Systas: Density-Based Algorithm For Clusters Discovery In Wireless Networks

The Internet of Things will comprise billions of randomly placed devices, firming a dense and unstructured network environment with overlapping wireless topologies. In such demanding environment, the grouping of IoT devices into clusters is a promising approach for the management and the control of network resources in the context of an autonomous system. This paper proposes the SYSTAS algorithm for the distributed discovery and formation of clusters in random geometric graphs of fixed wireless nodes by exploiting local topology knowledge and without having any information about the expected number of clusters. The density of the network graph, discovered by interacting with neighboring nodes and the topological features, as well as the model of preferential attachment are used by the proposed scheme. The effectiveness of SYSTAS is evaluated in various topologies. Experimental evaluation demonstrates that SYSTAS outperforms other clustering schemes; in some occasions these solutions have comparable results with SYSTAS but they require global network view, which leads to higher signaling cost.