Schiboni G, Suarez Mora JC, Zhang R, Amft O (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 20
Pages Range: 1-26
Article Number: 6104
Journal Issue: 21
DOI: 10.3390/s20216104
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.
APA:
Schiboni, G., Suarez Mora, J.C., Zhang, R., & Amft, O. (2020). Dyndse: Automated multi-objective design space exploration for context-adaptivewearable IOT edge devices. Sensors, 20(21), 1-26. https://dx.doi.org/10.3390/s20216104
MLA:
Schiboni, Giovanni, et al. "Dyndse: Automated multi-objective design space exploration for context-adaptivewearable IOT edge devices." Sensors 20.21 (2020): 1-26.
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