TY - GEN
T1 - Modeling the Operating Characteristics of IoT for Underwater Sound Classification
AU - Constantinou, Christos C.
AU - Michaelides, Erricos
AU - Alexopoulos, Ilias
AU - Pieri, Theofylaktos
AU - Neophytou, Stelios
AU - Kyriakides, Ioannis
AU - Abdi, Ehson
AU - Reodica, Jerald
AU - Hayes, Daniel R.
N1 - Funding Information:
This work is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation projects with grant numbers ENTERPRIZES/0916/0066 and INTEGRATED/0918/0032. This work was also partially funded by the EU H2020 Research and Innovation Programme under GA No. 857586 (CMMI-MaRITeC-X). Copyright notice: 978-0-7381-4394-1/21/$31.00 ©2021 IEEE
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/27
Y1 - 2021/1/27
N2 - In remote sensing applications, constraints of power, processing, and communications limit information acquisition. Pre-training the IoT improves the performance in information acquisition tasks such as detection, classification, and estimation. However, light and inexpensive IoT hardware still need to operate with strict resource constraints. In this paper, we provide a method for modeling the IoT operating characteristics that link information acquisition performance to resource use. The goal of modeling is to improve understanding of how to optimally utilize constrained resources to improve information acquisition performance. The proposed method is demonstrated using field, simulation, and lab-based experiments with real data and practical hardware for an underwater sound classification application utilizing deep learning.
AB - In remote sensing applications, constraints of power, processing, and communications limit information acquisition. Pre-training the IoT improves the performance in information acquisition tasks such as detection, classification, and estimation. However, light and inexpensive IoT hardware still need to operate with strict resource constraints. In this paper, we provide a method for modeling the IoT operating characteristics that link information acquisition performance to resource use. The goal of modeling is to improve understanding of how to optimally utilize constrained resources to improve information acquisition performance. The proposed method is demonstrated using field, simulation, and lab-based experiments with real data and practical hardware for an underwater sound classification application utilizing deep learning.
KW - Data Processing
KW - Edge Computing
KW - Internet of Things
KW - Machine Learning
KW - Monte Carlo Methods
KW - Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85103460905&partnerID=8YFLogxK
U2 - 10.1109/CCWC51732.2021.9376070
DO - 10.1109/CCWC51732.2021.9376070
M3 - Conference contribution
AN - SCOPUS:85103460905
T3 - 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021
SP - 1016
EP - 1022
BT - 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021
Y2 - 27 January 2021 through 30 January 2021
ER -