Towards A Better Sensor Data Accuracy Via Quality Of Monitoring And Semantic Clustering

Semantic clustering organizes wireless sensor nodes in clusters to detect a relevant event for an application. These nodes send only a message reporting it to the sink node while the other nodes decrease their resource utilization to save energy. In this work, we enhance semantic clustering with quality of monitoring (QoM) attributes of the event detection probability with variance reduction or dissimilarity measure. This approach allows each node to evaluate the accuracy of detection and the correlation level of the data gathered locally and by its neighbors. Experiments in a WLAN and FIT/IoT-Lab show QoM attributes improve the accuracy, especially in an extensive network where dissimilarity measure can keep it high independently of the cluster size. QoM also increases the sampling of these events by up to 60.7% while it decreases the number of messages sent to the sink between 13.1% and 32.9% without affecting the cluster formation or power consumption in almost all experiments.