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Event driven querying of semantic sensor web services.

机译：事件驱动的语义传感器Web服务查询。

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摘要

In today's world, there is a tremendous increase in the usage of sensor technology in several fields including agriculture, medicine, and weather. Sensors either in-site or remotely placed are usually deployed in the form of sensor clusters containing dozens of sensor nodes. The observed data from the various sensors need to be thoroughly analyzed for understanding the content they carry. Human analysis of the observed data is not always feasible or convincible, and hence, although there has been a rapid growth of sensor technology, the practical applications are far from reality. Therefore, there has been an imminent need for an intelligent approach to understand the data from the sensor clusters. At the same time, controlling the amount of the observation data is also necessary.;To address this integral issue, we present the Event Driven Querying of Semantic Sensor Web Services. The Event Condition Action (ECA) based model is intended for providing a platform for querying the cluster of sensors in an efficient and timely manner. Processing observation data is surpassed by semantically annotating data and using rule based reasoning as an inference tool. ECA enables a shift of the main focus from a large cluster section to a precise smaller section of the cluster, and thus eliminates the necessity to obtain data from entire sensor cluster every time to make an inference. This model can be used to measure and track numerous events like earthquakes, floods, stock market crashes, Christmas shopping trends that are set off by a pre-condition that in turn triggers a set of events.;In order to validate the efficiency and preciseness of the proposed model, we introduced an example of detection and prorogation of fire in a closed two dimensional building. The interior rooms of the building are modeled as sensor nodes for maintaining a state. We found the occurrences of certain events, like rise in temperature and production of smoke using proactive approach, were the forerunners of fire in the room. These events are captured by the reasoning engine from the data obtained from the sensors, resulting in a change of state in the sensor nodes. The change then triggers new events, bringing about a cascading waterfall like effect.;Finally, we measured the accuracy of the model by considering a sensor networks consisting of 11, 25, 50 and 100 nodes with up to two sources of fire and while tracking its propagation. We also measured the F-measure for each of the above sensor networks. Thus, the ECA model based coupled with proactive querying helps in not only curtailing the amount of observation data, but also helps in accurately determining the source of an event and tracking its spread effectively.
机译：在当今世界，传感器技术在农业，医药和气象等多个领域的使用已大大增加。现场或远程放置的传感器通常以包含数十个传感器节点的传感器群集的形式部署。需要对来自各种传感器的观察数据进行彻底分析，以了解其携带的内容。对观察到的数据进行人工分析并不总是可行或令人信服的，因此，尽管传感器技术得到了快速发展，但实际应用还远远没有实现。因此，迫切需要一种智能方法来理解来自传感器群集的数据。同时，还需要控制观察数据的数量。为了解决这一整体问题，我们提出了语义传感器Web服务的事件驱动查询。基于事件条件操作（ECA）的模型旨在提供一个平台，以高效，及时的方式查询传感器集群。在语义上注释数据并使用基于规则的推理作为推理工具可以超越对观察数据的处理。 ECA可以将主要焦点从较大的群集部分转移到群集的较小的精确部分，因此消除了每次进行推理都需要从整个传感器群集获取数据的麻烦。该模型可用于测量和跟踪众多事件，例如地震，洪水，股市崩盘，圣诞节购物趋势，这些事件是由触发一系列事件的前提条件引起的；为了验证效率和准确性在所提出模型的基础上，我们介绍了一个在封闭的二维建筑物中检测和处理火灾的示例。建筑物的内部空间被建模为用于保持状态的传感器节点。我们发现某些事件的发生是房间着火的先驱，例如温度升高和使用主动方法产生烟雾。这些事件由推理引擎从从传感器获得的数据中捕获，从而导致传感器节点状态发生变化。最后，我们通过考虑由11、25、50和100个节点组成的传感器网络以及最多两个火源并同时跟踪来测量模型的准确性，从而测量了模型的准确性。它的传播。我们还测量了上述每个传感器网络的F量度。因此，基于ECA模型与主动查询的结合不仅有助于减少观测数据的数量，而且还有助于准确确定事件的来源并有效地跟踪事件的传播。

著录项

  • 作者

    Padmanabha, Shruthi.;

  • 作者单位

    University of Missouri - Kansas City.;

  • 授予单位 University of Missouri - Kansas City.;
  • 学科 Computer science.
  • 学位 M.S.
  • 年度 2012
  • 页码 126 p.
  • 总页数 126
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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