TY  - CONF
Y1  - 2021///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113710402&doi=10.1109%2FI2MTC50364.2021.9460080&partnerID=40&md5=786b53d80b228036c6c430f8bc899eb2
TI  - Inter-Batch Gap Filling Using Compressive Sampling for Low-Cost IoT Vibration Sensors
PB  - Institute of Electrical and Electronics Engineers Inc.
N1  - Cited by: 1
VL  - 2021-M
AV  - none
SN  - 10915281
ID  - scholars20439
A1  - Ooi, Boonyaik Yaik
A1  - Liew, Soung Yue
A1  - Beh, W. L.
A1  - Shirmohammadi, Shervin
N2  - To measure machinery vibration, a sensor system consisting of a 3-axis accelerometer, ADXL345, attached to a self-contained system-on-a-chip with integrated Wi-Fi capabilities, ESP8266, is a low-cost solution. In this work, we first show that in such a system, the widely used direct-read-and-send method which samples and sends individually acquired vibration data points to the server is not effective, especially using Wi-Fi connection. We show that the micro delays in each individual data transmission will limit the sensor sampling rate and will also affect the time of the acquired data points not evenly spaced. Then, we propose that vibration should be sampled in batches before sending the acquired data out from the sensor node. The vibration for each batch should be acquired continuously without any form of interruption in between the sampling process to ensure the data points are evenly spaced. To fill the data gaps between the batches, we propose the use of compressive sampling technique. Our experimental results show that the maximum sampling rate of the direct-read-and-send method is 350Hz with a standard uncertainty of 12.4, and the method loses more information compared to our proposed solution that can measure the vibration wirelessly and continuously up to 633Hz. The gaps filled using compressive sampling can achieve an accuracy in terms of mean absolute error (MAE) of up to 0.06 with a standard uncertainty of 0.002, making the low-cost vibration sensor node a cost-effective solution. © 2021 IEEE.
KW  - Compressed sensing; Cost effectiveness; Costs; Machinery; Sensor nodes; System-on-chip; Ventilation exhausts; Wireless local area networks (WLAN); 3-axis accelerometer; Compressive sampling; Cost-effective solutions; Low-cost solution; Mean absolute error; Self-contained systems; Standard uncertainty; Wi-Fi connections; Internet of things
ER  -