Li Huiting, Liu Liu, Li Yiqian, Yuan Ze, Zhang Kun
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2019 Jul 13;19(14):3104. doi: 10.3390/s19143104.
Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz-3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.
边缘计算和物联网（IoT）为当今智能制造的发展提供了技术基础。为了支持工业领域各类设备的智能互联与应用，边缘计算应部署在靠近工业无线网络中各类设备节点的位置或嵌入其中。因此，研究工业物联网的无线网络设计具有重要意义。低功耗无线传感器设备在工业物联网（IIoT）中被广泛使用，它们对电磁噪声敏感。工业场景中的电磁噪声与传统假设的白噪声有显著差异。本文给出了在一家汽车工厂三个不同测试位置的电磁噪声测量结果。通过频域测量获得了该工厂300 MHz - 3 GHz频段无线环境的频谱占用情况。在时域测量中，测量了315 MHz、433 MHz和916 MHz三个频段的四个统计参数，并分析了不同厂区和不同频段的电磁噪声分布。根据测量结果，电磁噪声的时变特性可用连续隐马尔可夫模型（CHMM）来表征。这些结果对工业物联网边缘计算网络的设计和优化具有参考价值。
