CAREER: Addressing Data and Energy Management Challenges in Hierarchical Sensor Networks

该项目根据最近的技术趋势和试点部署的经验，对传感器网络的挑战进行了全新的审视。技术趋势表明，闪存的容量将继续上升，而其成本和能源消耗将继续直线下降。这将使传感器节点有可能配备高能效、高容量的NAND闪存存储。试验部署表明，可扩展的传感器网络体系结构将是分层的，由数百个资源受限的传感器和数十个资源丰富的传感器代理组成。这促使需要开发新的方法来利用代理上的资源，同时尊重传感器上的约束。这个项目有两个贡献。第一个贡献是档案传感器数据的存储和检索系统。这项研究包括传感器节点档案存储子系统的设计、原型和评估，支持大规模分布式档案传感器数据高效访问的算法，以及高效检索此类数据的压缩技术。其次，本项目提出了一种不确定性驱动的能源管理体系结构，将感知、通信、路由、数据处理和查询处理任务中的能源优化统一起来。这项研究使用预测模型和不确定性作为基本构建块，并在此基础上构建一系列能源优化服务。该项目将在科学和工程领域的数据密集型传感器网络应用中产生广泛影响，并将对五所学院联盟的教育产生广泛影响。该项目的成果，包括出版物、软件和硬件原型，将免费提供给研究界。

This project takes a fresh look at challenges in sensor networks in light of recent technology trends and experiences in pilot deployments. Technology trends indicate that the capacities of flash memories will continue to rise while their costs and energy consumption continue to plummet. This will make it possible to equip sensor nodes with energy-efficient, high-capacity NAND flash memory storage. Pilot deployments have shown that scalable sensor network architectures will be hierarchical, and comprise several hundreds of resource-constrained sensors as well as several tens of resource-rich sensor proxies. This motivates the need to develop novel methods to exploit resources at proxies while respecting constraints at sensors. This project has two contributions. The first contribution is a system for storage and retrieval of archival sensor data. This research includes the design, prototyping and evaluation of archival storage subsystems for sensor nodes, algorithms to enable efficient access of large distributed archival sensor data, and compression techniques for efficiently retrieving such data. Second, this project proposes an uncertainty-driven energy management architecture that unifies energy optimization across sensing, communication, routing, data processing and query processing tasks. This research uses prediction models and uncertainty as fundamental building blocks and builds a spectrum of energy-optimized services over this foundation. The project will have broad impact across data-intensive sensor network applications in science and engineering, as well as on education across the Five College consortium. The results of this project including publications, software and hardware prototypes will be made freely available to the research community.