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Dead Reckoning mechanisms are usually used to estimate the position ofsimulated entity in virtual environment. However, this technique often ignoresavailable contextual information that may be influential to the state of anentity, sacrificing remote predictive accuracy in favor of low computationalcomplexity. A novel extension of Dead Reckoning is suggested in this paper toincrease the network availability and fulfill the required Quality of Servicein large scale distributed simulation application. The proposed algorithm isreferred to as ANFIS Dead Reckoning, which stands for Adaptive Neuro-basedFuzzy Inference System Dead Reckoning is based on a fuzzy inference systemwhich is trained by the learning algorithm derived from the neuronal networksand fuzzy inference theory. The proposed mechanism takes its based on theoptimization approach to calculate the error threshold violation in networkinggames. Our model shows it primary benefits especially in the decision making ofthe behavior of simulated entities and preserving the consistence of thesimulation.

QoS-enabled ANFIS Dead Reckoning Algorithm for Distributed Interactive Simulation