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The outermost construct of InfluxDB is called organization, which is a dashboard consisting of buckets, which are named locations where all time series data is stored. All buckets have a time duration, called a retention period, during which each data point remains, and when a point has a timestamp that is longer than the retention period, it is deleted by InfluxDB to reduce overall disk usage. Measures are conceptually similar to tables in a relational database, and are named buckets for tags, fields, and timestamps.

Figure 13 The outermost construct of InfluxDB is called organization, which is a dashboard consisting of buckets, which are named locations where all time series data is stored. All buckets have a time duration, called a retention period, during which each data point remains, and when a point has a timestamp that is longer than the retention period, it is deleted by InfluxDB to reduce overall disk usage. Measures are conceptually similar to tables in a relational database, and are named buckets for tags, fields, and timestamps.

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MQTT (Message Queue Telemetry Transport) is a publish/subscribe based communication protocol. The MQTT client has a very small memory size, is light and efficient, requires minimal resources so that it can be used on even small microcontrollers. MQTT message headers are small, optimizing network bandwidth. The data sent is a minimum size of 1 kb. MQTT two-way communication enables device-to-cloud and cloud-to-device messaging. It can support less reliable networks, so this very useful advantage is useful even in slow network conditions. MQTT security makes it easy to encrypt messages using TLS and authenticate clients using modern authentication protocols, such as OAuth. (https://matt.org/). Explanation of Figure 2 is the initialization of IP, Port, Username and Password of MQTT Broker on ESP8266. Initialize SSID, WLAN Password used. Next, the WLAN and MQTT Broker connection authentication process based on the initialized data. Data is received by ESP8266 and then published to MQTT Broker. The process of publishing data will repeat itself every time with a delay in the program.
MQTT (Message Queue Telemetry Transport) is a publish/subscribe based communication protocol. The MQTT client has a very small memory size, is light and efficient, requires minimal resources so that it can be used on even small microcontrollers. MQTT message headers are small, optimizing network bandwidth. The data sent is a minimum size of 1 kb. MQTT two-way communication enables device-to-cloud and cloud-to-device messaging. It can support less reliable networks, so this very useful advantage is useful even in slow network conditions. MQTT security makes it easy to encrypt messages using TLS and authenticate clients using modern authentication protocols, such as OAuth. (https://matt.org/). Explanation of Figure 2 is the initialization of IP, Port, Username and Password of MQTT Broker on ESP8266. Initialize SSID, WLAN Password used. Next, the WLAN and MQTT Broker connection authentication process based on the initialized data. Data is received by ESP8266 and then published to MQTT Broker. The process of publishing data will repeat itself every time with a delay in the program.
Explanation of Figure 3 is the initialization of the IP, Port, Username and Password of the MQTT Broker on the ESP8266 Pi. Initialize SSID, WLAN Password used. Next, the WLAN and MQTT Broker connection authentication process based on the initialized data. Subscription data from MQTT Broker and then received to the Mobile application. Subscribing the data process will repeat at any time with a program delay.
Explanation of Figure 3 is the initialization of the IP, Port, Username and Password of the MQTT Broker on the ESP8266 Pi. Initialize SSID, WLAN Password used. Next, the WLAN and MQTT Broker connection authentication process based on the initialized data. Subscription data from MQTT Broker and then received to the Mobile application. Subscribing the data process will repeat at any time with a program delay.
D. Driver Switching for Lighting and Water Pump Control Figure 4 shows the relay driver design for lighting and water pump control integrated with the NODEMCU ESP8266 controller with an application interface schematic created using flutter. The Relay Driver functions as a breaker or connector for the flow of electric current which is controlled by providing a certain voltage and current to the coil. In this circuit, a DC relay is used with a coil voltage of 12 Volt DC and a current treated of about 20 — 30 milli Ampere. Therefore, in general we cannot directly connect the NodeMCU output to the relay because the current is not large enough. Therefore, it is necessary to use a driver as a current amplifier which is usually a transistor.
D. Driver Switching for Lighting and Water Pump Control Figure 4 shows the relay driver design for lighting and water pump control integrated with the NODEMCU ESP8266 controller with an application interface schematic created using flutter. The Relay Driver functions as a breaker or connector for the flow of electric current which is controlled by providing a certain voltage and current to the coil. In this circuit, a DC relay is used with a coil voltage of 12 Volt DC and a current treated of about 20 — 30 milli Ampere. Therefore, in general we cannot directly connect the NodeMCU output to the relay because the current is not large enough. Therefore, it is necessary to use a driver as a current amplifier which is usually a transistor.
E. Shield HYSRFOS
E. Shield HYSRFOS
Vol. 10, Issue 4, pp: (23-32), Month: October - December 2022, Available at: www.researchpublish.com
Vol. 10, Issue 4, pp: (23-32), Month: October - December 2022, Available at: www.researchpublish.com
Figure 8. Dimensions of GWT (Ground Water Tank) Table 1. Average Actual Distance with Ultrasonic Sensor Reading Distance HYSRF05
Figure 8. Dimensions of GWT (Ground Water Tank) Table 1. Average Actual Distance with Ultrasonic Sensor Reading Distance HYSRF05
Figure 9. Visualization of Flutter Interface Application with MQTT Waterlevel Monitoring Dashboard on Mobile Apps
Figure 9. Visualization of Flutter Interface Application with MQTT Waterlevel Monitoring Dashboard on Mobile Apps
C. Lighting Control Dashboard Figure 10. Visualization of the Lighting Control Interface Flutter Application with MQTT
C. Lighting Control Dashboard Figure 10. Visualization of the Lighting Control Interface Flutter Application with MQTT
D. Direct Notify from Apps
D. Direct Notify from Apps
Based on the amount of load on each device, we get a calculation of the power consumption of each device by comparing power consumption based on the use of water pump and lighting equipment loads before and after the design of the Internet of Things-based Lighting and Watering Automation System. With the PLN line to line voltage input 380V and line to netral input 230V with cos (10,972 the power consumption per day is obtained through equation (4): Table 2. Data Load Panel Device
Based on the amount of load on each device, we get a calculation of the power consumption of each device by comparing power consumption based on the use of water pump and lighting equipment loads before and after the design of the Internet of Things-based Lighting and Watering Automation System. With the PLN line to line voltage input 380V and line to netral input 230V with cos (10,972 the power consumption per day is obtained through equation (4): Table 2. Data Load Panel Device
We simulate notifications with the upper and lower limits of the water level. In our setting the water level is less than 93%. we mark it as a water shortage condition. For water levels above 98%, we mark it as full. Figure 11 is a graph on the dashboard showing when the water level is less than 93%. It detects that the water level is less than 93% at 1:21 pm and automatically turns the water pump on. And Figure 10. shows that the water level is over 98% at 2:31 p.m. and automatically turns the water pump off. Figure 12. Node Red Flow Visualization for Sending Notifications on Mobile Apps with NodeJs API
We simulate notifications with the upper and lower limits of the water level. In our setting the water level is less than 93%. we mark it as a water shortage condition. For water levels above 98%, we mark it as full. Figure 11 is a graph on the dashboard showing when the water level is less than 93%. It detects that the water level is less than 93% at 1:21 pm and automatically turns the water pump on. And Figure 10. shows that the water level is over 98% at 2:31 p.m. and automatically turns the water pump off. Figure 12. Node Red Flow Visualization for Sending Notifications on Mobile Apps with NodeJs API
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