Designing Parallel Data Processing for Enabling Large-Scale Sensor Applications - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Personal and Ubiquitous Computing Year : 2017

Designing Parallel Data Processing for Enabling Large-Scale Sensor Applications


Masses of sensors are being deployed at the scale of cities to manage parking spaces, transportation infrastructures to monitor traffic, and campuses of buildings to reduce energy consumption. These large-scale infrastructures become a reality for citizens via applications that orchestrate sensors to deliver high-value, innovative services. These applications critically rely on the processing of large amounts of data to analyze situations, inform users, and control devices. This paper proposes a design-driven approach to developing orchestrating applications for masses of sensors that integrates parallel processing of large amounts of data. Specifically, an application design exposes declarations that are used to generate a programming framework based on the MapReduce programming model. We have developed a prototype of our approach, using Apache Hadoop. We applied it to a case study and obtained significant speedups by parallelizing computations over twelve nodes. In doing so, we demonstrate that our design-driven approach allows to abstract over implementation details, while exposing architectural properties used to generate high-performance code for processing large datasets. Furthermore, we show that this high-performance support enables new, personalized services in a smart city. Finally, we discuss the expressiveness of our design language, identify some limitations, and present language extensions.
Fichier principal
Vignette du fichier
main.pdf (798.29 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01470281 , version 1 (17-02-2017)



Milan Kabáč, Charles Consel, Nic Volanschi. Designing Parallel Data Processing for Enabling Large-Scale Sensor Applications. Personal and Ubiquitous Computing, 2017, 21 (3), pp.457-473. ⟨10.1007/s00779-017-1009-1⟩. ⟨hal-01470281⟩


332 View
365 Download



Gmail Mastodon Facebook X LinkedIn More