Fog Computing Model and Efficient Algorithms for Directional Vehicle Mobility in Vehicular Network

Vehicular fog computing (VFC) has become an appealing paradigm to provide services for vehicles and traffic systems. However, high mobility is one of the great challenges to the communication and computation service qualities in VFC. A network model for directional vehicle mobility is proposed in this paper to guarantee the service qualities of vehicles in VFC. In the model, vehicles are configured into three vehicular subnetworks according to their turning directions at the next crossing. For each subnetwork, vehicles communicate with each other via vehicle-to-vehicle communication, and with roadside units via vehicle-to-infrastructure communication. The aim is to minimize the average response time of the tasks originated from vehicles. By carefully choosing neighboring vehicles as task processing helpers, a greedy algorithm is proposed to solve the mentioned optimization problem. Besides, two bipartite matching based algorithms, named BMA
₁
and BMA
₂
, are proposed by exploiting Kuhn-Munkras approach and minimum-cost maximum-flow approach, respectively. Performance of the proposed model and the offloading algorithms are evaluated on the combined simulation platform by open street map, SUMO and NS-3. Simulation results show that, the proposed model outperforms four existing models in terms of average response time, when the five models have similar number of unsuccessful tasks. Moreover, the proposed BMA
₁
and BMA
₂
are superior to the existing greedy algorithm in terms of the average response time of tasks, and the proposed greedy algorithm significantly accelerates the generation of offloading decisions in comparison to BMA
₁
, BMA
₂
and the existing greedy algorithm.