A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks


The research community focused on vehicle NetWorking has always placed a primary emphasis on ensuring passenger comfort, optimizing traffic management, and increasing road safety as their top priorities. The dream of modern intelligent vehicles has become a reality thanks to developments in computer and Communication technologies. These developments have made it possible to equip vehicles with intelligent sensors, cameras, NetWorking devices, and storage capabilities. The use of massive computations that involve outsourcing of tasks is required for the autonomous operation of modern intelligent vehicles. In recent years, numerous computing paradigms, such as mobile Cloud Computing (MCC), vehicular Cloud Computing (VCC), multi-access or mobile edge computing (MEC), vehicular edge computing (VEC), vehicular fog computing (VFC), and volunteer computing based VANET (VCBV), have been developed to move computational resources close to the user and handle the delay-sensitive applications of modern intelligent vehicles. Examples of these computing paradigms include mobile Cloud Computing (MCC), vehicular Cloud Computing (VCC), multi-access or As a result, in this study, we present an all-encompassing summary of the various computing paradigms that are associated with vehicular networks. In addition to this, we discuss the architectural specifics, similarities, and differences, as well as the primary characteristics of each computing paradigm. In the end, we wrap up the study by discussing some open research questions pertaining to vehicular networks as well as some potential future research directions.

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