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Fog computing environments in healthcare support critical operations by processing data closer to medical devices and patients. An Effective Resource Allocation Strategy, termed ERAS, improves computing efficiency within these specialised fog settings. The method combines real-time resource distribution with predictive algorithms to manage digital workloads. To enhance system performance, particle swarm optimisation is employed to tune the hyperparameters of a reinforcement learning model, helping to prevent the algorithm from settling into local minima through balanced exploration and exploitation. Evaluated against contemporary allocation algorithms, the strategy successfully minimises processing makespan while simultaneously improving both the average resource utilisation and the load balancing level across computing nodes. Although reinforcement learning shows substantial promise for enhancing critical care decisions, further validation in authentic clinical settings remains essential.
Modern healthcare operations increasingly rely on connected devices and fog computing to support real-time clinical decisions. Bottlenecks or delayed data processing can hinder patient care in fast-paced environments. Optimising how digital resources are allocated ensures that critical data streams are handled efficiently and evenly, lowering system delays and maintaining computational reliability when clinicians need to make rapid assessments.
This approach could eventually be integrated into fog computing platforms, medical IoT networks, and healthcare operational software to manage computing tasks efficiently. Potential adopters include health-tech software vendors and hospital infrastructure administrators managing edge networks. Given the explicit acknowledgment that reinforcement learning algorithms still require validation in authentic clinical environments, the technology currently sits at an early, computational research stage rather than near-market deployment.
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Abstract The healthcare industry has always been an early adopter of new technology and a big benefactor of it. The use of reinforcement learning in the healthcare system has repeatedly resulted in improved outcomes.. Many challenges exist concerning the architecture of the RL method, measurement metrics, and model choice. More significantly, the validation of RL in authentic clinical settings needs further work. This paper presents a new Effective Resource Allocation Strategy (ERAS) for the Fog environment, which is suitable for Healthcare applications. ERAS tries to achieve effective resource management in the Fog environment via real-time resource allocating as well as prediction algorithms. Comparing the ERAS with the state-of-the-art algorithms, ERAS achieved the minimum Makespan as compared to previous resource allocation algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). For each application, we further compared and contrasted the architecture of the RL models and the assessment metrics. In critical care, RL has tremendous potential to enhance decision-making. This paper presents two main contributions, (i) Optimization of the RL hyperparameters using PSO, and (ii) Using the optimized RL for the resource allocation and load balancing in the fog environment. Because of its exploitation, exploration, and capacity to get rid of local minima, the PSO has a significant significance when compared to other optimization methodologies.
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DOI: 10.1007/s11042-022-13000-0
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