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无线传感器网络不同地形下近地传播模型的测量与分析

Measurement and Analysis of Near-Ground Propagation Models under Different Terrains for Wireless Sensor Networks.

作者信息

Tang Weisheng, Ma Xiaoyuan, Wei Jianming, Wang Zhi

机构信息

Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Sensors (Basel). 2019 Apr 22;19(8):1901. doi: 10.3390/s19081901.

DOI:10.3390/s19081901
PMID:31013589
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6515328/
Abstract

The propagation model is an essential component in the design and deployment of a wireless sensor network (WSN). Although much attention has been given to near-ground propagation models, few studies place the transceiver directly on the ground with the height of antennas at the level of a few centimeters, which is a more realistic deployment scenario for WSNs. We measured the Received Signal Strength Indication (RSSI) of these truly near-ground WSNs at 470 MHz under four different terrains, namely flat concrete road, flat grass and two derived scenarios, and obtained the corresponding path loss models. By comprehensive analysis of the influence of different antenna heights and terrain factors, we showed the limit of existing theoretical models and proposed a propagation model selection strategy to more accurately reflect the true characteristics of the near-ground wireless channels for WSNs. In addition, we implemented these models on Cooja simulator and showed that simplistic theoretical models would induce great inaccuracy of network connectivity estimation.

摘要

传播模型是无线传感器网络(WSN)设计与部署中的一个重要组成部分。尽管近地面传播模型已受到广泛关注,但很少有研究将收发器直接放置在地面上,使天线高度处于几厘米的水平,而这对无线传感器网络来说是更现实的部署场景。我们在470MHz频率下,在四种不同地形(即平坦水泥路、平坦草地以及两种衍生场景)中测量了这些真正近地面的无线传感器网络的接收信号强度指示(RSSI),并获得了相应的路径损耗模型。通过综合分析不同天线高度和地形因素的影响,我们揭示了现有理论模型的局限性,并提出了一种传播模型选择策略,以更准确地反映无线传感器网络近地面无线信道的真实特性。此外,我们在Cooja模拟器上实现了这些模型,并表明简单的理论模型会导致网络连通性估计出现很大误差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/36d3ce6280ec/sensors-19-01901-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/b5ea68af6d91/sensors-19-01901-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/0cebfc7ce38c/sensors-19-01901-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/00f31a550ae6/sensors-19-01901-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/033812779758/sensors-19-01901-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/f3d76842575f/sensors-19-01901-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/d6337ff8ab17/sensors-19-01901-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/ab478820877d/sensors-19-01901-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/3f950c0ed12f/sensors-19-01901-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/47014f7f15eb/sensors-19-01901-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/36d3ce6280ec/sensors-19-01901-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/b5ea68af6d91/sensors-19-01901-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/0cebfc7ce38c/sensors-19-01901-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/00f31a550ae6/sensors-19-01901-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/033812779758/sensors-19-01901-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/f3d76842575f/sensors-19-01901-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/d6337ff8ab17/sensors-19-01901-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/ab478820877d/sensors-19-01901-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/3f950c0ed12f/sensors-19-01901-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/47014f7f15eb/sensors-19-01901-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e10/6515328/36d3ce6280ec/sensors-19-01901-g010.jpg

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