Probabilistic Data Prefetching for Data Transportation in Smart Cities

Abstract

To deal with the ever increasing wireless traffic, we have recently designed a vehicular cognitive capability harvesting network (V-CCHN) architecture to leverage vehicles as an alternative “transmission medium” (i.e., an opportunistic data carrier), besides the wireless spectrum, to effectively transport data from the location where it is collected to the place where it is consumed or utilized in a smart city environment. In the V-CCHN, cognitive radio technologies are utilized so that a large amount of data can be exchanged between vehicles and roadside infrastructure through short-range high-speed transmissions. Considering the limited contact duration and the uncertain activities of primary users, how to facilitate efficient data exchange between vehicles and roadside infrastructure is very challenging. This problem is further complicated by the fact that the mobility of vehicles might not be accurately predicted. In this paper, we propose a probabilistic data prefetching (PDP) scheme for the V-CCHN to address these challenges. By considering the conditional value at risk, we formulate the PDP schematic design as an optimization problem which allows us to obtain the corresponding PDP scheme. Finally, we have conducted extensive study to evaluate the performance of the obtained PDP scheme under various parameter settings.