计算机科学
路径损耗
实时计算
无线传感器网络
无线
传输(电信)
频道(广播)
数据包丢失
管道运输
网络数据包
帧(网络)
计算机网络
信噪比(成像)
环境科学
电信
环境工程
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2021-04-15
卷期号:8 (8): 6565-6577
被引量:27
标识
DOI:10.1109/jiot.2020.3044647
摘要
A variety of industrial applications are deployed in underground environments, such as soil condition assessment and pipeline monitoring (PM). Wireless underground sensor networks (WUSNs) are capable of continuously monitoring pipelines and promptly alerting any anomaly of entities. However, underground soils significantly influence the traditional WUSNs connectivity success. Long range (LoRa), being a leading low-power wide-area networks (LPWANs) technology, provides a new solution for underground industrial monitoring with its advantages in long-range capability and ultralow power consumption. Nevertheless, the LoRa-based link quality characteristics have not yet been quantitatively evaluated for WUSNs. In this article, the channel models of both the underground-to-aboveground (UG2AG) and aboveground-to-underground (AG2UG) communications are investigated. We experimentally analyze the impact of the propagation direction, burial depth and LoRa physical layer parameters on the in-situ LoRa propagation performance. The received signal strength indictor (RSSI), signal-to-noise ratio (SNR), and packet deliver ratio (PDR) are characterized for both communication channels in LoRa-based WUSNs. The semiempirical path-loss models are successfully verified by our field results, and we demonstrate that the communication range can be greater than 50 m at the burial depth of 0.4 m by adjusting the LoRa transmission/receiving settings. The combination of RSSI and SNR can be a better indicator of PDR than relying on either of them alone. Finally, the frame error rate (FER) is calculated to estimate the link performance with EM interferences. These results successfully demonstrate the advantages of LoRa for PM applications, which serve the first step toward the efficient protocol development of LoRa-based WUSNs.
科研通智能强力驱动
Strongly Powered by AbleSci AI