Clustering-based Anomaly Detection for Sensor Networks: A Lightweight Density-Grounded Approach

The proposed system “DensifyGuard” stands as an innovative system designed specifically for heterogeneous sensor networks. The networks often face challenges like limited network, power resources making it to be tough in sustainable in dynamic varying conditions for standard setup. For making sure the data collection process performed by sensor nodal points will be reliable and consistent is crucial without any anomaly disruptions. The proposed DensifyGuard handles this by using a method based on clustering formation and likelihood density-based mechanism. It gathers data points that will be close together using techniques like DBSCAN. It helps DensifyGuard to find anomalies hidden in areas with low amounts of data, marking them as possible outliers. The proposed system meets this challenge head-on by using advanced techniques to detect anomalies threat even when the data quantity will not great amount. By using density-based clustering structure proposed system not only improves the accuracy of analyzing data. It also helps to make a better optimistic decision within WSNs in anomaly detection. By make use of this Density based approach, an effective mechanism will have its capability to spot anomalies quickly while also reducing the amount of computing power needed. This makes suit perfect for the use in real-world situations where resources are limited, like in Sensory networks. By combining advanced anomaly detection methods with a simple, density-focused approach, proposed DensifyGuard will brings a new level of reliability and accuracy to sensor network operations in anomaly detection.