Open AccessLearning-based Incentive Mechanism Design for Crowd Sensing
Author(s) -
Yan Han,
Hui Gao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1288/1/012008
Subject(s) - incentive , computer science , task (project management) , mechanism (biology) , set (abstract data type) , budget constraint , block (permutation group theory) , mechanism design , data collection , data set , computer security , data mining , artificial intelligence , engineering , microeconomics , philosophy , programming language , statistics , geometry , mathematics , systems engineering , epistemology , economics
Crowd sensing campaigns encourage ordinary people to collect and share sensing data by using their portable smart devices like smartphones, iPad and iWatch. However, how to encourage participants to contribute sensing data is still a challenge. In this paper, we propose a learning-based incentive mechanism to maximize the collected amount of data under the constrained condition of limited task budget. Essentially, the mechanism sets different data collected price of every block based on the historical collection condition. The set price on one hand should make sure of that the total cost doesn’t exceed the limited budget, on the other hand, that participants are willing to contribute sensing data. It has been shown by simulation results that our proposed algorithm collects more amount of sensing data than that of the other two comparison algorithms.