IoT-Based Environmental Monitoring System with Machine Learning for Accurate and Real-time Data Analysis

Abstract

The integration of IoT with machine learning has revolutionized environmental monitoring, enabling precise and real-time data assessment. The investigations seeks to systematically review the advancements and barriers connected with IoT-based environmental monitoring systems, particularly those employing machine learning techniques for enhanced data processing and prediction accuracy. The research focuses on various applications, including air and water quality monitoring, weather prediction, and pollution forecasting. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 28 relevant studies were analyzed from reputable journals and conferences spanning from 2017 to 2024. The findings indicate significant progress in developing IoT and machine learning-driven environmental monitoring systems, with a trend towards integrating AI for better accuracy and reliability. However, the review highlights the need for more comprehensive frameworks that address the challenges of data handling, real-time processing, and scalability. The report accentuates the value of maintaining data privacy concerns and ensuring the interoperability of IoT devices across different platforms. Furthermore, the analysis reveals a noticeable gap in the literature regarding the standardization of these systems across different environmental parameters and geographies, which poses a challenge for widespread adoption. This review provides a critical analysis of current technologies and alludes to directions for further investigation, particularly in enhancing the robustness and generalizability of these systems for global environmental monitoring.