Evidence map›Paper›PMID 40594005›Full record

ArticleScientific reports2025

A secured remote patient monitoring framework for IoMT ecosystems.

Pronaya Bhattacharya, Anwesha Mukherjee, Bharat Bhushan, Shashi Kant Gupta, Thippa Reddy Gadekallu, Zhu Zhu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Pronaya BhattacharyaDepartment of Computer Science and Engineering, Amity School of Engineering and Technology, and Research and Innovation Cell, Amity University, Kolkata, West Bengal, India.
Anwesha MukherjeeDepartment of Computer Science, Mahishadal Raj College, Mahishadal, Purba Medinipur, West Bengal, India.
Bharat BhushanComputer Science and Engineering Department, School of Engineering and Technology, Sharda University, Greater Noida, India.
Shashi Kant GuptaCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Thippa Reddy GadekalluCollege of Mechanical Engineering (College of Robotics Engineering), Jiaxing University, Jiaxing, 314001, China. thippareddy@ieee.org.
Zhu ZhuSchool of Haiyan, Jiaxing Vocational and Technical College, Jiaxing City, 314036, Zhejiang Province, China. zzzhupearl@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancement in the Internet of Medical Things (IoMT) allows patients to set up smart sensors and medical devices to connect to remote healthcare setups. However, existing remote patient monitoring solutions predominantly rely on persistent connectivity and centralized cloud processing, resulting in high latency and energy consumption, particularly in environments with intermittent network availability. There is a need for real-time IoMT computing closer to the dew, with secured and privacy-enabled access to healthcare data. To address this, we propose the DeW-IoMT framework, which includes a dew layer in the roof-fog-cloud systems. Notably, our approach introduces a novel roof computing layer that acts as an intermediary gateway between the dew and fog layers, enhancing data security and reducing communication latency. The proposed architecture provides critical services during disconnected operations and minimizes computational requirements for the fog-cloud system. We measure heart rate using the pulse sensor, where the dew layer sets up conditions for remote patient monitoring with low overheads. We experimentally analyze the proposed scheme's response time, energy dissipation, and bandwidth and present a simulation analysis of the fog layer through the iFogSim software. Our results at dew demonstrate a reduction in response time by 74.61%, a decrease in energy consumption by 38.78%, and a 33.56% reduction in task data compared to traditional cloud-centric models. Our findings validate the framework viability in scalable IoMT setups.

Indexed as

Computer SecurityInternet of ThingsCloud ComputingHumansMonitoring, PhysiologicRemote Patient MonitoringSoftwareTelemedicineCloud computingDew computingHealthcare 5.0Internet of medical thingsSensors

Identifiers

PMID40594005
PMCPMC12217842

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.