IoT Based Earthquake Prediction Technology

Pirmagomedov, Rustam; Blinnikov, Mikhail; Amelyanovich, Alexey; Glushakov, Ruslan I.; Loskutov, S. I.; Koucheryavy, Andrey; Kirichek, Ruslan; Bobrikova, Ekaterina V.

doi:10.1007/978-3-030-01168-0_48

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…Wearable applications work with noisy data; thus they are natively resilient to error [31], moreover, most of the applications do not require extremely precise results, thus the paradigm of an acceptable margin of error as introduced by AC promises significant energy-efficiency gains for AH systems.…”

Section: Improving Energy Effeciencymentioning

confidence: 99%

IoT technologies for Augmented Human: A survey

Pirmagomedov

Koucheryavy

2021

Internet of Things

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Internet of Things (IoT) technology has delivered new enablers for improving human abilities. These enablers promise an enhanced quality of life and professional efficiency; however, the synthesis of IoT and human augmentation technologies has also extended IoT-related challenges far beyond the current scope. These potential challenges associated with IoTempowered Augmented Human (AH) have so far not been wellinvestigated. Thus, this article attempts to introduce readers to AH concept as well as summarize notable research challenges raised by such systems, in order to facilitate readers further interest in this topic. The article considers emerging IoT applications for human augmentation, devices and design principles, connectivity demands, and security aspects.

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Section: Improving Energy Effeciencymentioning

confidence: 99%

IoT technologies for Augmented Human: A survey

Pirmagomedov

Koucheryavy

2021

Internet of Things

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“…IoT based models have been proposed in Zambrano et al (2017) , Atzori et al (2012) for the detection of earthquakes and generating alerts to the nearby smart devices. IoT involves many wearable devices that may affect data communication due to noisy data ( Pirmagomedov et al, 2018 ). Sarwar et al (2019) utilized IoT to present an intelligent fire warning application.…”

Section: Related Workmentioning

confidence: 99%

A framework for the prediction of earthquake using federated learning

Tehseen

Farooq

Abid

2021

PeerJ Computer Science

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Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

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“…If theses parameters reach critical levels then it notifies the fire service station. These sensors provide GPS coordinates which helps the rescue team and firefighters to localize the fire [7]. The combination between IOT and new advanced technologies like Wireless Sensor Networks (WSN), is one of the most promising solution to help deal with the problem of natural disasters.…”

Section: Disaster Managementmentioning

confidence: 99%

June 2021

2021

JTCSST

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The disasters can be either natural or man-made. Control and management of disaster of any kind is possible in effective and robust way by the implementation of IoT in the system. The objective of implementing IoT into the disaster management system is the quick and effective recovery from the disaster. The various methods that can be deployed after the disaster is outlined through utilization of IoT. This paper gives an insight on the various methods that can be effectively used after the disaster using IoT. The existing techniques are very well monitored and has the ability to react to the situation as per needs, this paper significantly provides the contribution in analyzing these techniques for appropriate disaster management development block.

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IoT Based Earthquake Prediction Technology

Cited by 9 publications

References 17 publications

IoT technologies for Augmented Human: A survey

IoT technologies for Augmented Human: A survey

A framework for the prediction of earthquake using federated learning

June 2021

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