Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR

• Published in: JOIV : international journal on informatics visualization Online Vol. 7; no. 1; pp. 15 - 21
• Main Authors: Wedashwara, Wirarama, Yadnya, Made Sutha, Sudiarta, I Wayan, Arimbawa, I Wayan Agus, Mulyana, Tatang
• Format: Journal Article
• Language: English
• Published: Politeknik Negeri Padang 03.03.2023
• Subjects: fuzzy association rule mining; internet of things; wireless sensor networks
• ISSN: 2549-9610; 2549-9904
• DOI: 10.30630/joiv.7.1.1592

Prevention of the effects caused by natural disasters such as earthquakes and landslides requires analysis of vibration propagation. In outdoor applications, internet sources such as WIFI are not always available, so it requires alternative data communications such as nRF24l01. The system also requires a portable power source such as solar power. This research aims to develop a vibration propagation analysis system based on the nRF24l01 wireless sensor network and solar power by implementing the fuzzy rule-based regression (FRBR) algorithm. The system consists of two piezoelectric and nrf24l01 vibration sensors. The system also uses a third node equipped with temperature and soil moisture sensors, air temperature and humidity, and light intensity as environmental variables. The evaluation results show the Quality of Services (QoS) results with a throughput of 99.564%, PDR 99.675%, and a delay of 0.0073s. The Fuzzy Association Rule (FAR) extraction results yield nine rules with average support of 0.319 and confidence of 1 for vibration propagation. The availability of solar power was evaluated with an average current value of 0.250A and a voltage of 3.266V. The results of FRBR are based on the propagation of the vibration that propagated and produced a mean square error (MSE) of 0.141 and a mean absolute error (MAE) of 0.165. The correlation matrix and FAR results show that only soil moisture has a major effect on the magnitude and duration of propagation. However, other variables can regress soil moisture with MSE 0.232 and MAE 0.287.