Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3437959.3459260acmconferencesArticle/Chapter ViewAbstractPublication PagespadsConference Proceedingsconference-collections
research-article

Hot Area Targeting Dead Reckoning for Distributed Virtual Environments

Published: 01 June 2021 Publication History

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.

Supplementary Material

MP4 File (SIGSIM-PADS21-pads768.mp4)
Presentation video

References

[1]
Wentong Cai, Francis Lee, and Lian Chen. 1999. An auto-adaptive dead reckoning algorithm for distributed interactive simulation. In Proceedings of the thirteenth workshop on Parallel and distributed simulation. IEEE Computer Society, 82--89.
[2]
J. Calvin, A. Dickens, R. Gaines, P. Metzger, D. Miller, and D. Owen. 1993. The SIMNET Virtual World Architecture. In Proceedings of the IEEE Virtual Reality Annual International Symposium. 450--455.
[3]
Kuan-Ta Chen and Li-Wen Hong. 2007. User identification based on game-play activity patterns. In Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games. ACM, 7--12.
[4]
Youfu Chen and Elvis S. Liu. 2018. Comparing Dead Reckoning Algorithms forDistributed Car Simulations. In Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS '18). 105--111.
[5]
Anders Drachen and Alessandro Canossa. 2011. Evaluating motion: Spatial user behaviour in virtual environments. International Journal of Arts and Technology 4, 3 (2011), 294--314.
[6]
R.M. Fujimoto. 2000. Parallel and distributed simulation systems. Wiley.
[7]
Robert L Goldstone and Benjamin C Ashpole. 2004. Human foraging behavior in a virtual environment. Psychonomic bulletin & review 11, 3 (2004), 508--514.
[8]
Elvis S Liu and Georgios K Theodoropoulos. 2014. Interest management for distributed virtual environments: A survey. ACM computing surveys (CSUR)46, 4(2014), 51.
[9]
Aaron Mccoy, Tomas Ward, Seamus Mcloone, and Declan Delaney. 2007. Multistep-ahead Neural-network Predictors for Network Traffic Reduction in Distributed Interactive Applications. ACM Trans. Model. Comput. Simul. 17, 4,Article 16 (Sept. 2007). https://doi.org/10.1145/1276927.1276929
[10]
Seamus C. McLoone, Patrick J. Walsh, and Tomas E. Ward. 2012. An Enhanced Dead Reckoning Model for Physics-Aware Multiplayer Computer Games. In Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications(Dublin, Ireland)(DS-RT '12). IEEE Computer Society, Washington, DC, USA, 111--117. https://doi.org/10.1109/DS-RT.2012.22
[11]
John L Miller and Jon Crowcroft. 2009. Avatar movement in World of Warcraft battlegrounds. In Proceedings of the 8th annual workshop on Network and systems support for games. IEEE Press, 1.
[12]
João B. Pinto Neto, Nathalie Mitton, Miguel Elias M. Campista, and Luís Henrique M. K. Costa. 2018. Dead Reckoning Using Time Series Regression Models. In Proceedings of the 4th ACM MobiHoc Workshop on Experiences with the Design and Implementation of Smart Objects(Los Angeles, California)(SMARTOBJECTS '18). Association for Computing Machinery, New York, NY, USA, Article 6, 6 pages. https://doi.org/10.1145/3213299.3213305
[13]
David L. Neyland. 1997. Virtual Combat: A Guide to Distributed Interactive Simulation. Stackpole Books.
[14]
Charles A. Ramsey and Alan D. Hewitt. 2005. A Methodology for Assessing Sample Representativeness. Environmental Forensics6, 1 (2005), 71--75. https://doi.org/10.1080/15275920590913877 arXiv:https://doi.org/10.1080/15275920590913877
[15]
Jorma Rissanen. 1986. Order estimation by accumulated prediction errors. Journal of Applied Probability 23, A (1986), 55--61. https://doi.org/10.2307/3214342
[16]
Dave Roberts, Rob Aspin, Damien Marshall, Seamus Mcloone, Declan Delaney, and Tomas Ward. 2008. Bounding Inconsistency Using a Novel Threshold Metric for Dead Reckoning Update Packet Generation. Simulation 84, 5 (May 2008), 239--256. https://doi.org/10.1177/0037549708092221
[17]
Cheryl Savery and T. C. Nicholas Graham. 2013. Timelines: simplifying the programming of lag compensation for the next generation of networked games. Multimedia Systems 19, 3 (2013), 271--287.
[18]
Sandeep Singhal and Michael Zyda. 1999. Networked Virtual Environments: Design and Implementation. ACM Press/Addison-Wesley Publishing Co., New York, NY,USA.
[19]
Sandeep K Singhal and David R Cheriton. 1994.Using a position history-based protocol for distributed object visualization. Technical Report. DTIC Document.
[20]
Mirko Suznjevic and Maja Matijasevic. 2013. Player behavior and traffic characterization for MMORPGs: a survey. Multimedia systems 19, 3 (2013), 199--220.
[21]
Ruck Thawonmas, Junichi Oda, and Kuan-Ta Chen. 2009. Analysis of User Trajectories Based on Data Distribution and State Transition: a Case Study with a Massively Multiplayer Online Game Angel Love Online. In GAMEON. 56--62.
[22]
Samir Torki, Patrice Torguet, and Cédric Sanza. 2007. Adaptive Classifier System-based Dead Reckoning. In Proceedings of the 13th Eurographics Conference on Virtual Environments(Weimar, Germany)(EGVE'07). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 101--108. https://doi.org/10.2312/EGVE/IPT_EGVE2007/101--108
[23]
Amir Yahyavi, Kévin Huguenin, and Bettina Kemme. 2013. Interest Modeling in Games: The Case of Dead Reckoning. Multimedia Syst. 19, 3 (June 2013), 255--270. https://doi.org/10.1007/s00530-012-0275-z
[24]
Xiaoyu Zhang, Denis Gra?anin, and Thomas P Duncan. 2004. Evaluation of a pre-reckoning algorithm for distributed virtual environments. In Parallel and Distributed Systems, 2004. ICPADS 2004. Proceedings. Tenth International Conference on. IEEE, 445--452.
[25]
Suiping Zhou, Wentong Cai, Bu-Sung Lee, and Stephen J. Turner. 2004. Time-Space Consistency in Large-Scale Distributed Virtual Environments. ACM Trans. Model. Comput. Simul. 14, 1 (Jan. 2004), 31--47. https://doi.org/10.1145/974734.974736

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGSIM-PADS '21: Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
May 2021
181 pages
ISBN:9781450382960
DOI:10.1145/3437959
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. clustering
  2. dead reckoning
  3. distributed virtual environments

Qualifiers

  • Research-article

Conference

SIGSIM-PADS '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 398 of 779 submissions, 51%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 68
    Total Downloads
  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media