Modeling the Operating Characteristics of IoT for Underwater Sound Classification
| Autor: | Jerald Reodica, Ilias Alexopoulos, Daniel Hayes, Erricos Michaelides, Ioannis Kyriakides, Ehson Abdi, Theofylaktos Pieri, Stelios Neophytou, Christos C. Constantinou |
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| Rok vydání: | 2021 |
| Předmět: |
Data processing
Data Processing Neural Networks Artificial neural network business.industry Remote sensing application Computer science Deep learning Internet of Things Real-time computing 020206 networking & telecommunications 02 engineering and technology Field (computer science) Machine Learning Monte Carlo Methods Limit (music) 0202 electrical engineering electronic engineering information engineering Edge Computing 020201 artificial intelligence & image processing Artificial intelligence Underwater business Edge computing |
| Zdroj: | CCWC 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) |
| DOI: | 10.1109/ccwc51732.2021.9376070 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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