An Invulnerability Algorithm for Wireless Sensor Network\u27s Topology Based on Distance and Energy

To improve the topological stability of wireless sensor networks, an anti-destructive algorithm based on energy-aware weighting is proposed. The algorithm takes the Weighted Dynamic Topology Control (WDTC) algorithm as a reference, and calculates the weight of nodes by using the distance between nodes and the residual energy of nodes. Then chooses optimal weights and constructs a stable balanced topological network with multiple-connectivity paths using the K-connection idea. The simulation results show that the proposed algorithm improves the average connectivity of the topological network, enhances the robustness of the network, ensures the stable transmission of network information, and optimizes the betweenness centrality of the network nodes, making the network has a good invulnerability

Scale-free topology optimization for software-defined wireless sensor networks: A cyber-physical system

Due to the limited resource and vulnerability in wireless sensor networks, maximizing the network lifetime and improving network survivability have become the top priority problem in network topology optimization. This article presents a wireless sensor networks topology optimization model based on complex network theory and cyber-physical systems using software-defined wireless sensor network architecture. The multiple-factor-driven virtual force field and network division–oriented particle swarm algorithm are introduced into the deployment strategy of super-node for the implementation in wireless sensor networks topology initialization, which help to rationally allocate heterogeneous network resources and balance the energy consumption in wireless sensor networks. Furthermore, the preferential attachment scheme guided by corresponding priority of crucial sensors is added into scale-free structure for optimization in topology evolution process and for protection of vulnerable nodes in wireless sensor networks. Software-defined wireless sensor network–based functional architecture is adopted to optimize the network evolution rules and algorithm parameters using information cognition and flow-table configure mode. The theoretical analysis and experimental results demonstrate that the proposed wireless sensor networks topology optimization model possesses both the small-world effect and the scale-free property, which can contribute to extend the lifetime of wireless sensor networks with energy efficiency and improve the robustness of wireless sensor networks with structure invulnerability

Research on the Complexity Characteristics of Urban Metro Network Based on Complex Network Theory

It is to provide decision support for later planning of metro network. Firstly, the space-L method is used to model the metro network topology. Secondly, four different indicators are used to analyze the complexity of metro network. The results show that the degree of metro network nodes in Xuzhou is generally low, and the degree distribution and power distribution are quite different. The network has no scale network properties. In Xuzhou metro network, the path between random station pairs is long, and the degree of node aggregation is low. There is a positive correlation between degree and betweenness, which can make more accurate importance assessment of the site

SPS phase control system performance via analytical simulation

A solar power satellite transmission system which incorporates automatic beam forming, steering, and phase control is discussed. The phase control concept centers around the notation of an active retrodirective phased array as a means of pointing the beam to the appropriate spot on Earth. The transmitting antenna (spacetenna) directs the high power beam so that it focuses on the ground-based receiving antenna (rectenna). A combination of analysis and computerized simulation was conducted to determine the far field performance of the reference distribution system, and the beam forming and microwave power generating systems

Reverse Intervention for Dealing with Malicious Information in Online Social Networks

Malicious information is often hidden in the massive data flow of online social networks. In “We Media'' era, if the system is closed without intervention, malicious information may spread to the entire network quickly, which would cause severe economic and political losses. This paper adopts a reverse intervention strategy from the perspective of topology control, so that the spread of malicious information could be suppressed at a minimum cost. Noting that as the information spreads, social networks often present a community structure and multiple malicious information promoters may appear. Therefore, this paper adopts a divide and conquer strategy and proposes an intervention algorithm based on subgraph partitioning, in which we search for some influential nodes to block or release clarification. The main algorithm consists of two main phases. Firstly, a subgraph partitioning method based on community structure is given to quickly extract the community structure of the information dissemination network. Secondly, a node blocking and clarification publishing algorithm based on the Jordan Center is proposed in the obtained subgraphs. Experiments show that the proposed algorithm can effectively suppress the spread of malicious information with a low time complexity compared with the benchmark algorithms

Application of complex network principles to key station identification in railway network efficiency analysis

Network efficiency analysis becomes important in railways in order to contribute towards improving the safety and capacity of the rail network, making rail travel more attractive for passengers, and improving industry practice and informing policy development. However, a physical railway network structure is a complicated system, and the operation, maintenance, and management of such a network is a difficult task which may be affected by many influential factors. By using efficiency analysis technology for a railway network, combining physical structure with operation functions can help railway industry to optimize the railway network while improving its efficiency and reliability. This paper presents a new methodology based on complex network principles that combines the physical railway structure with railway operation strategy for a railway network efficiency analysis. In this method, two network models of railway physical and train flow networks are developed for the identification of key stations in the railway network based on network efficiency contribution in which the terms of degree, strength, betweenness, clustering coefficient, and a comprehensive factor are taken into consideration. Once the key stations have been identified and analysed, the railway network efficiency is then studied on the basis of selective and random modes of the station failures. A case study is presented in this paper to demonstrate the application of the proposed methodology. The results show that the identified key stations in the railway network play an important role in improving the overall railway network efficiency, which can provide useful information to railway designers, engineers, operators and maintainers to operate and maintain railway network effectively and efficiently