Chair of Digital Health, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany.
Sensors (Basel). 2020 Oct 27;20(21):6104. 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.
我们描述了一种基于模拟的可穿戴物联网边缘设备设计空间探索过程（DynDSE），该设备使用上下文自适应模式识别算法从流传感器数据中检索事件。我们提供了给定系统功能、组件及其参数的设计空间的正式描述。根据一组要求，在具有实际传感器数据的模拟中迭代搜索评估配置。我们探索了固有权衡，以在特定于应用的条件下找到最佳配置，这些权衡嵌入在冲突指标中。我们的指标包括检索性能、执行时间、能耗、内存需求和通信延迟。我们报告了一个案例研究，用于设计肌电监测眼镜，该眼镜在自动饮食监测方面有应用。设计空间包括两种定位算法和两种采样算法，旨在在三个微控制器上实时执行。DynDSE 生成了配置，这些配置在能耗比默认非优化配置低 70%的情况下，平衡了检索性能和资源消耗，F1 分数超过 80%。我们预计 DynDSE 方法可以应用于各种基于传感器的应用中，以找到合适的可穿戴物联网系统设计。
