SGER: Detecting and Maintaining Evolving Regions from Spatially and Temporally Varying Observations for Monitoring and Alerting

SGER：从空间和时间变化的观测中检测和维护不断变化的区域以进​​行监控和警报

• 批准号: 0844342
• 负责人: Yan Huang
• 金额: --
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0844342&HistoricalAwards=false
• 关键词: SGER; Detecting; Maintaining; Evolving; Regions

With the proliferation of wireless sensor networks and mobile devices enabled by global positioning systems (GPSs), the volume of real-time geo-referenced streams being collected is large and continues to increase. Individual readings from sensors represent discrete sampling points, whereas the phenomena that sensor networks monitor (e.g., floods, fires, and ocean currents) are often spatially and temporally continuous. This project aims at bridging the impedance mismatch of discrete sensor readings and the continuous phenomena. Specifically, we will explore incremental methods to detect and maintain evolving regions from discrete sensor readings in real time. This task is challenging and risky because (1) human intervention, which is important for region detection, needs to be minimal for the targeted monitoring applications; and (2) the alerting nature requires real-time responses, especially in disastrous situations when volumes of data are often high. The quality of service (QoS) requirement in terms of response time and accuracy of the regions detected needs to be balanced.A novel idea of virtual sensor insertion will be explored to improve the accuracy of region detection. To reduce human intervention, the system will be equipped with a learning ability by using and maintaining statistics needed for incremental polygonization. Measurements in information retrieval will be explored creatively for identifying qualitative region evolvements and creating region evolvement graph, which will result in a reduced number of alerts sent to users.The expected results will bridge the semantic gap of discrete readings and natural phenomena as well as provide a foundation for future work in geo-stream processing. Once the results are integrated into a geo-stream processing system, users can monitor evolving regions without being confined to querying discrete readings. The work will help sustain the growth of and support important time-critical applications such as disaster response and surveillance. Graduate students will be trained on various aspects of geo-stream processing. The project Web site (http://www.cse.unt.edu/~huangyan/eRegion) will be used for results dissemination.

随着无线传感器网络和全球定位系统（GPS）支持的移动的设备的激增，收集的实时地理参考流的量很大并且持续增加。来自传感器的单个读数代表离散的采样点，而传感器网络监测的现象（例如，洪水、火灾和洋流）通常在空间和时间上是连续的。本项目旨在桥接离散传感器读数和连续现象的阻抗失配。具体来说，我们将探索增量的方法来检测和维护不断发展的地区，从离散的传感器读数在真实的时间。这项任务具有挑战性和风险，因为（1）对于区域检测很重要的人为干预对于目标监控应用程序需要最小化;以及（2）警报性质需要实时响应，特别是在数据量通常很高的灾难性情况下。为了平衡区域检测的响应时间和准确性对服务质量（QoS）的要求，提出了一种虚拟传感器插入的新思想，以提高区域检测的准确性。为了减少人为干预，该系统将通过使用和维护增量自动化所需的统计数据来配备学习能力。本研究将创造性地探索信息检索中的度量方法，识别定性的区域演化并生成区域演化图，从而减少向用户发送的警报数量，并为地理流处理的未来工作奠定基础。一旦结果被集成到地理流处理系统中，用户就可以监测不断变化的区域，而不限于查询离散读数。这项工作将有助于维持发展和支持重要的时间紧迫的应用程序，如灾害响应和监测。研究生将接受地理流处理各个方面的培训。将利用项目网址（http：//www.cse.unt.edu/zhuangyan/eRegion）传播成果。