eprintid: 1503677
rev_number: 27
eprint_status: archive
userid: 608
dir: disk0/01/50/36/77
datestamp: 2017-06-02 16:27:41
lastmod: 2020-02-13 00:45:32
status_changed: 2017-06-02 16:27:41
type: proceedings_section
metadata_visibility: show
creators_name: Xu, L
creators_name: Hao, X
creators_name: Lane, ND
creators_name: Liu, X
creators_name: Moscibroda, T
title: More with less: Lowering user burden in mobile crowdsourcing through compressive sensing
ispublished: pub
divisions: UCL
divisions: A01
divisions: B04
divisions: C05
divisions: F48
keywords: Compressive sensing, mobile crowdsensing.
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Mobile crowdsourcing is a powerful tool for collecting data of various types. The primary bottleneck in such systems is the high burden placed on the user who must manually collect sensor data or respond in-situ to simple queries (e.g., experience sampling studies). In this work, we present Compressive CrowdSensing (CCS) - a framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios. CCS enables each user to provide significantly reduced amounts of manually collected data, while still maintaining acceptable levels of overall accuracy for the target crowd-based system. Näive applications of compressive sensing do not work well for common types of crowdsourcing data (e.g., user survey responses) because the necessary correlations that are exploited by a sparsifying base are hidden and non-Trivial to identify. CCS comprises a series of novel techniques that enable such challenges to be overcome. We evaluate CCS with four representative large-scale datasets and find that it is able to outperform standard uses of compressive sensing, as well as conventional approaches to lowering the quantity of user data needed by crowd systems.
date: 2015-09-11
date_type: published
publisher: Association for Computing Machinery
official_url: http://dx.doi.org/10.1145/2750858.2807523
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1122804
doi: 10.1145/2750858.2807523
isbn_13: 9781450335744
lyricists_name: Lane, Nicholas
lyricists_id: NLANE01
actors_name: Lane, Nicholas
actors_id: NLANE01
actors_role: owner
full_text_status: public
series: ACM International Joint Conference on Pervasive and Ubiquitous Computing
publication: UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
volume: 2015
place_of_pub: Osaka, Japan
pagerange: 659-670
event_title: UbiComp '15 : ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, September 07 - 11, 2015
book_title: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
editors_name: Miyahara, H
editors_name: Tokuda, H
editors_name: Mase, K
editors_name: Langheinrich, M
citation:        Xu, L;    Hao, X;    Lane, ND;    Liu, X;    Moscibroda, T;      (2015)    More with less: Lowering user burden in mobile crowdsourcing through compressive sensing.                     In: Miyahara, H and Tokuda, H and Mase, K and Langheinrich, M, (eds.) Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing.  (pp. pp. 659-670).  Association for Computing Machinery: Osaka, Japan.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1503677/1/ubicomp_ccs.pdf