SENSORS: RiverNet - Distributed Sensor Nets for Environmental Monitoring

传感器：RiverNet - 用于环境监测的分布式传感器网络

• 批准号: 0329837
• 负责人: Arthur Sanderson
• 金额: --
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0329837&HistoricalAwards=false
• 关键词: SENSORS; RiverNet; Distributed; Sensor; Nets

Robotics and Computer Vision ProgramABSTRACTProposal #: 0329837Title: SENSORS: RiverNet - Distributed Sensor Nets for Environmental MonitoringPI: Sanderson, ArthurRPI The RiverNet project addresses the development of distributed sensor networks for monitoring of environmental variables and will demonstrate this capability through observations of the Hudson River and Estuary. The project approach is based on the systematic development of a scalable sensor network architecture and multisensor fusion algorithms that support the detection and interpretation of environmentally important events and variables. Simulation tools will be utilized in development, and experiments using fixed and mobile underwater platforms will be employed. The impact of this research will be the exploration and application of distributed sensor networks for environmental monitoring, biological observation, and critical infrastructure management throughout the world. The monitoring of distributed environmental systems also specifically supports the challenge of cybersecurity for critical infrastructure. The educational program will include extensive outreach and opportunities for direct web-based access to river observations and models available for introductory, advanced, and public educational programs.

机器人和计算机视觉计划摘要提案编号：0329837标题：传感器：RiverNet -用于环境监测的分布式传感器网络PI：Sanderson，ArthurRPI RiverNet项目致力于开发用于监测环境变量的分布式传感器网络，并将通过对哈德逊河和河口的观测来证明这种能力。 该项目的方法是基于一个可扩展的传感器网络架构和多传感器融合算法，支持环境重要的事件和变量的检测和解释的系统开发。 模拟工具将用于开发，并将使用固定和移动的水下平台进行实验。 这项研究的影响将是探索和应用分布式传感器网络进行环境监测，生物观测和关键基础设施管理在世界各地。 对分布式环境系统的监测还特别支持关键基础设施的网络安全挑战。 教育计划将包括广泛的推广和机会，直接基于网络访问河流观测和模型，可用于介绍，先进和公共教育计划。