TY - GEN
T1 - Accelerating Deep Learning for Self-Calibration in Large-Scale Uncontrolled Wireless Sensor Networks for Environmental Monitoring
AU - Yar, Asif
AU - Henna, Shagufta
AU - McAfee, Marion
AU - Gharbia, Salem S.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Self-calibration poses one of the primary challenges in deploying wireless sensor networks (WSNs), particularly in uncontrolled environments. While existing deep learning methods have demonstrated their effectiveness in centralized scenarios, their efficiency diminishes when applied in distributed and uncontrolled environments. To tackle this issue, this work evaluates the application of extreme learning machines (ELM) and graph deep learning techniques for self-calibration. The experimental findings reveal that the graph learning approach outperforms state-of-the-art deep learning methods, including LSTM, MLP, and CNN, in terms of accuracy. Additionally, ELM showcases its effectiveness as a suitable machine learning approach under resource-constrained scenarios, with accelerated learning.
AB - Self-calibration poses one of the primary challenges in deploying wireless sensor networks (WSNs), particularly in uncontrolled environments. While existing deep learning methods have demonstrated their effectiveness in centralized scenarios, their efficiency diminishes when applied in distributed and uncontrolled environments. To tackle this issue, this work evaluates the application of extreme learning machines (ELM) and graph deep learning techniques for self-calibration. The experimental findings reveal that the graph learning approach outperforms state-of-the-art deep learning methods, including LSTM, MLP, and CNN, in terms of accuracy. Additionally, ELM showcases its effectiveness as a suitable machine learning approach under resource-constrained scenarios, with accelerated learning.
KW - Extreme Learning Machine
KW - Graph-based Deep Learning
KW - Self-calibration in Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85201163052&partnerID=8YFLogxK
U2 - 10.1109/ISSC61953.2024.10603082
DO - 10.1109/ISSC61953.2024.10603082
M3 - Conference contribution
AN - SCOPUS:85201163052
T3 - Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024
BT - Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024
A2 - Zheng, Huiru
A2 - Cleland, Ian
A2 - Moore, Adrian
A2 - Wang, Haiying
A2 - Glass, David
A2 - Rafferty, Joe
A2 - Bond, Raymond
A2 - Wallace, Jonathan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th Irish Systems and Signals Conference, ISSC 2024
Y2 - 13 June 2024 through 14 June 2024
ER -