Data quality optimized online task allocation method for mobile crowdsensing

Wei ZHANG; Zhuo LI; Xin CHEN

doi:10.11959/j.issn.2096-3750.2020.00185

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Data quality optimized online task allocation method for mobile crowdsensing

Theory and Technology | 更新时间：2024-06-05

- Data quality optimized online task allocation method for mobile crowdsensing
- Chinese Journal on Internet of Things Vol. 4, Issue 4, Pages: 91-97(2020)
- 作者机构：
  
  1. 网络文化与数字传播北京市重点实验室，北京 100101
  2. 北京信息科技大学计算机学院，北京 100101
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2020.00185
  CLC： TP393.01
- Published：30 December 2020，
  
  Published Online：2020-12，
- 稿件说明：
移动端阅览
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.
DOI：

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. DOI： 10.11959/j.issn.2096-3750.2020.00185.

摘要

感知质量优化和用户招募是移动群智感知的两个重要问题，随着数据量的大幅度增加，感知内容出现冗余，存在感知质量降低的风险。提出了一种感知质量优化的任务分发机制，在保证覆盖率的情况下，提高群体的感知质量。利用聚类算法评估任务真值，量化用户数据质量；基于汤普森抽样算法和贪婪算法设计并实现了一种用户招募策略，在保证任务空间覆盖率的基础上优化感知质量。针对TSUR（Thompson based user recruit）算法的性能进行仿真分析，并与已有的BBTA（bandit-based task assignment）算法和BUR（basic user recruitment）算法作比较。实验表明，在同一区域进行任务感知，与BBTA算法和BUR算法相比，累计感知质量分别提高了16%和20%，空间覆盖率分别提高了30%和22%。

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%.

关键词

移动群智感知任务分发数据质量在线学习

Keywords

mobile crowdsensingtask assignmentdata qualityonline learning

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