Hybrid Ant Colony Optimization and Deep Neural Network Model for 5G-IoT Optimization

Abstract

The research aims to develop a hybrid Ant Colony Optimization (ACO)-Deep Neural Network (DNN) for efficient resource allocation and the minimization of path loss in an Internet of Things (IoT)-driven 5G network. IoT-driven data are collected from the Zenodo repository, containing 8,484 datasets with noisy information. The data are preprocessed using Exploratory Data Analysis (EDA) to remove outliers and missing values. The processed data are split into an 80-20% set. The training set (80%) is utilized for the ACO and DNN models, while the testing set (20%) evaluates the system’s performance. ACO is used for feature selection and identification of suitable features for learning and prediction, which feed into the DNN model for learning and prediction. The model is then evaluated with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and root square (R2) value to determine the optimal path for resource allocation and reducing signal loss in 5G networks. In the results, the ACODNN-based model outperforms the others with the lowest RMSE (0.42) and MAE (0.35). It shows high accuracy and minimal error in optimizing path loss. The ACO-DNNbased model also achieves effective resource allocation with an R2 of 0.92 and low error rates. The results underscore the efficiency of the hybridized ACO-DNNbased model for resource allocation while minimizing the path loss in the transmission link.