CAREER: Operating System based Energy Virtualization for Sensor Networks

近年来，无线传感器网络以其成本低、体积小、易于部署等优点成为关键技术之一。然而，这些吸引人的优势意味着单个节点可用的资源严重有限。特别是，随着更多的晶体管意味着更多的功率消耗，能源很有可能继续成为系统的瓶颈。另一方面，人们越来越需要长期、可持续的传感器部署，以降低运营成本并确保服务的连续性。这项研究提出了一种基于操作系统的方法及其基本基础，通过在长期无人值守的传感器网络应用中提供能量虚拟化来解决这一问题。这项工作的基础是LiteOS操作系统：一种类似Unix的无线传感器网络操作系统。该项目的核心是基于操作系统的能源虚拟化，它代表了一个全面的框架，用于对共享同一平台的应用程序的能源资源进行核算、预留和隔离。该项目的成果是(I)改进的、高度可靠的LiteOS操作系统，具有对能源虚拟化的内置支持；(Ii)基于能源虚拟化的软件开发的架构原则、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成果；以及(Iv)本科生和研究生课程的教育试验台、课程和实验室设计。我们的发现将对需要长期和遥感的应用产生普遍影响，例如基础设施保护、辅助生活、智能建筑、工厂监控和众多军事应用。

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.