Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networks Article Swipe
Karthikeyan Vaithianathan
,
Julian Benadit Pernabas
,
Manjunath Ramanna Lamani
,
K. Venkatachalam
,
Sameh Askar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-27366-2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-27366-2
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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Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networksWork title
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2025-12-08Full publication date if available
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Karthikeyan Vaithianathan, Julian Benadit Pernabas, Manjunath Ramanna Lamani, K. Venkatachalam, Sameh AskarList of authors in order
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https://doi.org/10.1038/s41598-025-27366-2Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1038/s41598-025-27366-2Direct OA link when available
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