ArticleScientific reports2025
Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networks.
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.
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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.
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Who cites it
1 citing paper in PubMed.
- Beyond the digital divide: multi-group SEM examination of socioeconomic status, mHealth utilization, and urban-rural physical activity disparities in Indonesia.Frontiers in digital health · 2026Article
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6 authors.
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Abstract
Simultaneous task allocation is crucial for enhancing service quality in Industrial Internet of Things (IIoT) environments. The distribution and management of tasks remain among the biggest challenges in the IIoT era. Efficient allocation strategies are needed to enable transparent network configurations and maximize task throughput. Although recent methods address the dynamic management of objects, they often overlook the correlations between tasks and their associated functionalities. This paper introduces a novel Connected Harmonical Adaptive Task Allocation (CHATA) model for IIoT health networks to ensure fair task distribution. CHATA leverages similarity measures of object functionalities to identify the most suitable object to perform each task. Simulations conducted in NS-3 demonstrate that CHATA achieves up to 90% allocation efficiency in 5G Radio Access Technologies IIoT health environments and significantly outperforms recent approaches in task assignment performance.
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Registered trials
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