MLWiNS: Distributed Learning for the Nomadic Edge

边缘计算是一种使计算更接近数据生成位置的范例，例如蜂窝网络、互联网服务提供商和企业网络。典型的边缘节点是静态的，并且位于有线网络上的固定点上。该项目创建了边缘计算的新概念Nautilus，以支持移动节点(如车辆)上的分布式机器学习。该项目旨在创造新的技术，使一类新的应用能够利用安装在车辆上的传感器。应用范围从智能交通到城市规划，并广泛支持更智能的城市和社区。这个项目的更广泛影响包括：(1)创建以计算机视觉技术和机器学习为中心的实践课程；(2)教育更广泛的公众了解无线系统和机器学习技术及其对我们社会的影响；(3)与工业伙伴和监管机构接触；(4)公开传播开发的源代码和原型设计；(V)通过各种校园计划和全球计划指导STEM女学生，并帮助她们寻找在该领域实现职业生涯的机会。该项目采用三层结构，由游牧边缘(在车辆中)、静态边缘(与无线电接入网络共存)和云组成。以支持跨域(例如，智能交通、城市规划，以及更广泛地跨智能社区的不同方面)的各种新兴应用程序，这些应用程序可以发出跨大时空区域的查询。研究议程包括五项补充任务：(I)从车辆环境中设计分布式和动态计算机视觉，它涉及在不同条件下的事件和对象检测的协作和联合机器学习方法；(Ii)设计协作和分布式训练和推理以解决高级查询，它将利用各种冗余和编码计算和通信技术来支持效率和可扩展性；(Iii)无线网络环境的估计和预测，以推断协作游牧边缘节点之间以及游牧边缘和静态边缘之间的通信机会；(Iv)针对数据和模型的隐私和安全的设计；以及(V)系统集成和现场试验，以实现技术改进、评估和重现性。通过与当地交通网络-麦迪逊地铁交通的合作，研究团队计划使用项目研究成果，并帮助他们了解规划各种交通和交通决策的各种挑战、机会和优化。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。

Edge computing is a paradigm that brings computing closer to where the data is generated, such as cellular networks, internet service providers, and enterprise networks. Typical edge nodes are static and are located in fixed points over a wired network. This project creates a new notion of edge computing, Nautilus, to support distributed machine learning over mobile nodes, such as vehicles. The project aims to create new techniques that can enable a new class of applications that leverage sensors mounted on vehicles. The applications range from smart transportation, to urban planning, and to broadly support smarter cities and communities. The broader impact of this project includes: (i) creation of a hands-on curriculum centered around computer vision techniques and machine learning; (ii) educating the broader public on wireless systems and machine learning technologies and their impact on our society; (iii) engagement with industrial partners and with regulatory bodies; (iv) public dissemination of the developed source code and prototype design; (v) mentoring of women STEM students through various campus programs as well as global ones and helping them identify opportunities for fulfilling careers in this domain.This project is structured in a three-tiered architecture consisting of the nomadic edge (in vehicles), the static edge (co-located with Radio Access Networks), and the cloud. To support diverse emerging applications across domains (e.g., smart transportation, urban planning, and more broadly across different aspects of smarter communities) which can issue queries spanning large spatio-temporal regions. The research agenda includes five complementary tasks: (i) Design of distributed and dynamic computer vision from the vehicular context, which involves collaborative and federated machine learning approaches to event and object detection under diverse conditions; (ii) Design of collaborative and distributed training and inference to address high level queries which will utilize various techniques of redundant and coded computing and communication to support efficiency and scalability; (iii) Estimation and prediction of the wireless network context to infer opportunities for communication between collaborating nomadic edge nodes, as well as between the nomadic and static edges; (iv) Design for privacy and security of both data and models; and (v) Systems integration and field trials to allow for technique refinement, evaluation, and reproducibility. Through a partnership with the local transit network — Madison Metro Transit, the research team plans to use the project research results and assist them in understanding various challenges, opportunities, and optimizations in planning various transportation and transit decisions.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.