Distance-Based Routing Strategy for Traffic Transport in Spatial Networks

It is well known that routing strategies based on global topological information is not a good choice for the enhancement of traffic throughput in large-scale networks due to the heavy communication cost. On the contrary, acquiring spatial information, such as spatial distances among nodes, is more feasible. In this paper, we propose a novel distance-based routing strategy in spatial scale-free networks, called LDistance strategy. The probability of establishing links among nodes obeys the power-law in the spatial network under study. Compared with the LDegree strategy (Wang et al., 2006) and the mixed strategy (a strategy combining both greedy routing strategy and random routing strategy), results show that our proposed LDistance strategy can further enhance traffic capacity. Besides, the LDistance strategy can also achieve a much shorter delivering time than the LDegree strategy. Analyses reveal that the superiority of our strategy is mainly due to the interdependent relationship between topological and spatial characteristics in spatial scale-free networks. Furthermore, along transporting path in the LDistance strategy, the spatial distance to destination decays more rapidly, and the degrees of routers are higher than those in the LDegree strategy. 1. Introduction In the last few years, the analysis and modelling of dynamics in networked systems have attracted much attention in the field of theoretic physics [1–3]. Such networked systems include the Internet, high-way networks, airline networks, and social, biology, and some other infrastructure networks. In some real networks such as the Internet [4], electric-power grid [5], and airline networks [6], each node has its individual precise position in the space and the spatial distances among nodes cannot be arbitrarily neglected. Moreover, the spatial distances among nodes are not identical. In such networks, the network is embedded into a space with some (e.g., Euclidean) metric. This is why people usually call these networks spatial networks [7]. Until now, most previous work only focused on the effects of topological characteristics on dynamical occurring in networks, while the effects of spatial characteristics begin to attract much attention only in recent years. It has been reported that in real networks, the topological and spatial characteristics are closely related [8]. Two nodes close to each other are likely to be connected even though both nodes have low degrees, whereas there may not exist any link between two high-degree nodes far away from each other. For example, in an airline