Recently, it has been suggested to use Information Centric Networking approaches for enabling communication in DTN (Delay Tolerant Networking) type of scenarios. In particular, disaster scenarios are being studied, where ICN data mules disseminate content based on interests received from end-users or during encounters of other ICN data mules. In such a scenario, it is very useful to estimate the overall popularity (among end-users) of a given ICN interest message. This enables data mules to optimize content dissemination with limited caching capabilities by prioritizing interests based on their popularity. We present a scheme that enables to estimate the popularity of ICN interest messages in a completely decentralized manner among data mules in a scenario with random, unpredictable movements of ICN data mules. Our solution achieves scalable, distributed counting/aggregating of interests (in the sense of a popularity count) with loop detection. We also present analytical storage overhead estimations as well as evaluation results based on a simulation of our proposed approach.
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