Evidence map›Paper›PMID 40969047›Full record

ArticleSensors (Basel, Switzerland)2025

Energy-Aware Ultra-Reliable Low-Latency Communication for Healthcare IoT in Beyond 5G and 6G Networks.

Adeel Iqbal, Tahir Khurshaid, Ali Nauman, Sang-Bong Rhee

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
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

4 authors.

Adeel IqbalSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.ORCID 0000-0001-8692-173X
Tahir KhurshaidDepartment of Electrical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.ORCID 0000-0001-6113-123X
Ali NaumanSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.ORCID 0000-0002-2133-5286
Sang-Bong RheeDepartment of Electrical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultra-reliable low-latency communication (URLLC) is a cornerstone of beyond 5G and future 6G networks, particularly for mission-critical applications such as the healthcare Internet of Things. In applications such as remote surgery, emergency services, and real-time health monitoring, it is imperative to ensure stringent latency and reliability requirements. However, the energy constraints of wearable and implantable medical devices pose stringent challenges to conventional URLLC methods. This paper proposes an energy-aware URLLC framework that dynamically prioritizes healthcare traffic to optimize transmission energy and reliability. The framework integrates a priority-aware packet scheduler, adaptive transmission control, and edge-enabled reliability management. Extensive Monte Carlo simulations are carried out on various network loads and varying edge computing delays to evaluate performance metrics, like latency, throughput, reliability score, energy consumption, delay violation rate, and Jain's fairness index. Results illustrate that the suggested technique achieves lower latency, energy consumption, and delay violation rates and higher throughput and reliability scores, sacrificing Jain's fairness index graciously at peak network overload. This study is a potential research lead for green URLLC in healthcare IoT systems to come.

Indexed as

Computer Communication NetworksInternet of ThingsWireless TechnologyAlgorithmsHumansMonte Carlo MethodWearable Electronic Devices6G networksbeyond 5G (B5G)delay violation rateedge computingenergy efficiencyhealthcare IoT (H-IoT)Monte Carlo simulationpriority-aware schedulingreliability scoreultra-reliable low-latency communication (URLLC)

Identifiers

PMID40969047
PMCPMC12158281

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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.