基于无设备定位神经网络和云计算的城市排水系统监测

Mr. Rahul Sharma
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引用次数: 14

摘要

无线传感器网络是一个由空间传播和自给自足的设备组成的Wi-Fi社区,使用传感器来检测物理或环境条件。在大雨期间,城市排水系统无法排水。具有许多相互连接的无线传感器节点的无线传感器从网络环境中捕获实时数据,并将这些数据传输到基站进行分析和操作。通过无线传感器节点,可以捕获和监测排水系统中两点之间的水量以及水流量的差异。然而,大多数定位技术的目标是基于设备的定位,可以找到目标设备。它不适合诸如地形,排水流和洪水等应用。本文提出了一种利用人工神经网络和基于集群的无线传感器网络进行城市排水监测的无线定位系统。这个系统有两个阶段。在离线准备阶段,可接受信号强度(RSS)差分指标在RSS指标之间一起计算,而监视器区域为空,由该区域的专业人员计算。在RSS差分矩阵中选择一些RSS不相似值。将RSS不相似度标准和相关矩阵指标作为人工神经网络表示的输入,并将识别的位置坐标作为其输出。从无线传感器网络收集的实时数据用于检测溢出,并在出现干扰之前提供警报。
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Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing
Wireless Sensor Network is a Wi-Fi community consisting of spatially propagated and self-sufficient devices using sensors to detect physical or environmental conditions. During heavy rainfall, the urban drainage system cannot drain the water. A wireless sensor with many interconnected wireless sensor nodes captures real-time data from the network environment and transmits this data to a base station for analysis and operation. With wireless sensor nodes, it is possible to capture and monitor the amount of water in drainages and the difference in water flow between the two points in the drainage system. Nevertheless, the majority localization techniques aims on device based localization, which can find target with festinated devices. It is not suitable for applications such as terrain, drainage flow and flooding. Here device free wireless localization system using artificial neural networks and a cluster based wireless sensor network system to monitor urban drainage is proposed. There are two stages in the system. During the off-line preparation stage, Acceptable Signal Strength (RSS) differential metrics are calculated between the RSS metrics together while the monitor area is empty and calculated by a specialized in the region. Some RSS dissimilarity values ​​are selected in the RSS Difference Matrix. The RSS dissimilarity standards ​​and associated matrix indices are taken as the inputs of the ANN representation in addition to the identified position coordinate are in its outputs. The real-time data collected from the wireless sensor network is used to detect overflow and provide alarms before disturbances arise.
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