Probability routing strategy for scale-free networks

The performance of routing strategies on complex networks can be characterized by two measurements, i.e., the traffic capacity and the average packets travel time. By efficiently synthesizing the degree and the dynamic queue length of nodes, we propose the global hybrid (GH) routing strategy. It can achieve higher traffic capacity, as well as shorter average packets, travel time compared with the state-of-the-art global dynamic (GD) routing strategy and efficient routing (ER) strategy. Moreover, such superiority can be maintained through the queue length information and the corresponding routing paths are updated periodically. The simulation results show that our GH routing strategy can provide the same traffic capacity as the GD routing strategy does, which is more than twice as high as the ER strategy. At the same time, the average packets travel time of the GH routing strategy is more than 20% smaller than that of the GD routing strategy. It is worth noted that longer updating delay makes our GH routing strategy have a greater decline in the average packets travel time. With the updating delay equals 100, the decline can be up to 40%. To illustrate the practicability of our GH routing strategy, we also applied it to a scale-free network-based data center network. The simulation results reveal that it is practical, effective, and can be used in real scenarios to improve network performance.

This paper investigates the impact of network structure on latency in complex networks under two different routing strategies. The performances of both scale-free networks and random networks are compared and evaluated under the shortest path routing strategy and least cost routing strategy. Our study shows that under the shortest path routing strategy, the scale-free network fares much worse compared to the random network. Conversely, under the least cost routing, the performance of the scale-free network far exceeds that of the random network because of its unique network structure.

Opportunistic network is the current area of research which facilitates message delivery between the nodes where there is no end-to-end connectivity. The most of the existing routing schemes in Opportunistic networks consume high resources and leads to decline in network efficiency. So, designing an efficient routing scheme for OppNets always remains a stimulating task due to high mobility of nodes, absence of an end-to-end connectivity and the lack of further knowledge about the network topology. In this research paper we propose a novel Fuzzy based Social relationshipAware Routing (F-SAR) scheme in which the decision of the best forwarder to forward the message depend on fuzzy inference system considering residual energy, buffer availability and social relationship of nodes. This routing scheme is simulated in “The ONE” simulator and the results shows that it outperforms SnW, Prophet, and FLDEAR in terms of delivery rate, message transmission overhead, and average latency

We propose a new scheme for optical signal routing within linear lightwave network (LLN) subnets. The LLN is a fiber optic network which performs only linear operations on optical signals: power combining, splitting and possibly linear optical amplification. The LLN can be partitioned into several subnets where each subnet is a tree providing full broadcast among all stations connected to it. We study the synchronization problem that exists in these subnets which prevents efficient implementation of time division multiple access schemes for sharing a common broadcast medium. A solution to this problem is proposed, based on a new optical signal routing scheme, called rooted routing. The impact of rooted routing on power losses is analyzed, and an approach for minimizing power loss in LLNs with rooted routing is presented. It is shown that when link and excess losses are small, rooted routing power budget can be made close to the power budget yielded by the original, shortest path routing scheme. It is also shown that rooted routing power budget can be significantly improved using a single optical amplifier.

Software-Defined Vehicular Networks (SDVNs) technology has been attracting significant attention as it can make Vehicular Ad Hoc Network (VANET) more efficient and intelligent. SDVN provides a flexible architecture which can decouple the network management from data transmission. Compared to centralized SDVN, hybrid SDVN is even more flexible and has less overhead. This hybrid technology can eliminate the burden on the central controller by moving regional routing tasks from the central controller to local controllers or vehicular nodes. In the literature, different routing protocols have been reported for SDVNs. However, these existing routing protocols lack flexibility and adaptive approaches to deal with changing and dynamic traffic conditions. Thus, this paper proposes a new software-defined routing method, namely, Novel Adaptive Routing and Switching Scheme (NARSS), deployed in the controller. This adaptive method can dynamically select routing schemes for a specific traffic scenario. To achieve this, this paper firstly presents a method for collecting road network information to describe traffic condition where the method extracts the feature data used to generate the routing scheme switching model. Secondly, we train the feature data through an artificial neural network with high training speed and accuracy. Finally, we use the model as a basis for establishing the NARSS and deploy it in the controller. Simulation results show that the proposed scheme outperforms the single traditional routing protocol in terms of both packet delivery ratio and end-to-end delay.

In randomly-deployed wireless mission critical networks, the crucial steps of ad hoc route setup and node localization are vulnerable to various security breaches and attacks. In this paper, we introduce SIRLoS, a lightweight secure integrated routing and localization scheme which addresses this problem by exploiting the security benefits of link directionality in directed networks that have found popularity for multimedia networking. SIRLoS is a circuit-based algorithm that leverages the resources of the base station and the hierarchical structure of the network to reconstruct the graph of the network, and detect any security violations in the neighborhood discovery and routing schemes. We demonstrate the performance of our algorithm, and provide security and attack analysis.

