DETECTING TEMPORAL AND SPATIAL ANOMALIES IN SENSOR DATA FROM SMART SYSTEMS

The detection of temporal and spatial anomalies in sensor data collected from smart systems protects operational efficiency, maximizes resource utilization, and stops system breakdowns.HVAC sensor networks in smart buildings produce massive data collections.When automated systems encounter unexpected temperature, humidity, or energy consumption changes, these temporal anomalies will disrupt their decision-making abilities.Anomalies must be identified instantly because this enables disruption prevention and increases system reliability.Traditional anomaly detection methods that use rules encounter difficulties in detecting complex non-linear patterns across dynamic systems.Unsupervised learning models deliver strong anomaly detection because they assess complex relationships within sensor information.The detection models provide improved anomaly accuracy together with lower numbers of false positives.The integration of real-time anomaly detection frameworks that combine statistical techniques with machine learning approaches is hybrid and improves system reliability and efficiency.Contextual information helps the recent anomalies to refine the anomaly detection.The key contribution of this study is that it highlights the significance of a multi-faceted anomaly detection strategy combining statistical, machine learning, and deep learning approaches for anomaly identification.These methodologies, once implemented, enable smart systems to reduce downtime and decrease energy consumption and operational performance in general.The techniques can be extended to wider areas of IoT applications, such as smart cities and industrial automation, where a massive number of sensors need to be continuously monitored and adaptively learned.