Combined Banzhaf & Diversity Index (CBDI) for critical node detection

The identification of critical nodes in the network is of great significance for the research of the invulnerability and structural stability of complex networks. Most of the existing studies evaluate the importance of nodes in networks from a single structural perspective. Considering the local and global characteristics of the network, an identification algorithm of critical nodes in complex networks based on comprehensive topological importance (CTI) is proposed. The algorithm uses the theory of multiple attribute decision making (MADM) to quantitatively analyze the influence of different attributes on nodes and determine the comprehensive topology importance of nodes, which represents their structural importance in complex networks. Based on two simulation networks and five classical real networks, the proposed algorithm is proved to be superior to DC, LLS, P and WL indexes based on local information and BC indexes based on global information through the change of network efficiency under critical nodes attacking.

Several active queue management (AQM) schemes have been proposed to address the bufferbloat issue, a long network delay and performance degradation issue caused by excess buffering of packets in network nodes. The solutions proposed up to now, however, satisfy only a subset of the key network performance metrics, i.e., end-to-end throughput, throughput fairness, and delay, in 802.11s-based multi-radio multi-channel wireless mesh networks (MRMC WMN). In this letter, we propose FD-AQM, a fairness-aware delay-controlled AQM to satisfy all three key network performance metrics simultaneously in the 802.11s-based MRMC WMN. The proposed FD-AQM achieves the goal by adopting a per-queue drop interval which is recalibrated according to the average round-trip time of flows, and a drop decision mechanism which considers average throughput as well as target delay. Through simulations, we show that FD-AQM is superior to other AQM schemes in terms of throughput and fairness.

Serum protein expression profiling and bioinformatics analysis in workers occupationally exposed to chromium (VI)

In communication networks, cyber attacks, such as resource depleting attacks, can cause failure of nodes and can damage or significantly slow down the convergence of the average consensus algorithm. In particular, if the network topology information is learned, an intelligent adversary can attack the most critical node in the sense that deactivating it causes the largest destruction, among all the network nodes, to the convergence speed of the average consensus algorithm. Although a centralized method can undoubtedly identify such a critical node, it requires global information and is computationally intensive and, hence, is not scalable. In this paper, we aim to identify the most critical node in a distributed manner. The network algebraic connectivity is used to assess the destruction caused by node removal and further the importance of a node. We propose three low-complexity algorithms to estimate the descent of the algebraic connectivity due to node removal and theoretically analyze the corresponding estimation errors. Based on these estimation algorithms, distributed power iteration, and maximum-consensus, we propose a fully distributed algorithm for the nodes to iteratively find the most critical one. Extensive simulation results demonstrate the effectiveness of the proposed methods.

River drainage networks are important landscape features that have been studied for several decades from a range of geomorphological and hydrological perspectives. However, identifying the most vital (critical) nodes on river networks and analyzing their relationships with geomorphic and climatic properties have not yet been extensively addressed in the literature. In this study, we use an algorithm that determines the set of critical nodes whose removal results in maximum network fragmentation and apply it to various topologies of simulated and natural river networks. Specifically, we consider simulated river networks obtained from optimal channel network (OCN) approach as well as extracted networks from several natural basins across the United States. Our results indicate a power-law relationship between the number of connected node pairs in the remaining network and the number of removed critical nodes. We also investigate the characteristics of sub-basins resulted from the removal of critical nodes and compare them with those of central nodes (in the context of betweenness centrality) for both natural basins and OCNs with varying energy exponent γ to understand vulnerability and resilience of river networks under potential external disruptions.

Parallel Optical Burst Switching (POBS) is a variant of Optical Burst Switching (OBS) which is proposed as a new optical switching strategy for Ultra-Dense Wavelength Division Multiplexing (U-DWDM) to support the enormous bandwidth demand of the next generation Internet. As opposed to OBS, POBS transmits bursts in two dimensions: the wavelength dimension and the time dimension. POBS network uses an one-way resource reservation mechanism to set up the resources for each data burst transmission. The use of this mechanism may cause bursts to contend for the same resources at the same time at core (intermediate) nodes of the network. Therefore, the performance of POBS networks depends on the contention resolution policies to reach acceptable levels for bandwidth usage. These policies may increase both the cost and complexities of the core nodes in POBS networks. Most literatures on OBS networks apply contention resolution at the core nodes based on reactive strategies that are activated after contention takes place in core nodes. This study proposes the use of a proactive contention resolution technique at ingress nodes of POBS network as well as reactive contention resolution technique at core nodes for reducing the probability of burst drop in the network in order to increase the performance of the network. The simulation results show that the use of POBS network with the proposed Reactive Odd/Even Node ID Wavelength Assignment Technique (POBS-ROENIDWAT) shows a better performance in terms of reduced data loss rate and increased throughput compared to the performances of both POBS network with Sequential Wavelength Assignment Technique (POBS-SWAT) and OBS network with Burst Segmentation (OBS-BS).

Critical nodes have global significance for the network, the existing methods on the identification of critical nodes in Ad-hoc network usually based on the premise of topology is known, considering that there exists a link if the two nodes are in the other’s communication range, so the topology we get at last is relatively complex, but there are many links that aren’t exist in real which leading the identification of critical nodes with deviation. For this problem, in the paper we used Delaunay triangulation to optimize the network topology, based on identifying virtual backbone nodes by the unified connected dominating set, we measured the importance of the nodes by the average betweenness centrality. Experimental results show that the method proposed can identify the critical nodes in Ad-hoc networks effectively.

