NeTS-NOSS: Collaborative Research: Lightweight Monitoring Tools for Sensor Networks

提案编号：合作研究：0626954,0626151和0627155PI: Ramesh Govindan， Leonidas gu， Subhash suri机构：USC， Stanford， ucsb标题：NeTS-NOSS：用于传感器网络的轻量级监测工具摘要：微型廉价智能传感器网络引领了新一代低成本、大规模、高分辨率、实时传感和驱动。随着它们的经济重要性随着它们的规模和复杂性而增长，通过持续监控来确保它们的运行健康和健壮性变得至关重要。基于这一动机，该项目为传感器网络开发了轻量级监控算法和工具，使网络运营商能够观察网络的大规模行为，并检测网络属性或性能中的重大异常。这些工具本身是通用的，可以组合起来提供定制的解决方案，以满足各种特定于应用程序的需求。这些监控工具的设计需要通过各个节点上可用的本地信息推断网络的全局方面。为了以轻量级、节能的方式从局部视图合成全局摘要，该项目采用了三种方法：(1)智能采样，(2)信息聚合，(3)信号景观的稀疏表示。这些方法利用几何学、拓扑学、统计学和分布式信号处理的数学技术。网络监控工具可以实现更好的设计，更强大，更可靠，更长寿的传感器网络。这反过来又降低了成本，扩大了用户群体以及联网传感的潜在商业应用。本项目的研究成果包括新算法、软件工具及其在测试平台上的实证评估。

Proposal Number: Collaborative Research: 0626954, 0626151, and 0627155PI: Ramesh Govindan, Leonidas Guibas, Subhash SuriInstitution: USC, Stanford, UCSBTitle: NeTS-NOSS: Lightweight Monitoring Tools for Sensor NetworksAbstract:Networks of tiny and inexpensive smart sensors have ushered a new generation of low-cost, large-scale, high-resolution, real-time sensing and actuation. As their economic importance grows along with their size and complexity, it becomes critical to ensure their operational health and robustness through continuous monitoring. With that motivation, this project develops lightweight monitoring algorithms and tools for sensor networks that allow the network operators to observe the large-scale behavior of the network and detect significant anomalies in network attributes or performance. The tools themselves are generic and can be composed to provide tailored solutions to meet various application-specific needs.The design of these monitoring tools requires inferring global aspects of the network through local information available at individual nodes. In order to synthesize a global summary from local views in a lightweight, energy-efficient manner, the project employs three methods: (1) intelligent sampling, (2) information aggregation, and (3) sparse representation of the signal landscape. These methods utilize mathematical techniques from geometry, topology, statistics, and distributed signal processing.The network monitoring tools enable better designed, more robust, trustworthy, and longer-lived sensor networks in operation. That, in turn lowers costs and enlarges the community of users as well as the set of potential commercial applications for networked sensing.The research output of this project includes novel algorithms, software tools, and their empirical evaluation using testbeds.