A Shapley Value-based Strategy for Resource Allocation in Vehicular Clouds

The continuous emergence of new applications for Internet-connected road vehicles is imposing unprecedented re-source demand. Motivated by the incorporation of ever more resources into vehicles, this is a trend that, on the downside, is causing vehicular networks to become increasingly more challenging to manage. Departing from the proposition that computing capabilities can help overcome resource allocation problems in vehicular clouds (VCs), in this paper, we formulate ALTAIC, a coalition game to maximize resource utilization while dynamically load-balancing the usage among the VCs. First, we define a Shapley value-based strategy to determine the order in which the tasks are allocated. Then, with the marginal contribution of each task calculated, we employ a simple queue to allocate the tasks in VCs using these values. Finally, we conduct a comparative performance analysis of ALTAIC and relevant approaches. Simulation results show that the proposed solution allocates more tasks than the others and reduces 27.12% the load average of the VCs.