基于物联网方法的无线传感器网络模块评估

L. Anand, Padmalal S, J. Seetha, R. Juliana, PS Naveen Kumar, Gayatri Parasa
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引用次数: 0

摘要

带有在特定无线电显示器上操作的信号收发器的微型计算机和医疗设备构成了监测环境条件(温度、压力、光线、振动水平、位置)的无线传感器网络(wsns)的骨干。由于其设计灵活、设置费用低,在广域网传感器网络中得到了广泛的应用。u200b触摸网络允许连接多达65,000个设备,而其他无线网络上的智能传感器用于传输数据端口和分配无线网络。由于无线解决方案的价格不断下降,功能不断增强,在遥测数据采集系统和远距离探测通信中,无线解决方案正逐渐取代有线解决方案。在本研究中使用了深度学习模型来防止传感器节点操纵数据。传感器节点包含大量的参数和估计。如果这些预测的数据值被改变,网络性能就会受到影响,节点的生命周期也会缩短。当传感器节点分布时,数据安全成为优先考虑的问题。这种新方法比以前的方法效率提高了98.82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Wireless Sensor Networks Module using IoT Approach
Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.
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