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: 1717197
• 负责人: Guoliang Xue
• 金额: $ 25.2万
• 依托单位: Arizona State University
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717197&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是美国联邦通信委员会倡导的解决频谱短缺、提高频谱利用率的最有前途的方法之一。在这样的系统中，频谱服务提供商(SSP)接受来自主用户的注册，并基于他们对频谱的使用来确定频谱可用性。有兴趣使用频谱的二次用户需要联系SSP，以查询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以获得利益，但会降低性能。该项目的目标是通过设计抗Sybil的激励机制来增强众包频谱感知，以克服现有激励机制的缺点。该项目将提高人们对众包频谱传感系统中可能的Sybil攻击的认识。预期的结果还将在设计基于众包的应用的激励机制方面开辟新的天地。该项目将吸引少数族裔学生和代表性不足的群体参与。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日益增长的课程。Struts 1专注于通过直接众包架构下的抗Sybil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通信。在接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选择获奖用户集合并决定向每个获奖卖家支付相应的费用。获奖用户执行感知任务，并获得相应的奖励。Struts 2专注于通过分级众包架构下的Sybil防Sybil激励机制设计来增强众包，其中移动用户在频谱感知和请求方面都做出了贡献。该模型包含了赢得DARPA网络挑战的方法，但容易受到Sybil攻击。绩效考察员将设计防梅毒的激励机制。所提出的研究将通过仿真和试验台实验进行评估和验证。

项目成果

Provisioning QoS-Aware and Robust Applications in Internet of Things: A Network Perspective

• DOI: 10.1109/tnet.2019.2936015
• 发表时间: 2019-08
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Ruozhou Yu;G. Xue;Xiang Zhang

Inferring User Activities from IoT Device Events in Smart Homes: Challenges and Opportunities

• DOI: 10.1109/icccn54977.2022.9868917
• 发表时间: 2022-07
• 期刊: 2022 International Conference on Computer Communications and Networks (ICCCN)
• 作者: Xuanli Lin;Yinxin Wan;Kuai Xu;Feng Wang;G. Xue

The Fog of Things Paradigm: Road toward On-Demand Internet of Things

• DOI: 10.1109/mcom.2018.1701140
• 发表时间: 2018-09
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Ruozhou Yu;G. Xue;Vishnu Teja Kilari;Xiang Zhang

Robust resource provisioning in time-varying edge networks

• DOI: 10.1145/3397166.3409146
• 发表时间: 2020-10
• 期刊: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
• 作者: Ruozhou Yu;G. Xue;Yinxin Wan;Jian Tang;Dejun Yang;Yusheng Ji

EARS: Enabling Private Feedback Updates in Anonymous Reputation Systems

• DOI: 10.1109/cns48642.2020.9162328
• 发表时间: 2020-06
• 期刊: 2020 IEEE Conference on Communications and Network Security (CNS)
• 作者: Vishnu Teja Kilari;Ruozhou Yu;S. Misra;G. Xue