Evidence map›Paper›PMID 42393101›Full record

ArticleScientific reports2026

A blockchain-enabled IoT framework for smart electro-medical waste management.

K Suresh Kumar, T Ananth Kumar, Christo Ananth, Mathiyazhagan Narayanan, Hari Mohan Rai, Saurabh Agarwal, Wooguil Pak

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

K Suresh KumarComputer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, 641202, India.
T Ananth KumarComputer Science and Engineering, IFET College of Engineering, Villupuram, 605108, India.
Christo AnanthFaculty of Artificial Intelligence and Digital Technologies, Samarkand State University, 140104, Samarkand, Uzbekistan.
Mathiyazhagan NarayananDepartment of Biotechnology, Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Chennai, 602105, India.
Hari Mohan RaiDepartment of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, 010000, Astana, Kazakhstan. harimohanrai@gmail.com.
Saurabh AgarwalSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. saurabhnsit2510@gmail.com.
Wooguil PakSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. wooguilpak@yu.ac.kr.

Funding

National Research Foundation of Korea RS-2025-24683865
6 · The paper itself

Abstract

Electro-medical waste (EMW) management presents critical challenges related to traceability, regulatory compliance, and operational safety in healthcare environments, where improper handling can pose serious risks to public health and the environment. To address these challenges, this work proposes a blockchain-integrated Internet of Things framework, termed BIOT-EMW, which combines IoT sensing, blockchain-based auditability, and edge-level intelligence to enable secure and transparent EMW lifecycle management. A convolutional neural network-based computer vision module is deployed at the edge to automate EMW classification and reduce manual intervention. The performance evaluation of the proposed BIOT-EMW system emphasizes system-level operational metrics, including latency, delay, bandwidth, power consumption, resource utilization, and scalability, rather than exhaustive model-level benchmarking. Experimental results show that edge-level processing latency ranges from 3.45 to 9.33 ms, end-to-edge communication delay from 6.43 to 12.11 ms, bandwidth usage from 24.54 Mbps to 34.87 Mbps, and device-level power consumption from 6.5 to 13.31 mW, with resource utilization between 78 and 92% and scalability reaching up to 97%. In a prototype-scale experimental testbed, the BIOT-EMW framework shows feasible and efficient operation with promising scalability for automated electro-medical waste management in smart healthcare facilities.

Indexed as

BlockchainComputer visionConvolutional neural networksElectro-medical wastesInternet of things

Identifiers

PMID42393101
PMCPMC13328551

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.