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Vehicular cloud computing (VCC) is a promising paradigm for efficiently utilizing and sharing computing and storage resources on vehicles. However, the network topology and the available computing resources change rapidly due to vehicular mobility. In this paper, we study the task offloading problem in the vehicular cloud (VC), in which computing missions that are exclusively divided into interdependent tasks can be offloaded from the edge cloud and executed on vehicles in the VC to minimize the overall response time. A mobility-aware model based on vehicles’ stay time is adopted by considering the instability of computing resources caused by the high vehicular mobility. We formulate an NP-hard optimization problem for task offloading that considers the heterogeneity of vehicular computing capabilities and the interdependency of computing tasks. For this, a Mobility-Aware Vehicular Cloud task Offloading (MAVCO) scheme is designed for low complexity that provides the optimal solution. We also consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to augment the system performance. The simulation findings demonstrate that the proposed algorithm can efficiently minimize the tasks’ response time while releasing the edge cloud burden by comparing it with benchmark approaches.

The flow of data traffic has been vastly increased because of the implementation of the Internet of Things (IoT)-and security is a top need for Internet communication. The encryption-decryption process ensures that data access is restricted to legitimate users. But there is a tradeoff between the level of security and the communication overhead.

In an automated steering system of the self-driving vehicles, the steering wheel angle is measured by the absolute angular displacement sensors or relative angle sensors. However, these sensors either encompass global navigation satellite systems (GNSS)/gyroscope – Micro Electromechanical-Sensor (MEMS) based solutions or comprise of the complex gear-based mechanical structure which results in latency and additive bias in the accumulative steering angle assessment. To address these issues, we propose a novel steering angle assessment system based on enhanced gear mechanism along with the adapted rotation paradigm for the customized self-driving vehicles. Additionally, a digital signal processing system has been introduced to resolve the issues in the identification of absolute central and max-bounding steering wheels position in self-driving vehicles. In assistance with the proposed mechanism, an algorithm has also been proposed to optimize the computed steering angle to minimalize the effect of additive bias in the accuracy. The proposed mechanism has been installed in the customized self-driving testbed vehicle and rigor validation has been performed in the straight and curvy road scenarios. Finally, the comparison study has been carried out between the conventional relative sensor and the proposed mechanism to show the accuracy and effectiveness of the proposed mechanism in terms of error rate, stability, and deviation.