CAREER: Towards Proactive and Collaborative Mobility-Aware Edge Intelligence

职业：迈向主动、协作的移动感知边缘智能

• 批准号: 2145268
• 负责人: Lena Mashayekhy
• 金额: $ 67.18万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145268&HistoricalAwards=false
• 关键词: CAREER; Towards; Proactive; Collaborative; Mobility

The significant increase in both IoT (Internet of Things) network size and data volume opens up attractive opportunities for data analysis and learning to support decision making and derive scientific discovery and innovations. Multiaccess Edge Computing (MEC) enables IoT devices to offload their delay-sensitive resource-intensive tasks to nearby edge devices. However, spatio-temporal uncertainties due to user mobility bring the most challenging obstacles for MEC in providing predictable performance and guaranteed Quality-of-Service (QoS). The research mission of this CAREER proposal is to develop foundations of proactive and collaborative mobility-aware MEC systems to improve QoS for IoT applications, minimize data movement, and optimize the overall system performance. This project focuses on all aspects of mobility in MEC, which include these scenarios: (i) mobile IoT devices/edge devices move as their own decisions; (ii) the system makes the use of mobile edge devices to move to a specific demand area; and (iii) the system incentives the mobile IoT devices to move to receive better QoS. In support of the project mission, this project will design a novel data-driven approach to infer vital mobility patterns leading to QoS violations, and will develop a unique proactive offloading and service deployment approach based on an integrated community detection and multidimensional flow approach. This project will investigate the design of a proactive learning-based approach to act autonomously based on any changes that may lead to QoS violation. This project will facilitate dynamic placement and relocation of mobile edge devices in providing low-latency edge services. This project will also investigate the effects of suggesting location changes to IoT devices and edge devices with location flexibility, and will design a reverse-auction mechanism and a two-sided mechanism to balance the system load and further improve response time.This CAREER project will integrate teaching, research, and outreach activities to broaden exposure to systems research and increase student retention and representation of underrepresented students by providing meaningful and exciting student involvement and finely-tuned mentorship. The proposed research plan will address the technical challenges in building mobility-aware MEC systems for realtime processing demands of mobile IoT applications to enable edge intelligence along the cloud-to-thing continuum and will enrich the scientific knowledge of advanced computing system design. The availability of collaborative mobile edge devices will improve the quality of edge services for end-users and will empower many more innovative applications that are simply not yet possible today. The proposed educational plan will educate students on modern notions of MEC and prepare them for related professions. A substantial quantity of the materials of this project will be made publicly available online in the form of tutorials, talks, publications, codes, datasets, and testbeds.This project is jointly funded by the Faculty Early Career Development Program (CAREER) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

物联网（IoT）网络规模和数据量的显着增加为数据分析和学习提供了诱人的机会，以支持决策并得出科学发现和创新。多路访问边缘计算 (MEC) 使物联网设备能够将延迟敏感的资源密集型任务卸载到附近的边缘设备。然而，由于用户移动性导致的时空不确定性给 MEC 提供可预测的性能和有保证的服务质量 (QoS) 带来了最具挑战性的障碍。该职业提案的研究任务是开发主动、协作的移动感知 MEC 系统的基础，以提高物联网应用的服务质量、最大限度地减少数据移动并优化整体系统性能。该项目重点关注 MEC 中移动性的各个方面，包括以下场景：(i) 移动物联网设备/边缘设备根据自己的决定移动；(ii) 系统利用移动边缘设备移动到特定的需求区域；(iii) 系统激励移动物联网设备移动以获得更好的 QoS。为了支持项目任务，该项目将设计一种新颖的数据驱动方法来推断导致服务质量违规的重要移动模式，并将开发一种基于集成社区检测和多维流方法的独特的主动卸载和服务部署方法。该项目将研究一种基于主动学习的方法的设计，以根据可能导致 QoS 违规的任何变化自主采取行动。该项目将促进移动边缘设备的动态放置和重新定位，以提供低延迟边缘服务。该项目还将研究建议位置变化对具有位置灵活性的物联网设备和边缘设备的影响，并将设计反向拍卖机制和双边机制来平衡系统负载并进一步缩短响应时间。该职业项目将整合教学、研究和外展活动，以扩大对系统研究的接触，并通过提供有意义和令人兴奋的学生参与和精心调整的指导来提高学生保留率和代表性不足的学生的代表性。拟议的研究计划将解决构建移动感知MEC系统的技术挑战，以满足移动物联网应用的实时处理需求，以实现云到物连续体的边缘智能，并将丰富先进计算系统设计的科学知识。协作移动边缘设备的可用性将提高最终用户的边缘服务质量，并将支持更多目前尚不可能实现的创新应用程序。拟议的教育计划将教育学生现代 MEC 概念，并为他们从事相关职业做好准备。该项目的大量材料将以教程、讲座、出版物、代码、数据集和测试平台的形式在网上公开提供。该项目由教师早期职业发展计划 (CAREER) 和刺激竞争性研究既定计划 (EPSCoR) 共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的评估进行评估，认为值得支持。 影响审查标准。

项目成果

Physics-Inspired Mobile Cloudlet Placement in Next-Generation Edge Networks

• DOI: 10.1109/edge55608.2022.00031
• 发表时间: 2022-07
• 期刊: 2022 IEEE International Conference on Edge Computing and Communications (EDGE)
• 作者: Dixit Bhatta;Lena Mashayekhy