NeTS: Medium: Collaborative Research: Building an Intelligent, Uncertainty-Resilient Detection and Tracking Sensor Network

NeTS：媒介：协作研究：构建智能、抗不确定性的检测和跟踪传感器网络

• 批准号: 0963812
• 负责人: Sartaj Sahni
• 金额: $ 24.98万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963812&HistoricalAwards=false
• 关键词: NeTS; Medium; Collaborative; Research; Building

Detection, identification, and tracking of CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive) plumes can be accomplished by combining the modalities of sensor and cyber networks. The sensor network provides information about physical-space activities, e.g., locations and movements of the plume sources. The cyber network provides storage and computational resources to analyze and infer where the plume originated, the trajectory of its movement, and the prediction of its future movement. The challenges in realizing such a sensor cyber network include intelligent sensing (intelligent sensor selection and coverage) and the capability to deal with uncertainties (uncertainty in measurement as well as in modeling). This project proposes to leverage the convergence between the sensor and the cyber networks to achieve these goals. In particular, the plan is to carry out three synergistic research tasks: 1) network formation by sensor selection, placement, and coverage; 2) sensor tasking protocol with temporal/spatial uncertainty management; 3) protocols for reliable sensor-cyber communication supporting the above two tasks. The PIs will prototype the research results and integrate the prototypes for different components to build the sensor cyber network for plume detection, identification and tracking. They will also evaluate the sensor cyber network using various test scenarios in collaboration with Oak Ridge National Lab. If successful, the project will provide technology for building detection and tracking sensor networks that can give great protection to people and the environment against harmful plumes.

检测、识别和跟踪CBRNE（化学、生物、放射、核和爆炸）羽流可以通过结合传感器和网络的模式来完成。传感器网络提供有关物理空间活动的信息，例如羽流源的位置和运动。网络提供存储和计算资源，以分析和推断烟羽的起源，其运动轨迹以及预测其未来的运动。实现这种传感器网络的挑战包括智能传感（智能传感器选择和覆盖）和处理不确定性（测量和建模中的不确定性）的能力。该项目建议利用传感器和网络网络之间的融合来实现这些目标。具体而言，该计划将开展三个协同研究任务：1)通过传感器的选择、放置和覆盖形成网络；2)具有时空不确定性管理的传感器任务协议；3)支持上述两项任务的可靠传感器网络通信协议。pi将对研究结果进行原型设计，并将不同组件的原型集成在一起，构建用于羽流检测、识别和跟踪的传感器网络。他们还将与橡树岭国家实验室合作，使用各种测试场景评估传感器网络。如果成功，该项目将为建立检测和跟踪传感器网络提供技术，可以极大地保护人类和环境免受有害羽流的侵害。