Granular location-based cluster in wireless sensor networks

Abstract

The capability of sensor nodes to collect ambient data makes environmental monitoring as one of the prime applications of wireless sensor networks. Mobile sink traverses an area of interest in collecting data. With the help of virtual infrastructure (VI), an area is divided into cluster. However, the nature of the structure causes various sizes of localized cluster; coarser as a cluster is farther from the center (distance dependent). This causes inconsistency and approximate in cluster-based localization. Distance independent clustering provides consistent granularity which creates almost equal size of clusters called enhanced VI (eVI) is proposed. Applications that can benefit from this granularity include accuracy in aggregation function, and shorter safe path charting. Our previous work intelligently handled out-of-bound sensor nodes that reside outside an area of interest. However, the excluded region varies due to the structure. Distance independent clustering can enhance the exclusion process further for efficient data collection of the queried area through dividing the cluster into smaller sub-cluster appropriately. Additionally, utilizing the eVI for autonomous navigation enables mobile sink to detect more precise location of dangerous object and thus result shorter safe travelled distance. Simulation result demonstrates that the granular clusters perform better than coarse-grained clusters.