Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring
OPEN ACCESS
Loading...
Author / Producer
Date
2022
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
This article proposes a proof-of-concept device to continuously assess the usage of handheld power tools and detect construction working tasks (e.g., different drilling works) along with potential misusages, e.g., drops, with an energy-efficient architecture design. The designed device is based on Bluetooth low energy (BLE) and NFC connectivity. BLE is used to exchange data with a gateway, whereas NFC has been chosen as an energy-efficient wake-up mechanism. A temperature and humidity sensor is embedded to monitor storage conditions and an accelerometer for tool usage monitoring. The ARM Cortex-M4 core embedded in the BLE module is exploited to process the information at the edge. A Tiny Machine Learning (TinyML) algorithm is proposed to process the data directly on board and achieve low latency and high energy efficiency. The TinyML algorithm has been developed embedded in the proposed device to detect four different usage classes (tool transportation, no-load, metal, and wood drilling). A dataset containing more than 280 min of three-axis accelerations during different activities has been acquired with the device attached to a construction rotary hammer drill and used to train and validate the algorithm. A neural architecture search has been performed to optimize the trade-off between accuracy and complexity, achieving an accuracy of 90.6% with a model size of roughly 30 kB. The experimental results showed an ultralow power consumption in sleep mode of 550 nA and a peak power consumption of 8 mA while running TinyML on the edge. This results in a balanced combination of edge processing capabilities and low power consumption, enabling to obtain a smart Internet of Things (IoT) device in the field with a long lifetime of up to four years in operation and 17 years in shelf mode with a standard 250-mAh coin battery. This work enables a long battery lifetime operation of device degradation and utility analysis, further closing the gap between edge processing and fine granularity data evaluation.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
71
Pages / Article No.
2510711
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Monitoring; Sensors; Intelligent sensors; Drilling; Batteries; Wireless sensor networks; Neural networks; Asset management; condition monitoring; construction 40; energy efficiency; low-power design; smart sensors; tool usage monitoring; wireless sensor networks
Organisational unit
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning