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The current earthquake monitoring system uses a seismometer that can capture seismic vibrations very well but is expensive, heavy, and difficult to launch. Therefore, earthquake monitoring stations can only be launched in a few places in small numbers. This study aims to implement a Wireless Sensor Network (WSN) system for earthquake monitoring. Th...
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... system used in this study consists of earthquake detection and monitoring system. Figure 1 shows an overview of the designed system. Figure 1 point (1) describes a collection of sensor nodes placed separately at several locations. ...
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... 1 shows an overview of the designed system. Figure 1 point (1) describes a collection of sensor nodes placed separately at several locations. Point (2) describes the Sink, which is in charge of receiving data from each sensor node and forwarding it to the monitoring website via the internet. ...
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... detection results for sensor node 1 for the first test scenario are shown in Table 1. The values in the table are presented in graphical form in Figure 9 and Figure 10. First, the line chart in Figure 9 describes the value of the frequency (Hz) of vibration observed by the sensor every time. ...
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... 2 shows the detection results of sensor node 2 in the first scenario. The values in the table are displayed in graphical form in Figure 11 and Figure 12. Like sensor node 1, sensor node 2 is active when given a shock and can determine the frequency of vibrations it observes. ...
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... 2 shows the detection results of sensor node 2 in the first scenario. The values in the table are displayed in graphical form in Figure 11 and Figure 12. Like sensor node 1, sensor node 2 is active when given a shock and can determine the frequency of vibrations it observes. ...
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... sensor node 1, sensor node 2 is active when given a shock and can determine the frequency of vibrations it observes. Unfortunately, the warning graph for sensor node 2 in Figure 12 indicates that the sensor observed many vibrations. This may be caused by noise on the SW-420 vibration sensor; thus, the microcontroller is active when there is no vibration. ...
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... 3 shows the detection data from sensor node 3 in the first scenario. The data is displayed in the form of a graph in Figure 13 and Figure 14. Figure 14 also indicates that the sensor detects a large amount of vibration. The following paragraphs will discuss the system testing results for the second experimental scenario. ...
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... 3 shows the detection data from sensor node 3 in the first scenario. The data is displayed in the form of a graph in Figure 13 and Figure 14. Figure 14 also indicates that the sensor detects a large amount of vibration. ...
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... data is displayed in the form of a graph in Figure 13 and Figure 14. Figure 14 also indicates that the sensor detects a large amount of vibration. The following paragraphs will discuss the system testing results for the second experimental scenario. ...
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... 4 shows the detection results from sensor node 1 for the second scenario. Figure 15 and Figure 16 present the data in graphical form. Figure 15 shows that sensor node 1 detects a vibration with a frequency of 4500Hz at minute 4:24 and 235Hz at minute 4:26. ...
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... 4 shows the detection results from sensor node 1 for the second scenario. Figure 15 and Figure 16 present the data in graphical form. Figure 15 shows that sensor node 1 detects a vibration with a frequency of 4500Hz at minute 4:24 and 235Hz at minute 4:26. ...
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... 15 and Figure 16 present the data in graphical form. Figure 15 shows that sensor node 1 detects a vibration with a frequency of 4500Hz at minute 4:24 and 235Hz at minute 4:26. Figure 16 indicates that the sensor successfully sends an alert to the monitoring website when vibration is detected. ...
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... 15 shows that sensor node 1 detects a vibration with a frequency of 4500Hz at minute 4:24 and 235Hz at minute 4:26. Figure 16 indicates that the sensor successfully sends an alert to the monitoring website when vibration is detected. Table 5 shows the data generated by sensor node 2 for the second scenario. ...
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... 5 shows the data generated by sensor node 2 for the second scenario. The data is presented in the form of a graph in Figure 17 and Figure 18. In Figure 17, it is shown that sensor node 2 detects the highest vibration wave of 4500Hz at minute 4:24-4:25, then 246Hz vibration wave was detected at minute 4:17-4:18, and 235Hz at minute 4:26. ...
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... 5 shows the data generated by sensor node 2 for the second scenario. The data is presented in the form of a graph in Figure 17 and Figure 18. In Figure 17, it is shown that sensor node 2 detects the highest vibration wave of 4500Hz at minute 4:24-4:25, then 246Hz vibration wave was detected at minute 4:17-4:18, and 235Hz at minute 4:26. ...
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... data is presented in the form of a graph in Figure 17 and Figure 18. In Figure 17, it is shown that sensor node 2 detects the highest vibration wave of 4500Hz at minute 4:24-4:25, then 246Hz vibration wave was detected at minute 4:17-4:18, and 235Hz at minute 4:26. However, the sensor node 2 warning graph in Figure 18 still shows that the sensor gives a warning even though there is no vibration. ...
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... Figure 17, it is shown that sensor node 2 detects the highest vibration wave of 4500Hz at minute 4:24-4:25, then 246Hz vibration wave was detected at minute 4:17-4:18, and 235Hz at minute 4:26. However, the sensor node 2 warning graph in Figure 18 still shows that the sensor gives a warning even though there is no vibration. Shows the detection data from sensor node 3 for the second scenario. ...
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... the detection data from sensor node 3 for the second scenario. The data is displayed in Figure 19 and Figure 20. In Figure 19, it is shown that there are vibration waves of 4500Hz at minute 4:24, 207-246Hz waves at minute 4:17, and 235Hz waves at minute 4:26. ...
Citations
The frequency of extxxcsreme climate phenomena is rising due to global climate change, and these phenomena are becoming more severe in terms of both human casualties and economic losses. To handle these tragedies, authorities need to be better prepared. A reliable system for catastrophe detection and alerting can aid in minimizing the destruction of lives and property. Wireless sensor networks are quite helpful in this area and play a significant role in wireless data transfer. With wireless sensor networks, we can instantly start the rescue effort and lessen the impact of natural disasters like floods, tsunamis, and hurricanes. With the use of a disaster detection and warning system that uses wireless sensors, Tectonic shifts in the Earth's crust that occur suddenly are what generate earthquakes and landslides. Telecommunications infrastructures suffer severe losses after major disasters. Location of the largest earthquakes by magnitude: Sumatra, Indonesia, and the Indian Ocean 9.2 magnitude. While measuring the earth's shock waves, such as p- and s-waves and soil water levels, earthquake sensors and soil moisture sensors are employed. These sensors are connected to sensor nodes, which are connected to cluster nodes, which are connected to base stations. Wireless sensor network is the name of this network (WSN). This study will examine technical possibilities for disaster management. WSN can take the position of the conventional network for disaster management, which uses a lot of expensive infrastructure like landlines and optical cable networks. Wireless networks have many benefits over conventional wired networks, including being inexpensive and simple to maintain. Keywords: Sensing Node, Wireless Sensor Network (WSN).
