Collaborative Research: SHF: Small: Decentralized Edge Computing Platform for Privacy-Preserving Mobile Crowdsensing

移动众测利用移动设备（如智能手机和可穿戴设备）收集用户的传感数据并测量时空现象（如空气质量和交通速度）。然而，现有的众感解决方案主要建立在以云为中心的方法上，这带来了重大的安全和隐私挑战。例如，对洪水和野火的准确和实时的态势感知对于事件指挥官和居民抗击这些自然灾害非常重要，移动人群传感可以通过移动用户提供的图片和/或输入提供大规模的灾害监测。虽然大多数用户都愿意帮忙，但他们可能会因为隐私问题而犹豫不决，因为他们的私人信息，包括GPS位置，可能会在传输到云服务器或服务器上存储的过程中泄露。该项目通过集成软硬件设计、边缘计算、分布式时空优化和基于机器学习的隐私保护，研究无聚合和隐私感知移动众测的硬件和软件架构。该项目没有将原始传感器数据聚合到中央服务器，而是在边缘服务器之间传递用户数据的潜在表示，直到它们通过时空插值恢复所有区域的数据。该项目的教育部分包括地方外展计划（例如，佛罗里达大学的大学少数族裔导师计划和加州大学默塞德分校的研究周博览会）和夏季实习，以增加代表性不足的人群，包括少数族裔和女学生的研究机会。该项目研究了一种新的软硬件架构，该架构将时空预测和分布式优化集成到边缘计算中，用于无聚合和隐私感知的移动众测。该项目设计了一个机器学习管道，可以使用部分可用的众感数据预测传感测量，同时提供隐私意识，而无需将传感器数据聚合到中央服务器。为了有效地管理上述机器学习管道，并自动扩展多个边缘服务器的计算资源，开发了边缘计算平台。两个重要的应用，自然灾害（洪水）和公共卫生（体温）监测，将实施评估系统的有效性和展示社会影响。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。

Mobile crowdsensing leverages mobile devices (e.g., smartphones and wearables) to collect sensing data from users and measure spatiotemporal phenomena (e.g., air quality and traffic speed). Yet, existing crowdsensing solutions are mainly built on a cloud-centric approach that raises significant security and privacy challenges. For example, accurate and real-time situational awareness of flooding and wildfires is important for incident commanders and residents to fight these natural hazards, and mobile crowdsensing can provide large-scale monitoring of hazards by pictures and/or input provided by mobile users. Although most users are willing to help, they may hesitate to participate in such a crowdsensing task due to privacy concerns, as their private information including GPS locations may be leaked during the transmissions to a cloud server or from the storage on the server. This project investigates a hardware and software architecture for aggregation-free and privacy-aware mobile crowdsensing by integrating software and hardware design, edge computing, distributed spatiotemporal optimization, and machine-learning-based privacy protection. Without aggregating raw sensor data to a central server, this project passes latent representations of user data among edge servers until they recover the data of all areas by spatiotemporal interpolation. The educational components of this project include local-outreach programs (e.g., the University Minority Mentor Program at the University of Florida and the research week fair at the University of California, Merced) and summer internships to enhance research opportunities for underrepresented populations, including minority and female students. This project investigates a novel software and hardware architecture that integrates spatiotemporal prediction and distributed optimization into edge computing for aggregation-free and privacy-aware mobile crowdsensing. This project designs a machine-learning pipeline that predicts sensing measurements with partially-available crowdsensed data, at the same time providing privacy-awareness without aggregating sensor data to a central server. An edge computing platform is developed to efficiently manage the above machine-learning pipeline and automatically scale up the computing resources of multiple edge servers. Two important applications, natural hazard (flood) and public health (body temperature) monitoring, will be implemented to evaluate system effectiveness and demonstrate societal impact.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.