A survey on Vehicular Fog Computing: Current state-of-the-art and future directions

Abstract

Vehicular fog computing (VFC) enhances Intelligent Traffic System (ITS) by computing the real-time traffic information for accident alerts, path navigation and etc. It utilizes parked and moving vehicles as a fog node to perform computation rather than the core cloud. VFC consumes less bandwidth, decreased response time, and congestion of core cloud due to sort distance. VFC performs resource allocation to select the appropriate vehicle for computation according to the available computational resource. It performs data retrieval operations to transfer traffic data from vehicle to vehicular fog because direct communication is limited by the transmission range of the vehicle. VFC faces various issues during resource allocation and data retrieval operation such as vehicle's high mobility, dynamic topology and etc. This causes uneven resource distribution due to inefficient resource allocation and late data delivery due to unfeasible data retrieval. The security and privacy leakage of traffic data threaten due to sharing in vehicular fog. To overcome these issues, we provide in-depth exploration at each phase of the VFC operation. Hence, we present a new classification of VFC operation, which classifies the challenges every phase such as resource allocation in task division, scheduling, and load balancing; result-retrieval in phase, node selector, node selection and path recovery; secure data sharing in authentication, encipherment, auditing, and data privacy. This state-of-the-art gives a better understanding of open research issues and future direction to efficiently handle the computation at VFC.