Skewness-aware clustering tree for unevenly distributed spatial sensor nodes in smart city

Efficient spatial index is essential for querying spatial sensor nodes in the context of smart city. Sensor nodes are usually unevenly distributed in real situations. In this setting, R-tree and its variants may cause large overlap and coverage among branch nodes, which impact the query efficiency greatly. To address this challenge, this paper proposes a novel skewness-aware clustering tree (SWC-tree) by clustering sensor nodes. Sensor nodes in a dense region will be put into the same node. Thus, overlap and coverage among node regions are less than that of R-tree and its variants. As dense regions contain more sensor nodes, we assign a higher priority to these region nodes for facilitating the query operation. Experimental results show that in the context of skewed distribution, SWC-tree is efficient in performance for conducting insertion, deletion, and query operations of sensor nodes.