Pheromone Static Routing Strategy for Complex Networks

Effective local dynamic routing strategy for air route networks

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.

Alliance networks are the underlying structures of social systems in business, management, and society. The sustainability and dynamics of a social system rely on the structural evolutions of the topologies. Understanding the evolution sheds light on the dynamics and sustainability of a social system. Minority game models have been successfully applied across social science, economy, management, and engineering. They provide simple yet applicable modeling to articulate the evolutionary cooperation dynamics of competitive players in binary decision situations. By extending the minority games played in alliance networks, the cooperation in structured systems of different network topologies is analyzed. In this model, local and global score strategies are considered with and without cooperation rewiring options. The cooperation level, the score, and the topological properties are investigated. The research uses a numerical simulation approach on random networks, scale-free networks, and small-world networks. The results suggest that the network rewiring strategy leads to higher systemic performance with a higher score and a higher level of stability in decision-making. Competitive decision-making can lead to a higher level of cooperation from a poor initial start. However, stubbornness in decision-making can lead to a poor situation when cooperation is discouraged. Players with local or global information adopt local and global score strategies. The results show that local strategies might lead to imbalance, while a global strategy might achieve a relatively stable outcome. This work contributes to bridge minority games in structured networks to study the cooperation between formation and evolution, and calls for future minority game modeling on social networks.

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This paper first constructs a tiered network model of the interbank market. Then, from the perspective of contagion risk, it studies numerically the resilience of four types of interbank market network models to shocks, namely, tiered networks, random networks, small‐world networks, and scale‐free networks. This paper studies the interbank market with homogeneous and heterogeneous banks and analyzes random shocks and selective shocks. The study reveals that tiered interbank market networks and random interbank market networks are basically more vulnerable against selective shocks, while small‐world interbank market networks and scale‐free interbank market networks are generally more vulnerable against random shocks. Besides, the results indicate that, in the four types of interbank market networks, scale‐free networks have the highest stability against shocks, while small‐world networks are the most vulnerable. When banks are homogeneous, faced with selective shocks, the stability of the tiered interbank market networks is slightly lower than that of random interbank market networks, whereas, in other cases, the stability of the tiered interbank market networks is basically between that of random interbank market networks and that of scale‐free interbank market 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.

Modelling heterogeneous information spreading abilities of social network ties

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

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R(0), does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only.

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.

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

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.

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.

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.