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联邦通信委员会倡导的解决频谱短缺和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，并且基于他们对频谱的使用来确定频谱可用性。有兴趣使用频谱的次用户需要联系SSP，以询问任何感兴趣的频带中的频谱可用性。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使能器。在众包频谱感测中，SSP将频谱感测任务外包给大量招募的移动的用户。然而，许多现有的众包激励机制容易受到Sybil攻击，其中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Sybil证明激励机制，克服现有激励机制的缺点，以提高众包频谱感知。该项目将提高对众包频谱感知系统中可能的Sybil攻击的认识。预期结果还将为多方联动外包应用的激励机制设计开辟新的天地。该项目将吸引少数民族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不断增长的课程。拟议的研究包括两个相互关联的研究重点。推力1集中于在直接众包架构下通过Sybil证明激励机制设计来增强众包频谱感知，其中移动的用户直接与SSP通信。在接收到感测任务描述时，每个移动的用户提出感测计划以及奖励请求。SSP选择一组获胜用户并决定向每个获胜卖家支付相应的款项。获胜的用户执行感测任务并获得相应的奖励。推力2集中在增强众包通过Sybil证明激励机制设计下的分层众包架构，其中移动的用户在频谱感知和征求。这种模型包含了赢得DARPA网络挑战的方法，但容易受到Sybil攻击。PI将设计防止Sybil的激励机制。所提出的研究将通过模拟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