人群中的交易数据:移动人群感知的利润驱动数据获取

IF 13.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Zhenzhe Zheng, Yanqing Peng, Fan Wu, Shaojie Tang, Guihai Chen
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引用次数: 60

摘要

数据交易作为一种重要的商业模式,越来越受到人们的关注。然而,对数据市场中数据获取的研究仍处于初级阶段。移动众包感知已被公认为获取大规模数据的一种高效且可扩展的方式。为众感数据市场设计一个实用的数据采集方案必须考虑三个主要挑战:众感数据交易格式的确定、多项式计算复杂性的利润最大化和战略环境中的支付最小化。在本文中,我们共同考虑了这些设计挑战,并提出了VENUS,这是第一个用于人群感知数据市场的利润驱动VEN数据acqUiSition框架。具体而言,VENUS由两个互补机制组成:实现利润最大化的VENUS-PRO和实现支付最小化的VENUS-PAY。考虑到每个数据采集点的预期付款,VENUS-PRO贪婪地选择最“成本效益”的数据采集点,以实现次优利润。为了确定每个数据采集点的最低付款额,我们进一步设计了VENUS-PAY,这是一种贝叶斯环境下的数据采购拍卖。我们的理论分析表明,VENUS-PAY可以同时实现策略验证和最优预期支付。我们在英特尔研究所伯克利实验室收集的公共感官数据集上评估VENUS。我们的评估结果表明,VENUS-PRO接近最优利润,并且在总付款方面,VENUS-PAY优于规范的第二价格反向拍卖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing
As a significant business paradigm, data trading has attracted increasing attention. However, the study of data acquisition in data markets is still in its infancy. Mobile crowdsensing has been recognized as an efficient and scalable way to acquire large-scale data. Designing a practical data acquisition scheme for crowd-sensed data markets has to consider three major challenges: crowd-sensed data trading format determination, profit maximization with polynomial computational complexity, and payment minimization in strategic environments. In this paper, we jointly consider these design challenges, and propose VENUS, which is the first profit-driVEN data acqUiSition framework for crowd-sensed data markets. Specifically, VENUS consists of two complementary mechanisms: VENUS-PRO for profit maximization and VENUS-PAY for payment minimization. Given the expected payment for each of the data acquisition points, VENUS-PRO greedily selects the most “cost-efficient” data acquisition points to achieve a sub-optimal profit. To determine the minimum payment for each data acquisition point, we further design VENUS-PAY, which is a data procurement auction in Bayesian setting. Our theoretical analysis shows that VENUS-PAY can achieve both strategy-proofness and optimal expected payment. We evaluate VENUS on a public sensory data set, collected by Intel Research, Berkeley Laboratory. Our evaluation results show that VENUS-PRO approaches the optimal profit, and VENUS-PAY outperforms the canonical second-price reverse auction, in terms of total payment.
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来源期刊
CiteScore
30.00
自引率
4.30%
发文量
234
审稿时长
6 months
期刊介绍: The IEEE Journal on Selected Areas in Communications (JSAC) is a prestigious journal that covers various topics related to Computer Networks and Communications (Q1) as well as Electrical and Electronic Engineering (Q1). Each issue of JSAC is dedicated to a specific technical topic, providing readers with an up-to-date collection of papers in that area. The journal is highly regarded within the research community and serves as a valuable reference. The topics covered by JSAC issues span the entire field of communications and networking, with recent issue themes including Network Coding for Wireless Communication Networks, Wireless and Pervasive Communications for Healthcare, Network Infrastructure Configuration, Broadband Access Networks: Architectures and Protocols, Body Area Networking: Technology and Applications, Underwater Wireless Communication Networks, Game Theory in Communication Systems, and Exploiting Limited Feedback in Tomorrow’s Communication Networks.
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