TY - GEN
T1 - Machine Learning at Resource Constraint Edge Device Using Bonsai Algorithm
AU - Naveen, Soumyalatha
AU - Kounte, Manjunath R.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/11
Y1 - 2020/12/11
N2 - In the worldwide billions of devices connected each other to interact with the surrounding environment to collect the data based on the context. Using machine learning algorithm intelligence can be incorporated in these Internet of Things (IoT) devices to get valuable insights from these data for accurate predictions. Machine learning model is deployed onto the devices for making the decisions locally. This enables fast, accurate prediction within few milliseconds by evading data transmission to the cloud and makes perfectly applicable for real time applications. In this paper, the experiment is conducted with publicly available dataset with Bonsai algorithm. This algorithm is implemented in Linux environment with core is processor in python 2.7 and achieved 92% accuracy with model size of 6.25KB, which can be easily deployed on resource constraint IoT devices.
AB - In the worldwide billions of devices connected each other to interact with the surrounding environment to collect the data based on the context. Using machine learning algorithm intelligence can be incorporated in these Internet of Things (IoT) devices to get valuable insights from these data for accurate predictions. Machine learning model is deployed onto the devices for making the decisions locally. This enables fast, accurate prediction within few milliseconds by evading data transmission to the cloud and makes perfectly applicable for real time applications. In this paper, the experiment is conducted with publicly available dataset with Bonsai algorithm. This algorithm is implemented in Linux environment with core is processor in python 2.7 and achieved 92% accuracy with model size of 6.25KB, which can be easily deployed on resource constraint IoT devices.
UR - https://www.scopus.com/pages/publications/85101110917
UR - https://www.scopus.com/inward/citedby.url?scp=85101110917&partnerID=8YFLogxK
U2 - 10.1109/ICAECC50550.2020.9339514
DO - 10.1109/ICAECC50550.2020.9339514
M3 - Conference contribution
AN - SCOPUS:85101110917
T3 - Proceedings of 2020 3rd International Conference on Advances in Electronics, Computers and Communications, ICAECC 2020
BT - Proceedings of 2020 3rd International Conference on Advances in Electronics, Computers and Communications, ICAECC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Advances in Electronics, Computers and Communications, ICAECC 2020
Y2 - 11 December 2020 through 12 December 2020
ER -