Heuristic algorithms for efficient allocation of multicast-capable nodes in sparse-splitting optical networks

This paper presents the design, implementation and evaluation of Limiting Greedy Connections (LGC), an active mechanism for controlling unresponsive connections and minimizing the degradation in network performance caused by bandwidth‐greedy applications. The primary objectives of the LGC mechanism are to limit the impact of greedy connections on a congested node, to keep a loose upper bound on the packet queue occupancy at the intermediate nodes of the network and to minimize packet loss. The LGC mechanism is evaluated for a variety of network topologies, transmitting sources and node queue parameters, using a Java‐based active network testbed. Copyright © 2003 John Wiley &Sons, Ltd.

The highway electromechanical system is an important support to ensure normal operation of the highway. Currently, the cyclical and integral maintenance strategy was mainly adopted. In order to improve the equipment utilization and economic performance, a key equipment search algorithm based on complex network structure is presented in this paper, which gives a new way with differentiation for highway electromechanical maintenance. Based on the topological structure of mechanical and electrical system, the centrality, closeness centrality and distance centrality is combined as effective indicators of critical nodes. Moreover, in order to measure the topology of network and importance of neighboring nodes, mutual information of nodes in networks is calculated via the Markov chain interaction algorithm. By analysis on this project, different conditions lead to little difference of the research result, but acquire the same effect for critical nodes analysis. This paper proposes a new method to search the critical nodes in electromechanical system from the perspective of complex network, which provides a new theoretical basis for the reasonable design of highway electromechanical maintenance.

Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion based on link quality prediction. The link's quality is classified based on the available bandwidth and latency between two network nodes. We have implemented and compared two of the most popular supervised learning based classification methods, i.e. logistic regression and support vector machine (SVM). We have compared the performance of both methods and their suitability to apply in link quality prediction. The experimental results show that SVM outperforms logistic regression for binary and multiclass classification in terms of accuracy.

This dissertation presents novel approaches to modeling and analyzing security and resource allocation in mobile ad hoc networks (MANETs). The research involves the design, implementation and simulation of different models resulting in resource sharing and security’s strengthening of the network among mobile devices. Because of the mobility, the network topology may change quickly and unpredictably over time. Moreover, data-information sent from a source to a designated destination node, which is not nearby, has to route its information with the need of intermediary mobile nodes. However, not all intermediary nodes in the network are willing to participate in data-packet transfer of other nodes. The unwillingness to participate in data forwarding is because a node is built on limited resources such as energy-power and data. Due to their limited resource, nodes may not want to participate in the overall network objectives by forwarding data-packets of others in fear of depleting their energy power. To enforce cooperation among autonomous nodes, we design, implement and simulate new incentive mechanisms that used game theoretic concepts to analyze and model the strategic interactions among rationale nodes with conflicting interests. Since there is no central authority and the network is decentralized, to address the concerns of mobility of selfish nodes in MANETs, a model of security and trust relationship was designed and implemented to improve the impact of investment into trust mechanisms. A series of simulations was carried out that showed the strengthening of security in a network with selfish and malicious nodes. Our research involves bargaining for resources in a highly dynamic ad-hoc network. The design of a new arbitration mechanism for MANETs utilizes the Dirichlet distribution for fairness in allocating resources. Then, we investigated the problem of collusion nodes in mobile ad-hoc networks with an arbitrator. We model the collusion by having a group of nodes disrupting the bargaining process by not cooperating with the arbitrator. Finally, we investigated the resource allocation for a system between agility and recovery using the concept of Markov decision process. Simulation results showed that the proposed solutions may be helpful to decision-makers when allocating resources between separated teams.

The topology of multi-region opportunistic sensor networks is evolving, and it is difficult to identify the key nodes in the networks by traditional key node identification methods. In this paper, a novel method based on the improved TOPSIS method is proposed to identify the key node from the ferry node. The dynamic topology information is represented by the graph model which is modeled by the temporal reachable graph. Based on the temporal reachable graph, three attributes are constructed to identify the key node, which are average degree, betweenness centrality and message forwarding rate. The game theory with a combination weighting method is employed to combine the subjective weight and objective weight, so as to obtain the combined weight of each attribute. The TOPSIS method is improved by the combined weight. The key node is identified by the improved TOPSIS. The experiments in three simulation situations show that, compared with the TOPSIS method and MADM_TOPSIS method, the proposed method has better accuracy for the key node identification in the network.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

The Asian social networks are dominated by the society’s collectivist culture, and this interestingly introduces an influence mechanism aided by word-of-mouth and opinion leaders. An opinion leader can help to generate and shape other people’s opinion and achieve a high information spread on any topic. In this work, a modified spider monkey optimization based opinion leader detection approach is proposed. Firstly, we employ the modified node2vec graph embedding to generate the lower dimensional vectors which act as the initial features for the nodes in a typical Asian social network. Next, the entire population is broken down into several groups using the k-means++ algorithm where the number of clusters is equal to the number of opinion leaders to be selected. The local and global leaders are chosen by using the coordinates of the cluster centres of these clusters. The coordinates of the centroids of the clusters are then used to detect the local and global leaders in the network. The local leaders then form the seed set of opinion leaders for the network. The positions of the nodes in the network, including the local and global leaders, are updated over a number of iterations. At the end of these iterations, the seed set generating the maximum influence forms the set of opinion leaders in the network. We test our proposed approach using the popular information diffusion and cognitive opinion dynamics (COD) models. We perform intensive experiments on several real-life social networks based on various performance metrics. The results obtained reveal that the proposed approach outperforms several existing techniques of opinion leader detection.