Narrow Results

Network navigation is one of the main problems in large communication networks. We propose a new routing strategy in which some smart nodes in networks deliver messages to next hops on the paths towards destinations according to Yan's algorithm while the other nodes just deliver messages randomly. We test our routing strategy in a large scale-free network. Simulations show that the average delivery time decreases with increase of number of smart nodes, while the maximal network capacity increases with number of smart nodes in the network. Moreover our strategy is much more efficient when employed with target selection than with random selection of the smart nodes.

Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.

With the increasing complexity and demand for transportation networks, effective routing planning has attracted more and more attention. Different modes of transportation, such as the airplane, railway and so on, work together, forming a multi-modal transportation network. Therefore, this paper studies the routing and congestion problems in the multi-modal transportation network, and shows how to increase the network capacity as much as possible while saving time and economic costs, so as to avoid congestion and realize the effective use of different modes of transportation. This paper simulates the influence of two main factors on the network capacity, the parameter which shows the importance between time costs and economic costs, and the difference between different transportation modes. The results show the change of the network capacity when the time cost and economic cost are of different importance. There exists a critical point that can balance time costs and economic costs, so as to maximize network capacity. Then this paper further finds out the method of estimating the condition when the network takes the maximum capacity through theoretical analysis.