OPSCAN: Density-based Spatial Clustering in Opportunistic Networks

Abstract

In modern opportunistic networks, network operations can be improved through knowledge of spatial information of low and high density areas, predictions of the mobility of nodes in the space, as well as the spatial distribution of nodes. Such information can be used to adapt forwarding decisions. In this paper, we introduce an efficient opportunistic spatial clustering algorithm, OPSCAN (Opportunistic Spatial Clustering of Applications with Noise). Based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a density-based clustering algorithm that discovers arbitrary-shaped clusters in a dataset and distinguishes noise points. OPSCAN is capable of clustering network nodes into high density clusters, while maintaining sparse areas of nodes between clusters. Clusters share spatial information of the network such as area density, mobility statistics and information about other clusters and their nodes. Knowledge of edge nodes in the clusters is also made available for utilization in more efficient forwarding decisions. Simulations show that our algorithm is capable of producing dense, homogeneous clusters and accurately outlining cluster edges. We have used the Silhouette Coefficient to measure cluster homogeneity against density-based clustering algorithms DBSCAN and ST-DBSCAN (Spatial-Temporal DBSCAN), a DBSCAN-based spatial-temporal variant on GeoLife dataset. We have found OPSCAN outperforms DBSCAN by a coefficient of 0.81 to 0.73 for the same minimum distance, under-performing ST-DBSCAN by 0.87 to 0.81 for that distance. OPSCAN requires only two inputs as compared to four for ST-DBSCAN. As the distance parameter is increased, OPSCAN produces homogeneous clusters more closely to ST-DBSCAN.