Offloading AI for peer-to-peer games with dead reckoning
IPTPS'09 Proceedings of the 8th international conference on Peer-to-peer systems
Dead Reckoning-Based Update Scheduling against Message Loss for Improving Consistency in DVEs
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
Comparison of predictive contract mechanisms from an information theory perspective
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
An information-based dynamic extrapolation model for networked virtual environments
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fair and Efficient Dead Reckoning-Based Update Dissemination for Distributed Virtual Environments
PADS '12 Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation
Adaptive classifier system-based dead reckoning
EGVE'07 Proceedings of the 13th Eurographics conference on Virtual Environments
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In this paper, we studied the relationship between the accuracy of the dead reckoning data and the update interval in a distributed virtual environment (DVE).We have already shown that numerical analysis can be applied to the dead reckoning data between frames using parametrics calculated from the data over the last several frames based on polynomial models. Based on the above polynomial models, we proposed the new dead reckoning method to extrapolate the attribute data which arrives at discrete time period. We showed that theoretical models which approximate the statistical error of dead reckoning data can be formulated based on parameters such as the update interval and changes in the data. Theoretical models showed that the average error of proposed method is less than that of current method. Finally, we evaluate the validity of the proposed method with the comparison of current method by conducting experiments with the pen motion of a series of letters written by human. We confirmed that the proposed method can decrease the extrapolation error in comparison with current method in average.