Energy Aware Clustering with Medical Data Classification Model in IoT Environment

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