Big data analytics for smart home energy management system based on IOMT using AHP and WASPAS

Abstract

The convergence of edge computing and 5G network speed provides an innovative way to address the energy efficiency and low latency requirements in medical data processing, especially from the perspective of the Internet of Medical Things (IoMT). Together, these technologies allow for the quick and effective handling of the enormous volumes of medical data produced by different IoMT devices in the context of smart healthcare systems. The IoMT is bringing cutting-edge technologies, social benefits, and economic advantages to transform modern healthcare systems entirely. Digital healthcare is transforming due to machine learning, which uses sophisticated algorithms to forecast patients’ health status efficiently. These approaches predict the onset of disease, hospital readmissions, and treatment customization by analyzing large medical datasets. Strong data security and good forecast accuracy are still issues. The quality and variety of training data are key factors in making accurate predictions, and strict encryption, safe storage, and regulatory compliance are necessary for data security. By including various significant components from existing research, the current study seeks to determine the most collective features. The goal of the study is to offer a systematic approach for assessing these features identified by using the approaches of the AHP and WASPAS. These approaches are effective for efficient big data analytics in the context of smart home energy management system based on IOMT.