NeTS: Small: Collaborative Research: Enhancing Crowdsourced Spectrum Sensing through Sybil-proof Incentives

NetS：小型：协作研究：通过防女巫激励措施增强众包频谱感知

• 批准号: 1717315
• 负责人: Dejun Yang
• 金额: $ 24.8万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717315&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Enhancing

Database-driven dynamic spectrum sharing has been advocated by the Federal Communications Commission as one of the most promising methods to address the spectrum shortage and improve the spectrum utilization. In such a system, a spectrum service provider (SSP) accepts registrations from primary users and determines spectrum availability based on their use of spectrum. Secondary users interested in using the spectrum are required to contact the SSP to inquire about spectrum availability in any band of interest. Leveraging the power of crowd-sourcing, spectrum sensing becomes a key enabler for effectively improving the spectrum-estimation accuracy. In crowd-sourced spectrum sensing, an SSP outsources spectrum-sensing tasks to a large number of recruited mobile users. However, many existing incentive mechanisms for crowd-sourcing are vulnerable to Sybil attacks, where an attacker illegitimately forges multiple identities to gain benefits but degrades the performance. The goal of this project is to enhance crowd-sourced spectrum sensing by designing Sybil-proof incentive mechanisms that overcome the shortcomings of current incentive mechanisms. This project will raise awareness about the possible Sybil attacks in crowd-sourced spectrum sensing systems. The anticipated results will also break new grounds in designing incentive mechanisms for crowdsourcing-based applications. This project will engage minority students and under-represented groups. The proposed research activities will complement and enrich the growing curricula at Arizona State University and Colorado School of Mines through course development and special topic seminars.The proposed research consists of two inter-related research thrusts. Thrust 1 concentrates on enhancing crowd-sourced spectrum sensing through Sybil-proof incentive mechanism design under a direct crowd-sourcing architecture, where mobile users communicate directly with the SSP. Upon receiving the sensing task description, each mobile user proposes a sensing plan together with a request for reward. The SSP selects the set of winning users and decides the corresponding payment to each winning seller. The winning users perform the sensing tasks and receive the corresponding rewards. Thrust 2 concentrates on enhancing crowd-sourcing through Sybil-proof incentive mechanism design under a hierarchical crowd-sourcing architecture, where mobile users contribute in both spectrum sensing and solicitation. This model subsumes the approach that won the DARPA networking challenge, but is vulnerable to Sybil attacks. The PIs will design Sybil-proof incentive mechanisms. The proposed research will be evaluated and validated via simulations and testbed experiments.

联邦通信委员会已主张数据库驱动的动态频谱共享，这是解决频谱短缺并改善频谱利用率的最有前途的方法之一。在这样的系统中，Spectrum服务提供商（SSP）接受主要用户的注册，并根据频谱的使用来确定频谱的可用性。有兴趣使用频谱的二级用户需要与SSP联系以查询任何感兴趣的乐队中的频谱可用性。利用众包的力量，频谱感应成为有效提高频谱估计精度的关键推动因素。在众包频谱传感中，SSP将频谱感应任务外包给了许多招募的移动用户。但是，许多现有的众包动力机制容易受到Sybil攻击的影响，在这些机制中，攻击者非法锻造多种身份以获得收益，但会降低表现。该项目的目的是通过设计克服当前激励机制的缺点来增强人群的频谱传感。该项目将提高人们对众包频谱传感系统中可能发动的Sybil攻击的认识。预期的结果还将在设计基于众包的应用程序的激励机制方面打破新的基础。该项目将吸引少数民族学生和代表性不足的群体。拟议的研究活动将通过课程开发和特殊主题研讨会来补充和丰富亚利桑那州立大学和科罗拉多州矿业的不断增长的课程。拟议的研究包括两个相关的研究推力。推力1集中于通过直接的人群架构结构下的Sybil-Prove激励机制设计增强人群的光谱传感，移动用户直接与SSP进行通信。收到传感任务描述后，每个移动用户都提出了一个传感计划，并要求奖励。 SSP选择获奖用户的集合，并决定每个获奖卖方的相应付款。获奖用户执行传感任务并接收相应的奖励。推力2集中在通过Sybil-Prof-Prof-Prove激励机构设计下增强众源，在分层群体架构结构下，移动用户在频谱感测和招标中都做出了贡献。该模型包含赢得DARPA网络挑战的方法，但容易受到Sybil攻击的影响。 PI将设计防系统的激励机制。拟议的研究将通过模拟和测试实验进行评估和验证。

项目成果

Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing

• DOI: 10.1109/tmc.2018.2861393
• 发表时间: 2019-06-01
• 期刊: IEEE TRANSACTIONS ON MOBILE COMPUTING
• 影响因子: 7.9
• 作者: Wang, Zhibo;Hu, Jiahui;Qi, Hairong
• 通讯作者: Qi, Hairong

Online Incentive Mechanism for Mobile Crowdsourcing Based on Two-Tiered Social Crowdsourcing Architecture

• DOI: 10.1109/sahcn.2018.8397102
• 发表时间: 2018-06
• 期刊: 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
• 作者: Jia Xu;Chengcheng Guan;Haobo Wu;Dejun Yang;Lijie Xu;Tao Li

Pay On-demand: Dynamic Incentive and Task Selection for Location-dependent Mobile Crowdsensing Systems

• DOI: 10.1109/icdcs.2018.00066
• 发表时间: 2018-01-01
• 期刊: 2018 IEEE 38TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS)
• 作者: Wang, Zhibo;Hu, Jiahui;Wang, Qian
• 通讯作者: Wang, Qian

A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing

• DOI: 10.1109/icdcs.2019.00091
• 发表时间: 2019-01-01
• 期刊: 2019 39TH IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2019)
• 作者: Lin, Jian;Yang, Dejun;Xue, Guoliang
• 通讯作者: Xue, Guoliang

Incentive Mechanism Design for Truth Discovery in Crowdsourcing With Copiers

复印机众包真相发现激励机制设计

• DOI: 10.1109/tsc.2021.3075741
• 发表时间: 2022-09-01
• 期刊: IEEE TRANSACTIONS ON SERVICES COMPUTING
• 影响因子: 8.1
• 作者: Jiang, Lingyun;Niu, Xiaofu;Xu, Lijie
• 通讯作者: Xu, Lijie