ArticleScientific reports2025
Energy-efficient clustering and routing for IoT-enabled healthcare using adaptive fuzzy logic and hybrid optimization.
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 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Elephant herd clustering with adaptive optimization for secure routing protocol in IoT network.Scientific reports · 2026Article
- Optimized cluster based routing protocol for IoT enabled healthcare data networks.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leveraging Internet of Things technology in healthcare, including wireless sensor networks and next-generation networks, enhances the seamless integration of medical equipment and enables intelligent interaction among devices. This advancement plays a crucial role in assisting healthcare professionals in improving patient outcomes. However, ensuring efficient data transmission and communication in Internet of Things-based healthcare systems is essential to meet the critical requirements of real-time monitoring and emergency response. This paper proposes an adaptive model to optimize cluster head selection and routing in internet of things-enabled healthcare applications. The cluster head selection process employs an adaptive fuzzy logic mechanism that incorporates factors such as energy levels, SN density, mobility, and link stability to handle uncertainties in SN characteristics and dynamically adapt to changing network conditions in healthcare environments. Furthermore, a hybrid optimization method is introduced that combines particle swarm optimization and a genetic algorithm to discover optimal routing paths, leveraging Particle Swarm Optimization fast convergence and Genetic Algorithm global search capability to minimize energy consumption and delay. Extensive simulations have been conducted in MATLAB and Google Collaboration to evaluate the proposed model in terms of packet delivery ratio, average delay, throughput, and energy efficiency. The results demonstrate significant improvements over the existing methods. Specifically, the proposed model achieves a PDR of 92.5%, an average minimum delay of 0.10 s, a throughput of 61.5 bps and an energy efficiency of 9.1 J/bit. These findings highlight the effectiveness of the proposed model in optimizing communication reliability, reducing energy consumption, and improving overall network performance in IoT-based healthcare applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.