CAREER: Operating System based Energy Virtualization for Sensor Networks

近年来，无线传感器网络以其低成本、低姿态、易于部署等优点成为一项关键技术。然而，这些有吸引力的优势意味着可用于单个节点的资源是非常有限的。特别是，能源很可能继续成为系统瓶颈，因为更多的晶体管意味着更多的功耗。 另一方面，人们越来越需要长期、可持续地部署传感器，以降低运营成本并确保服务连续性。这项研究提出了一个基于操作系统的方法和它的基础，这个问题，通过提供能源虚拟化在长期无人值守的传感器网络应用。这项工作的基础是LiteOS操作系统：一个用于无线传感器网络的类Unix操作系统。该项目的核心是基于操作系统的能源虚拟化，它为共享同一平台的应用程序提供了一个全面的能源资源核算、预留和隔离框架。该项目的交付内容包括：（i）一个改进的、高度可靠的LiteOS操作系统，内置对能源虚拟化的支持;（ii）基于能源虚拟化的软件开发的架构原理、设计方法、编程API和自适应通信协议;（iii）能源虚拟化的理论和算法研究结果;以及（iv）教育测试平台。本科和研究生课程的课程和实验室设计。我们的研究结果将对需要长期和遥感的应用产生广泛的影响，例如基础设施保护，辅助生活，智能建筑，工厂监控和许多军事应用。

In recent years, wireless sensor networks have emerged as one key technology with the advantages of being low-cost, low-profile, and easy to deploy. These attractive advantages, however, imply that resources available to individual nodes are severely limited. In particular, it is highly likely that energy will continue to be a system bottleneck, as more transistors indicate more power consumption. On the other hand, there is a growing need for long-term, sustainable deployments of sensors to reduce operational costs and ensure service continuity. This research presents an operating system based approach and its underlying foundations for this problem, by providing energy virtualization in long-term unattended sensor network applications. The foundation of this work is the LiteOS operating system: a Unix-like operating system for wireless sensor networks. The centerpiece of this project is operating system based energy virtualization, which represents a comprehensive framework for accounting, reservation, and isolation of energy resource for applications sharing the same platform. The deliveries of this project are (i) an improved, highly reliable LiteOS operating system with built-in support for energy virtualization, (ii) architecture principles, design methodologies, programming APIs, and adaptive communication protocols for software development based on energy virtualization, (iii) results from theoretical and algorithmic investigations of energy virtualization, and (iv) educational testbeds, curriculum and laboratory designs for undergraduate and graduate courses. Our findings will have pervasive impact on applications that are in need of long-term and remote sensing, such as infrastructure protection, assisted living, smart buildings, factory monitoring, and numerous military applications.