Application of Wireless Sensor Network based on LoRa in City Gas Meter Reading

Authors

  • Kun Wang Xi'an Aeronautical University

DOI:

https://doi.org/10.3991/ijoe.v13i12.7887

Keywords:

LoRa, wireless sensor network, gas meter reading

Abstract


At present, the common meter reading method in gas meter reading system is manual. The meter reader enters the meter reading system to calculate the cost after getting the reading. This work is not only labour-intensive, but also inefficient. In addition, incorrect reading may occur due to human errors. With the development of the wireless communication technology, a wireless communication technology named LoRa for long-distance and low-power-consumption devices appeared. LoRa is a wireless communication technology with long transmission distance, low power consumption, low transmission speed, low complexity and low cost. It is mainly used in automatic control and internet of things. Through the comparison and analysis of several wireless communication technologies, a gas meter reading platform based on LoRa spread spectrum and wireless sensors is proposed. First of all, this paper briefly introduces the LoRa wireless communication technology and machine vision technology. Secondly, it gives a detailed introduction to the overall design of the system which includes system architecture design, information acquisition terminal, image acquisition module and wireless sensor module. Finally, an experiment is carried out in a residential area. The results show that the gas meter reading platform based on LoRa and wireless sensor network has a high practical value.

Author Biography

Kun Wang, Xi'an Aeronautical University

Kun Wang is Xi'an Aeronautical University of China, Xi'an 710077. She is manly engaged in the research of electronis and communication.

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Published

2017-12-11

How to Cite

Wang, K. (2017). Application of Wireless Sensor Network based on LoRa in City Gas Meter Reading. International Journal of Online and Biomedical Engineering (iJOE), 13(12), pp. 104–115. https://doi.org/10.3991/ijoe.v13i12.7887

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Section

Papers