Wireless sensor network design for landslide warning system in IoT architecture

Sofwan, Aghus; Sumardi, -; Ridho, Mukhammad Faizal; Goni, Abdul; Najib, Najib

doi:10.1109/icitacee.2017.8257718

Cited by 24 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, there is a lot of research on how to detect the vulnerability of landslides. One of them is research on the design of wireless sensor networks for landslide early warning based on the internet of things [5]. In this study, a tool was used to collect data using several sensors to detect soil moisture, soil slope, humidity, and temperature.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic Method Design for Landslide Vulnerability

2019

Self Cite

View full text Add to dashboard Cite

Landslide is one of the most frequently happened disasters in Indonesia due to weather and climate characteristics, regional topography, and geological structures that make this country have many potential landslide areas. The main goal of this study is to apply fuzzy logic to the landslide detection sensor so that it is easier to find out areas prone to landslides. The fuzzy logic method uses five parameters to analyze the landslide area. These parameters are rainfall, land slope, moisture content in two different soil depths, and ground vibration. They are used to determine the vulnerability level of landslide area is very safe, relatively safe, relatively potential, potentially and very potential. Fuzzification, inference, and defuzzification performed on each data in the analysis process to determine the vulnerability of landslides, and the parameters and weighting rules are according to government regulations. From the results of the study, it was concluded that intelligent systems based on fuzzy logic can be used to determine the level of vulnerability of landslides in a specified area.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic Method Design for Landslide Vulnerability

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Borujeni and Nateghi (2019), the ANN model was selected as a computational model for landslide forecasting, which only measured the soil layer displacement. Moreover, much practical research on the landslide warning system has been studied, such as in Chaturvedi et al (2017), Chen et al (2017), Hemalatha et al (2019), and Sofwan et al (2017). Sofwan et al (2017) developed a wireless sensor network that applied Internet of Things architecture for landslide warnings.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, much practical research on the landslide warning system has been studied, such as in Chaturvedi et al (2017), Chen et al (2017), Hemalatha et al (2019), and Sofwan et al (2017). Sofwan et al (2017) developed a wireless sensor network that applied Internet of Things architecture for landslide warnings. Chen et al (2017) used a probability regression model to detect landslide and applied it in practical cases.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Sofwan¹,

Sumardi²,

Najib³

et al. 2021

JST

Self Cite

View full text Add to dashboard Cite

Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver these factor values into the levels of vulnerability of landslide disasters is needed. The system uses Arduino Mega 2560 to process the level of vulnerability. It can predict the moment and the probability of the disaster occurring as an early warning system. The artificial neural network (ANN) intelligent system can expect an event of a disaster. The designed ANN used five parameters causing landslide as input data: rainfall, slope, soil moisture on the surface, soil moisture in the ground’s depth, and soil vibration. The ANN system output delivered three-level conditions: the safe, the standby, and the hazardous. The feed-forward backpropagation (FFBP) and the cascade forward backpropagation (CFBP) methods were analyzed. The performance of both methods was compared in terms of minimum square error (MSE). The MSE results of FFBP and CFBP in the safe, the standby, and the hazardous conditions were 0.017076 and 0.034952; 0.049597 and 0.046764; 0.062105 and 0.060355; respectively. The results point to the supremacy of CFBP to FFBP in standby and hazardous conditions. Therefore, the CFBP is implemented into the hardware of the early warning system.

show abstract

“…The design of wireless sensor networks for landslide warning systems in the IoT architecture was investigated by Sofwan. Landslides frequently occur in Indonesia, which has climate characteristics, regional topography, and geological structures that make this country bear many landslide potential areas [11]. A landmine robot controlled by Arduino was examined by Abilash to detect landmines.…”

Section: Introductionmentioning

confidence: 99%

Design Of An Automatic Bell Warning System For Prayer Times In A Net Centric Computing Lab

Juhariansyah

Ritzkal

Hendrawan

2020

jrc

View full text Add to dashboard Cite

The development of science and technology has been increasing rapidly, providing convenience in everyday life. Various household appliances and office work tools use electronic devices to ease the works, including bell warning systems for prayer times. The system notifies the people when the times for prayer comes. The warning systems used presently have not operated automatically. The research aimed to design and implement a series of automatic warning bell system for prayer times, that sent a notification to the telegram. It used Arduino Mega 2560 as the data processor. The result found that the tool, equipped with an RTC, a Dfplayer, a loud-speaker, and an Ethernet Shield, worked satisfactorily by sending a notification to the Telegram.

show abstract

Wireless sensor network design for landslide warning system in IoT architecture

Cited by 24 publications

References 11 publications

Fuzzy Logic Method Design for Landslide Vulnerability

Fuzzy Logic Method Design for Landslide Vulnerability

Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Design Of An Automatic Bell Warning System For Prayer Times In A Net Centric Computing Lab

Contact Info

Product

Resources

About