PLATOS improves healthcare IoT by cutting energy use and delay
PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing
Distributed, Parallel, and Cluster ComputingArtificial Intelligence
Summary
Healthcare devices connected to the internet help doctors better care for patients, but they can struggle with delays and using too much battery power. The authors created a method called PLATOS that sorts tasks into groups and picks the best computers nearby to quickly and efficiently handle those tasks. Their approach aims to reduce both waiting time and energy use for these devices. Tests show PLATOS lowers energy use by almost 19% and cuts delay by about 9%, making healthcare monitoring faster and more efficient.
Healthcare IoTFog computingTask schedulingLatencyPower consumptionEnergy efficiencyPriority tasksiFogSim2Edge computing
Authors
Mohammed Alaa Ala'anzy, Zulfiqar Ahmad, Zhanar Mukash
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.