Design of real time sensor system for detection and processing of seismic waves for earthquake early warning system

Sherki, Yogesh; Gaikwad, Nikhil B.; Chandle, J. O.; Kulkarni, Anil V.

doi:10.1109/icpace.2015.7274959

Cited by 15 publications

(3 citation statements)

References 2 publications

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“…Much research has been carried out in order to build earthquake early warning systems that make use of rapidly increasing IoT technology [140,141]. Zambrano et al [142,143] presented a mechanism for early earthquake warning that communicates via an IoT protocol.…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

Ariyachandra

Wedawatta

2023

Sustainability

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Natural hazard-induced disasters have caused catastrophic damage and loss to buildings, infrastructure, and the affected communities as a whole during the recent decades and their impact is expected to further escalate in the future. Thus, there is a huge demand for disaster risk management using digitalisation as a key enabler for effective and efficient disaster risk management systems. It is widely accepted that digital and intelligence technologies can help solve key aspects of disaster risk management such as disaster prevention and mitigation, and rescue and recovery. Digital Twin (DT) is one of the most promising technologies for multi-stage management which offers significant potential to advance disaster resilience. Smart Cities (SCs) use pervasive information and communications technology to monitor activities in the city. With increasingly large applications of DTs combined with big data generated from sensors in a SC, it is now possible to create Digital Twin Smart Cities (DTSCs). Despite the increasing prevalence of DTSC technologies and their profound impact on disaster risk management, a systematic and longitudinal view of the evolution to the current status of DTSC for disaster risk management does not exist. This review analyses 312 titles and abstracts and 72 full papers. To begin with, a scientific review of DT and SC is undertaken, where the evolution of DTSCs is reviewed. In addition, the intelligence technologies used in DTSCs for disaster risk management are assessed and their benefits are evaluated. Furthermore, the evolution and technical feasibility of DTSC-driven disaster risk management is evaluated by assessing current applications of DTSCs in disaster risk management. It was found that despite the significant potential benefits offered by DTSCs, they also add a new layer of complexities and challenges inherent to these technologies to the already complex web of complexities involved in disaster risk management. These challenges can be addressed by understanding how the process of utilising DTSCs in disaster risk reduction and sustainability is designed, which is essential for comprehending what DTSCs may offer, how it is implemented, and what it means to all involved stakeholders. This paper contributes to the knowledge by improving the understanding of the current status of DTSC technologies and their impact on disaster risk management, and articulating the challenges in implementing DTSC, which inspires the professional community to advance these technologies to address them in future research.

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Section: Internet Of Things (Iot)mentioning

confidence: 99%

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

Ariyachandra

Wedawatta

2023

Sustainability

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“…Generally triangulation of distance is the method that is used for the prediction of earthquakes. The problem is that it is not very accurate [5]. R. Mallouhy et.…”

Section: Related Workmentioning

confidence: 99%

NatDisP – An Intelligent Natural Disaster Predictor

Mittapalli

Mutha

Maheswari

2021

Preprint

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Natural disasters are adverse actions that happen due to the natural processes of the earth. In today’s world with so much pollution, global warming and because of so many reasons, natural disasters are happening far more than they used to happen before and many people in the world face problems, lose their houses, livelihoods and even their houses. It is really painful to get to know the effects of these natural disasters. So, this paper proposes a model which helps us predict natural disasters before they happen using wireless technologies. In this paper modern technologies like IOT, artificial intelligence and machine learning are used. Here for the prediction of each disaster, data/signals given by nature are used , for example for the earthquake module the seismic signals from the earth are used, systems like UNITE are used where the sensors placed in the earth to get the seismic data. For the other disasters also such data is taken from signals given by nature. A detailed explanation on how disasters are predicted based on these simple signals and data from nature is given. In this paper solutions based on wireless technologies to solve some after effects of these natural disasters are also suggested . So, basically our idea is to have all disasters predicted in one place using modern computer science technologies.

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“…From the paper, it is not clear whether the prototype has been deployed or not. Another work that leverages accelerometers and Arduino microcontrollers is that presented by Sherki et al [3]. Instead of ZigBee, a GSM module is employed, and the system is tested against a vibration generator machine.…”

Section: Related Work: An Overview On Sassmentioning

confidence: 99%

Processing and Communication Delays in EWS: On the Performance of the Earthcloud Prototype

Klapez

Grazia

Casoni

et al. 2019

Wireless Communications and Mobile Computing

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A Seismic Alert System (SAS), also called Earthquake Warning System (EWS) or Earthquake Early Warning System (EEW or EEWS), represents one of the most important measures that can be taken to prevent and minimize earthquake damage. These systems are mainly used to detect P-waves and the faster seismic waves and to subsequently trigger an alarm about the incoming S-waves, the slower and most dangerous seismic waves. In some cases, distributed systems are also able to alert some locations before the impending P-waves strike them. This paper presents Earthcloud, a cloud-based SAS that aims to provide all the former capabilities while retaining financial accessibility. Earthcloud first results, generated from four months of data acquisition, are compared with those coming from other systems. In particular, the paper focuses on processing and communication delays, showing how the Earthcloud new detection strategy may minimize delays. Although a thorough test campaign with more sensor nodes is needed to assess performance reliably, especially for highly dense urban scenarios, initial results are promising, with total latencies for Earthcloud always kept under the 1-second mark, despite being at the expense of solid magnitude estimation.

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Design of real time sensor system for detection and processing of seismic waves for earthquake early warning system

Cited by 15 publications

References 2 publications

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

NatDisP – An Intelligent Natural Disaster Predictor

Processing and Communication Delays in EWS: On the Performance of the Earthcloud Prototype

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