Show simple item record

dc.contributor.authorNguyen, Quoe Viet Hung
dc.contributor.authorSathe, Saket
dc.contributor.authorDuong, Chi Thang
dc.contributor.authorAberer, Karl
dc.date.accessioned2021-07-15T04:18:52Z
dc.date.available2021-07-15T04:18:52Z
dc.date.issued2014
dc.identifier.isbn9781631900433
dc.identifier.doi10.4108/icst.collaboratecom.2014.257239
dc.identifier.urihttp://hdl.handle.net/10072/405995
dc.description.abstractParticipatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for world-wide applications, without spending cost to buy dedicated sensors. Despite of this benefit, the data collected from human sensors are inherently uncertain due to no quality guarantee from the participants. Moreover, the participatory sensing data are time series that not only exhibit highly irregular dependencies on time, but also vary from sensor to sensor. To overcome these issues, we study in this paper the problem of creating probabilistic data from given (uncertain) time series collected by participatory sensors. We approach the problem in two steps. In the first step, we generate probabilistic times series from raw time series using a dynamical model from the time series literature. In the second step, we combine probabilistic time series from multiple sensors based on the mutual relationship between the reliability of the sensors and the quality of their data. Through extensive experimentation, we demonstrate the efficiency of our approach on both real data and synthetic data.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencename10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing
dc.relation.ispartofconferencetitle10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing
dc.relation.ispartofdatefrom2014-10-22
dc.relation.ispartofdateto2014-10-25
dc.relation.ispartoflocationMiami, FL, USA
dc.relation.ispartofpagefrom114
dc.relation.ispartofpageto123
dc.subject.fieldofresearchNetworking and communications
dc.subject.fieldofresearchcode460609
dc.subject.keywordsScience & Technology
dc.subject.keywordsEngineering, Electrical & Electronic
dc.subject.keywordsparticipatory sensing
dc.titleTowards Enabling Probabilistic Databases for Participatory Sensing
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationNguyen, QVH; Sathe, S; Duong, CT; Aberer, K, Towards Enabling Probabilistic Databases for Participatory Sensing, 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2014, pp. 114-123
dc.date.updated2021-07-15T04:15:57Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2014IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record