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

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