Wireless Sensor System for Prediction of Carbon Monoxide Concentration using Fuzzy Time Series

Abstract

Carbon monoxide (CO) concentration produced from incomplete material burning affects both work health and safety. A smart system capable of early detection of carbon monoxide (CO) concentration is therefore required. This research develops a carbon monoxide sensor detection capability using a wireless sensor system that transmits data to the web server via internet connection. A semiconductor CO sensor is installed in a remote terminal unit. A computer application is developed for data acquisition and sending via online and in real time to a web server using an internet modem. For a web-based prediction of CO concentration, a Fuzzy Time Series algorithm induced by Pritpal Sing matrix is used. This research uses CO concentration data for two months. The resulting carbon monoxide concentration prediction is displayed in real time on a dashboard. This prediction is for the next day's forecast. Results show that the Fuzzy Time Series that is induced by Pritpal Sing matrix has an average error of 2.67 %, calculated with its average forecasting error rate (AFER). This error value varies, depending on the number of data and data characteristics.

Integration of internet of things with wireless sensor network

“…b. ETSI M2M TC: Focuses on M2M standardization, definition, examples of applications using it and analysis of demand and requirement of this technology. As mentioned in "Application Study of Internet of Things in Home", Tele communications Science [5], [6], [7], the centre for information aggregation is the gateway and it should be deigned with one major features such as protocol conversion for one system to another, device addressing and verification by authenticating mechanisms, collection of information, state control so on. In another proposal called "Web of Things [8]", an intelligent gateway will be devised which will access the physical objects and turn them to RESTful resources.…”

Web Based Information System of Carbon Monoxide Pollution

“…The forecasting of air quality became popular, many methods have been purposed for forecasting air quality such as hidden Markov model [15], first-order and one-variable grey model [16], developed support vector machine [17], fuzzy time series model [18][19] [25], Solar Radiation [26], and Fuzzy-AHP [27].…”