Name
Title | ||
|---|---|---|
End-to-end delay boundary prediction using maximum entropy principle (MEP) for internet-based teleoperation |
Abstract | ||
|---|---|---|
Since data packets may get lost somewhere in the Internet connections, for real-time applications such as Internet-based teleoperation, delay boundary prediction plays an important role in determining properly whether a packet is lost or not. The predictors currently employed are lowpass filters based on the autoregressive and moving average (ARMA) models. However, recent studies and the results of the experiments in this paper show that the traditional ARMA model is not suitable because sometimes delays develop with quick and evident variation. In this paper, we present a novel adaptive algorithm for delay boundary prediction based on the maximum entropy principle (MEP). The results of our 3 successive working day experiments on 9 links which consists of academic, commercial and governmental ones among Northern America, Asia and Europe show that the MEP algorithm proposed has a better performance than the traditional ARMA method |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ROBOT.2002.1013640 | Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference |
Keywords | Field | DocType |
Internet,autoregressive moving average processes,low-pass filters,maximum entropy methods,telecontrol,Internet-based teleoperation,adaptive algorithm,autoregressive and moving average models,data packets,delay boundary prediction,end-to-end delay boundary prediction,lowpass filters,maximum entropy principle | Teleoperation,Autoregressive–moving-average model,Autoregressive model,End-to-end delay,Control theory,Network packet,Control engineering,Adaptive algorithm,Principle of maximum entropy,Moving average,Mathematics | Conference |
Volume | Issue | Citations |
3 | 1 | 1 |
PageRank | References | Authors |
0.37 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Xiaoping Liu | 1 | 1158 | 91.78 |
| Max Q.-H. Meng | 2 | 1477 | 202.72 |
| Xiufen Ye | 3 | 42 | 10.31 |
| Jason Gu | 4 | 421 | 74.77 |