Wireless Sensor Network System for Landslide Monitoring and Warning

Giri, Prapti; Ng, Kam; Phillips, William J.

doi:10.1109/tim.2018.2861999

Cited by 74 publications

(36 citation statements)

References 17 publications

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“…An insight into these technologies reveals that, the WiFi sensors are not only free from occlusion, but are also non-intrusive and cost effective [8], [9]. Successful applications of wireless sensor networks in sensing and monitoring have already been shown for: pedestrian counting in urban traffic [10], industrial process automation [11], landslide monitoring [12], and distributed environmental monitoring [13]. Motivated from such applications, this paper contributes to the existing knowledge by utilizing WiFi sensors for not only estimating but also forecasting the crowd count in large scale public events.…”

Section: A Overview Of the Motivationmentioning

confidence: 99%

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Singh

Determe

Horlin

et al. 2020

IEEE Trans. Instrum. Meas.

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To ensure effective management and security in large scale public events, it is imperative for the event organizers to be aware of potentially critical crowd densities. This paper, therefore, presents a solution to the above problem in terms of WiFi based crowd counting and LSTM neural network based forecasting. Monitoring of an actual event organized in Brussels has been described, wherein crowd counts are obtained using WiFi sensors in a privacy-preserved manner. The time-stamped crowd counts are used to develop univariate time-series, which are in-turn utilized for forecasting. Five different LSTM models are utilized for crowd time-series forecasting and analyzed for their suitability. A random walk model is used as reference for performance assessment. Among different LSTM models, Convolutional LSTM delivered the best performance. Overall results and analysis show that the developed system is suitable for crowd monitoring.

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Section: A Overview Of the Motivationmentioning

confidence: 99%

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Singh

Determe

Horlin

et al. 2020

IEEE Trans. Instrum. Meas.

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“…Possible reasons for this is that WiFi systems are not only free from occlusion, but are also non-intrusive and more cost effective [19]. Some relevant applications of WiFi technology include counting pedestrians in urban traffic [20], indoor counting system [21], crowd speed estimation [22], and occupancy based lightning control in smart buildings [23].…”

Section: Crowd Monitoring Systemmentioning

confidence: 99%

Crowd Monitoring: State-of-the-Art and Future Directions

Singh

Determe

Horlin

et al. 2020

IETE Technical Review

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With the growing concerns over public safety, the importance of crowd monitoring is being realized by various security and event management agencies worldwide. Estimation of crowd dynamics can help such agencies in prevention of any unanticipated accidents or issues. Research on crowd monitoring has been underway since the past few decades. Conventional crowd monitoring systems mainly rely on computer vision approach. Due to predominant use of videos/ image sequences, the existing techniques may raise data privacy concerns. This has led to development of new crowd monitoring techniques which are privacy preserving and require minimum public participation. This paper aims to serve as a single and sufficient source of information to the concerned researchers on various aspects of crowd monitoring and also provide future directions which can be helpful in developing advanced crowd monitoring techniques.

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“…Giri (2018) [9], presents a wireless sensor network system (WSNS) for effective, reliable and efficient landslide monitoring [9]. The system incorporates a network of wireless Inertial Measurement Unit (IMU) sensor devices for collecting movement data, local base stations for data collection, capture servers for data processing and storage, and warning systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic Method Design for Landslide Vulnerability

2019

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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.

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Wireless Sensor Network System for Landslide Monitoring and Warning

Cited by 74 publications

References 17 publications

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Crowd Monitoring: State-of-the-Art and Future Directions

Fuzzy Logic Method Design for Landslide Vulnerability

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