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Machine learning in disruption-tolerant MANETs

Machine learning in disruption-tolerant MANETs In this article we study the data dissemination problem in which data items are flooded to all the moving objects in a mobile ad hoc network by peer-to-peer transfer. We show that if memory and bandwidth are bounded at moving objects, then the problem of determining whether a set of data items can be disseminated to all the moving objects is NP-complete. For a heuristic solution we postulate that a moving object should save and transmit the data items that are most likely to be new (i.e., previously unknown) to future encountered moving objects. We propose a method to be used by each moving object to prioritize data items based on their probabilities of being new to future receivers. The method employs a machine learning system for estimation of the novelty probability and the machine learning system is progressively trained by received data items. Through simulations based on real mobility traces, we show the superiority of the method against some natural alternatives. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Distributed databases
ISSN
1556-4665
DOI
10.1145/1636665.1636669
Publisher site
See Article on Publisher Site

Abstract

In this article we study the data dissemination problem in which data items are flooded to all the moving objects in a mobile ad hoc network by peer-to-peer transfer. We show that if memory and bandwidth are bounded at moving objects, then the problem of determining whether a set of data items can be disseminated to all the moving objects is NP-complete. For a heuristic solution we postulate that a moving object should save and transmit the data items that are most likely to be new (i.e., previously unknown) to future encountered moving objects. We propose a method to be used by each moving object to prioritize data items based on their probabilities of being new to future receivers. The method employs a machine learning system for estimation of the novelty probability and the machine learning system is progressively trained by received data items. Through simulations based on real mobility traces, we show the superiority of the method against some natural alternatives.

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Nov 1, 2009

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