感知质量优化的移动群智感知任务在线分发算法

doi:10.11959/j.issn.2096-3750.2020.00185

Chinese Journal on Internet of Things ›› 2020, Vol. 4 ›› Issue (4): 91-97.doi: 10.11959/j.issn.2096-3750.2020.00185

• Theory and Technology • Previous Articles Next Articles

Data quality optimized online task allocation method for mobile crowdsensing

Wei ZHANG^1,²,Zhuo LI^1,²(),Xin CHEN²

¹ Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing 100101,China
² Beijing Information Science and Technology University,Beijing 100101,China

Revised:2020-02-18 Online:2020-12-30 Published:2020-12-14
Supported by:
The National Natural Science Foundation of China(61872044);The National Natural Science Foundation of China(61502040);The Beijing Municipal Program for Top Talent,Beijing Municipal Program for Top Talent Cultivation(CIT＆TCD201804055);The Open Program of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(ICDDXN001);The Qinxin Talent Program of Beijing Information Science and Technology University

Abstract

Abstract:

Optimization of the perceived quality and the recruitment of user are two important issues of mobile crowdsensing.As the amount of data increases rapidly,perceived data becomes redundant,and perceived quality is at risk of decreasing.A mechanism of task assignment based on the perceptive quality optimization was proposed to improve the perceived quality under the condition of full coverage.The clustering algorithm was used to evaluate the truth value of the task and quantify the quality of the user data.Based on Thompson sampling algorithm and greedy algorithm,a user recruitment strategy was designed and implemented to optimize the perceived quality on the basis of ensuring the spatial coverage of the task.The performance of Thompson based user recruit (TSUR) algorithm was simulated and analyzed that compared with the existing algorithms of BBTA and basic user recruitment (BUR).Experiments show that in the same area,compared with bandit-based task assignment (BBTA) algorithm and BUR algorithm,the quality of the cumulative sensing data was improved by 16% and 20%,and the spatial coverage was improved by 30% and 22%.

Key words: mobile crowdsensing, task assignment, data quality, online learning

CLC Number:

TP393.01

Wei ZHANG,Zhuo LI,Xin CHEN. Data quality optimized online task allocation method for mobile crowdsensing[J]. Chinese Journal on Internet of Things, 2020, 4(4): 91-97.

Figures/Tables 5

References 21

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