On Collective Intellect for Task Offloading in Vehicular Fog Paradigm

A vehicular fog network is an emerging paradigm adopted to facilitate delay-sensitive and innovative applications. Since vehicular environments are inherently dynamic, it becomes a challenge to effectively utilize all available resources. Often a centralized resource distribution model is adopted for effective resource utilization but this comes with significant overhead. Thus, a distributed model seems more suited for highly dynamic vehicular environments; however, a distributed model without collective intelligence can end up in uneven workload distribution. In this paper, we propose a distributed non-cooperative task offloading framework for efficient resource utilization. Here, vehicles with heterogeneous task requirements interact with one another without directly influencing the actions of the neighboring vehicles. That is, the framework allows vehicles to communicate with neighbors to gather contextual information to revisit their offloading decisions. The shared information includes resource type, task residence time, system cost, and offloading inference. The effectiveness of the framework is evaluated against baseline schemes in terms of service delay, transmission delay, system cost, delivery rate, and system efficiency. The results demonstrate significantly reduced task residence times by 50%, highest throughput at 83 Mbits/s, which in turn contributes to an improved system utilization up to 70% across the resource-sharing network.