Earthquake Detection at the Edge: IoT Crowdsensing Network

Bassetti, Enrico; Panizzi, Emanuele

doi:10.3390/info13040195

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…For example, the smart MEM accelerometers produced by QuakeSaver GmbH use a convolutional neural network to classify waveforms on the edge so that only higher-order data products are transmitted to end users 32 . A similar approach is described in the crowd-sensed earthquake detection study of Bassetti et al (2022).…”

Section: Slim Pickings For In-situ Eo?mentioning

confidence: 99%

When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

Kuglitsch,

Albayrak,

Luterbacher

et al. 2023

Environ. Res. Lett.

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Earth observations (EO) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g., radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is actually being used in this way. This topical review draws on use cases, workshop presentations, literature, and consultation with experts from key institutes to explore reasons for this discrepancy. Specifically, it evaluates the types of EO needed to train AI-based models for DRR applications and identifies the main characteristics, possible challenges, and innovative solutions for EO. Finally, it suggests ways to make EO more user ready and to facilitate its uptake in AI for DRR and beyond.

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Section: Slim Pickings For In-situ Eo?mentioning

confidence: 99%

When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

Kuglitsch,

Albayrak,

Luterbacher

et al. 2023

Environ. Res. Lett.

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show abstract

“…An example of this type of solution is an early warning system for railway bridges [ 105 ]. Edge computing also allows the real-time monitoring of natural disasters such as fires [ 106 ] or earthquakes [ 107 ]. These solutions tolerate multiple node faults and partial network disruptions and store all data locally, which also enhances privacy [ 107 ].…”

Section: Iot Devices and Cloud Processingmentioning

confidence: 99%

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

Prauzek,

Kucova,

Konecny

et al. 2023

Sensors

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Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.

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“…The model can detect the probability of occurring preceding significant shocks and accurately predict P-waves between 1.5 and 2.5 s before their arrival. In [182], the authors used detector nodes to detect earthquakes locally by probing the environment and assessing data from probes in the surrounding area. The method stores all data locally, making it resistant to node failures and partial network outages, thus increasing privacy.…”

Section: Iot-cloud-based Eewsmentioning

confidence: 99%

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Abdalzaher,

Krichen,

Yiltas-Kaplan

et al. 2023

Sustainability

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Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

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Earthquake Detection at the Edge: IoT Crowdsensing Network

Cited by 14 publications

References 17 publications

When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

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