Vessel to shore data movement through the Internet of Floating Things: A microservice platform at the edge. (7th September 2020)

• Record Type: Journal Article
• Authors: Di Luccio, Diana; Kosta, Sokol; Castiglione, Aniello; Maratea, Antonio; Montella, Raffaele
• Other Names: Jeon Gwanggil guestEditor.; Abdellah Chehri guestEditor.; Cuomo Salvatore guestEditor.; Din Sadia guestEditor.; Jabbar Sohail guestEditor.; Kosta Sokol guestEditor.; Laccetti Giuliano guestEditor.; Lapegna Marco guestEditor.; Mele Valeria guestEditor.; Montella Raffaele guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-07
• Subjects: cloud database -- data crowdsourcing -- Internet of Floating Things -- microservices -- ocean data -- security -- transfer protocol; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5988
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The rise of the Internet of Things has generated high expectations about the improvement in people's lifestyles. In the last decade, we saw several examples of instrumented cities where different types of data were gathered, processed, and made available to inspire the next generation of scientists and engineers. In this framework, sensors and actuators became leading actors of technologically pervasive urban environments. However, in coastal areas, marine data crowdsourcing is difficult to apply due to the challenging operational conditions, extremely unstable network connectivity, and security issues in data movement. To fill this gap, we present a novel version of our DYNAMO transfer protocol (DTP), a platform‐independent data mover framework where data collected on board of vessels are stored locally and then moved from the edge to the cloud when the operating conditions are favorable. We evaluate the performance of DTP in a controlled environment with a private cloud by measuring the time it takes for the clouds ide to process and store a fixed amount of data while varying the number of microservice instances. We show that the time decreases exponentially when the number of microservice instances goes from 1 to 16 and it remains constant above that number.