DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices
Digital Health, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany
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Sensors 2020, 20(21), 6104; https://doi.org/10.3390/s20216104
Received: 22 September 2020
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Revised: 14 October 2020
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Accepted: 21 October 2020
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Published: 27 October 2020
(This article belongs to the Special Issue Advanced Machine Learning Tools and Methods for IoMT Sensor Applications)
We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.
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Keywords:
health monitoring; automatic dietary monitoring; physiological sensing; pattern spotting; energy saving; embedded machine learning
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MDPI and ACS Style
Schiboni, G.; Suarez, J.C.; Zhang, R.; Amft, O. DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices. Sensors 2020, 20, 6104. https://doi.org/10.3390/s20216104
AMA Style
Schiboni G, Suarez JC, Zhang R, Amft O. DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices. Sensors. 2020; 20(21):6104. https://doi.org/10.3390/s20216104
Chicago/Turabian StyleSchiboni, Giovanni, Juan Carlos Suarez, Rui Zhang, and Oliver Amft. 2020. "DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices" Sensors 20, no. 21: 6104. https://doi.org/10.3390/s20216104
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