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Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

机译:分布式交互式应用中用于减少网络流量的多步神经网络预测器

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Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms.
机译:预测合同机制(例如航位推算)已广泛用于支持分布式交互式应用程序(DIA)中的可伸缩远程实体建模。通过采用受控的不一致形式,可以减少网络流量。但是,依靠即时导数信息的分布,航位推算以较低的计算复杂度和易于实现性来权衡远程外推精度。在本文中,我们提出了航位推算的新扩展,称为神经推斥,其目的是用预测速度信息代替瞬时速度信息的使用,以提高远程主机上实体位置外推的准确性。根据我们提出的神经干扰方法,每个控制主机都使用一组神经网络预测器,这些预测器受过训练,可以估计直至并包括某个最大预测范围的实体速度的未来变化。模拟每个估计的速度变化对当前实体位置的影响,以在某个短时间范围内对实体的可能位置产生一个估计。在检测到错误阈值违规时,与发送瞬时速度矢量相反,控制主机发送通过估计位置外推的预测速度矢量。这样的方法成功地减少了与实体状态的远程外推相关联的空间误差。因此,可以实现网络流量的进一步减少。与使用IEEE DIS标准航位推算相比,在高度互动的DIA中使用多个人类用户进行的仿真结果表明,改进可伸缩性的巨大潜力。我们提出的神经干扰框架展示了用于实时使用的低计算资源开销,并且可以无缝集成到许多现有的航位推测机制中。

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