On the analysis of received signal strength indicator  from ESP8266

Rösli, R; Habaebi, Mohamed Hadi; Islam, Md. Rafiqul

doi:10.11591/eei.v8i3.1511

Cited by 6 publications

(3 citation statements)

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“…The system is designed to monitor LPG pressure. The NodeMCU microcontroller [8], [9], [33]- [36] then processes the pressure value from the LPG cylinder read by the pressure sensor. If the pressure value in Figure 3 is 5 psi, then the green LED indicator on the Blynk application lights up.…”

Section: Lpg Gas Monitoring System Flowchartmentioning

confidence: 99%

Internet of things with NodeMCU ESP8266 for MPX-5700AP sensor-based LPG pressure monitoring

Sutikno,

Ar-Rasyid,

Wahono

et al. 2023

IJAAS

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The use of liquefied petroleum gas (LPG) cylinders as fuel has become a basic need for the community. LPG is more efficient than oil stoves, but LPG also poses a danger. The dangers contained in gas cylinders cause users to be afraid to check the availability of gas in cylinders because the checking process must be directly close to the gas cylinder. Because of this danger, users do not check gas availability, causing it to run out of gas when cooking. To solve this problem, a system is needed to detect the availability of LPG contents, which can be monitored remotely so that users will feel safe because they are not close to gas cylinders. The condition of gas cylinder availability can be remotely monitored using the internet of things (IoT) method. Therefore, an IoT-based LPG pressure monitoring tool was designed. A tool designed using the MPX-5700AP sensor is useful for detecting gas pressure values in LPG cylinders. IoT is used to monitor LPG pressure using the Blynk application. The buzzer module is a tool for sending sound signals as information on the condition of gas cylinders. The NodeMCU ESP8266 microcontroller processes and sends data to the Blynk application. System testing is carried out in three conditions: full, close to empty, and empty. The results of this test showed an error value of 3.41% and an accuracy rate of 96.59%.

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Section: Lpg Gas Monitoring System Flowchartmentioning

confidence: 99%

Internet of things with NodeMCU ESP8266 for MPX-5700AP sensor-based LPG pressure monitoring

Sutikno,

Ar-Rasyid,

Wahono

et al. 2023

IJAAS

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“…First, the RSSI of the empty room is recorded, then the database in some points where a person is standing is also recorded from the available access points (APs). For the localization process, a person or an object walks through the area of interest and change the power properties in the measurement area because of propagation phenomena such as diffrac-tion, reflection, scattering, and shadowing (Rosli et al, 2019).…”

Section: Wi-fi-based Dfilmentioning

confidence: 99%

“…First, the target's RSSI values are different from the RSSI of fingerprint as the positions are different. Second, the propagation phenomena that yield the RSSI fluctuation are concerned (Rosli et al, 2019). In this paper, we do not consider applying the vast propagation model.…”

Section: Scenariomentioning

confidence: 99%

Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

Suroso

Rupaksi²,

Krisnawan

et al. 2021

IJoCED

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The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.

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Web application system for urban best location determination based on internet of things: Case study shopping mall site selection

Saleh

Abdullah

2023

AIP Conference Proceedings

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On the analysis of received signal strength indicator from ESP8266

Cited by 6 publications

References 0 publications

Internet of things with NodeMCU ESP8266 for MPX-5700AP sensor-based LPG pressure monitoring

Internet of things with NodeMCU ESP8266 for MPX-5700AP sensor-based LPG pressure monitoring

Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

Web application system for urban best location determination based on internet of things: Case study shopping mall site selection

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