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Title: Workflow-based automatic processing for Internet of Floating Things crowdsourced data

Abstract

Data from sensors incorporated into mobile devices, such as networked navigational sensors, can be used to capture detailed environmental information. We describe here a workflow and framework for using sensors on boats to construct unique new datasets of underwater topography (bathymetry). Starting with a large number of measurements of position, depth, etc., obtained from such an Internet of Floating Things, we illustrate how, with a specialized protocol, data can be communicated to cloud resources, even when using delayed, intermittent, or disconnected networks. We then propose a method for automatic sensor calibration based on a novel reputation approach. Sampled depth data are interpolated efficiently on a cloud computing platform in order to provide a continuously updated bathymetric database. Our prototype implementation uses the FACE-IT Galaxy workflow engine to manage network communication and exploits the computational power of GPGPUs in a virtualized cloud environment, working with a CUDA-parallel algorithm, for efficient data processing. We report on an initial evaluation involving data from a sailing vessel in Italian coastal waters.

Authors:
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Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
Parthenope University of Naples; National Science Foundation (NSF)
OSTI Identifier:
1556907
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Future Generations Computer Systems
Additional Journal Information:
Journal Volume: 94
Country of Publication:
United States
Language:
English
Subject:
Bathymetry interpolation; Cloud computing; Data crowd sourcing; GPGPU virtualization; Internet of Things; Mobile computing; Workflows

Citation Formats

Montella, Raffaele, Di Luccio, Diana, Marcellino, Livia, Galletti, Ardelio, Kosta, Sokol, Giunta, Giulio, and Foster, Ian. Workflow-based automatic processing for Internet of Floating Things crowdsourced data. United States: N. p., 2019. Web. doi:10.1016/j.future.2018.11.025.
Montella, Raffaele, Di Luccio, Diana, Marcellino, Livia, Galletti, Ardelio, Kosta, Sokol, Giunta, Giulio, & Foster, Ian. Workflow-based automatic processing for Internet of Floating Things crowdsourced data. United States. doi:10.1016/j.future.2018.11.025.
Montella, Raffaele, Di Luccio, Diana, Marcellino, Livia, Galletti, Ardelio, Kosta, Sokol, Giunta, Giulio, and Foster, Ian. Wed . "Workflow-based automatic processing for Internet of Floating Things crowdsourced data". United States. doi:10.1016/j.future.2018.11.025.
@article{osti_1556907,
title = {Workflow-based automatic processing for Internet of Floating Things crowdsourced data},
author = {Montella, Raffaele and Di Luccio, Diana and Marcellino, Livia and Galletti, Ardelio and Kosta, Sokol and Giunta, Giulio and Foster, Ian},
abstractNote = {Data from sensors incorporated into mobile devices, such as networked navigational sensors, can be used to capture detailed environmental information. We describe here a workflow and framework for using sensors on boats to construct unique new datasets of underwater topography (bathymetry). Starting with a large number of measurements of position, depth, etc., obtained from such an Internet of Floating Things, we illustrate how, with a specialized protocol, data can be communicated to cloud resources, even when using delayed, intermittent, or disconnected networks. We then propose a method for automatic sensor calibration based on a novel reputation approach. Sampled depth data are interpolated efficiently on a cloud computing platform in order to provide a continuously updated bathymetric database. Our prototype implementation uses the FACE-IT Galaxy workflow engine to manage network communication and exploits the computational power of GPGPUs in a virtualized cloud environment, working with a CUDA-parallel algorithm, for efficient data processing. We report on an initial evaluation involving data from a sailing vessel in Italian coastal waters.},
doi = {10.1016/j.future.2018.11.025},
journal = {Future Generations Computer Systems},
number = ,
volume = 94,
place = {United States},
year = {2019},
month = {5}
}