Hot Area Targeting Dead Reckoning for Distributed Virtual Environments

Abstract

Dead reckoning (DR) is a key technique to increase scalability in Distributed Virtual Environments (DVE). Replacing data transmission with prediction, DR relies on its prediction capability to reduce the bandwidth consumption in the cost of inconsistency among participants. We propose a hot area targeting DR (HATDR) approach to increase the prediction capability by the hot area targeting pattern discovered with a noise-resistant clustering approach. This approach is shown to be robust against hyperparameters. Experiments carried out with a real-life MMOG dataset show that HATDR is comparable to the state-of-the-art DR approaches.