Cyber-physical system with IoT-based smart vehicles

Nowadays, smart vehicles can exchange data through various communication protocols in response to technological developments in smart transport systems. Smart vehicles show the perfect model for a cyber-physical system when electronic components and physical devices are embedded in them. The incorporation of computer, network and physical methods are the cyber-physical systems (CPS). Integrated Systems and Network control and manage physical processes, feedback mechanisms that influence physical computation processes and vice versa. Cyber-devices, including hardware, software applications, are protected from cyber-attacks through cybersecurity. Individuals and businesses use this strategy to shield themselves from unauthorized access to data centers and other computerized networks. As the IoT and data remain fundamentally related, virtual vehicle hijacking is possible in the transport network's evolving design. Hence in this paper, IoT-based Advanced Electronic Cyber-Physical System (AE-CPS) has been proposed to minimize the cyber-attack and enhance security for the smart vehicle. Cyber-assault involves cyber-criminals launching a single computer or several computers or networks with one or more computers. A cyber-attack can maliciously deactivate computers, stolen data, or use a broken computer for other attacks. A cyber-attack happens when a hacker attempt to obtain unauthorized access to a computer or network data. It happens when information without permission is accessed. Personal details, such as social security numbers, passwords, and financial account numbers, may be included. First, a state-space structure reflects the driverless, smart vehicle with the board viewing system. The IoT embedded sensors monitor the device states since the smart vehicles and control center has been far from everyone. The vehicle's sensing data is sent through an insecure communication channel to the control center, where attacks occur. The optimal status estimate algorithm is derived from the mean square error theory for the vehicle conditions' information and visualization. The optimization algorithm focused on the partial definite programming approach is designed to govern the vehicle states. The experimental results show that there are less delay and a high-performance rate of 98.97%. high security rate (95.23%), time stages (8.12%), state reaction rate (90.9%), error rate (6.45%), evaluation metrics (93.6%), reaction time (1.44%), feedback period (96.88%) when compared to other methods.