Evidence map›Paper›PMID 35336296›Full record

ReviewSensors (Basel, Switzerland)2022

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review.

Marco Esposito, Lorenzo Palma, Alberto Belli, Luisiana Sabbatini, Paola Pierleoni

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Marco EspositoDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0003-4984-8304
Lorenzo PalmaDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0003-2851-3310
Alberto BelliDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0002-6142-0987
Luisiana SabbatiniDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0001-8554-0564
Paola PierleoniDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.ORCID 0000-0002-1436-8864

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Indexed as

DisastersInternet of ThingsNatural DisastersHumansearly warning systemsearthquake early warningflood early warningInternet of Thingslandslide early warningtsunami early warning

Identifiers

PMID35336296
PMCPMC8954208

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.