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.
Opportunistic networks are characterized by the dynamic connectivity created when mobile devices encounter each other, as they are within close proximity. During these transient opportunities, devices are typically within one-hop wireless range of their neighbors. Opportunistic networks are an effective way, in terms of bandwidth and battery consumption to distribute large volume content among peers. Many existing proposals consider opportunistic networks as a best-effort content delivery approach, which limits their applications. We exploit characteristics of human mobility to derive an effective data forwarding scheme that achieves Combined Optimal Stability and Capacity (COSC) for opportunistic networks. COSC includes a path selection algorithm to maximize the utility of link capacity and stability. We validate theoretical findings with rigorous simulation studies using synthetic and real-world mobility traces. When compared with other approaches, COSC shows significant improvement due to the consideration of link capacity and stability.
Knowledge of user movement in mobile environments paves the way for intelligent resource allocation and event scheduling for a variety of applications. Existing schemes for estimating user mobility are limited in their scope as they rely on repetitive patterns of user movement. Such patterns neither exist not easy to recognize in soft-real time, in open environments such as parks, malls, or streets. We propose a novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME). MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME is also used to make predictions about the absolute orientation of users. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. MOEME has been tested on real world and synthetic mobility traces -makes predictions about direction and count of users with up to 90% accuracy, enhances successful video downloads in shared environments.
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