TY - GEN
T1 - Powering the IoT through embedded machine learning and LoRa
AU - Suresh, Vignesh Mahalingam
AU - Sidhu, Rishi
AU - Karkare, Prateek
AU - Patil, Aakash
AU - Lei, Zhang
AU - Basu, Arindam
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/5/4
Y1 - 2018/5/4
N2 - The Internet of Things (IoT) technology is rapidly changing the way we live and the number of connected devices are increasing at an exponential pace. However, two key challenges are the battery life for off-grid IoT applications and the ability of edge devices to communicate over long range. Raw data transmission poses as a very power hungry activity for any device. The conventional cellular wide area networks are power-hungry and incompatible for battery-operated IoT devices. There is a need for low-power edge computing devices that reduce the transmission payload and integrate Low-Power Wide-Area Network (LPWAN) technologies, which offer a wide range connectivity while still providing a long battery life. One of the most promising LPWAN technologies today is LoRa. In this paper, we present a solution that employs machine learning on the edge device and performs low power transmission through LoRa. We demonstrate the use case of our solution through a field trial conducted in China for sow activity classification. By implementing embedded machine learning with LoRa, we could compress the transmitted data by 512 times and extend the battery life by three times. A very low energy expenditure of 5.1 mJ per classification result is achieved.
AB - The Internet of Things (IoT) technology is rapidly changing the way we live and the number of connected devices are increasing at an exponential pace. However, two key challenges are the battery life for off-grid IoT applications and the ability of edge devices to communicate over long range. Raw data transmission poses as a very power hungry activity for any device. The conventional cellular wide area networks are power-hungry and incompatible for battery-operated IoT devices. There is a need for low-power edge computing devices that reduce the transmission payload and integrate Low-Power Wide-Area Network (LPWAN) technologies, which offer a wide range connectivity while still providing a long battery life. One of the most promising LPWAN technologies today is LoRa. In this paper, we present a solution that employs machine learning on the edge device and performs low power transmission through LoRa. We demonstrate the use case of our solution through a field trial conducted in China for sow activity classification. By implementing embedded machine learning with LoRa, we could compress the transmitted data by 512 times and extend the battery life by three times. A very low energy expenditure of 5.1 mJ per classification result is achieved.
KW - Edge Computing
KW - IoT
KW - LoRa
KW - LPWAN
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85050387410&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85050387410&origin=recordpage
U2 - 10.1109/WF-IoT.2018.8355177
DO - 10.1109/WF-IoT.2018.8355177
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467399449
VL - 2018-January
T3 - IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings
SP - 349
EP - 354
BT - IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings
PB - IEEE
T2 - 4th IEEE World Forum on Internet of Things, WF-IoT 2018
Y2 - 5 February 2018 through 8 February 2018
ER -