Evaluation of arima and ann stream analytics for air quality monitoring system

There are many environmental monitoring systems available in the market with Internetof-Things (IoT) enabled technology. However, the existing system is not equipped with online data analytics. Some of them provide analytics but are done in offline mode through third-party software or devices known as batch analytics. Pricewise, the existing monitoring system alone is expensive even though none of them are furnished with stream analytics. The thesis presents the design and development of an accurate air quality monitoring system equipped with streaming machine learning predictive analytics called Smart Environmental System (SES). The developed SES is divided into two sections End-Node Unit (ENU) and Gateway Unit (GWU). ENU consisted of calibrated sensors of NO2, CO, CO2, PM2.5, PM10, O3, temperature humidity integrated with Raspberry Pi Single-Board Computer (SBC) and Long-Range (LoRa) Transmitter (Tx) module. Meanwhile, GWU consisted of Raspberry Pi SBC, LoRa Receiver (Rx) and 4G module. The ENU transferred the data wirelessly to the GWU through LoRa communication, and GWU stored the data immediately in MySQL, which was installed in the Linux Apache MySQL PHP (LAMP) server. Investigation on evaluating senso rs’ accuracy is executed by comparing the collected data by SES vs data from the Department of Environment (DoE). The SES’s accuracy percentage error of CO, NO2, O3, PM10 are 5.1%, 7%, 6.1% and 6% correspondingly compared to DoE. Such accuracy of sensors is acceptable with an accuracy below 10%. Once accuracy has been validated, the data stored in MySQL database is successfully exported to the R query table in R-Server by using dbGetQuery() command, checked and aligned with the MySQL database. It is observed that the data in MySQL are successfully exported to the R query table based on the similar number of variables between those two tables. The data stored in the query table act as input to the analytics algorithm, which runs in R-server as well. In this thesis, two algorithms have been implemented and compared. Auto -Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). It is identified that ARIMA has better prediction accuracy (PA) percentage of 99.45%, 99.87%, 99.75%, 98.92% for CO, NO2, O3 and PM10 over ANN thus chosen as a predictive analytics algorithm for SES. Once embedded in SES, ARIMA performances are evaluated based on Mean Absolute Percentage Error (MAPE) and Prediction Accuracy (PA). It is observed that ARIMA MAPE is 1.64%,9.67%, 9.59%, 7.09%, for CO, NO2, O3 and PM10, respectively which led PA to achieve 96.78%, 90.33%, 90.41% and 92.91% correspondingly. The results proved that the proposed SES is able to precisely predict those gases for the next 24 hours above the 90% prediction accuracy. It can be concluded the proposed SES could be implemented as a future for the Air Pollutant Index (API) system

Water surface platform for internet-based environmental monitoring system

Currently, environmental monitoring plays such an important role in human life. This research work was carried out to monitor the environment of air and water quality that displayed the data through the mobile phone or computer. This is due to several challenges while monitoring the environment such as accessibility to a site and the safety of the workers. This research work consists of several sensors that were attached to the water surface platform (WSP); carbon monoxide sensor, temperature and humidity sensor, pH sensor, and depth sensor that acts as an input. Besides that, a GPS module also attached to the platform to track down the position of the platform in terms of latitude and longitude. Through this input, the WSP also collects the data of the carbon monoxide released to determine the quality of air, while the data on the pH value and the value of temperature and humidity were collected to determine the quality of water based on Class IV- irrigation. This research work also detects the water level for flood warning and sends a warning to the authorities through Short Message Service (SMS). As a result, all the data from the sensors were successfully sent to the ThingSpeak IoT platform to be monitored by the authorities. The graph for each sensor was generated in the ThingSpeak channel to easier the authorities. The SMS of the parameters’ value also sent to the mobile phone. The power load of the WSP is 10.84W with the total time consumed of one hour and 36 seconds by using Li-ion battery. There is a slightly difference in transferring the data to the ThingSpeak channel and sending SMS due to some delays in coding part. Based on the results obtained when the WSP was deployed at FKAAS Lake in UTHM, the lake can be classified as class IV type. The long-term goal of this research is to ensure that the authorities can monitor the changes that happened on the website without the needs to be at the site

Atmospheric Pollution Interventions in the Environment: Effects on Biotic and Abiotic Factors, Their Monitoring and Control

Atmosphere is polluted for all living, non-living entities. Concentrations of atmospheric pollutants like PM2.5, PM10, CO, CO2, NO, NO2, and volatile organic compounds (VOC) are increasing abruptly due to anthropogenic activities (fossil fuels combustion, industrial activities, and power generation etc.). These pollutants are causing soil (microbial diversity disturbance, soil structure), plants (germination, growth, and biochemistry), and human health (asthma, liver, and lungs disorders to cancers) interventions. All the effects of these pollutants on soil, plants, animals, and microbes needed to be discussed briefly. Different strategies and technologies (HOPES, IOT, TEMPO and TNGAPMS) are used in the world to reduce the pollutant emission at source or when in the atmosphere and also discussed here. All gaseous emissions control mechanisms for major exhaust gases from toxic to less toxic form or environmental friendly form are major concern. Heavy metals present in dust and volatile organic compounds are converted into less toxic forms and their techniques are discussed briefly

LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring

This study proposes a smart long-range (LoRa) sensing node to timely collect the air quality information and update it on the cloud. The developed long-range wide area network (LoRaWAN)-based Internet of Things (IoT) air quality monitoring system (AQMS), hereafter called LoRaWAN-IoT-AQMS, was deployed in an outdoor environment to validate its reliability and effectiveness. The system is composed of multiple sensors (NO2, SO2, CO2, CO, PM2.5, temperature, and humidity), Arduino microcontroller, LoRa shield, LoRaWAN gateway, and The Thing Network (TTN) IoT platform. The LoRaWAN-IoT-AQMS is a standalone system powered continuously by a rechargeable battery with a photovoltaic solar panel via a solar charger shield for sustainable operation. Our system simultaneously gathers the considered air quality information by using the smart sensing unit. Then, the system transmits the information through the gateway to the TTN platform, which is integrated with the ThingSpeak IoT server. This action updates the collected data and displays these data on a developed Web-based dashboard and a Graphical User Interface (GUI) that uses the Virtuino mobile application. Thus, the displayed information can be easily accessed by users via their smartphones. The results obtained by the developed LoRaWAN-IoT-AQMS are validated by comparing them with experimental results based on the high-technology Aeroqual air quality monitoring devices. Our system can reliably monitor various air quality indicators and efficiently transmit the information in real time over the Internet