Open Access

ARTICLE

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

by C. Rajinikanth¹, P. Selvaraj², Mohamed Yacin Sikkandar³, T. Jayasankar⁴, Seifedine Kadry⁵, Yunyoung Nam^6,*

1 Department of Electronics and Communications Engineering, Erode Sengunthar Engineering College (Autonomous), Erode, 638057, India
2 Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, India
3 Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
4 Department of Electrical Communication Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, 620024, India
5 Faculty of Applied Computing and Technology (FACT), Noroff University College, Kristiansand, 4608, Norway
6 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Yunyoung Nam. Email:

Computers, Materials & Continua 2021, 69(2), 2013-2029. https://doi.org/10.32604/cmc.2021.018719

Received 19 March 2021; Accepted 20 April 2021; Issue published 21 July 2021

Abstract

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.

---

Keywords

---