Online incentive mechanism based on quality perception in spatio-temporal crowdsourcing

doi:10.11772/j.issn.1001-9081.2022071095

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2091-2099.DOI: 10.11772/j.issn.1001-9081.2022071095

Special Issue: 第39届CCF中国数据库学术会议(NDBC 2022)

• The 39th CCF National Database Conference (NDBC 2022) • Previous Articles Next Articles

Online incentive mechanism based on quality perception in spatio-temporal crowdsourcing

Yanan PAN, Qingxian PAN(), Zhaoyi YU, Jiajing CHU, Song YU

School of Computer and Control Engineering，Yantai University，Yantai Shandong 264005，China

Received:2022-07-12 Revised:2022-08-04 Accepted:2022-08-16 Online:2023-07-20 Published:2023-07-10
Contact: Qingxian PAN
About author:PAN Yanan， born in 1997， M. S. candidate. Her research interests include machine learning， crowd sensing.
PAN Qingxian， born in 1979， Ph. D.， associate professor. His research interests include artificial intelligence， machine learning.
YU Zhaoyi， born in 1995， M. S. His research interests include mobile crowdsourcing.
CHU Jiajing， born in 1997， M. S. candidate. Her research interests include crowd sensing
YU Song， born in 1998， M. S. candidate. His research interests include mobile crowdsourcing.
Supported by:
National Natural Science Foundation of China(62072392)

时空众包中基于质量感知的在线激励机制

潘亚楠, 潘庆先(), 于兆一, 褚佳静, 于嵩

烟台大学计算机与控制工程学院，山东烟台 264005

通讯作者: 潘庆先
作者简介:潘亚楠（1997—），女，山东济南人，硕士研究生，CCF会员，主要研究方向：机器学习、群智感知；
潘庆先（1979—），男，山东武城人，副教授，博士，CCF会员，主要研究方向：人工智能、机器学习；
于兆一（1995—），男，山东德州人，硕士，CCF会员，主要研究方向：移动众包；
褚佳静（1997—），女，山东威海人，硕士研究生，CCF会员，主要研究方向：群智感知；
于嵩（1998—），男，山东烟台人，硕士研究生，CCF会员，主要研究方向：移动众包。
基金资助:
国家自然科学基金资助项目(62072392)

Abstract

Abstract:

In the real-time and complex network environment， how to motivate workers to participate in tasks and obtain high-quality perception data is the focus of spatio-temporal crowdsourcing research. Based on this， a spatio-temporal crowdsourcing’s online incentive mechanism based on quality perception was proposed. Firstly， in order to adapt to the real-time characteristics of spatio-temporal crowdsourcing， a Phased Online selection of workers Algorithm （POA） was proposed. In this algorithm， the entire crowdsourcing activity cycle was divided into multiple stages under budget constraints， and workers were selected online in each stage. Secondly， in order to improve the accuracy and efficiency of quality prediction， an Improved Expected Maximum （IEM） algorithm was proposed. In this algorithm， the task results submitted by workers with high reliability were given priority in the process of algorithm iteration. Finally， the effectiveness of the proposed incentive mechanism in improving platform utility was verified by comparison experiments on real datasets. Experimental results show that in terms of efficiency， compared with the Improved Two-stage Auction （ITA） algorithm， the Multi-attribute and ITA （M-ITA） algorithm， Lyapunov-based Vickrey-Clarke-Groves （L-VCG） and other auction algorithms， the efficiency of POA has increased by 11.11% on average， and the amount of additional rewards for workers has increased by 12.12% on average， which can encourage workers to move to remote and unpopular areas； In terms of quality estimation， the IEM algorithm has an average improvement of 5.06% in accuracy and 14.2% in efficiency compared to other quality estimation algorithms.

Key words: spatio-temporal crowdsourcing, online auction, quality prediction, incentive mechanism, platform utility

摘要：

在实时、复杂的网络环境中，如何激励工人参与任务并得到高质量的感知数据是时空众包研究的重点。基于此，提出一种基于质量感知的时空众包在线激励机制。首先，为了适应时空众包实时性的特点，提出一种阶段性在线选择工人算法（POA），该算法在预算约束下将整个众包活动周期分为多个阶段，每个阶段在线选择工人；其次，为了提高质量预估的精度与效率，提出一种改进的最大期望（IEM）算法，该算法在算法迭代的过程中优先考虑可信度高的工人提交的任务结果；最后，通过真实数据集上的对比实验，验证了所提激励机制在提高平台效用方面的有效性。实验结果表明，POA相较于改进的两阶段拍卖（ITA）算法、多属性与两阶段相结合的拍卖（M-ITA）算法，以及L-VCG（Lyapunov-based Vickrey-Clarke-Groves）等拍卖算法，效率平均提高了11.11%，工人的额外奖励金额平均提升了12.12%，可以激励工人向冷门偏远地区移动；在质量预估方面，IEM算法相比其他质量预估算法，在精度和效率上分别平均提高了5.06%和14.2%。

关键词: 时空众包, 在线拍卖, 质量预估, 激励机制, 平台效用

CLC Number:

TP18

Yanan PAN, Qingxian PAN, Zhaoyi YU, Jiajing CHU, Song YU. Online incentive mechanism based on quality perception in spatio-temporal crowdsourcing[J]. Journal of Computer Applications, 2023, 43(7): 2091-2099.

潘亚楠, 潘庆先, 于兆一, 褚佳静, 于嵩. 时空众包中基于质量感知的在线激励机制[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2091-2099.

Figures/Tables 13

Fig. 1 Architecture of crowdsourcing system based on quality perception

Fig. 2 Flow of online incentive mechanism based on quality perception

Fig. 3 Influence of parameter ρ on efficiency

Fig. 4 Influence of maximum number of bidding tasks β on efficiency

Tab. 1 Setting of experimental parameters

参数	设置
B	500，1 000，1 500，2 000
r_i	（0，1）
ρ	1
λ₁+λ₂+λ₃	1
β	8

Fig. 5 Efficiency comparison results under different budgets

Fig. 6 Number of winning workers and difference in bonuses

Fig. 7 Workers' reliability under different budgets

Fig. 8 Proportion of correct answers

Tab. 2 Datasets information

数据集	任务类别	任务选项	任务数
Adult2	标记网站	4	333
Duck	识别图像	2	39
LabelMe	注释图像	8	2 688

Tab. 3 Workers’ history information

工作者	历史信息
w₁	（0.87，0.82，0.92，0.87，0.92，0.9，0.95，0.87，0.9， 0.92，0.9，0.92）
w₂	（1，0.87，0.9）
w₃	（0.92，0.97，0.85，0.92，0.95，0.9，0.97）
w₄	（0.85，0.92，0.9，0.95，1）
w₅	（1，0.85，0.9，0.92，0.72，0.825）
w₆	（0.75，0.82，0.9）
w₇	（0.84，0.89）

Fig. 9 Accuracy comparison results on different datasets

Fig. 10 Efficiency comparison results on different datasets

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Online incentive mechanism based on quality perception in spatio-temporal crowdsourcing

时空众包中基于质量感知的在线激励机制

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