A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks
Large scale dense wireless sensor networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to this high density of nodes, it is very likely that both spatially correlated information and redundant data can be detected by several nearby nodes, which can be exploited to save energy. In this work we consider the problem of constructing a spatial correlation aware dynamic and scalable routing structure for data collection and aggregation in WSNs. Although there are some solutions for data aggregation in WSNs, most of them build their structures based on the order of event occurrence. This can lead to both low quality routing trees and a lack of load balancing support, since the same tree is used throughout the network lifetime. To tackle these challenges we propose a novel algorithm called dYnamic and scalablE tree Aware of Spatial correlaTion (YEAST). Results show that the routing tree built by YEAST provides the best aggregation quality compared with other evaluated algorithms. With YEAST an event can be sensed with 97% accuracy, and 75% of the nodes residual energy can be saved within the phenomena area when compared with the classical approach for data collection.
spatial correlation wireless sensor networks data aggregation routing protocol