Hierarchical spatial clustering in multi-hop wireless sensor networks

Abstract

In wireless sensor networks, the resource-limited sensor nodes collect and transmit sensing readings to Sink node collaboratively in multi-hop manner to conserve energy. For more energy-efficient data collection, we consider the problem of spatial clustering, which aims to group the strong correlated sensor nodes into the same cluster for rotatively reporting representative data later, and propose a hierarchical spatial clustering algorithm named as HSC. Specifically, with similarity measure on both magnitude and trend of sensing readings, HSC groups the similar sensor nodes in distributed and hierarchical manner by exploiting a pre-built data collection tree, which dispenses HSC from the extra requirements, such as global network topology and strict time synchronization, during clustering. Extensive simulation results show that HSC performs superiorly on clustering quality when compared with the alternative algorithms. Furthermore, approximate data collection scheme combined with HSC can reduce much more communication overhead while incurring modest data error than with other algorithms. In general, HSC possesses comprehensive advantage on the aspects of both clustering quality and approximation performance.