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Modelling of extreme data in a wireless sensor network through the application of random field theory

机译:应用随机场理论对无线传感器网络中的极端数据建模

摘要

Wireless Sensor Networks (WSNs) consist of a large number of small, simple sensor nodes which support sensing, processing, and wireless transmission capabilities in order to monitor some physical environment. The data that they collect will be transmitted to an information sink where it can be accessed by the user. Due to the vast number of nodes expected in many WSN applications, there is the possibility that at certain times the network may have potentially huge amounts of data to transmit to the sink. The fact that the nodes have limited energy resources means that when a huge amount of data is generated, the nodes' batteries will be depleted at aggressive rates as nodes try to forward this data to the sink. This problem will be particularly severe in regions of the network close to the sink, as nodes in these regions will be responsible for routing the data from large areas of the network to the sink. This phenomenon is often described as a "data-implosion" around the sink. We develop a model of the node data in a wireless sensor network based on a stochastic model of the underlying phenomenon being observed by the network. The model is based on a stationary Gaussian random field and we use this model to study the size and spatial distribution of the sets of nodes that observe statistically high data. This knowledge is exploited in order to ameliorate the data-implosion problem. Effectively, we implement a data suppression scheme that only lets nodes which sense statistically high data attempt to transmit their data to the sink. Further, we also use our model to study network data that belongs to a given contour level and show that we can achieve further data suppression by only transmitting node data if it belongs to some predefined contour level. Finally, we show how the knowledge of the size and spatial distribution of statistically high node data in a WSN can be used to study the traffic in both schedule and contention based MAC protocols.
机译:无线传感器网络(WSN)由大量小型,简单的传感器节点组成,这些节点支持传感,处理和无线传输功能,以便监视某些物理环境。他们收集的数据将被传输到信息接收器,用户可以在其中访问它。由于许多WSN应用程序中预期有大量的节点,因此在某些时候,网络可能有潜在的大量数据要传输到接收器。节点具有有限的能源这一事实意味着,当生成大量数据时,随着节点尝试将这些数据转发到接收器,节点的电池将被消耗aggressive尽。该问题在靠近接收器的网络区域中尤其严重,因为这些区域中的节点将负责将数据从网络的较大区域路由到接收器。这种现象通常被描述为水槽周围的“数据内爆”。我们基于网络观察到的潜在现象的随机模型,开发了无线传感器网络中的节点数据模型。该模型基于平稳的高斯随机场,我们使用该模型来研究观察到统计上较高数据的节点集的大小和空间分布。利用该知识来缓解数据内爆问题。有效地,我们实现了一种数据抑制方案,该方案仅允许感知统计上较高的数据的节点尝试将其数据传输到接收器。此外,我们还使用我们的模型来研究属于给定轮廓级别的网络数据,并表明我们可以通过仅发送属于某个预定义轮廓级别的节点数据来实现进一步的数据抑制。最后,我们展示了如何将WSN中统计上较高的节点数据的大小和空间分布的知识用于研究基于调度和竞争的MAC协议中的流量。

著录项

  • 作者

    Patterson Glenn;

  • 作者单位
  • 年度 2007
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
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