NeTS-NOSS: Collaborative Research: Lightweight Monitoring Tools for Sensor Networks
NeTS-NOSS: Collaborative Research: Lightweight Monitoring Tools for Sensor Networks
批准号:
0626151
负责人:
Leonidas Guibas
金额:
$22.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-15 至 2010-12-31
中文摘要
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英文摘要
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.
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