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Efficient packet transportation on complex networks with nonuniform node capacity distribution

Existing routing schemes in opportunistic networks can be classified two categories: multi-copy based schemes and single-copy based schemes. Multi-copy based schemes can increase the robustness of networks and reduce average delay. However, they consume a large amount of network resources and induce large overhead. Single-copy based schemes can save network resources, but it is difficult to guarantee the transmission delay and packet delivery ratio for these schemes. Hence, how to design an efficient routing scheme which can make a tradeoff between multi-copy schemes and single-copy schemes in opportunistic networks with limited resources is a challenging issue. Inspired by this issue, in this paper, we propose a novel routing scheme based on game theory in opportunistic networks under limited resources, named GTR. In GTR, we map the message transmission to a multi-player bargaining model. By constructing proper utility function, GTR can save the network resources, and achieve better performance in dense opportunistic networks. Our extensive simulation results also demonstrate this point, and GTR performs better compared to some classical routing schemes in terms of packet delivery ratio, average delay, overhead ratio and average hops.

In this paper, we present a methodology employing statistical analysis and stochastic geometry to study geometric routing schemes in wireless ad hoc networks. In particular, we analyze the network-layer performance of one such scheme, the random [ 1/ 2]disk routing scheme, which is a localized geometric routing scheme in which each node chooses the next relay randomly among the nodes within its transmission range and in the general direction of the destination. The techniques developed in this paper enable us to establish the asymptotic connectivity and the convergence results for the mean and variance of the routing path lengths generated by geometric routing schemes in random wireless networks. In particular, we approximate the progress of the routing path toward the destination by a Markov process and determine the sufficient conditions that ensure the asymptotic connectivity for both dense and large-scale ad hoc networks deploying the random [ 1/ 2]disk routing scheme. Furthermore, using this Markov characterization, we show that the expected length (hop count) of the path generated by the random [ 1/ 2]disk routing scheme normalized by the length of the path generated by the ideal direct-line routing, converges to 3π/4 asymptotically. Moreover, we show that the variance-to-mean ratio of the routing path length converges to 9π <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /64-1 asymptotically. Through simulation, we show that the aforementioned asymptotic statistics are in fact quite accurate even for finite granularity and size of the network.

The routing strategy is important to information dissemination and exchange in complex communication networks. The traditional routing strategies are often insufficient to alleviate congestion and enhance the utilization efficiency of two-layer complex networks. In this paper, a novel efficient static weighted routing strategy is proposed. The weight of the logical edges mapped on the hub nodes at the physical layer is assigned a higher weight according to the degree of nodes. Then packets choose the routing path with the minimum sum weight of logical edges. It is found that the average packet transmission time and average throughput have been improved greatly compared to the shortest path routing strategy in two-layer complex networks.

Effective local dynamic routing strategy for air route networks

Effects of efficient edge rewiring strategies on network transport efficiency

How to enhance the network capacity is one of the most important issues. To achieve this, the existing works have focused on improving either the network structure or routing strategies with a common assumption of uniformly distributing the replicas of information among the nodes in the network. The nodes associated with information replicas are considered as source nodes (or server). However, for many networks such as the Internet, some nodes have much more traffics than the others, exhibiting an asymmetric phenomenon. In this letter, we study the optimal source selection strategy to enhance the network capacity, where an optimization model is proposed to find the optimal source selection probability distribution. Simulation results show that in homogeneous networks, most of the nodes can be the sources. While in heterogeneous networks such as the scale-free networks, only a small number of the nodes can be the sources. Moreover, an interesting phenomenon is observed that the optimal proportion of source nodes in Erdös-Rényi random network and Barabási-Albert scale-free network exhibits a power law relationship with the network size.

We present a link capacity estimation model and a hybrid routing scheme for wireless mesh networks which aims at improving throughput. The multi-source interference between links and frame capture behaviors of nodes are analyzed. Our model provides estimated values which can indicate the relative quality of wireless links. And we present a routing metric which combines multiple factors based on the estimated values. The proposed routing scheme which is independent of special routing protocols adopts different strategies for the traffic of accessing the Internet and the end-to-end traffic. Besides, we introduce a new gateway-assisted routing technique to further improve the performance. Simulation results show that the estimation model provides accurate estimated values enough for route selection, and the routing scheme improves end-to-end throughput by 185% compared with legacy AODV[14] protocol.