CAREER: Leveraging Context-Aware Sensing to Enhance QoE for Mobile Users: A Practical Framework Design

职业：利用上下文感知感知增强移动用户的体验质量：实用的框架设计

• 批准号: 1943509
• 负责人: Ming Li
• 金额: $ 50.22万
• 依托单位: University of Texas at Arlington
• 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: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943509&HistoricalAwards=false
• 关键词: CAREER; Leveraging; Context; Aware; Sensing; CAREER; Leveraging; Context; Aware; Sensing

Due to the proliferation of powerful mobile terminals, resource-demanding multimedia applications, such as video streaming, video calls, and online gaming, have been surging during the past decade. How to improve the satisfaction of these applications as perceived subjectively by end-users, widely interpreted as the quality of experience (QoE), is critical to the operators of wireless networks. Prevailing QoE enhancement methods are mostly operator-oriented, while users play somewhat limited roles. On the other hand, mobile devices are equipped with multiple embedded sensors that enable the collection and inference of rich contextual information. Motivated by these observations, this project aims to leverage user-centric context sensing to enhance QoE in wireless networks. The proposed user-assisted QoE enhancement, if successful, has the potential to serve as a promising complement to the existing centralized QoE management framework. Besides, it will open up new opportunities that incorporate mobile users for network measurement and management that has been mostly conducted by network operators alone. The project plans to engage female and under-represented minority students in the research activities and actively outreach to middle and high school students. The scholarly discovery will be disseminated broadly to the community.This project intends to develop a practical framework to leverage context sensing for QoE enhancement and address critical challenges involved therein. More specifically, the proposed research will be carried out through three closely related research thrusts. The first thrust studies fine-granular user context prediction based on historical records harvested from mobile devices. As another key ingredient, it further investigates how to utilize the prediction results to assist with resource provisioning and QoE improvement in wireless networks. Realizing that context sensing may incur extra communication and energy overhead, the second thrust intends to reduce the cost by developing cost-efficient context sampling and accurate reconstruction that adapt to context dynamics. The third thrust addresses the privacy concern during context sensing. A flexible context trading framework will be developed to allow individual users to decide how much privacy to disclose in the trade of QoE improvement. The proposed research is inter-disciplinary, which spans resource management, learning techniques, compressive sensing, differential privacy, and economic theories. The proposed mechanisms and designs will be thoroughly evaluated via a combination of measurement campaigns, simulations, and experimental studies. A medium-scale prototype of off-the-shelf mobile devices, routers, servers, and cloud will be implemented.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.

由于强大的移动终端的扩散，在过去的十年中，诸如视频流，视频通话和在线游戏之类的多媒体应用程序（例如视频流，视频呼叫和在线游戏）一直在增加。如何提高最终用户对这些应用程序的满意度（被广泛解释为体验质量（QOE））对无线网络的运营商至关重要。盛行的QoE增强方法主要是面向操作员的，而用户则扮演有限的角色。另一方面，移动设备配备了多个嵌入式传感器，可实现丰富的上下文信息的收集和推断。在这些观察结果的推动下，该项目旨在利用以用户为中心的上下文感测来增强无线网络中的QoE。提议的用户辅助QoE增强功能（如果成功）有可能成为现有集中式QoE管理框架的有希望的补充。此外，它将开放新的机会，将移动用户纳入用于网络测量和管理的移动用户，而网络测量和管理主要仅由网络运营商进行。该项目计划让女性和代表性不足的少数族裔学生参与研究活动，并积极向中学和高中生宣讲。学术发现将广泛地传播给社区。该项目打算开发一个实用的框架来利用上下文感知QOE的增强感，并应对其中涉及的关键挑战。更具体地说，拟议的研究将通过三个密切相关的研究推力进行。第一个推力研究基于从移动设备收获的历史记录的细粒用户上下文预测。作为另一种关键要素，它进一步研究了如何利用预测结果来协助无线网络的资源供应和QOE改进。意识到上下文感应可能会产生额外的沟通和能量开销，第二个推力旨在通过开发成本效益的上下文采样和准确的重建来降低成本，以适应上下文动态。第三个推力解决了上下文感测期间的隐私问题。将开发一个灵活的上下文交易框架，以允许各个用户决定在改进QOE交易中披露多少隐私。拟议的研究是跨学科的，它涵盖了资源管理，学习技术，压缩感应，差异隐私和经济理论。提出的机制和设计将通过测量活动，模拟和实验研究的结合进行彻底评估。将实施现成的移动设备，路由器，服务器和云的中等规模的原型。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。