Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity
OA Location
Author(s)
Venanzi, Matteo
Teacy, WTL
Rogers, Alex
Jennings, Nicholas R
Type
Conference Paper
Abstract
We propose a new Bayesian model for reliable aggregation of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user?s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the re- porting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application.
Date Issued
2015-07
Citation
2015, pp.717-724
Start Page
717
End Page
724
Identifier
http://eprints.soton.ac.uk/376365/
Source
International Joint Conference on Artificial Intelligence (IJCAI-15)
Publication Status
Unpublished