How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint

By Dong Zhao, Xiangyang Li, Huadong Ma. IEEE Conference on Computer Communications, 2014

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/27d699f990591433fe6267b0eb89b671f91045f4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huadong Ma
2. Guoliang Xue
3. Xi Fang
4. Yunhao Liu
5. Jian Tang
6. Dejun Yang
7. Zheng Yang

In Computer Science Applications

1. Guoliang Xue
2. Xi Fang
3. Yunhao Liu
4. Jian Tang
5. Dejun Yang
6. Zheng Yang
7. Iordanis Koutsopoulos

In Management Science and Operations Research

1. Guoliang Xue
2. Xi Fang
3. Yunhao Liu
4. Jian Tang
5. Dejun Yang
6. Iordanis Koutsopoulos

Citations in the corpus

• Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones (2016, background): “The aforementioned mechanisms are all off-line, the authors in [48] and [50] designed online incentive mechanisms where the crowdsourcer makes decisions instantly upon the user's arrival.”
• Opportunities in mobile crowd sensing (2014, background): “From the sensing perspective, proper incentives must be offered to users for completing specific sensing tasks [9].”
• Incentives for Mobile Crowd Sensing: A Survey (2016, background): “Recently, Some researchers [54], [65] investigate this online setting based on an offline budget feasible mechanism, which provides a starting point for online mechanisms.”