Number Of Nodes In Network Research Articles

Оценка надежности двумерной инфокоммуникационной сети на основе мобильных сенсоров в среде распространения с плоскими границами

A method for estimating the stability of a mobile infocommunication network (MIN), which is based on a model of a maze with a size of n×m cells on a discrete plane with a square covering is considered. The stability of the MIN based on mobile intelligent devices (MIU) is estimated by the energy criterion, which takes into account both direct and reflected waves, as well as by the criterion of completeness of the maze control. To obtain quantitative estimates, transport and communication graphs models were applied, which made it possible to calculate the required number of network nodes and the average recovery time for failures of the MIU given by removing arbitrary nodes or edges of the communication graph. The results of a computational experiment for various network topologies and parameters of flat boundaries were analyzed. It was shown that the presence of signal reflections from flat boundaries increases the stability of the network, even with less hardware redundancy compared with media that have absorbing flat boundaries.

How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network

This paper investigates how various consumer behaviors influence sales volume in social commerce through information dissemination. We use a modified SIR dynamic model to study these effects and how they vary under different maximum message lifetimes and as the number of network nodes varies. We conduct parameter sensitivity analysis to measure the influences of consumer behaviors on the sales volume. The simulation results indicate that consumer behaviors during information dissemination can significantly influence the outbreak size of the information and the sales volume. Specifically, a higher information-spread probability will increase the outbreak size of the message. A higher first-purchase probability could accelerate the increase in sales volume, especially when the maximum message lifetime is long and the number of nodes is large. Besides, the regular-purchase behavior has a positive impact on the acceleration, while the discard-information behavior after the first purchase has an opposite effect. We verify our model using data collected from two websites: Sina Weibo and Taobao. This study helps social commerce marketers understand how consumer behaviors can influence sales volume through information spread and aids them in modeling this mechanism.

The trapping problem and the average shortest weighted path of the weighted pseudofractal scale-free networks

In this paper, we study the trapping time in the weighted pseudofractal scale-free networks (WPSFNs) and the average shortest weighted path in the modified weighted pseudofractal scale-free networks (MWPSFNs) with the weight factor [Formula: see text]. At first, for exceptional case with the trap fixed at a hub node for weight-dependent walk, we derive the exact analytic formulas of the trapping time through the structure of WPSFNs. The obtained rigorous solution shows that the trapping time approximately grows as a power-law function of the number of network nodes with the exponent represented by [Formula: see text]. Then, we deduce the scaling expression of the average shortest weighted path through the iterative process of the construction of MWPSFNs. The obtained rigorous solution shows that the scalings of average shortest weighted path with network size obey three laws along with the range of the weight factor. We provide a theoretical study of the trapping time for weight-dependent walk and the average shortest weighted path in a wide range of deterministic weighted networks.

Structural optimisation algorithm of weight correlation analysis for DBN

The deep belief network (DBN) algorithm has slow convergence rate and low learning efficiency, and it is sensitive to model hyper-parameters. In view of the fact that DBN is a learning network model with a large number of network nodes and multiple hidden layers, this paper proposes a weight correlation analysis algorithm for DBN (WCA-DBN) to determine the approximately optimal network structure of DBN. This algorithm establishes a PS orthogonal projection space based on the 'arbitrary node set' using the linear discriminant analysis (LDA) method, projects the 'balanced node set' onto the PS plane, and solves the weights of the network nodes between sets through the correlation principle, to adjust the network structure of the hidden layer adaptively. The real-time traffic flow of six different types of roads in Jinan City, Shandong Province was selected as experimental materials to verify that the WCA-DBN algorithm has more obvious advantages over traditional algorithms in terms of accuracy and time complexity.

Layout optimization of large-scale oil–gas gathering system based on combined optimization strategy

Layout optimization of large-scale oil–gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells.

WSN Optimization Algorithm for Traffic Environmental Monitoring

In accordance with the features of variety and disparity of traffic information, using wireless sensor network to monitor traffic environment, this paper proposes an optimization algorithm of applying wireless sensor to monitor traffic environment. In this paper, to optimize the coverage, we analyze the irregularity of network sensing areas based on environmental factors and propose a wireless sensor network optimization algorithm. According to the irregularity of network node sensing areas, we build an irregular network coverage model, divide the node monitoring area to improve the network coverage and make corrections to the coverage according to the network coverage connectivity and other features. The simulation test proves that the method proposed in this paper can avoid too many redundant nodes - it only requires a reasonable number of network nodes. This will reduce network energy costs and increase network connectivity without affecting the coverage, thus improving the network performance. the algorithm can satisfy the requirements of real-time acquisition, processing and remote supervision of traffic information.

