3 research outputs found

    A HMRSVP approach to support QoS challenges in mobile environment

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    The current Internet architecture with its best effort service model is inadequate for real time applications that need certain Quality of Service (QoS) assurances. Several QoS models are proposed, however, these models were proposed for static environment. The main aim of this paper is to propose a set of protocols that enable the support of seamless mobility with the required QoS. To achieve this, first, the current static environment QoS models are studied, evaluated and compared. Their limitations to support mobility are identified and discussed. Second Mobile RSVP (MRSVP) and its extensions Hierarchal Mobile RSVP (HMRSVP) and Resource Reservation with Pointer Forwarding (HMRSVPpf) approaches are also studied and evaluated. It was shown that the main drawback of these approaches is the scalability issue. Lastly, this paper proposes an extension to the HMRSVP approach to overcome its drawbacks

    QoS Signaling for Parameterized Traffic in IEEE 802.11e Wireless LANs

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    Evaluating Mobility Predictors in Wireless Networks for Improving Handoff and Opportunistic Routing

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    We evaluate mobility predictors in wireless networks. Handoff prediction in wireless networks has long been considered as a mechanism to improve the quality of service provided to mobile wireless users. Most prior studies, however, were based on theoretical analysis, simulation with synthetic mobility models, or small wireless network traces. We study the effect of mobility prediction for a large realistic wireless situation. We tackle the problem by using traces collected from a large production wireless network to evaluate several major families of handoff-location prediction techniques, a set of handoff-time predictors, and a predictor that jointly predicts handoff location and time. We also propose a fallback mechanism, which uses a lower-order predictor whenever a higher-order predictor fails to predict. We found that low-order Markov predictors, with our proposed fallback mechanisms, performed as well or better than the more complex and more space-consuming compression-based handoff-location predictors. Although our handoff-time predictor had modest prediction accuracy, in the context of mobile voice applications we found that bandwidth reservation strategies can benefit from the combined location and time handoff predictor, significantly reducing the call-drop rate without significantly increasing the call-block rate. We also developed a prediction-based routing protocol for mobile opportunistic networks. We evaluated and compared our protocol\u27s performance to five existing routing protocols, using simulations driven by real mobility traces. We found that the basic routing protocols are not practical for large-scale opportunistic networks. Prediction-based routing protocols trade off the message delivery ratio against resource usage and performed well and comparable to each other
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