Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.