article · Multimedia Tools and Applications
As Internet of Things networks expand, healthcare applications require real-time data processing and analysis. Fog computing allows computation closer to the edge, but managing resources across distributed nodes remains difficult. A framework called the Effective Prediction and Resource Allocation Methodology, or EPRAM, addresses this challenge for smart healthcare systems. The methodology consists of three modules focused on data preprocessing, resource allocation, and predictive analysis. It combines deep reinforcement learning to optimise resource scheduling with a probabilistic neural network to assess incoming patient data. In tests predicting the probability of heart attacks from sensor data, the probabilistic neural network provided fast and accurate assessments. Compared to existing methods, this framework minimises completion time, enhances load balancing, and improves resource utilisation while optimising quality of service metrics such as response time, bandwidth efficiency, energy consumption, and allocation cost.
Real-time healthcare monitoring systems rely on swift data processing to detect critical conditions such as heart attacks. Cloud infrastructure can suffer from latency issues, but distributing tasks across fog computing nodes often creates resource bottlenecks. By intelligently predicting medical events and balancing computational workloads, this approach helps ensure vital patient alerts are delivered quickly while conserving energy and network bandwidth.
The method is designed for smart healthcare providers, remote patient monitoring services, and developers of IoT healthcare infrastructure. Based on the evaluation against benchmark algorithms, the work represents applied, tested research at an algorithmic simulation or experimental stage. Moving towards commercial deployment would require integration into clinical IoT hardware, testing across physical fog networks, and regulatory validation for medical diagnostic tools.
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Abstract Recently, many concepts in technology has been changed. According to the digital transformation trends, Internet of Things (IoT) represents an interested research issue. As the IoT grows, the data and the processes will need more space. The data in cases like healthcare, smart cities, autonomous vehicles, smart agriculture, etc. needs to be analyzed and processed in real-time. Cisco refers to the dependence of edge and cloud as “The Fog”. The data can be analyzed at the fog layer to maximize data utilization. This paper presents a new Effective Prediction and Resource Allocation Methodology (EPRAM) for Fog environment, which is suitable for Healthcare applications. Resource Allocation (RA) represents a hard mission as it involves a set of various resources and fog nodes to achieve the required computations for IoT systems. EPRAM tries to achieve effective resource management in Fog environment via real-time resource allocating as well as prediction algorithm. EPRAM is composed of three main modules, namely: (i) Data Preprocessing Module (DPM), (ii) Resource Allocation Module (RAM) and (ii) Effective Prediction Module (EPM). The EPM uses the PNN to predict a target field, using one or more predictors. In order to detect the probability of the heart attack, PNN is trained using the training dataset. Then PNN will be tested using the user’s sensing data coming from the IoT layer to predict the probability of heart attack and then take the most appropriate action accordingly. The main goal of the system is to achieve a low latency while improving the Quality of Service (QoS) metrics such as (the allocation cost, the response time, bandwidth efficiency and energy consumption). Unlike other RA techniques, EPRAM employs deep Reinforcement Learning (RL) algorithm in a new manner. It also uses the PNN for the prediction algorithm. It has achieved such acceptable performance due to using deep RL and PNN. Deep RL has shown impressive promises in resource allocation. PNN generates accurate predicted target and is much faster than multilayer perceptron networks. Comparing the EPRAM with the state-of-the-art algorithms, EPRAM achieved the minimum Makespan as compared to previous LB algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). Accordingly, EPRAM is a suitable algorithm in the case of real-time systems in FC which leads to load balancing. ERAM is effective in monitoring and predicting the status of the patient accurately and quickly.
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DOI: 10.1007/s11042-022-12223-5
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