ML Techniques in WSN

Machine learning techniques are valuable in wireless sensor networks (WSNs) for enhancing data analysis and network efficiency. They enable anomaly detection, identifying unusual patterns that indicate faults or intrusions. Clustering algorithms facilitate data aggregation, reducing redundancy and conserving energy. Predictive models forecast environmental changes, allowing for proactive management. Additionally, ML optimizes routing protocols and node operation, extending battery life and improving communication efficiency. Adaptive learning allows WSNs to adjust to dynamic conditions, ensuring reliability. Overall, integrating machine learning enhances decision-making, energy efficiency, and the overall performance of wireless sensor networks.