McCoy, Aaron and Ward, Tomas E. and McLoone, Seamus and Delaney, Declan (2006) Using Neural Networks to Reduce Entity State Updates in Distributed Interactive Applications. In: Proceedings 2006 IEEE International Workshop on Machine Learning for Signal Processing, September 6-8 2006, NUI Maynooth.

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Abstract

Dead reckoning is the most commonly used predictive contract mechanism for the reduction of network traffic in Distributed Interactive Applications (DIAs). However, this technique often ignores available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favour of low computational complexity. In this paper, we present a novel extension of dead reckoning by employing neuralnetworks to take into account expected future entity behaviour during the transmission of entity state updates (ESUs) for remote entity modeling in DIAs. This proposed method succeeds in reducing network traffic through a decrease in the frequency of ESU transmission required to maintain consistency. Validation is achieved through simulation in a highly interactive DIA, and results indicate significant potential for improved scalability when compared to the use of the IEEE DIS Standard dead reckoning technique. The new method exhibits relatively low computational overhead and seamless integration with current dead reckoning schemes.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Neural Networks; Reduce Entity State Updates; Distributed Interactive Applications;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	1446
Depositing User:	Dr Tomas Ward
Date Deposited:	18 Jun 2009 14:14
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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