Dynamic Behavior of Interacting between Epidemics and Cascades on Heterogeneous Networks

Epidemic spreading and cascading failure are two important dynamical processes over complex networks. They have been investigated separately for a long history. But in the real world, these two dynamics sometimes may interact with each other. In this paper, we explore a model combined with SIR epidemic spreading model and local loads sharing cascading failure model. There exists a critical value of tolerance parameter that whether the epidemic with high infection probability can spread out and infect a fraction of the network in this model. When the tolerance parameter is smaller than the critical value, cascading failure cuts off abundant of paths and blocks the spreading of epidemic locally. While the tolerance parameter is larger than the critical value, epidemic spreads out and infects a fraction of the network. A method for estimating the critical value is proposed. In simulation, we verify the effectiveness of this method in Barab\'asi-Albert (BA) networks

A Scale-Free Topology Construction Model for Wireless Sensor Networks

A local-area and energy-efficient (LAEE) evolution model for wireless sensor networks is proposed. The process of topology evolution is divided into two phases. In the first phase, nodes are distributed randomly in a fixed region. In the second phase, according to the spatial structure of wireless sensor networks, topology evolution starts from the sink, grows with an energy-efficient preferential attachment rule in the new node's local-area, and stops until all nodes are connected into network. Both analysis and simulation results show that the degree distribution of LAEE follows the power law. This topology construction model has better tolerance against energy depletion or random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure

Dynamical interplay between epidemics and cascades in complex networks

Epidemics and cascading failure are extensively investigated. Traditionally, they are independently studied, but in practice, there are many cases where these two dynamics interact with each other and neither of their effects can be ignored. For example, consider that a digital virus is spreading in a communication network, which is transferring data in the meantime. We build a model based on the epidemiological SIR model and a local load sharing cascading failure model to study the interplay between these two dynamics. In this model, when the dynamical process stops at equilibrium, the nodes both uninfected and unfailed form several clusters. We consider the relative size of the largest one, i.e. the giant component. A phenomenon is observed in both Erdős-Rényi (ER) random networks and Barabási-Albert (BA) scale-free networks that when the infection probability is over some critical value, a giant component forms only if the tolerance parameter α is within some interval $(\alpha_l,\alpha_u)$ . In this interval, the size of the remained giant component first increases and then decreases. After analyzing the cause of this phenomenon, we then present in ER random networks a theoretical solution of the key values of $\alpha_l$ and $\alpha_u$ , which are very important when we evaluate the robustness of the network. Finally, our theory is verified by numerical simulations

Analysis of Data Transmission Method based on GSM-R Network and Teaching Platform For Wireless Network

In recent years there has been growing concern over the lack of legal protection afforded the American consumer. Comprehensive consumer protection legislation has been introduced at all levels of government, and several significant proposals have been enacted into law. One such enactment at the municipal level is the New York City Consumer Protection Law of 1969, which establishes a framework for a broad ban against unfair trade practices and vests the city\u27s Commissioner of Consumer Affairs with extensive powers of enforcement. In this note, the New York City ordinance will be analyzed and evaluated against the general background of existing consumer protection legislation in the United States

A Novel Network Optimization Scheme Based on Anti-Flocking and Improved Nash Equilibrium Algorithm

Unmanned Aerial Vehicle (UAV) has very wide application prospect in aiding terrestrial cellular network communication, but it remains a challenge to optimize UAV locations and maximize user service rate during deployment. In this paper, a novel network optimization scheme based on anti-flocking model and improved Nash Equilibrium (NE) algorithm is proposed by studying the problem of dynamic UAV deployment and backhaul transmission. Firstly, the UAV-adaptive algorithm based on gray wolf optimization (U-GWO) is used to predeploy UAVs with limited number of UAVs. Secondly, ground mobile users are tracked by building a UAV-based anti-flocking (U-AF) model. Then, during the tracking of ground users by UAV, an improved NE strategy is used to establish the backhaul transmission links between UAVs, ground BSs and other UAVs to ensure that deployed UAVs can maximize the service rate and effective backhaul transmission rate of ground users. Simulation results show that the average service rate of User Equipment (UE) with U-GWO algorithm is improved from 1 % to 5.77 % compared to other different swarm intelligence optimization algorithms. And the service rate obtained with U-AF algorithm is 43.2 % improved compared to the baseline scenario without U-AF algorithm. For UAV backhaul transmission link construction, the simulation results show that the proposed improved NE strategy improves the average effective backhaul transmission rate by 12 %, the minimum backhaul transmission rate by 84 % and the overall iteration number by 5 % on average compared to a pure NE strategy