Modeling the Operating Characteristics of IoT for Underwater Sound Classification

Christos C. Constantinou, Erricos Michaelides, Ilias Alexopoulos, Theofylaktos Pieri, Stelios Neophytou, Ioannis Kyriakides, Ehson Abdi, Jerald Reodica, Daniel R. Hayes

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    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.

    Original languageEnglish
    Title of host publication2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021
    EditorsRajashree Paul
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1016-1022
    Number of pages7
    ISBN (Electronic)9780738143941
    DOIs
    Publication statusPublished - 27 Jan 2021
    Event11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021 - Virtual, Las Vegas, United States
    Duration: 27 Jan 202130 Jan 2021

    Publication series

    Name2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021

    Conference

    Conference11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021
    CountryUnited States
    CityVirtual, Las Vegas
    Period27/01/2130/01/21

    Keywords

    • Data Processing
    • Edge Computing
    • Internet of Things
    • Machine Learning
    • Monte Carlo Methods
    • Neural Networks

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