CAREER: Distributed Inference-Making via Crowdsensing

职业：通过群体感知进行分布式推理

• 批准号: 2047701
• 负责人: Swastik Brahma
• 金额: $ 48.98万
• 依托单位: Tennessee State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047701&HistoricalAwards=false
• 关键词: CAREER; Distributed; Inference; Making; via

Crowdsensing systems allow human crowd participants carrying smart devices to contribute sensing measurements from built-in sensors in their devices toward a distributed inference-making task. Some crowdsensing systems also employ human crowd participants as humans-as-sensors where humans themselves observe a phenomenon and contribute subjective inferences obtained. The human-powered nature of crowdsensing makes the performance of such systems to become dependent, in addition to aspects of signal processing for extracting information about a phenomenon from observations made, on factors pertinent to human nature. This makes optimization of crowdsensing performance to require jointly addressing signal processing and human aspects, which this project aims to address by converging interdisciplinary perspectives. The project will significantly advance human-powered societal-scale distributed sensing technologies that can sustain smarter, safer, and more resilient communities as well as will advance human-in-the-loop signal processing capabilities. The project also aims to significantly enhance student engagement, learning, recruitment, and retention through an elaborate plan on integrating research and educational activities as well as will enhance participation of underrepresented populations, including women and minorities. The project develops models, analytical approaches, and optimization techniques that address signal processing aspects jointly with traits pertinent to the nature of human crowd participants to lay the foundations of crowdsensing-based distributed inference-making systems. Specifically, the project aims to: 1) produce novel game theoretic market-based crowdsensing mechanisms that jointly address signal processing and selfishness aspects to enable optimal information acquisition from selfish human crowd participants, under factors such as participatory cost uncertainties, resource constraints, privacy concerns, and dependency structures, in a competitive market environment while providing optimal (monetary or nonmonetary) incentives to induce their desired participation; 2) produce prospect theoretic models and methods for optimally employing cognitively biased human crowd participants for distributed inference-making tasks; 3) analytically unravel the impact of attacks from malicious human crowd participants, who can challenge the integrity of the contributed data, and develop mitigation techniques; and, 4) produce a crowdsensing testbed that supports a variety of smart devices and applications for analysis and performance evaluation of crowdsensing techniques under real-world operating conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

群体传感系统允许携带智能设备的人群参与者将其设备内置传感器的传感测量数据贡献给分布式推理任务。一些群体感知系统还使用人群参与者作为人类传感器，人类自己观察现象并贡献获得的主观推论。人群感知的人力本质使得此类系统的性能除了用于从观察中提取有关现象的信息的信号处理方面之外，还依赖于与人性相关的因素。这使得众感知性能的优化需要共同解决信号处理和人类方面的问题，该项目旨在通过融合跨学科的观点来解决这一问题。该项目将显着推进人力社会规模的分布式传感技术，以维持更智能、更安全和更有弹性的社区，并将提高人在环信号处理能力。该项目还旨在通过整合研究和教育活动的精心计划，显着提高学生的参与度、学习、招生和保留率，并将提高包括妇女和少数民族在内的代表性不足的群体的参与度。该项目开发模型、分析方法和优化技术，解决信号处理方面的问题以及与人群参与者的性质相关的特征，为基于人群感知的分布式推理系统奠定基础。具体来说，该项目的目标是：1）产生新颖的基于博弈论市场的人群感知机制，共同解决信号处理和自私方面的问题，以便在参与成本不确定性、资源限制、隐私问题和依赖结构等因素下，在竞争性市场环境中，从自私的人群参与者那里获取最佳信息，同时提供最佳（货币或非货币）激励以诱导他们期望的参与； 2）产生前景理论模型和方法，以最佳地利用有认知偏差的人群参与者来执行分布式推理任务； 3）分析性地揭示恶意人群参与者攻击的影响，这些参与者可以挑战所贡献数据的完整性，并开发缓解技术； 4) 制作一个群智感知测试平台，支持各种智能设备和应用程序，以便在现实操作条件下对众智感知技术进行分析和性能评估。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optimal Auction Design with Malicious Sellers

• DOI: 10.1109/percomworkshops53856.2022.9767523
• 发表时间: 2022-03
• 期刊: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
• 作者: Swastik Brahma;L. Njilla;Satyaki Nan

Collaborative Human Decision-Making With Heterogeneous Agents

• DOI: 10.1109/tcss.2021.3098975
• 发表时间: 2021-07-27
• 期刊: IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
• 影响因子: 5
• 作者: Geng, Baocheng;Cheng, Xiancheng;Varshney, Pramod K.
• 通讯作者: Varshney, Pramod K.