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A Data Gathering Scheme in Wireless Sensor Networks Using a Spiking Neural Network with Simple Local Information

机译:使用具有简单本地信息的尖峰神经网络的无线传感器网络中的数据收集方案

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In wireless sensor networks (WSNs), compact wireless sensor nodes are deployed in observation area, form an impromptu network and gather sensing data periodically. Thereby, the environmental observation of large-scale area can be realized remotely. In the synchronization-based data gathering scheme, transmission timings for sensing data are synchronized by using spiking neural oscillators. Using this scheme, the number of the transmissions and receptions, and node power consumption can be reduced. However, in the conventional scheme, the duplicate transmissions of the same sensing data by plural wireless sensor nodes should be improved. In this paper, a new data gathering scheme reducing duplicate transmissions of sensing data is proposed. In the proposed scheme, simple local information is used for transmissions and receptions of sensing data. Then, the traffic of the whole network can be significantly reduced. In the simulation experiments, the effectiveness of the proposed scheme is verified.
机译:在无线传感器网络(WSN)中,紧凑型无线传感器节点部署在观察区域中,形成临时网络并定期收集传感数据。由此,可以远程实现大范围的环境观察。在基于同步的数据收集方案中,通过使用尖峰神经振荡器来同步感测数据的传输定时。使用该方案,可以减少发送和接收的数量以及节点功耗。然而,在常规方案中,应当改善多个无线传感器节点对相同感测数据的重复传输。在本文中,提出了一种新的数据收集方案,该方案减少了感测数据的重复传输。在提出的方案中,简单的本地信息用于感测数据的发送和接收。这样,可以显着减少整个网络的流量。在仿真实验中,验证了所提方案的有效性。

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