Scaling up scheduling in SnF OCS networks – An adjacency list based solution

It has been proved that, in networks with co-existing delay sensitive and delay tolerant traffic flows, the overall network utility can be significantly improved by putting off (or storing) delay tolerant flows until off-peak hours. In our previous work, we proposed Time-Shifted Multilayer Graph (TS-MLG), a routing framework for Store-and-Forward (SnF) optical circuit switched (OCS) networks. With TS-MLG, the scheduling of delay tolerant traffic flows can be determined through a single routing computation. TS-MLG has been shown to be useful in SnF OCS networks of medium size, but the computing time required per traffic flow increases quickly as the network size increases and the number of active requests grows. To control the computational complexity, the number of layers used in routing must be limited, resulting in degradation of network performance. Although the delay caused by computing the end-to-end spatial/temporal path is negligible compared to the overall end-to-end transmission delay, it decides the scalability of the control plane. This prevents TS-MLG from being used in large-scale networks. In this paper, we aim to increase the scalability of the TS-MLG, by taking into account the graph sparsity. Instead of running Dijkstra Algorithm on adjacency matrices, we propose to use adjacency list to represent the graph, and Breadth-First Search Algorithm to compute shortest paths. This reduces the computational complexity from O(N2 × L2) to O(N × L + E), in which N is the number of network nodes, L is the number of layers used in routing and E is the number of edges, with a slightly increased overhead in graph update. We verify the effectiveness of the proposed algorithm by performing a large number of computations on the same hardware platform. In a network containing 30 nodes, and when the number of layers is limited to 20, the average time needed to complete 1000 routing computations with the proposed solution is 1.597 s, while that for the original one is 244 s. The proposed solution effectively scales up TS-MLG and make it suitable for large-scale networks.

An Enhanced Virtual Impedance Optimization Method for Reactive Power Sharing in Microgrids

To solve the reactive power sharing issue of the conventional droop control method, communication is commonly used, especially in networked microgrids. However, there are still many occasions where communication has not been established in advance. In such cases, the wireless reactive power sharing strategy based on the virtual impedance optimization can be applied. By designing and optimizing the controller of virtual impedance, the global reactive power sharing error of the microgrid can be minimized. Thus, the reactive power sharing performance of the droop control method is improved accordingly. However, the original optimization method has not considered the different capacities of distributed generation (DG) units, and it is subject to the number of network nodes. In this paper, an enhanced virtual impedance optimization method is proposed to solve the two issues. First, the reactive power estimation method used in the fitness function is modified to adapt to the system that contains DG units with different capacities. Second, a network simplification step is added before the optimization to deal with the system with more nodes. With these improvements, the application scope of the virtual impedance optimization method is greatly extended. Finally, the effectiveness of the enhanced method is verified through a 38-bus system in MATLAB simulation.

A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images

Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo3 framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo3-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth.

Supercomputer Modeling of Large-Scale Wireless Sensor Networks

We consider a wireless sensor network (WSN) in the form of a chain of sensors with several sinks. Chiasserini–Garetto conditions are imposed on the functioning of the network nodes and the transmission environment. The number of network nodes is assumed in the thousands. A parallel simulation model is constructed to estimate the WSN performance parameters. Computer experiments conducted on a BlueGene/P supercomputer show good scalability of the model.

Estimation of coverage factor of a wireless sensor network

The subject matter of the study in the article are the processes of synthesis of the topological structure of the wireless sensor network (WSN). The goal is to develop a simulation model for estimating the coverage factor of randomly located WSN nodes. The task: to carry out an estimation of the coverage factor by randomly located WSN nodes. The methods used are: simulation modeling, Monte Carlo method. The following results are obtained. According to the developed simulation model, the coverage factor of randomly located WSN nodes is evaluated depending on the side length of the flat area of the WSN location, the number of sensor nodes of the network, the radius of sensitivity and the communication radius of the node, taking into account and without taking into account the boundary effect. The developed simulation model also allows to determine the connectivity of the WSN. Conclusions. The scientific novelty of the results obtained is as follows: the simulation model for estimating the coverage factor of the WSN with a random arrangement of sensory nodes and monitoring the connectivity of the network has been improved. The results of the computational experiment make it possible to evaluate the coverage coefficient of a wireless sensor network for a different number of network nodes, the radius of sensitivity and the radius of communication of a node with and without the boundary effect.

Collective Energy-Efficiency Approach to Data Center Networks Planning

Energy efficiency of data centers (DCs) has become a major concern as DCs continue to grow large often hosting tens of thousands of servers or even hundreds of thousands of them. Clearly, such a volume of DCs implies scale of data center network (DCN) with a huge number of network nodes and links. The energy consumption of this communication network has skyrocketed and become the same league as computing servers’ costs. With the ever-increasing amount of data that need to be stored and processed in DCs, DCN traffic continues to soar drawing increasingly more power. In particular, more than one-third of the total energy in DCs is consumed by communication links, switching and aggregation elements. In this paper, we concern the energy efficiency of data center explicitly taking into account both servers and DCN. To this end, we present VPTCA, as a collective energy-efficiency approach to data center network planning, which deals with virtual machine (VM) placement and communication traffic configuration. VPTCA aims particularly to reduce the energy consumption of DCN by assigning interrelated VMs into the same server or pod, which effectively helps reduce the amount of transmission load. In the layer of traffic message, VPTCA optimally uses switch ports and link bandwidth to balance the load and avoid congestions, enabling DCN to increase its transmission capacity, and saving a significant amount of network energy. In our evaluation via NS-2 simulations, the performance of VPTCA is measured and compared with two well-known DCN management algorithms, Global First Fit and ElasticTree. Based on our experimental results, VPTCA outperforms existing algorithms in providing DCN more transmission capacity with less energy consumption.

