated vehicles have recently gained a huge attention worldwide, and it has created a new wave of revolution in automobile industries. However, in order to fully establish automated vehicles and their connectivity to the surroundings, security, privacy, and dependability always remain a crucial issue. One cannot deny the fact that such automatic vehicles are highly vulnerable to different kinds of security attacks. Also, today's such systems are built from generic components. Prior analysis of different attack trends and vulnerabilities enables us to deploy security solutions effectively. Moreover, scientific research has shown that a "group" can perform better than individuals in making decisions and predictions. Therefore, this paper deals with the measurable security, privacy, and dependability of automated vehicles through the crowd-based intelligence approach that is inspired from swarm intelligence. We have studied three use case scenarios of automated vehicles and systems with vehicular fog and have analyzed the security, privacy, and dependability metrics of such systems. Our systematic approaches to measuring efficient system configuration, security, privacy, and dependability of automated vehicles are essential for getting the overall picture of the system such as design patterns, best practices for configuration of system, metrics, and measurements.