CNS Core: Small: Sensor-based Wireless Network Management via Edge Computing

随着物联网、移动医疗、自动驾驶汽车、网络物理系统、虚拟现实和智能环境的激增，无线频谱需求继续以显著的速度增长。该提案通过高级集成软件驱动的网络、无处不在的计算部署和传感器驱动的分析方面的新兴趋势，研究管理无线网络的新方法。该提案支持国家科学基金会的使命，即通过使无线行业能够更有效和灵活地控制其网络来促进繁荣。这位调查员将与无线通信行业密切合作，该行业每年为美国经济贡献4750亿美元，并在全国范围内支持多达470万个工作岗位。该项目通过开发基于传感器的计算教育模块进一步造福社会，该模块将在TeachEngineering上发布，TeachEngineering是一种面向K-12教育人员的免费可搜索工程课程库。将研究两个主要推动力，以构建一个能够实现基于传感器的实际无线网络管理的体系结构。第一个推力旨在利用传感器数据更有效和灵活地管理无线网络。首先，通过两个域中的行为分析技术，将感觉域中的对象与无线域中的对象对齐。其次，设计的技术将根据移动设备用户的操作灵活地调度网络流量。第三，可编程框架和相应的可行性工具允许管理员配置这种新型网络架构。该提案的第二个主要目的是提高网络边缘的感官数据分析效率。首先，使用用户和流量上下文来确定应该为传感器分析分配多少边缘资源。其次，通过上下文感知的采样率算法减少了对网络边缘资源的计算需求。此外，新技术利用无线信息来减少基于传感器的处理。研究成果将被纳入科罗拉多大学博尔德大学的课程、科学出版物，并以开源软件的形式发布。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。

As Internet-of-Things, mobile health, autonomous vehicles, cyber-physical systems, virtual reality, and smart environments proliferate, wireless spectrum demand continues to grow at a significant rate. This proposal investigates new ways of managing wireless networks through the advanced integration of emerging trends in software-driven networking, ubiquitous computing deployments, and sensor-driven analysis. The proposal supports the National Science Foundation's mission to advance prosperity by enabling wireless industries to more efficiently and flexibly control their networks. The investigator will work closely with the wireless communication industry that contributes $475 billion annually to America's economy each year and supports up to 4.7 million jobs nationally. The project further benefits society by developing sensor-based computing educational modules that will be released on TeachEngineering, a free searchable engineering curricula library for K-12 educators.Two major thrusts will be studied to build an architecture that enables practical sensor-based wireless network management. The first thrust aims to utilize sensor data to more effectively and flexibly manage the wireless network. First, objects in the sensory domain will be aligned with objects in the wireless domain through behavior analysis techniques in both domains. Second, techniques will be designed to flexibly schedule network traffic based on the actions of mobile device users. Third, a programmable framework and corresponding feasibility tools allow administrators to configure this new type of network architecture. A second major thrust in this proposal makes analyzing sensory data at the network edge more efficient. First, user and traffic context are used to determine how much edge resources should be allocated for sensor analysis. Second, computational demands on network edge resources are lessened via context-aware sampling rate algorithms. In addition, new techniques utilize wireless information to lessen sensor-based processing. The research results will be incorporated into courses at the University of Colorado Boulder, scientific publications, and released as open-source software.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.