Abstract
This paper explores a sequential decision making methodology of when to update statistical learning models in Intelligent Edge Computing devices given underlying changes in the contextual data distribution. The proposed model update scheduling takes into consideration the optimal decision time for minimizing the network overhead while preserving the prediction accuracy of the models. The paper reports on a comparison between the proposed approach with four other update delaying policies found in the literature, an evaluation of the performances using linear and support vector regression models over real contextual data streams and a discussion on the strengths and weaknesses of the proposed policy.
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Acknowledgements
This research is funded by the EU H2020 GNFUV Project RAWFIE–OC2–EXP–SCI (Grant No. 645220), under the EC FIRE+ initiative.
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Appendices
Appendix 1
1.1 CUSUM Policy
Function to Obtain Bad Distribution
Function to Obtain Good Distribution
Appendix 2
1.1 Time-Optimised Policy
Obtain Reward Distribution
Absolute error difference rate for sensors pi2 to pi5
Communication rate for sensors pi2 to pi5
Appendix 3
1.1 Results for Linear Regression
Absolute error difference rate for sensors R1 to R8
Communication rate for sensors R1 to R8
Appendix 4
1.1 Support Vector Regression
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Aleksandrova, E., Anagnotopoulos, C. (2020). Optimised Statistical Model Updates in Distributed Intelligence Environments. In: Mastorakis, G., Mavromoustakis, C., Batalla, J., Pallis, E. (eds) Convergence of Artificial Intelligence and the Internet of Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-44907-0_3
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