Abstract
Environment monitoring is an important area apart from environmental safety and pollution control. Such monitoring performed by the physical models of the atmosphere is unstable and inaccurate. Machine Learning (ML) techniques on the other hand are more robust in capturing the dynamics in the environment. In this paper, a novel approach is proposed to build a cost-effective standardized environment monitoring system (IoT-EMS) in volunteer computing environment. In volunteer computing, the volunteers (people) share their resources for distributed computing to perform a task (environment monitoring). The system is based on the Internet of Things and is controlled and accessed remotely through the Arduino platform (volunteer resource). In this system, the volunteers record the environment information from the surrounding through different sensors. Then the sensor readings are uploaded directly to a web server database, from where they can be viewed anytime and anywhere through a website. Analytics on the gathered time-series data is achieved through ML data modeling using R Language and RStudio IDE. Experimental results show that the system is able to accurately predict the trends in temperature, humidity, carbon monoxide level, and carbon dioxide. The prediction accuracy of different ML techniques such as MLP, k-NN, multiple regression, and SVM are also compared in different scenarios.
Highlights
Environment monitoring is the task of recording atmospheric parameters over a period of time and at a specified location [1,2,3,4,5]
A separate course of action is followed for regression-based algorithms (SVM and Multiple Regression) and for classification-based algorithms (MLP and k-Nearest Neighbor (k-neural network (NN)))
Machine Learning (ML) techniques are used for acquiring analytics on the gathered data
Summary
Environment monitoring is the task of recording atmospheric parameters over a period of time and at a specified location [1,2,3,4,5]. IoT creates a platform which integrates physical world with the computerbased system This platform provides an efficient, accurate, and economic approach towards environment monitoring and reduces human interference. This phenomenon comprises of tiny machines which have the ability to sense, respond, collect data and compute, and connect to the Internet
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