Clustering coefficient analysis in large wireless ad hoc network

Abstract

Large wireless networks such as Internet of Things and Cyber-Physical Systems emerge as a new technology challenge in networking, particularly for scalability. Researchers recently note the potential of social network analysis to design wireless networks more efficiently. In this paper, we present a very original view to take advantage of clustering coefficient in social network analysis to develop more effective ad hoc networking without the need of global network information. We start from random geometric graph to view the communication range of a wireless node as a disc, which is equivalent to examining the outage probability of links. Under stochastic geometry analysis of interference, the impacts of density and traffic of nodes on the connectivity can be therefore analyzed. Similar to the structural holes in social networks, the ad hoc networks suffer from the void region problem in the geographical routing, which is even more harmful in ultra dense networking environments. Discarding the detailed global routing table, we surprisingly show that the local estimation of clustering coefficient of nodes can significantly improve the void problem handling to result in more effective ad hoc networking.