Advances in Fuzzy-Based Internet of Medical Things (IoMT)

12IoMT Type-2 Fuzzy Logic Implementation

Sasanko Sekhar Gantayat¹*, K. M. Pimple² and Pokkuluri Kiran Sree³

¹Department of CSE (Honors), Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, Andhra Pradesh, India

²Department of Electronics and Telecommunication Engineering, Dr. Rajendra Gode Institute of Technology and Research, Amravati, India

³Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India

Abstract

Monitoring data streams lays the groundwork for creating clever context-aware apps. Multiple wireless sensors might be dispersed across a localized region and keep an eye on environmental variables to spot disasters like fire and flood. Measurements are sent to a back-end system, which then makes determinations about the presence or absence of irregularities that might have unfavorable consequences. A system present using data streams from several sensors can accurately identify events as they happen in real time. Time series prediction is used in the proposed framework to derive upcoming insights from total values and contextual information over consensus theory to efficiently aggregate data. A second type of fuzzy inference method is used to precisely identify events from the unanimously merged and forecasted components of context. Reasoning skills under uncertainty of phenomenon identification are provided by the type-2 inference process. The effectiveness of a ...