Faster Algorithm for Truth Discovery via Range Cover

2017 ◽  
pp. 461-472 ◽  
Author(s):  
Ziyun Huang ◽  
Hu Ding ◽  
Jinhui Xu
Keyword(s):  
Truth Discovery

scholarly journals Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

2016 ◽  
Vol 27 (10) ◽  
pp. 2984-2997 ◽  
Author(s):  
Robin Wentao Ouyang ◽  
Lance M. Kaplan ◽  
Alice Toniolo ◽  
Mani Srivastava ◽  
Timothy J. Norman
Keyword(s):  
Large Scale ◽  
Truth Discovery

Privacy-preserving Streaming Truth Discovery in Crowdsourcing with Differential Privacy

2021 ◽  
pp. 1-1
Author(s):  
Dan Wang ◽  
Ju Ren ◽  
Zhibo Wang ◽  
Xiaoyi Pang ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving ◽  
Truth Discovery

scholarly journals Modeling Truth Existence in Truth Discovery

2015 ◽  
Author(s):  
Shi Zhi ◽  
Bo Zhao ◽  
Wenzhu Tong ◽  
Jing Gao ◽  
Dian Yu ◽  
...  
Keyword(s):  
Truth Discovery

Combining Truth Discovery and RDF Knowledge Bases to Their Mutual Advantage

2018 ◽  
pp. 652-668
Author(s):  
Valentina Beretta ◽  
Sébastien Harispe ◽  
Sylvie Ranwez ◽  
Isabelle Mougenot
Keyword(s):  
Knowledge Bases ◽  
Truth Discovery ◽  
Mutual Advantage

scholarly journals Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 805
Author(s):  
Jia Xu ◽  
Shangshu Yang ◽  
Weifeng Lu ◽  
Lijie Xu ◽  
Dejun Yang
Keyword(s):  
Large Scale ◽  
Computationally Efficient ◽  
Reverse Auction ◽  
Mobile Crowdsensing ◽  
Computing Paradigm ◽  
Truth Discovery ◽  
Estimation Precision ◽  
Discovery Stage ◽  
Budget Feasible ◽  
Great Development

The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.

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