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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.

In order to alleviate traffic congestion on multilayer networks, designing an efficient routing strategy is one of the most important ways. In this paper, a novel routing strategy is proposed to reduce traffic congestion on two-layer networks. In the proposed strategy, the optimal paths in the physical layer are chosen by comprehensively considering the roles of nodes’ degrees of the two layers. Both numerical and analytical results indicate that our routing strategy can reasonably redistribute the traffic load of the physical layer, and thus the traffic capacity of two-layer complex networks are significantly enhanced compared with the shortest path routing (SPR) and the global awareness routing (GAR) strategies. This study may shed some light on the optimization of networked traffic dynamics.

The network topology and the routing strategy are major factors to affect the traffic dynamics of the network. In this work, we aim to design an optimal time-varying network structure and an efficient route is allocated to each user in the network. The network topology is designed by considering addition, removal and rewiring of links. At each time instant, a new node connects with an existing node based on the degree and correlation with its neighbor. Traffic congestion is handled by rewiring of some congested links along with the removal of the anti-preferential and correlated links. Centrality plays an important role to find the most important node in the network. The more a node is central, the more it can be used for the shortest route of the user pairs and it can be congested due to a large number of data coming from its neighborhood. Therefore, routes of the users are selected such that the sum of the centrality of the nodes appearing in the user’s route is minimum. Thereafter, we analyze the network structure by using various network properties such as the clustering coefficient, centrality, average shortest path, rich club coefficient, average packet travel time and order parameter.

The rise of socio-technical systems in which humans interact with various forms of Artificial Intelligence, including assistants and recommenders, multiplies the possibility for the emergence of large-scale social behavior, possibly with unintended negative consequences. In this work, we discuss a particularly interesting case, i.e., navigation services’ impact on urban emissions, showing through simulations that the sum of many individually “optimal” choices may have unintended negative outcomes because such choices influence and interfere with each other on top of shared resources. To prove this point, we demonstrate how the introduction of a random component in the path suggestion phase may help to relieve the effect of collective and individual choices on the urban environment in terms of urban emissions.

Multimedia applications present new challenges to the current networking technology. One of them is Quality of Service (QoS) requirement. Due to the increasing dynamics of traffic introduced by multimedia applications, dynamic and flexible QoS (Quality of Service) control is needed to ensure both QoS satisfaction and resource efficiency. Because integrated service networks are designed to support a wide range of traffic classes, which have different service requirements, developing metrics to evaluate them is complex. In this paper there are presented the results of some tests performed on Cisco routers in a quality of service environment.

The issue of routing is vitally important in Wireless Mesh Networks. But currently, most routing protocols such as OLSR and AODV cannot make the most of multiple paths between the source site and destination site because of the complexity and the cost. Software Defined Networking(SDN) structure promises to obtain the network configuration effectively, and with a centralized controller, it can deploy fine-grained routing algorithms to make full use of the network resources, while ensuring that the control overhead is acceptable. In this paper, we propose a new approach of SDN-based routing algorithm, or SDNR. We introduce link saturation to SDN, with which the SDN controller can figure out the congested path and reroute the following traffic to another non-congested path, which is the real-time optimal one, to ensure the network throughput. We compare SDNR to classic routing protocols and demonstrate its superiority.

Optimizing routing paths improves the network's topological properties and performance, and helps balance the network's energy. Traditional network routing algorithms use the energy balanced ring routing algorithm, which causes an uneven consumption of energy throughout the network. This paper thus proposes a method of optimizing network routing paths based on an improved ant colony algorithm with constraint feedback. First, a network model is designed, and its topology is established. The robust coefficients are determined, and route choices are used to update the GRAEB routing algorithm. Using the region segmentation method, the confidence interval of cluster routing task's random distribution is obtained. Through the ant feedback constraint, the probability of information being correctly transmitted is increased. According to Bayes' theorem, route node localization and saving of route location reliability recursive algorithm, network routing is optimized. Simulation experiments show that the algorithm can effectively increase the probability of information being transmitted correctly on the network, with transmission being almost distortion-free. As the number of routing hops increases, the advantages of this routing algorithm become more apparent, improving the quality of the information transmission.