Trust-based Data Management 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. An issue is that finding the required data depends on chance encounter with the source of data. Most existing research take the semantics of data into account while caching data onto mobile devices from the wired sources. In this work, we propose that the mobile devices decide what to cache based on the queries they encounter. The scheme involves a distributed technique for estimating the global query distribution in the network. The devices proactively increase the availability of popular data in the network. They use their estimate of query frequencies to push popular data and to guide their caching decisions. We also address the issue of data tampering in MANETs. The answers obtained from peer devices may not be reliable. This propagation of incorrect data may be either intentional or out of ignorance. We propose a Bayesian approach to infer the correct answer. The suggested answers and the reputation values of the sources themselves are used to determine the most likely answer. We implement these techniques in the network simulator, Glomosim and show that our scheme improves data availability, response latency and data accuracy.

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data validation, manet, predictive caching, query distribution estimation, reputation management


University of Maryland at Baltimore County

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