Abstract: Given wireless sensor networks' significant data transmission requirements, conventional direct transmission often leads to bandwidth constraints and excessive network energy consumption. This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan.

Shuling YIN, Renping YU and Longzhi WANG, “Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150419

@article{YIN2024,
title = {Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150419},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150419},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Shuling YIN and Renping YU and Longzhi WANG}
}