Data-Aware Clustering for Geosensor Networks Data Collection

Geosensor networks comprise small electro-mechanical devices that communicate over a wireless network. These devices collect environmental measures and send them to a base station. Energy consumption and data routing are critical factors for efficient geosensor networks. The usual cluster-based data routing protocols for sensor networks group the nodes based on their geographical closeness and aggregate their data to save energy. However, this clustering procedure does not produce the best data summaries. We propose to group the nodes into spatially homogeneous clusters, which consider both the geographical distance and the similarity of measurements between the nodes. Through simulated experiments, we have concluded that spatially homogeneous clusters produce better spatial zones identification and data summaries with a higher statistical quality if compared with the usual clustering methods. Besides, spatially homogeneous clustering can be seen as a tool for spatial sensor data mining, since their clusters represent the partition of the sensor field that has maximum internal homogeneity regarding the values of monitored variable. To make possible the use of our data-aware clustering proposal to collect the sensors’ data, we present a design guideline for a cluster-based data routing protocol, the HR-DASH. Palavras-chave: remote sensing, environment monitoring, spatio-temporal data, data aggregation.