Abstract
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.
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References
Bisgaard, M., Gerhardt, D., Hermanns, H., Krčál, J., Nies, G., Stenger, M.: Battery-aware scheduling in low orbit: the GomX–3 case. In: Fitzgerald, J., Heitmeyer, C., Gnesi, S., Philippou, A. (eds.) FM 2016. LNCS, vol. 9995, pp. 559–576. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48989-6_34
Boker, U., Henzinger, T.A., Radhakrishna, A.: Battery transition systems. In: ACM SIGPLAN Notices, vol. 49, pp. 595–606. ACM (2014)
Chiasserini, C.F., Rao, R.R.: Energy efficient battery management. IEEE J. Sel. Areas Commun. 19(7), 1235–1245 (2001)
David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B.: Uppaal SMC tutorial. Int. J. Softw. Tools Technol. Transf. 17(4), 397–415 (2015)
David, A., et al.: Statistical model checking for networks of priced timed automata. In: Fahrenberg, U., Tripakis, S. (eds.) FORMATS 2011. LNCS, vol. 6919, pp. 80–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24310-3_7
Doyle, M., Fuller, T.F., Newman, J.: Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 140(6), 1526–1533 (1993)
Eder, K., Gallagher, J.: Energy-aware software engineering. In: ICT - Energy Concepts for Energy Efficiency and Sustainability, pp. 103–127. InTechOpen (2017)
Fehnker, A., van Hoesel, L., Mader, A.: Modelling and verification of the LMAC protocol for wireless sensor networks. In: Davies, J., Gibbons, J. (eds.) IFM 2007. LNCS, vol. 4591, pp. 253–272. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73210-5_14
Gold, S.: A PSPICE macromodel for lithium-ion batteries. In: Twelfth Annual Battery Conference on Applications and Advances, pp. 215–222. IEEE (1997)
Haemmerlé, R., López-García, P., Liqat, U., Klemen, M., Gallagher, J.P., Hermenegildo, M.V.: A transformational approach to parametric accumulated-cost static profiling. In: Kiselyov, O., King, A. (eds.) FLOPS 2016. LNCS, vol. 9613, pp. 163–180. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29604-3_11
Heni, M., Bouallegue, A., Bouallegue, R.: Energy consumption model in ad hoc mobile network. Int. J. Comput. Netw. Commun. 4(3), 207–217 (2012)
Jaeger, M.G.B.: Wireless network medium access control protocol. https://www.google.com/patents/US20120120871. US Patent App. 12/945,989 (2012)
Jongerden, M., Haverkort, B., Bohnenkamp, H., Katoen, J.P.: Maximizing system lifetime by battery scheduling. In: Proceedings of 2009 IEEE/IFIP International Conference on Dependable Systems & Networks, pp. 63–72. IEEE (2009)
Jongerden, M.R., Haverkort, B.R.: Which battery model to use? IET Software 3(6), 445–457 (2009)
Jongerden, M.R., Haverkort, B.R.: Battery aging, battery charging and the kinetic battery model: a first exploration. In: Bertrand, N., Bortolussi, L. (eds.) QEST 2017. LNCS, vol. 10503, pp. 88–103. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66335-7_6
Jørgensen, K.H., Christoffersen, N.B., Petersen, R.D., Gjøderum, T.H.: VisuAAL - an application for visualizing realistic mesh network protocol behavior through Uppaal simulations, Master Thesis. Aalborg University, Department of Computer Science (2017)
Manwell, J.F., McGowan, J.G.: Lead acid battery storage model for hybrid energy systems. Solar Energy 50(5), 399–405 (1993)
Panigrahi, D., Dey, S., Rao, R., Lahiri, K., Chiasserini, C., Raghunathan, A.: Battery life estimation of mobile embedded systems. In: Fourteenth International Conference on VLSI Design 2001, pp. 57–63. IEEE (2001)
Rakhmatov, D.N., Vrudhula, S.B.: An analytical high-level battery model for use in energy management of portable electronic systems. In: Proceedings of the 2001 IEEE/ACM International Conference on Computer-Aided Design, pp. 488–493. IEEE (2001)
Rodrigues, L.M., Montez, C., Moraes, R., Portugal, P., Vasques, F.: A temperature-dependent battery model for wireless sensor networks. Sensors 17(2), 422 (2017)
Stoer, J., Bulirsch, R.: Introduction to Numerical Analysis, vol. 12. Springer, New York (2013). https://doi.org/10.1007/978-0-387-21738-3
TADIRAN SL-750 Data Sheet. https://tadiranbatteries.de/pdf/lithium-thionyl-chloride-batteries/SL-750.pdf. Accessed 12 June 2018
Wognsen, E.R., Haverkort, B.R., Jongerden, M., Hansen, R.R., Larsen, K.G.: A score function for optimizing the cycle-life of battery-powered embedded systems. In: Sankaranarayanan, S., Vicario, E. (eds.) FORMATS 2015. LNCS, vol. 9268, pp. 305–320. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22975-1_20
Xue, J., Yuan, Z., Zhang, Q.: Traffic load-aware power-saving mechanism for IEEE 802.16 E sleep mode. In: Proceedings of 4th International Conference on Wireless Communications, Networking and Mobile Computing 2008, pp. 1–4. IEEE (2008)
Acknowledgements
We thank Kevin H. Jørgensen, Niels B. Christoffersen, Rasmus D. Petersen and Tim H. Gjøderum for their help with integrating our tool with VisuAAL and numerous discussions about the annotation of battery modes in Uppaal SMC models of the two wireless network protocols. We thank Neocortec for allowing us to use their MAC protocol in our experiments and Thomas Steen Halkier for discussions about the battery consumption patterns and for providing us with current consumption graphs. The work was funded by the center IDEA4CPS, the Innovation Fund Denmark center DiCyPS and ERC Advanced Grant LASSO. The last author is partially affiliated with FI MU, Brno.
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Ivanov, D., Larsen, K.G., Schupp, S., Srba, J. (2018). Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems. In: McIver, A., Horvath, A. (eds) Quantitative Evaluation of Systems. QEST 2018. Lecture Notes in Computer Science(), vol 11024. Springer, Cham. https://doi.org/10.1007/978-3-319-99154-2_11
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