CAREER: Distributed Inference-Making via Crowdsensing

人群传感系统允许携带智能设备的人群参与者通过其设备中的内置传感器为分布式推理任务提供传感测量。一些群体感知系统还采用人类群体参与者作为人类作为传感器，在这种系统中，人类自己观察到一种现象，并贡献得出的主观推断。除了从观察中提取有关现象的信息的信号处理方面之外，群体感知的人力本质使得这类系统的性能变得依赖于与人性相关的因素。这使得优化众感性能需要共同解决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个问题。该项目将显著推进人力社会规模分布式传感技术，以维持更智能、更安全、更有弹性的社区，并将推进人在环路信号处理能力。该项目还旨在通过一项整合研究和教育活动的精心计划，显著提高学生的参与度、学习、招生和留校率，并将提高代表性不足人群（包括妇女和少数民族）的参与度。该项目开发模型、分析方法和优化技术，将信号处理方面与人类群体参与者的性质相关的特征结合起来，为基于群体感知的分布式推理系统奠定基础。具体而言，该项目旨在：1)建立基于市场的新型博弈论众感机制，共同解决信号处理和自私方面的问题，在竞争的市场环境中，在参与成本不确定性、资源约束、隐私问题和依赖结构等因素下，从自私的人类群体参与者那里获得最佳信息，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)建立前景理论模型和方法，以最优地利用认知偏差的人群参与者进行分布式推理任务；3)分析地揭示来自恶意人群参与者的攻击的影响，他们可以挑战所提供数据的完整性，并开发缓解技术；并且，4)建立一个支持各种智能设备和应用的众感测试平台，用于分析和评估现实操作条件下的众感技术的性能。该奖项反映了美国国家科学基金会的法定使命，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.