Monitoring System Using Internet of Things For Potential Landslides

Moulat, Meryem El; Debauche, Olivier; Mahmoudi, Saïd; Brahim, L. Aït; Manneback, Pierre; Lebeau, Frédéric

doi:10.1016/j.procs.2018.07.140

Cited by 59 publications

(38 citation statements)

References 16 publications

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“…Moreover, we have also validated and tested the scalability and the adaptability of our architecture on different use cases such as cattle behavior, 23 animal' behavior, 24 connected pivot-center irrigation, 26 digital phenotyping, 28 the health of bee hives, 25 and landslides monitoring. 27 European legislation that will be translated into national law will require us to ensure the safety and traceability of the use of the data.…”

Section: Discussionmentioning

confidence: 99%

“…In the past, our architecture has been progressively developed through different use cases such as cattle behavior, 23 Farm animals' behavior, 24 the health of bee hives, 25 connected pivot-center irrigation, 26 landslides monitoring, 27 and digital phenotyping. 28 In all these use cases, the lambda architectures were used to ingest streaming time series data from the Internet of Things.…”

Section: Introductionmentioning

confidence: 99%

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Cloud architecture for plant phenotyping research

Debauche

Mahmoudi

Cock

et al. 2020

Concurrency and Computation

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Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud architecture for plant phenotyping research

Debauche

Mahmoudi

Cock

et al. 2020

Concurrency and Computation

Self Cite

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“…Singh et al [9] considered the Internet of Things for improving worker safety in mining operations with enhanced productivity. El Moulat et al [10] et al established a new approach for monitoring to detect sliding hillslopes. Sun et al [11] and others introduced a tailings dam monitoring and pre-alarm system (TDMPAS),which monitors the impounded water level and dam deformation with the Internet of Things and cloud computing.…”

Section: A Related Workmentioning

confidence: 99%

Modeling and Application of the Underground Emergency Hedging System Based on Internet of Things Technology

et al. 2019

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The application of the Internet of Things technology in intelligent mines has become an important research area with the rapid development of the cloud platform and Internet +. The Internet of Things technology is a core technology that specializes in model building and algorithm implementation for the application of the mine Internet of Things. The framework of the mine Internet of Things integrated information platform from the perception layer, the transmission layer, the data layer, and the application layer is constructed according to the complexity and particularity of the underground working environment in this paper, which establishes the technical flow of the Internet of Things for underground emergency hedging subsystems and designs the main functions of five subsystems based on the Internet of Things technology. This paper proposes the structure and function of the underground emergency hedging system and introduces key technology combined with the Internet of Things technology. A novel algorithm for underground geomagnetic GRPM positioning and a mathematical model of the underground emergency hedging path are proposed according to the concepts of spatial data mining and the intelligent mine, which verify the validity of the model in intelligent decision-making in the simulation test. The simulation results show that the algorithm and model provided an effective idea for positioning and hedging in an underground emergency and help improve the intelligent analysis of the mine Internet of Things data. INDEX TERMS Mine Internet of Things, underground emergency hedging system, underground geomagnetic GRPM positioning, hedging path, spatial data mining.

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“…One of the major advancements introduced by innovative instrumentations is the possibility to overcome the so-called “manual philosophy”, thanks to the integration of Internet of things (IoT) technologies in the design process of automated monitoring systems [ 4 , 5 , 6 , 7 ]. When it comes to landslide monitoring, the integration of IoT-based devices results in a multilayered structure, where each component has a key role in guaranteeing the system functionality and efficiency [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Advantages of IoT-Based Geotechnical Monitoring Systems Integrating Automatic Procedures for Data Acquisition and Elaboration

Carri¹,

Valletta

Cavalca

et al. 2021

Sensors

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Monitoring instrumentation plays a major role in the study of natural phenomena and analysis for risk prevention purposes, especially when facing the management of critical events. Within the geotechnical field, data collection has traditionally been performed with a manual approach characterized by time-expensive on-site investigations and monitoring devices activated by an operator. Due to these reasons, innovative instruments have been developed in recent years in order to provide a complete and more efficient system thanks to technological improvements. This paper aims to illustrate the advantages deriving from the application of a monitoring approach, named Internet of natural hazards, relying on the Internet of things principles applied to monitoring technologies. One of the main features of the system is the ability of automatic tools to acquire and elaborate data independently, which has led to the development of dedicated software and web-based visualization platforms for faster, more efficient and accessible data management. Additionally, automatic procedures play a key role in the implementation of early warning systems with a near-real-time approach, providing a valuable tool to the decision-makers and authorities responsible for emergency management. Moreover, the possibility of recording a large number of different parameters and physical quantities with high sampling frequency allows to perform meaningful statistical analyses and identify cause–effect relationships. A series of examples deriving from different case studies are reported in this paper in order to present the practical implications of the IoNH approach application to geotechnical monitoring.

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Monitoring System Using Internet of Things For Potential Landslides

Cited by 59 publications

References 16 publications

Cloud architecture for plant phenotyping research

Cloud architecture for plant phenotyping research

Modeling and Application of the Underground Emergency Hedging System Based on Internet of Things Technology

Advantages of IoT-Based Geotechnical Monitoring Systems Integrating Automatic Procedures for Data Acquisition and Elaboration

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