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[STI17] Opportunistic Multiparty Calibration for Robust Participatory Sensing
Conférence Internationale avec comité de lecture : In proc. of the 14th IEEE International Conference on Mobile Ad Hoc Networks and Sensor Networks (MASS), July 2017, pp.10, USA,Mots clés: Calibration ; Mobile Participatory Sensing
Résumé:
While bringing massive-scale sensing at low cost,
mobile participatory sensing is challenged by the low accuracy
of the sensors embedded in and/or connected to the smartphones.
The mobile measurements that are collected need to be corrected
so as to accurately match the phenomena being observed.
This paper addresses this challenge by introducing a
multi-hop,
multiparty calibration
method that operates in the background in
an automated way. Using our method, sensors that are within
a relevant sensing (and communication) range coordinate so
that the observations of the participating (previously) calibrated
sensors serve calibrating the other participants. As a result, our
method is particularly well suited for participatory sensing within
crowd meetings, as as for instance within public spaces. Our solu-
tion leverages multivariate linear regression, together with robust
regression so as to discard the measurements that are of too low
quality for being meaningful. To the best of our knowledge, we
are the first to introduce a multiparty calibration algorithm, while
previous work in the area focused on pairwise calibration. The
paper further introduces a supporting prototype implemented
over Android, and related experiment in the context of noise
sensing. We show that the proposed multiparty calibration system
enhances the accuracy of the mobile noise sensing application.
BibTeX
| @inproceedings { | |||
| STI17, | |||
| title | = | "{Opportunistic Multiparty Calibration for Robust Participatory Sensing }", | |
| author | = | " F. Sailhan and O. Tavares and V. Issarny ", | |
| booktitle | = | "{ In proc. of the 14th IEEE International Conference on Mobile Ad Hoc Networks and Sensor Networks (MASS)}", | |
| year | = | 2017, | |
| month | = | "July", | |
| pages | = | "10", | |
| address | = | " USA", | |
| } | |||
