Mobile network estimation

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Abstract
Mobile systems must adapt their behavior to changing network conditions. To do this, they must accurately estimate available network capacity. Producing quality estimates is challenging because network observations are noisy, particularly in mobile, ad hoc networks. Current systems depend on simple, exponentially-weighted moving average (EWMA) filters. These filters are either able to detect true changes quickly or to mask observed noise and transients, but cannot do both. In this paper, we present four filters designed to react quickly to persistent changes while tolerating transient noise. Such filters are agile when possible, but stable when necessary, adapting their behavior to prevailing conditions. These filters are evaluated in a variety of networking situations, including persistent and transient change, congestion, and topology changes. We find that one filter, based on techniques from statistical process control provides performance superior to the other three. Compared to two EWMA filters, one agile and the other stable, it is able to offer the agility of the former in four of five scenarios and the stability of the latter in three of four scenarios.

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