PCV: Predicting contact volume for reliable and efficient data transfers in opportunistic networks

Exploiting opportunistic contacts between mobile devices to enable deployment of real applications through reliable and efficient data transfers poses a significant research challenge. Indeed, accurate prediction of contact volume, defined as the maximum amount of data transferable during a contact, can improve performance of deployments. However, existing schemes for estimating contact volume that make use of preconceived patterns or contact time distributions may not be applicable in uncertain environments. In this paper, we propose a novel scheme called PCV that predicts contact volume in soft real-time to enable efficient and reliable data transfers in opportunistic networks. An Android Application that learns data rate profiles has been developed to facilitate PCV. In addition, an analytical model has been developed to depict variable data rates between mobile devices. Extensive simulations are carried out on both synthetic and real world mobility traces to validate the usefulness of PCV. Experimental results show the effectiveness of our approach in terms of reliable data transfers.