Abstract
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.
Highlights
Autonomous wearable IoT devices are being used for physiological and behavioural health-monitoring [1] and provide relevant health status information to their wearers [2,3]
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 introduced a general methodology for multi-objective DynDSE applied to context-adaptive wearable IoT edge devices, which retrieve events from streaming sensor data using pattern recognition algorithms
Summary
Autonomous wearable IoT devices are being used for physiological and behavioural health-monitoring [1] and provide relevant health status information to their wearers [2,3]. Miniaturised electronics embedded in wearable accessories, garments, etc., provide the resources to retrieve pattern events from streaming sensor data and to interact with the wearer, which led to the concept of edge computing [4]. Edge computing aims to process data at the devices end, rather than the cloud to reduce network load and service response time. In medical IoT monitoring applications [5], a device may retrieve relevant events using embedded machine learning methods, sending only abstract event information to the cloud. A wearable IoT device typically consists of multiple sensors, a microcontroller (μC), which runs data processing algorithms, memory, and a radio module for data communication (Figure 1)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call