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

随着全球定位系统(GPS)支持的无线传感器网络和移动设备的激增，收集的实时地理参考流的数量很大，并且还在继续增加。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)往往在空间和时间上是连续的。该项目旨在弥合离散传感器读数的阻抗失配和连续现象。具体地说，我们将探索增量方法，从离散的传感器读数中实时检测和维护不断变化的区域。这项任务具有挑战性和风险性，因为(1)对区域检测很重要的人为干预对于目标监控应用来说需要最小化；(2)警报的性质要求实时响应，特别是在数据量往往很大的灾难性情况下。为了提高区域检测的准确性，提出了一种虚拟传感器插入的新思路，以提高区域检测的准确性。为了减少人为干预，系统将配备学习能力，通过使用和维护增量多边形化所需的统计数据。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，创建区域演化图，从而减少发送给用户的警报数量，预期的结果将弥合离散读数和自然现象之间的语义鸿沟，并为未来的地理流处理工作提供基础。一旦结果被整合到地球流处理系统中，用户就可以监测不断变化的区域，而不必局限于查询离散的读数。这项工作将有助于维持重要的时间关键型应用程序的增长并为其提供支持，例如灾害应对和监视。研究生将接受关于地质流处理各个方面的培训。项目网站(http://www.cse.unt.edu/~huangyan/eRegion))将用于传播成果。

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.