Analytical Solution for Long Battery Lifetime Prediction in
Nonadaptive Systems

D 2018

Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems

IVANOV, Dmitry, Kim G. LARSEN, Sibylle SCHUPP and Jiří SRBA

Basic information

Original name

Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems

Authors

IVANOV, Dmitry (643 Russian Federation), Kim G. LARSEN (208 Denmark), Sibylle SCHUPP (276 Germany) and Jiří SRBA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Netherlands, Proceedings of the 15th International Conference on Quantitative Evaluation of SysTems (QEST'18), p. 173-189, 17 pp. 2018

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/18:00106626

Organization unit

Faculty of Informatics

ISBN

978-3-319-99153-5

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-99154-2_11

UT WoS

000548912200011

Keywords in English

UPPAAL SMC; battery models; statistical model checking; wireless protocols

Abstract

V originále

Uppaal SMC is a state-of-the-art tool for modelling and statistical analysis of hybrid systems, allowing the user to directly model the expected battery consumption in battery-operated devices. The tool employs a numerical approach for solving differential equations describing the continuous evolution of a hybrid system, however, the addition of a battery model significantly slows down the simulation and decreases the precision of the analysis. Moreover, Uppaal SMC is not optimized for obtaining simulations with durations of realistic battery lifetimes. We propose an analytical approach to address the performance and precision issues of battery modelling, and a trace extrapolation technique for extending the prediction horizon of Uppaal SMC. Our approach shows a performance gain of up to 80% on two industrial wireless sensor protocol models, while improving the precision with up to 55%. As a proof of concept, we develop a tool prototype where we apply our extrapolation technique for predicting battery lifetimes and show that the expected battery lifetime for several months of device operation can be computed within a reasonable computation time.

Citovat

IVANOV, Dmitry, Kim G. LARSEN, Sibylle SCHUPP and Jiří SRBA. Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems. In Proceedings of the 15th International Conference on Quantitative Evaluation of SysTems (QEST'18). Netherlands: Springer, 2018, p. 173-189. ISBN 978-3-319-99153-5. Available from: https://dx.doi.org/10.1007/978-3-319-99154-2_11.

@inproceedings{1506125,
   author = {Ivanov, Dmitry and Larsen, Kim G. and Schupp, Sibylle and Srba, Jiří},
   address = {Netherlands},
   booktitle = {Proceedings of the 15th International Conference on Quantitative Evaluation of SysTems (QEST'18)},
   doi = {http://dx.doi.org/10.1007/978-3-319-99154-2_11},
   keywords = {UPPAAL SMC; battery models; statistical model checking; wireless protocols},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Netherlands},
   isbn = {978-3-319-99153-5},
   pages = {173-189},
   publisher = {Springer},
   title = {Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems},
   url = {https://link.springer.com/chapter/10.1007%2F978-3-319-99154-2_11},
   year = {2018}
}

TY  - JOUR
ID  - 1506125
AU  - Ivanov, Dmitry - Larsen, Kim G. - Schupp, Sibylle - Srba, Jiří
PY  - 2018
TI  - Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems
PB  - Springer
CY  - Netherlands
SN  - 9783319991535
KW  - UPPAAL SMC
KW  - battery models
KW  - statistical model checking
KW  - wireless protocols
UR  - https://link.springer.com/chapter/10.1007%2F978-3-319-99154-2_11
L2  - https://link.springer.com/chapter/10.1007%2F978-3-319-99154-2_11
N2  - Uppaal SMC is a state-of-the-art tool for modelling and statistical analysis of hybrid systems, allowing the user to directly model the expected battery consumption in battery-operated devices. The tool employs a numerical approach for solving differential equations describing the continuous evolution of a hybrid system, however, the addition of a battery model significantly slows down the simulation and decreases the precision of the analysis. Moreover, Uppaal SMC is not optimized for obtaining simulations with durations of realistic battery lifetimes. We propose an analytical approach to address the performance and precision issues of battery modelling, and a trace extrapolation technique for extending the prediction horizon of Uppaal SMC. Our approach shows a performance gain of up to 80% on two industrial wireless sensor protocol models, while improving the precision with up to 55%. As a proof of concept, we develop a tool prototype where we apply our extrapolation technique for predicting battery lifetimes and show that the expected battery lifetime for several months of device operation can be computed within a reasonable computation time.
ER  -

IVANOV, Dmitry, Kim G. LARSEN, Sibylle SCHUPP and Jiří SRBA. Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems. In \textit{Proceedings of the 15th International Conference on Quantitative Evaluation of SysTems (QEST'18)}. Netherlands: Springer, 2018, p.~173-189. ISBN~978-3-319-99153-5. Available from: https://dx.doi.org/10.1007/978-3-319-99154-2\_{}11.

Detailed Information on Publication Record