{"title":"SGCS: A Cost-Effective Quality Control System for Strategic Workers in Mobile Crowd Sensing","authors":"Han Wang;Anfeng Liu;Neal N. Xiong","doi":"10.1109/TNSE.2024.3524576","DOIUrl":null,"url":null,"abstract":"Many game-based data collection schemes aim to optimize system profits in Mobile Crowd Sensing (MCS). These schemes often assume that the platform knows the data quality upon receipt from workers, ignoring the verification costs; and assume all low-quality submissions are detected. However, due to the challenge of Information Elicitation Without Verification (IEWV), previous game strategies fail to address two key issues in real-world MCS: (1) Verification incurs costs, so the Nash equilibrium from previous studies may not hold. (2) Cheating workers may not be detected, leading to poor-quality data submissions, a scenario not considered in previous models. To address these challenges, we propose a new <underline>S</u>tackelberg <underline>G</u>ame-based quality <underline>C</u>ontrol <underline>S</u>ystem (SGCS). Theoretically, we derive the minimum verification rate required for workers to submit high-quality data, considering their strategic responses to the platform's verification rate. We also design a Worker-Dependent Verification Rates (WDVR) algorithm that identifies honest workers focusing on long-term gains, reducing verification rates for them to lower average verification costs and enhance platform utilities. Our approach is validated through a drone-assisted data collection application, demonstrating that: (1) A minimum effective verification rate ensures strategic workers submit high-quality data. (2) There is a complex trade-off between data quality, verification rates, and platform utilities. Higher data quality increases platform income but also raises verification costs more rapidly, potentially reducing overall utilities. The proposed SGCS provides a practical game-theoretic method for MCS data collection.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1146-1158"},"PeriodicalIF":6.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10819632/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Many game-based data collection schemes aim to optimize system profits in Mobile Crowd Sensing (MCS). These schemes often assume that the platform knows the data quality upon receipt from workers, ignoring the verification costs; and assume all low-quality submissions are detected. However, due to the challenge of Information Elicitation Without Verification (IEWV), previous game strategies fail to address two key issues in real-world MCS: (1) Verification incurs costs, so the Nash equilibrium from previous studies may not hold. (2) Cheating workers may not be detected, leading to poor-quality data submissions, a scenario not considered in previous models. To address these challenges, we propose a new Stackelberg Game-based quality Control System (SGCS). Theoretically, we derive the minimum verification rate required for workers to submit high-quality data, considering their strategic responses to the platform's verification rate. We also design a Worker-Dependent Verification Rates (WDVR) algorithm that identifies honest workers focusing on long-term gains, reducing verification rates for them to lower average verification costs and enhance platform utilities. Our approach is validated through a drone-assisted data collection application, demonstrating that: (1) A minimum effective verification rate ensures strategic workers submit high-quality data. (2) There is a complex trade-off between data quality, verification rates, and platform utilities. Higher data quality increases platform income but also raises verification costs more rapidly, potentially reducing overall utilities. The proposed SGCS provides a practical game-theoretic method for MCS data collection.
在移动人群传感(MCS)中,许多基于游戏的数据收集方案旨在优化系统收益。这些方案通常假设平台在收到工作人员的数据时就知道数据质量,而忽略了验证成本;并假设所有低质量的提交都被检测到。然而,由于没有验证的信息激发(Information Elicitation Without Verification, IEWV)的挑战,以往的博弈策略未能解决现实MCS中的两个关键问题:(1)验证会产生成本,因此以往研究的纳什均衡可能不成立。(2)可能无法检测到作弊的工作人员,导致提交的数据质量低下,这是以前的模型没有考虑到的情况。为了应对这些挑战,我们提出了一个新的基于Stackelberg游戏的质量控制系统(SGCS)。从理论上讲,我们推导出工人提交高质量数据所需的最小验证率,考虑他们对平台验证率的战略响应。我们还设计了一个工人依赖的验证率(WDVR)算法,用于识别专注于长期收益的诚实工人,降低他们的验证率,以降低平均验证成本并增强平台效用。我们的方法通过无人机辅助数据收集应用程序进行了验证,证明:(1)最低有效验证率确保战略工作人员提交高质量数据。(2)数据质量、验证率和平台效用之间存在复杂的权衡。更高的数据质量增加了平台收入,但也更快地提高了验证成本,潜在地降低了整体效用。该方法为MCS数据采集提供了一种实用的博弈论方法。
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.