Skip to Main content Skip to Navigation
Conference papers

IAM - Interpolation and Aggregation on the Move: Collaborative Crowdsensing for Spatio-temporal Phenomena

Abstract : Crowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. This limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized postprocessing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. The evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces-and may even eliminate-the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach.
Complete list of metadata
Contributor : Yifan Du Connect in order to contact the contributor
Submitted on : Wednesday, December 2, 2020 - 3:08:59 AM
Last modification on : Friday, January 21, 2022 - 3:18:27 AM
Long-term archiving on: : Wednesday, March 3, 2021 - 6:27:13 PM


Files produced by the author(s)


  • HAL Id : hal-03035035, version 1



Yifan Du, Francoise Sailhan, Valerie Issarny. IAM - Interpolation and Aggregation on the Move: Collaborative Crowdsensing for Spatio-temporal Phenomena. MobiQuitous 2020 - EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Dec 2020, Virtual, Germany. pp.337-346. ⟨hal-03035035⟩



Les métriques sont temporairement indisponibles