A Fuzzy Markup Language-Based Approach for a Quality of Location Inference as An Environmental Health Awareness

Abstract

In a recent study conducted over a two-month period, the authors have concluded that there exists a correlation between two environmental parameters, namely air quality and noise. The correlation efficiency results obtained were quantitative in nature, and thus cannot be intuitively interpreted by humans. In this article, the authors propose a Fuzzy-markup language (FML) model that aims to translate the degree of correlation that may exist among IoT environmental parameters into a linguistic set of indicators. For that purpose, the authors have developed a Fuzzy-Inference System (FIS) that infers the quality of location status according to people's surroundings and provide health-aware notifications accordingly. The initial FIS results show the significance of timeframe that should be considered according to each sensory data source. In other words, the correlation degree of the two sensory set is clearly affected by time frame variations.