Nowadays, with the rapid urbanisation and industrialisation in the recent decades, the need of effective environmental monitoring has increased for sustainable development and public health. The work presented here is the design and implementation of an intelligent Internet of Things (IoT) environmental monitoring system, which is intended to provide real-time, accurate and scalable solutions for the acquisition and analysis of environmental data. The proposed system combines state-of-the-art sensor networks with embedded intelligence to monitor important environmental parameters such as air quality, temperature, humidity and noise levels. Remote monitoring and centralised control are achieved through the use of wireless communication protocols and cloud-based data management, which allows for early detection of environmental anomalies and trends. The main contribution of this paper is the integration of machine learning algorithms for predictive analytics and anomaly detection, improving the system's responsiveness and reliability. The modular architecture allows for easy integration of new sensors and capabilities and provides flexibility for different deployment scenarios, from urban centres to industrial zones and rural areas. Extensive field experiments and performance evaluations have demonstrated the system\\\'s effectiveness in providing high-resolution environmental insights at low power consumption and with little maintenance. This research indicates the transformative potential of intelligent IoT systems in environmental monitoring, providing a scalable and cost-effective framework for policy makers, environmental agencies and researchers. Results pave the way for future work on smart environmental management and contribute to the broader vision of sustainable, data-driven urban ecosystems.
Mohapatra et al. (Sat,) studied this question.