Cited by 318IoT and Edge/Fog ComputingInternet of Things and AIInnovation in Digital Healthcare Systems

The DOI matches its Crossref record; 60 of the 61 references it cites were found in a registry or an index.

Trends in IoT based solutions for health care: Moving AI to the edge

Open access
Greco, Luca, Percannella, Gennaro, Ritrovato, Pierluigi, Tortorella, Francesco, Vento, Mario·2020·Pattern Recognition Letters

Abstract

In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.patrec.2020.05.016

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 61 references

Research works printed but not linked: 3

  1. Azimi, 2017, HiCH: hierarchical fog-assisted computing architecture for healthcare IoT, ACM Trans. Embed. Comput. Syst. (TECS), 16, 174
  2. Park, 2019, Wireless network intelligence at the edge, Proc. IEEE, 107, 2204, 10.1109/JPROC.2019.2941458 · DOI · Paper page
  3. Wang, 2020, Convergence of edge computing and deep learning: acomprehensive survey, IEEE Commun. Surv. Tutor. (Early Access), 1

Cited by

Sorted by citations

Metadata

Authors
Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M.
Affiliations
University of Salerno (IT)
Corresponding author
Greco, Luca
Venue
Pattern Recognition Letters
Publisher
Elsevier BV
Published
2020-07
Volume
135
Pages
346-353
ISSN
0167-8655 (ISSN-L)
OpenAlex
W3025245379
Cited by
318
References
61
Citations by year
2026: 15 · 2025: 36 · 2024: 53 · 2023: 72 · 2022: 65 · 2021: 67 · 2020: 10
FWCI
25.14
Type
journal article
Language
en
Open access
Locations
  • Pattern Recognition Letters (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
Keywords
Internet of Medical Things, edge computing, fog computing, wearable sensors, smart health monitoring, artificial intelligence in healthcare, response time, privacy and security, near real-time processing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review