International Journal of Science, Engineering and TechnologyAuthors: Deepti Tripathi, Professor Amit Thakur
Abstract: Wireless Sensor Networks (WSNs) require efficient data routing and intelligent event detection to ensure energy conservation and timely response in critical applications. This paper proposes a hybrid method that integrates the Threshold-sensitive Energy Efficient sensor Network protocol (TEEN) with Support Vector Machine (SVM) classification for optimized event-driven data transmission and accurate decision-making. The TEEN protocol is employed to minimize energy consumption by transmitting data only when predefined hard and soft thresholds are crossed, thereby reducing redundant communication. The collected threshold-triggered data is then processed using SVM to classify events and detect anomalies with high precision. This hybrid approach enhances both the responsiveness and reliability of WSNs by ensuring that only relevant, high-quality data is analyzed, while SVM’s robust classification capability improves event detection accuracy. Simulation results indicate that the TEEN-SVM method significantly prolongs network lifetime, reduces communication overhead, and achieves superior detection rates compared to conventional routing or classification techniques alone, making it suitable for time-critical applications such as environmental monitoring, disaster management, and industrial automation.
DOI: http://doi.org/10.5281/zenodo.16925433
