Paper Info
Title
An Efficient Truth Discovery Mechanism for Crowdsensing Tasks With Temporal and Spatial Correlations
Abstract
Crowdsensing is a promising sensing paradigm to efficiently collect and monitor the physical world by using the embedded sensors in mobile devices. However, the observations (sensory data) submitted by mobile device users may not be reliable. For the same sensing task, users with different reliabilities may submit conflicting information. Thus, we need to estimate the truth based on the submitted observations. Temporal and spatial correlations among tasks are widely observed in crowdsensing applications. However, most of the existing truth discovery mechanisms assume that the tasks are independent, which is not suitable for all crowdsensing applications. To solve this problem, we propose an efficient truth discovery mechanism for crowdsensing tasks with temporal and spatial correlations. To improve the reliability of the estimated truth, we first filter the outliers based on the temporal correlations among tasks, then estimate the truth based on the weighted observations, and finally refine the estimated truth based on the spatial correlations among tasks. Our experiments on a real transportation dataset show the efficiency of the proposed mechanism.
Year
DOI
Venue
2019
10.1109/ICTAI.2019.00076
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
Field
DocType
Crowdsensing, truth discovery, temporal correlation, spatial correlation
Spatial correlation,Computer science,Crowdsensing,Outlier,Mobile device,Artificial intelligence,Machine learning
Conference
ISSN
ISBN
Citations 
1082-3409
978-1-7281-3799-5
0
PageRank 
References 
Authors
0.34
10
6
Name
Order
Citations
PageRank
Runzhi Wang100.34
Yu-e Sun2337.07
He Huang382965.14
Le Lu400.34
Yang Du5146.47
Danlei Huang601.35