Energy Aware Clustering with Medical Data Classification Model in IoT Environment

Abstract

With the exponential developments of wireless networking and inexpensive Internet of Things (IoT), a wide range of applications has been designed to attain enhanced services. Due to the limited energy capacity of IoT devices, energy-aware clustering techniques can be highly preferable. At the same time, artificial intelligence (AI) techniques can be applied to perform appropriate disease diagnostic processes. With this motivation, this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification (SSAC-MDC) model in an IoT environment. The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment. The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering (SSAC) technique to choose the proper set of cluster heads (CHs) and construct clusters. Besides, the medical data classification process involves three different subprocesses namely pre-processing, autoencoder (AE) based classification, and improved beetle antenna search (IBAS) based parameter tuning. The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work. For showcasing the improved performance of the SSAC-MDC technique, a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.