Evidence map›Paper›PMID 37896525›Full record

ArticleSensors (Basel, Switzerland)2023

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

Paola Pierleoni, Roberto Concetti, Alberto Belli, Lorenzo Palma, Simone Marzorati, Marco Esposito

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Paola PierleoniDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0002-1436-8864
Roberto ConcettiIstituto di Istruzione Superiore Carlo Urbani, 63821 Porto Sant'Elpidio, Italy.
Alberto BelliDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0002-6142-0987
Lorenzo PalmaDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0003-2851-3310
Simone MarzoratiIstituto Nazionale di Geofisica e Vulcanologia (INGV), Osservatorio Nazionale Terremoti, 60131 Ancona, Italy.ORCID 0000-0002-5803-4882
Marco EspositoDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0003-4984-8304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Indexed as

cloud computingearly warning systemsearthquake localizationInternet of Things

Identifiers

PMID37896525
PMCPMC10610707

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.