In this paper, we analyze the throughput of data dissemination at the level of users’ interests. We show that users’ interests have the ability to drastically improve upon existing throughput scaling’s established under the assumption that users show the same preference in any type of data they encounter. More precisely, we consider the scenario where each data source estimates the recipients that will be interested in its data based on user interest probability, which is described by a Zipf-distributed data popularity that decays of exponent
a can be disseminated efficiently to more recipients in a user-centric network.
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