CAREER: Wireless Collaborative Mixed Reality Networking: Foundations and Algorithms for Joint Communication, Computation, and Learning

职业：无线协作混合现实网络：联合通信、计算和学习的基础和算法

• 批准号: 1942383
• 负责人: Bin Li
• 金额: $ 50.59万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942383&HistoricalAwards=false
• 关键词: CAREER; Wireless; Collaborative; Mixed; Reality

Wireless collaborative mixed reality (WCMR) provides an interactive and immersive experience for a group of people that can move freely in an open space and will potentially revolutionize existing collaborative mission-critical training, such as firefighter drills and disaster response training. In order to provide the best immersive experience, WCMR differs drastically from traditional wireless applications in that they demand not only coordinated information to be transferred to collaborating agents in real-time but also fast computations in mobile mixed reality devices. Hence, WCMR requires fundamentally different designs than existing approaches that mainly focus on communication demands and predominantly assume that they are independently generated at different network agents. This project aims to develop joint communication, computation, and learning algorithms that explicitly exploit unique characteristics of WCMR and support emerging WCMR applications. Research outcomes from this CAREER project are constantly integrated into both undergraduate and graduate courses taught by the PI. This CAREER project also establishes outreach programs for both K-12 and college students to be exposed to state-of-the-art wireless and mixed reality technologies. Different from traditional wireless networks, the design of efficient WCMR needs to enable both concurrent wireless communications and fast computations. Therefore, the system performance relies heavily on the extremely low-delay completion of all concurrent communication and computation tasks across the network, instead of independent communication tasks as in traditional wireless networks. As such, the proposed research is organized into the following three interdependent thrusts: (i) Serving concurrent WCMR traffic. This thrust focuses on the communication aspect of WCMR and will establish analytical foundations of adaptive algorithm design that efficiently serves the concurrent traffic with the goal of optimizing throughput, latency, and seamless user experience. (ii) Offloading compute-intensive WCMR tasks. This thrust addresses both communication and computation needs of WCMR, and will develop joint offloading and scheduling schemes that significantly boost the performance of WCMR to alleviate heavy computations in mobile mixed reality devices by leveraging powerful servers. (iii) Leveraging predictable WCMR user behavior. This thrust focuses on joint communication, computation, and learning design, and will further enhance network performance by exploiting predictable user behavior. Finally, we will implement the algorithms developed in this project in our existing platforms, and evaluate their corresponding performance.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.

无线协作混合现实（WCMR）为一群可以在开放空间中自由移动的人提供交互式和沉浸式体验，并可能彻底改变现有的协作关键任务培训，例如消防员演习和灾难响应培训。为了提供最佳的沉浸式体验，WCMR与传统的无线应用程序有很大的不同，因为它们不仅需要将协调的信息实时传输给协作代理，还需要在移动的混合现实设备中进行快速计算。因此，WCMR需要从根本上不同的设计比现有的方法，主要集中在通信需求，并主要假设它们是独立地产生在不同的网络代理。该项目旨在开发联合通信，计算和学习算法，明确利用WCMR的独特特性，并支持新兴的WCMR应用程序。从这个职业生涯项目的研究成果不断融入PI教授的本科和研究生课程。这个CAREER项目还为K-12和大学生建立了外展计划，让他们接触到最先进的无线和混合现实技术。与传统的无线网络不同，高效WCMR的设计需要同时支持并发无线通信和快速计算。因此，系统性能在很大程度上依赖于网络上所有并发通信和计算任务的极低延迟完成，而不是像传统无线网络中那样独立的通信任务。因此，拟议的研究被组织成以下三个相互依赖的推力：（i）服务并发WCMR流量。这一推力集中在WCMR的通信方面，并将建立自适应算法设计的分析基础，有效地服务于并发流量，目标是优化吞吐量，延迟和无缝用户体验。(ii)卸载计算密集型WCMR任务。这一推力解决了WCMR的通信和计算需求，并将开发联合卸载和调度方案，通过利用强大的服务器，显着提高WCMR的性能，以减轻移动的混合现实设备中的繁重计算。(iii)利用可预测的WDMR用户行为。这一重点是联合通信，计算和学习设计，并将通过利用可预测的用户行为进一步提高网络性能。最后，我们将在我们现有的平台上实现该项目中开发的算法，并评估其相应的性能。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。

项目成果

Thompson-Sampling-Based Wireless Transmission for Panoramic Video Streaming

• 发表时间: 2020-06
• 期刊: 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT)
• 作者: Jiangong Chen;Bin Li;R. Srikant

Waiting But Not Aging: Optimizing Information Freshness Under the Pull Model

• DOI: 10.1109/tnet.2020.3041654
• 发表时间: 2021-02-01
• 期刊: IEEE-ACM TRANSACTIONS ON NETWORKING
• 影响因子: 3.7
• 作者: Li, Fengjiao;Sang, Yu;Ji, Bo
• 通讯作者: Ji, Bo

An Interactive and Immersive Remote Education Platform based on Commodity Devices

• DOI: 10.1109/infocomwkshps51825.2021.9484492
• 发表时间: 2021-05
• 期刊: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: Jiangong Chen;Feng Qian;Bin Li

Motion-Prediction-based Wireless Scheduling for Multi-User Panoramic Video Streaming

• DOI: 10.1109/infocom42981.2021.9488771
• 发表时间: 2021-05
• 期刊: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
• 作者: Jiangong Chen;Xudong Qin;Guangyu Zhu;Bo Ji;Bin Li