TY - JOUR
T1 - Workflow-based automatic processing for Internet of Floating Things crowdsourced data
AU - Montella, Raffaele
AU - Di Luccio, Diana
AU - Marcellino, Livia
AU - Galletti, Ardelio
AU - Kosta, Sokol
AU - Giunta, Giulio
AU - Foster, Ian
PY - 2019/5/1
Y1 - 2019/5/1
N2 - © 2018 Elsevier B.V. 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.
AB - © 2018 Elsevier B.V. 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.
KW - Bathymetry interpolation
KW - Cloud computing
KW - Data crowd sourcing
KW - GPGPU virtualization
KW - Internet of Things
KW - Mobile computing
KW - Workflows
UR - http://www.scopus.com/inward/record.url?scp=85057471607&partnerID=8YFLogxK
U2 - 10.1016/j.future.2018.11.025
DO - 10.1016/j.future.2018.11.025
M3 - Journal article
AN - SCOPUS:85057471607
SN - 0167-739X
VL - 94
SP - 103
EP - 119
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -