QoS-enabled ANFIS Dead Reckoning Algorithm for Distributed Interactive Simulation

TL;DR

This paper introduces a QoS-enabled ANFIS Dead Reckoning algorithm that enhances distributed simulation accuracy and network availability by integrating fuzzy inference systems trained with neural network principles.

Contribution

It presents a novel ANFIS-based Dead Reckoning method that incorporates contextual information and optimization to improve simulation accuracy and QoS in large-scale distributed environments.

Findings

Improved prediction accuracy in distributed simulations.

Enhanced network availability and QoS.

Better decision-making for simulated entity behaviors.

Abstract

Dead Reckoning mechanisms are usually used to estimate the position of simulated entity in virtual environment. However, this technique often ignores available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favor of low computational complexity. A novel extension of Dead Reckoning is suggested in this paper to increase the network availability and fulfill the required Quality of Service in large scale distributed simulation application. The proposed algorithm is referred to as ANFIS Dead Reckoning, which stands for Adaptive Neuro-based Fuzzy Inference System Dead Reckoning is based on a fuzzy inference system which is trained by the learning algorithm derived from the neuronal networks and fuzzy inference theory. The proposed mechanism takes its based on the optimization approach to calculate the error threshold violation in networking games. Our model shows it primary benefits especially in the decision making of the behavior of simulated entities and preserving the consistence of the simulation.