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.

数据库驱动的动态频谱共享已被美国联邦通信委员会倡导为解决频谱短缺和提高频谱利用率最有前途的方法之一。在这样的系统中，频谱服务提供商（SSP）接受主要用户的注册，并根据他们的频谱使用情况确定频谱可用性。有兴趣使用频谱的二级用户需要联系 SSP，询问任何感兴趣频段的频谱可用性。利用众包的力量，频谱感知成为有效提高频谱估计精度的关键推动因素。在众包频谱感知中，SSP 将频谱感知任务外包给大量招募的移动用户。然而，许多现有的众包激励机制很容易受到 Sybil 攻击，攻击者非法伪造多个身份来获取利益，但却降低了性能。该项目的目标是通过设计克服当前激励机制缺点的防女巫激励机制来增强众包频谱感知。该项目将提高人们对众包频谱传感系统中可能发生的 Sybil 攻击的认识。预期结果也将为基于众包的应用程序设计激励机制开辟新天地。该项目将吸引少数族裔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专题研讨会补充和丰富亚利桑那州立大学和科罗拉多矿业学院不断发展的课程。拟议的研究由两个相互关联的研究主旨组成。 Thrust 1 专注于通过直接众包架构下的防女巫激励机制设计来增强众包频谱感知，其中移动用户直接与 SSP 进行通信。在收到感知任务描述后，每个移动用户提出感知计划以及奖励请求。 SSP 选择一组获胜用户并决定向每个获胜卖家相应的付款。获胜用户执行感知任务并获得相应奖励。 Thrust 2 专注于通过分层众包架构下的防女巫激励机制设计来增强众包，其中移动用户在频谱感知和征集方面做出贡献。该模型采用了赢得 DARPA 网络挑战的方法，但容易受到 Sybil 攻击。 PI 将设计防女巫激励机制。拟议的研究将通过模拟和测试台实验进行评估和验证。

项目成果

Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing

移动群智感知的个性化隐私保护任务分配

• 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: Zhibo Wang;Jiahui Hu;Ruizhao Lv;Jian Wei;Qian Wang;Dejun Yang;H. Qi
• 通讯作者: H. Qi

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

基于两层社交众包架构的移动众包在线激励机制

• DOI: 10.1109/sahcn.2018.8397102
• 发表时间: 2018-06-01
• 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
• 通讯作者: Tao Li

Sybil-Proof Online Incentive Mechanisms for Crowdsensing

针对群体感知的防女巫在线激励机制

• DOI: 10.1109/infocom.2018.8486418
• 发表时间: 2018-04
• 期刊: IEEE Conference on Computer Communications (INFOCOM
• 作者: Lin, Jian;Li, Ming;Yang, Dejun;Xue, Guoliang
• 通讯作者: Xue, Guoliang

A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing

移动群智感知的抗女巫真相发现框架

• DOI: 10.1109/icdcs.2019.00091
• 发表时间: 2019-07-01
• 期刊: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Jian Lin;Dejun Yang;Kun Wu;Jian Tang;G. Xue
• 通讯作者: G. Xue

Privacy-Preserving and Trustworthy Mobile Sensing with Fair Incentives

具有公平激励的隐私保护和值得信赖的移动传感

• DOI: 10.1109/icc.2019.8761094
• 发表时间: 2019-05-01
• 期刊: ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
• 作者: Haiqin Wu;Liangmin Wang;G. Xue;Jian Tang;Dejun Yang
• 通讯作者: Dejun Yang