A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT

Abstract

In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.