The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning

Abstract

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

“…A relevant point to consider in the implementation of ML estimation models in IoT is the architecture structure with hardware devices, networking standards and communication protocols. To develop a suitable IoT implementation, it is necessary to examine which factors affect the problem in more detail and how stable and economical the models are in real life (Gangwar et al, 2023). This perspective has a special emphasis on the cost of IoT solutions that are energy efficient and scalable to enable rapid changes to the sensing network without additional infrastructure requirements (Scislo and Szczepanik-Scislo, 2021), with the additional great challenge that is the use of low-cost and commercial off-the-self (COTS) solutions to implement the measurement setup.…”