CAREER: Distributed Inference-Making via Crowdsensing

树冠传感系统允许携带智能设备的人群参与者将来自设备中内置传感器的传感测量贡献给分布式推理任务。一些人群感应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提供获得的主观推论。除了从观察中提取有关现象的信息的信号处理方面外，众感的人力性质使这种系统的性能变得依赖于与人性有关的因素。这使得群体感知性能的优化需要联合处理信号处理和人的方面，本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社会规模的分布式传感技术，这些技术可以维持更智能、更安全和更具弹性的社区，并将提高人在环路中的信号处理能力。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，显著提高学生的参与度、学习、招生和保留率，并将加强代表不足的人群，包括妇女和少数群体的参与。该项目开发了模型、分析方法和优化技术，结合与人类群体参与者的性质相关的特征来处理信号处理方面的问题，以奠定基于群体感知的分布式推理系统的基础。具体地说，该项目的目标是：1)产生新颖的基于博弈论的市场群体感知机制，该机制共同解决信号处理和自私方面的问题，从而能够在竞争的市场环境中从自私的人类群体参与者那里获得最优的信息获取，同时提供最优的(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，用于在分布式推理任务中最优地使用认知偏向的人类群体参与者；3)分析揭示恶意人类群体参与者的攻击的影响，他们可以挑战所贡献的数据的完整性，并开发缓解技术；以及，4)生产支持各种智能设备和应用的人群感应试验台，用于在真实世界的运行条件下对人群感应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。

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.