CAREER: Distributed Inference-Making via Crowdsensing

人群感知系统允许携带智能设备的人类人群参与者将来自其设备中的内置传感器的感测测量结果贡献给分布式推理任务。一些群体感测系统还采用人类群体参与者作为人类传感器，其中人类自己观察现象并贡献所获得的主观推断。人群感知的人力性质使得这种系统的性能除了用于从所进行的观察中提取关于现象的信息的信号处理的方面之外，还取决于与人性有关的因素。这使得优化众测性能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。该项目将显著推进人力驱动的社会规模分布式传感技术，这些技术可以维持更智能、更安全、更有弹性的社区，并将推进人在环信号处理能力。该项目还旨在通过一项关于整合研究和教育活动的详细计划，大大提高学生的参与、学习、招聘和保留，并将提高包括妇女和少数民族在内的代表性不足的人口的参与。该项目开发模型，分析方法和优化技术，解决信号处理方面与人类群体参与者的性质相关的特征，为基于众测的分布式推理系统奠定基础。具体而言，该项目旨在：1）产生新颖的基于博弈论市场的群体感知机制，其联合解决信号处理和自私方面，以在诸如参与成本不确定性、资源约束、隐私问题和依赖结构的因素下，实现从自私的人类群体参与者的最佳信息获取，在竞争激烈的市场环境中，（货币或非货币）激励措施，以促使他们按预期参与; 2）产生前景理论模型和方法，用于最佳地使用认知偏差的人类群体参与者进行分布式推理任务; 3）分析来自恶意人群参与者的攻击的影响，这些参与者可以挑战贡献数据的完整性，并开发缓解技术;并且，在本发明中，4）制作一个支持各种智能设备和应用的人群感知测试平台，用于在真实的环境下对人群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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.