PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing

Abstract:Healthcare Internet of Things (HIoT) technology is revolutionising the healthcare industry by enabling real-time data collection and analysis for personalised patient care. However, the rapid expansion of HIoT technology introduces challenges such as increased latency and higher energy consumption in fog computing environments, particularly when managing battery-operated devices. To address these issues, this work proposes a novel scheduling strategy that optimises both power consumption and latency through task-oriented scheduling for HIoT tasks. The proposed strategy, named PLATOS (Power and Latency Aware Task Oriented Scheduling), is implemented in four sequential phases. In the first phase, HIoT tasks are categorised into three groups: priority-oriented, storage-oriented, and computational-oriented. The second phase focuses on latency optimisation by identifying the fog computing resources that yield the lowest execution delay for each task category. In the third phase, power optimisation is achieved by selecting the resources that minimise energy consumption. Finally, in the decision-making phase, high-performance fog resources are allocated to high-priority tasks while the remaining tasks are scheduled based on a mapped list derived from the latency and power optimisation phases. Simulation experiments conducted in iFogSim2 demonstrate that PLATOS reduces energy consumption by 18.72% and latency by 8.65% when compared to the state-of-the-art. These improvements enhance the efficiency and responsiveness of HIoT systems and contribute to more effective patient care and proactive healthcare service delivery.

Published in Eurasian Journal of Mathematical and Computer Applications, Vol. 13, No. 3, pp. 4-21 (2025). 18 pages, 6 figures

Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)

arXiv:2609.07318 [cs.DC]

(or arXiv:2609.07318v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2609.07318 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Eurasian Journal of Mathematical and Computer Applications, Vol. 13, No. 3, pp. 4-21 (2025)

https://doi.org/10.32523/2306-6172-2025-13-1-4-21 Focus to learn more DOI(s) linking to related resources

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