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Workflow-based automatic processing for Internet of Floating Things crowdsourced data
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DOI：10.1016/j.future.2018.11.025.png)
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Abstract
En 中文
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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Workflows
Data crowd sourcing
Mobile computing
Cloud computing
GPGPU virtualization
Internet of Things
Bathymetry interpolation
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