2571-1075ISSN (Online)
Volume 10 , Issue 1 , PP: 18-25, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Tatiraju V. Rajani Kanth 1 * , K. Dhineshkumar 2 , Haritima Mishra 3 , Chandra Sekar P. 4
Doi: https://doi.org/10.54216/IJBES.100103
Wireless Sensor Networks (WSNs) are pivotal for real-time environmental monitoring, providing valuable data on variables like temperature, humidity, and pollution levels. However, ensuring timely and accurate data transmission and analysis remains a challenge due to resource constraints in WSNs. This study introduces a machine learning-enhanced WSN framework that leverages predictive algorithms for efficient data processing and anomaly detection in real time. By integrating machine learning models, the system can predict environmental trends, detect sensor faults, and identify unusual events, improving data reliability and reducing network load. Experimental evaluations in a simulated environment show a 40% improvement in anomaly detection accuracy and a 35% reduction in data redundancy. Furthermore, this framework achieved a 25% increase in energy efficiency, enhancing network longevity. This machine learning-optimized WSN framework provides an effective solution for continuous environmental monitoring in applications such as wildlife tracking, pollution control, and smart agriculture.
Machine Learning , Wireless Sensor Networks (WSN) , Real-Time Environmental Monitoring , Predictive Algorithms , Anomaly Detection , Data Reliability , Network Load Reduction , Energy Efficiency , Wildlife Tracking , Pollution Control , Smart Agriculture
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