Crowd-based Network Prediction : a Comparison of Data-exchange Policies
Abstract: Network performance maps can be used as a tool to predict network conditions at a given location, based on previous measurements at that location. By using measurement data from other users in similar locations, these predictions can be significantly improved. This thesis looks into the accuracy of predictions when using different approaches to distribute this data between users, we compare the accuracy of predictions achieved by using a central server containing all known measurements to the accuracy achieved when using a crowd-based approach with opportunistic exchanges between users. Using data-driven simulations, this thesis also compares and evaluates the impact of using different exchange policies. Based on these simulations we conclude which of the exchange policies provides the most accurate predictions.
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