Volume 18, Number 2

A Multi-Model Regression Approach for Predicting Resource Allocation Efficiency in IoT-Driven 6G Networks

  Authors

Hussain AlSalman, King Saud University, Saudi Arabia

  Abstract

Enabling healthcare services over emerging Sixth Generation (6G) networks and Internet of Things (IoT) introducesa strict requirementthetimely and reliable allocation of medical resources. Prediction of resource allocation efficiency based on rule-based or manual policies often fails to be adaptive to heterogeneous demands and dynamic conditions of IoT networks. To address this challenge, a multi-model regression-based approach is proposed to predict the efficiency of resource allocation for optimizing the MR infrastructures of IoT and 6G networks. The approach consists of data pre-processing, exploratory data analysis, multi-model regression learning, and operational factors interpretation. First, the dataset is loaded and non-informative identifier attributes are removed to reduce noise and improve generalization. Correlation analysis is performed through a heat map plot of numerical features to identify features that are strongly related to the target variable. Extensive experiments are conducted on a publicly available dataset to evaluate the proposed approach according to a number of performance metrics, such as the root mean square error (RMSE), determination coefficient (R-squared), and mean absolute error (MAE). Experimental results showed that the best regression model of proposed approach attains the highest prediction performance compared with other models and state-of-the-art work. In addition to predictive superiority, interpretation of best model’s outputs regarding to throughput and utilization of the network is reported to show the association between predicted efficiency, network speed, and utilization status, which will help to design an actionable plan for deploying intelligent allocation policies.

  Keywords

Medical Resource Allocation, Internet of Things (IoT),Sixth Generation (6G) Networks, Multi-model Regression, Coefficient Determination