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Marginal probabilistic modeling of the delays in the sensory data transmission of networked telerobots.

Gago-Benítez A, Fernández-Madrigal JA, Cruz-Martín A - Sensors (Basel) (2014)

Bottom Line: This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot.Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method.The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

View Article: PubMed Central - PubMed

Affiliation: Systems Engineering and Automation Department, University of Málaga, Campus Teatinos, Boulevard Luis Pasteur s/n, Málaga 29071, Spain. anagagobenitez@gmail.com.

ABSTRACT
Networked telerobots are remotely controlled through general purpose networks and components, which are highly heterogeneous and exhibit stochastic response times; however their correct teleoperation requires a timely flow of information from sensors to remote stations. In order to guarantee these time requirements, a good on-line probabilistic estimation of the sensory transmission delays is needed. In many modern applications this estimation must be computationally highly efficient, e.g., when the system includes a web-based client interface. This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot. Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method. The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

No MeSH data available.


Related in: MedlinePlus

PDGs for those regimes obtained by the confidence interval method where more than one parametrical model explain successfully the data obtaining their best p-values.
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f9-sensors-14-02305: PDGs for those regimes obtained by the confidence interval method where more than one parametrical model explain successfully the data obtaining their best p-values.

Mentions: The probability difference graph (Figure 9) is a plot of the difference between an empirical cumulative distribution function (cdf) and a theoretical cdf. This graph can be used to put in order the theoretical distributions in case we have to choose a model, since it yields a qualitative measure of distance between them. It also provides a general, qualitative indication of their goodness to fit the data: the closest to the zero horizontal, the better the fitting. Although all PDGs are for models that have been successful in modeling scenarios (according to the hypothesis test, reflected here in the small values of the curves in the ordinate axis), we can observe in the figure that in some cases the modeling has been better than in others. As happened in the Q-Q plots, scenario #4 has produced the worse fittings, which is reflected in the pronounced belly of all model curves around the middle of the delay values: if they are used for prediction they will be conservative in that area, which could be an interesting property as explained before. In that particular scenario, the gamma distribution seems slightly better than the other two, but their difference is rather small. In scenario #5 we obtain a pdf farther from zero when the erlang model is chosen as hypothesis. In scenario #17 the lognormal has been better than the weibull. In scenario #18 the lognormal is also better for higher values of the delays, which is important in prediction.


Marginal probabilistic modeling of the delays in the sensory data transmission of networked telerobots.

Gago-Benítez A, Fernández-Madrigal JA, Cruz-Martín A - Sensors (Basel) (2014)

PDGs for those regimes obtained by the confidence interval method where more than one parametrical model explain successfully the data obtaining their best p-values.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958226&req=5

f9-sensors-14-02305: PDGs for those regimes obtained by the confidence interval method where more than one parametrical model explain successfully the data obtaining their best p-values.
Mentions: The probability difference graph (Figure 9) is a plot of the difference between an empirical cumulative distribution function (cdf) and a theoretical cdf. This graph can be used to put in order the theoretical distributions in case we have to choose a model, since it yields a qualitative measure of distance between them. It also provides a general, qualitative indication of their goodness to fit the data: the closest to the zero horizontal, the better the fitting. Although all PDGs are for models that have been successful in modeling scenarios (according to the hypothesis test, reflected here in the small values of the curves in the ordinate axis), we can observe in the figure that in some cases the modeling has been better than in others. As happened in the Q-Q plots, scenario #4 has produced the worse fittings, which is reflected in the pronounced belly of all model curves around the middle of the delay values: if they are used for prediction they will be conservative in that area, which could be an interesting property as explained before. In that particular scenario, the gamma distribution seems slightly better than the other two, but their difference is rather small. In scenario #5 we obtain a pdf farther from zero when the erlang model is chosen as hypothesis. In scenario #17 the lognormal has been better than the weibull. In scenario #18 the lognormal is also better for higher values of the delays, which is important in prediction.

Bottom Line: This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot.Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method.The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

View Article: PubMed Central - PubMed

Affiliation: Systems Engineering and Automation Department, University of Málaga, Campus Teatinos, Boulevard Luis Pasteur s/n, Málaga 29071, Spain. anagagobenitez@gmail.com.

ABSTRACT
Networked telerobots are remotely controlled through general purpose networks and components, which are highly heterogeneous and exhibit stochastic response times; however their correct teleoperation requires a timely flow of information from sensors to remote stations. In order to guarantee these time requirements, a good on-line probabilistic estimation of the sensory transmission delays is needed. In many modern applications this estimation must be computationally highly efficient, e.g., when the system includes a web-based client interface. This paper studies marginal probability distributions that, under mild assumptions, can be a good approximation of the real distribution of the delays without using knowledge of their dynamics, are efficient to compute, and need minor modifications on the networked robot. Since sequences of delays exhibit strong non-linearities in these networked applications, to satisfy the iid hypothesis required by the marginal approach we apply a change detection method. The results reported here indicate that some parametrical models explain well many more real scenarios when using this change detection method, while some non-parametrical distributions have a very good rate of successful modeling in the case that non-linearity detection is not possible and that we split the total delay into its three basic terms: server, network and client times.

No MeSH data available.


Related in: MedlinePlus