scholarly journals A Smart IoT Device for Detecting and Responding to Earthquakes

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1546 ◽  
Author(s):  
Jangsoo Lee ◽  
Irshad Khan ◽  
Seonhwa Choi ◽  
Young-Woo Kwon
Keyword(s):  
Low Cost ◽  
Warning System ◽  
Acceleration Sensor ◽  
Earthquake Early Warning System ◽  
Detection Model ◽  
Earthquake Detection ◽  
Warning Message ◽  
Acceleration Sensors ◽  
Learning Technique ◽  
Noise Data

The advancement of hardware and software technologies makes it possible to use smartphones or Internet of things for monitoring environments in realtime. In recent years, much effort has been made to develop a smartphone based earthquake early warning system, where low-cost acceleration sensors inside a smartphones are used for capturing earthquake signals. However, because a smartphone comes with a powerful CPU, spacious memory, and several sensors, it is waste of such resources to use it only for detecting earthquakes. Furthermore, because a smartphone is mostly in use during the daytime, the acquired data cannot be used for detecting earthquakes due to human activities. Therefore, in this article, we introduce a stand-alone device equipped with a low-cost acceleration sensor and least computing resources to detect earthquakes. To that end, we first select an appropriate acceleration sensor by assessing the performance and accuracy of four different sensors. Then, we design and develop an earthquake alert device. To detect earthquakes, we employ a simple machine learning technique which trains an earthquake detection model with daily motions, noise data recorded in buildings, and earthquakes recorded in the past. Furthermore, we evaluate the four acceleration sensors by recording two realistic earthquakes on a shake-table. In the experiments, the results show that the developed earthquake alert device can successfully detect earthquakes and send a warning message to nearby devices, thereby enabling proactive responses to earthquakes.

Design and Simulation of MEMS Accelerometer for the Application of Seismic vibrations

2020 ◽  
Vol 12 ◽  
Author(s):  
K. Srinivasa Rao ◽  
M. Harsha Vardhan ◽  
CH. Mani Kanta Uma Mahesh ◽  
S. Nikhil ◽  
P. Ashok Kumar ◽  
...  
Keyword(s):  
Early Warning ◽  
Low Cost ◽  
Warning System ◽  
Early Warning Systems ◽  
Vulnerable Groups ◽  
Acceleration Sensor ◽  
Disaster Reduction ◽  
Short Period ◽  
Acceleration Sensors ◽  
Seismic Vibrations

Aim: Using accelerometer to detect the vibrations produced during earthquake. Backgorund: The big challenge facing the Earthquake Early Warning Systems (EEWS) is to accurately detect seismic tremors at the edge of the early warning system or outside of the earthquake seismic system.[1]. During the last years in development, early warning earthquake (EEW) has proved to be one of the potential means of disaster reduction. Objectives: During the last years in development, early warning earthquake (EEW) has proved to be one of the potential means of disaster reduction. The EEW network intensity usually determines the efficiency of both the EEW system An upgraded low-cost Micro Electro Mechanical System MEMS based device called GL-P2B to reduce sensor costs and establish a dense EEW In the this study, a dense EEW network was also developed to maximize the density of the seismic network for EEW applications. A tri-axial sensor with high-dynamic MEMS called GL-P2B was developed. This sensor was upgraded from the previous version by enhancing the CPU's processing capability and correcting certain errors found during the initial test cycle. Methodology: An autonomous sensor with an acceleration sensor is launched in this article First; we systematically evaluated a series of acceleration sensors to select the most suitable acceleration sensor using mems by analyzing their quality and accuracy, and then created a dedicated tool that can monitor earthquakes. Our result shows that an earthquake can be detected with a low-cost acceleration detector, thereby enhancing the safety of vulnerable groups against earthquakes. Results: There have been two earthquakes recently: Gyeongju Earthquake in magnitude 5.6 and Pohang South Korea Earthquake in magnitude 5.4 respectively in 206 and 2017. As a result, earthquake detection and response in a relatively short period of time was highly demanded. The use of seismic smart phone systems is one solution, but it is expensive to use a smartphone to track seismic events and to allow participants to use their smartphones as just another seismic detector. However, because of the existence of smartphones which are used extensively in our everyday lives, a large percentage of smartphones are useless for earthquakes to be detected. We also built a clever tool in this paper that can be mounted to a wall or to a ceiling. The system is only fitted with sensors that include an accelerator and the price in comparison to intelligent telephones is very small. Conclusion: In this paper, we have used an appropriate capacitive sensing technology to create the accelerometer. We have designed and simulated the accelerometer to measure the seismic vibrations by FEM Tool. The simulated results show that the unit is modelled on a 3Hz resonant frequency and therefore it senses the acceleration between 2 and 8 Hz.

scholarly journals Crowdsourcing an Earthquake early warning system for the Indian Subcontinent

