Reliable Adaptive Resource Management for Cognitive Cloud Vehicular Networks

Abstract

In this paper, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of cognitive radio and soft-input/soft-output data fusion in vehicular access networks. The goal is to allow energy and computing-limited car smartphones to utilize the available vehicle-to-infrastructure (V2I) WiFi connections for performing traffic offloading toward local or remote clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple roadside units (i.e., cloudlets). We cast the resource management problem into a suitable constrained stochastic network utility maximization problem and derive the optimal cognitive resource manager that dynamically allocates the access time windows at the serving roadside units (i.e., the access points), together with the access rates and traffic flows at the served vehicular clients (i.e., the secondary users of the wireless backbone). The developed controller provides hard reliability guarantees to the cloud service provider (i.e., the primary user of the wireless backbone) on a per-slot basis. Furthermore, it is able to acquire context information about the currently available bandwidth-energy resources to quickly adapt to the mobility-induced abrupt changes in the state of the vehicular network.