Query Distribution Estimation and Predictive Caching in Mobile Ad Hoc Networks

The problem of data management has been studied widely in the field of mobile ad-hoc networks and pervasive computing. The issue addressed is that finding the data required by a device depends on chance encounter with the source of data. Most existing research has focused on specifying the required data by specifying the user or application intentions. These approaches take the semantics of data into account while caching data onto mobile devices from the wired sources. We propose a scheme by which mobile devices proactively increase the availability of data by pushing and caching the most popular data in the network. It involves a local distributed technique for estimating global query distribution in the network. The devices have a finite sized cache to store the pushed data and use their estimation of queries for prioritizing the data to cache. We implement this technique in the network simulator, Glomosim and show that our scheme improves data availability as well as the response latency.