2019 ◽  
Author(s):  
Abdul Aleem ◽  
Paul George ◽  
Prasanna Natarajan
Keyword(s):  
Indian Subcontinent ◽  
Low Cost ◽  
Warning System ◽  
Strong Motion ◽  
Deep Penetration ◽  
Ground Shaking ◽  
Earthquake Early Warning System ◽  
Ongoing Work ◽  
Motion Characteristics ◽  
Main Components

Earthquakes are potentially very destructive natural events. The risk fromearthquakes is aggravated because they are unpredictable and can cause tremendousloss of life and property within seconds, particularly in dense urban settings. Wepresent our ongoing work to develop a comprehensive earthquake early warningsystem (EEWS) for the Indian subcontinent. The impetus for this work comes fromthe fact that India has just 82 seismic stations for a land area of about 3.2 million sq.km, with no dedicated EEWS, plus low-cost accelerometers are now easily available,and smartphones have deep penetration. The planned system will use a network ofmobile smartphones and stationary low-cost MEMS-based strong motion sensors.The main components of this project are: creating a high-density network of low-costsensors, real-time transmission of data, algorithms to analyze ground shaking data,compute ground motion characteristics, and determine if the source of shaking is anearthquake.

Performance of a Low‐Cost Earthquake Early Warning System (P‐Alert) during the 2016ML 6.4 Meinong (Taiwan) Earthquake

2016 ◽  
Vol 87 (5) ◽  
pp. 1050-1059 ◽  
Author(s):  
Yih‐Min Wu ◽  
Wen‐Tzong Liang ◽  
Himanshu Mittal ◽  
Wei‐An Chao ◽  
Cheng‐Horng Lin ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Low Cost ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Earthquake Early Warning System ◽  
System P ◽  
Taiwan Earthquake

Extending near fault earthquakes catalogs using convolutional neural network and single-station waveforms

2020 ◽  
Author(s):  
Josipa Majstorović ◽  
Piero Poli
Keyword(s):  
Central Italy ◽  
Warning System ◽  
Night Time ◽  
Earthquake Early Warning System ◽  
Near Fault ◽  
Earthquake Detection ◽  
Single Station ◽  
Seismic Catalogs ◽  
Near Fault Earthquakes ◽  
The City

<p>The machine learning (ML) algorithms have already found their application in standard seismological procedures, such as earthquake detection and localization, phase picking, earthquake early warning system, etc. They are progressively becoming superior methods since one can rapidly scan voluminous data and detect earthquakes, even if buried in highly noisy time series.</p><p>We here make use of ML algorithms to obtain more complete near fault seismic catalogs and thus better understand the long-term (decades) evolution of seismicity before large earthquakes occurrence. We focus on data recorded before the devastating L’Aquila earthquake (6 April 2009 01:32 UTC, Mw6.3) right beneath the city of L’Aquila in the Abruzzo region (Central Italy). Before this event sparse stations were available, reducing the magnitude completeness of standard catalogs. </p><p>We adapted existing convolutional neural networks (CNN) for earthquake detection, localisation and characterization using a single-station waveforms. The CNN is applied to 29 years of data (1990 to 2019) recorded at the AQU station, located near the city of L’Aquila (Italy). The pre-existing catalog maintained by Istituto nazionale di geofisica e vulcanologia is used to define labels and train and test the CNN. We are here interested in classifying the continuous three-component waveforms into four categories, noise/earthquakes, distance (location), magnitude and depth, where each category consists of several nodes. Existing seismic catalogs are used to label earthquakes, while the noise events are randomly selected between the catalog events, evenly represented by daytime and night-time periods.</p><p>We prefer CNN over other methods, since we can use seismograms directly with very minor pre-processing (e.g. filtering) and we do not need any prior knowledge of the region.</p><p><br><br></p>

scholarly journals Earthquake Detection and Warning System for Automatic Cut-Off of Electricity and Gas Supply Lines for Safety Measures

2020 ◽  
Vol 9 (3) ◽  
pp. 85-90
Keyword(s):  
Low Cost ◽  
Warning System ◽  
Financial Loss ◽  
Electrical Transmission ◽  
Safety Measures ◽  
Tectonic Plates ◽  
Earthquake Detection ◽  
Electrical Transmission Line ◽  
Gas Leaks ◽  
Gas Supply

The project aims at designing an earthquake monitoring and warning system that is capable of detecting earthquakes as well as warning people to take necessary precautions. The designed system will not only try to save human lives, but will also store the data for later use by professionals working at this sector. India is a country with a high frequency of earthquakes. Since the country lies at the junction of three tectonic plates, the intensity of earthquakes felt in this region is moderate. But surprisingly, the number of deaths and financial loss in this region by earthquakes is not due to building crashes or being crushed under homes. Rather, major reasons of losses are due to indirect effects such as induction of fear, as well as fire induced from a cracked gas line or faulty electrical transmission line damaged by earthquakes. Hence, a low cost automatic microcontroller based system has been designed and implemented using low cost locally sourced electronic components, which senses earthquakes and gas leaks through accelerometer and gas sensor respectively. The microcontroller operates a relay and a motor that cuts off electricity and gas supplies respectively during the event of an earthquake, helping to prevent associated potential disasters

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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