Optimized Resource Allocation in Vehicular Fog Computing Environments Using Hybrid MOSP Algorithm

Due to the appearance of new concepts such as fog computing and rapid progress toward the Internet of Vehicles (IOV), cloud computing becomes faced with the problem of resource allocation. Fog computing offers a solution by providing and offering computing storage and networking facilities near to the end-users and the connected devices. This work mainly focuses on the resource management for parked vehicles in via vehicular fog computing so as to improve resource utilization, QoS, delay, and energy consumption. The algorithm that is called MOSP and implemented the Multi-objective Grey Wolf Optimizer (MOGWO) solves the problem of allocating the resources for the parked and slow-moving vehicles taking into consideration the limitations concerning computation, storage, and mobility of the fog nodes. For the purpose of comparison, the performance of the proposed MOSP algorithm is compared with other approaches available in the literature. The evaluation of the performance has revealed the successful achievement of less energy consumption and considerable elimination of delays, which are critical issues in vehicular fog computing environments. This paper offers an original approach to resource management in V2V fog computing for parked cars through the employment of MOSP algorithm that enhances resource efficiency while enhancing QoS, delay, and energy consumption.