A novel technique for detecting the misbehavior nodes in wireless adhoc networks

Wireless adhoc network acts as the most emerging platform in the current trend that plays a vital role in the field of sensing, analyzing the network route, rectifying the problem within the network. This also pays route for many advanced studies. Observing aid depletion attacks on the routing layers is the main objective of this paper. It also attempts to entirely disable community nodes by draining their battery strength immediately. Such an attempt to drain their battery strength is said to be as depletion attack, and these attacks are not specific to any particular protocol. Under the worst case, even a single depletion can empower a community-wide usage by O(N), where N is the total number of network nodes used.

Towards a minimal order distributed observer for linear systems

In this paper we consider the distributed estimation problem for continuous-time linear time-invariant (LTI) systems. A single linear plant is observed by a network of local observers. Each local observer in the network has access to only part of the output of the observed system, but also receives information on the state estimates of its neighbors.Each local observer should in this way generate an estimate of the plant state. In this paper we study the problem of existence of a reduced order distributed observer. We show that if the observed system is observable and the network graph is a strongly connected directed graph, then a distributed observer exists with state space dimension equal to Nn−∑i=1Npi, where N is the number of network nodes, n is the state space dimension of the observed plant, and pi is the rank of the output matrix of the observed output received by the ith local observer. In the case of a single observer, this result specializes to the well-known minimal order observer in classical observer design.

Denial of service attack solution in OLSR based manet by varying number of fictitious nodes

The Mobile Ad Hoc Network (MANET) is formed by group of mobile nodes and such group of nodes is flexible in creating links with the other nodes in the network frequently. The routing protocols in the network layer helps in transmitting the data packets between the nodes in the network. The wireless devices use electromagnetic waves or the infrared waves as medium of transmission and each device have antennas for communication. This wireless channel is very unreliable and also unprotected from the interferences from outside. The optimized link state routing (OLSR) protocol is an optimization of pure link state routing protocol. In this paper, the focus is on the active denial of service (DoS) attacks in the network layer routing protocol OLSR. Fictitious node based detection of DoS attacks are proposed by varying the number of fictitious nodes for particular number of network nodes and the parameters throughput,delay,packet delivery ratio and average delay are evaluated using network simulator and the results are compared.The number of fictitious nodes required for the maximum throughput of the given network is finally evaluated.

Optimizing the Number of Fog Nodes for Cloud-Fog-Thing Networks

Fog networking has recently received considerable attention from a theoretical perspective, but in order for such networks to be practical several open areas need to be addressed. This paper determines the optimum number of nodes that should be upgraded to fog nodes with additional computing capabilities in order to maximize the average data rate and minimize the transmission delay. The optimization is performed for a given set of wireless channel conditions and a fixed total number of network nodes. It will be shown that, having more or less fog nodes than the optimum degrades the data rate. The numerical results indicate that the average data rate can increase nearly an order of magnitude for an optimized number of fog nodes in case of shadowing and fading. It is further shown that the optimum number of fog nodes does not increase in direct proportion to the increase in the total number of nodes. Furthermore, the optimum number of fog nodes decreases when channels have high path loss exponents. These findings suggest that the fog nodes must be selected among those that have the highest computation capability for densely deployed networks and high path loss exponent channels.

Authentication in dynamic peer-to-peer networks based on homomorphic secret sharing

We have proposed a new method for authentication and secret key establishment in ad-hoc networks through public channels based only on verifiable homomorphic threshold secret sharing. The secrecy of the method has been analyzed in standard assumptions with regard to the model of an active adversary represented by a coalition consisting of a pre-threshold number of network nodes. The perfect security of the method is proved in the passive adversary model.

Performance Evaluation of Ethernet Network for Mobile Fronthual Networks

<p>Increasing mobile data traffic due to the rise of both smartphones and tablets has led to high-capacity demand of mobile data network. To meet the ever-growing capacity demand and reduce the cost of mobile network components, Cloud Radio Access Network (C-RAN) has emerged as a promising solution. In such network, the mobile operator’s Remote Radio Head (RRH) and Base Band Unit (BBU) are often separated and the connection between them has very tight timing and latency requirements imposed by Common Public Radio Interface (CPRI) and 3rd Generation Partnership Project (3GPP). This fronthaul connection is not yet provided by packet based network. To employ packet-based network for C-RAN fronthaul, the carried fronthaul traffic are needed to achieve the requirements of fronthaul streams. For this reason, the aim of this paper is focused on investigating and evaluating the feasibility of Ethernet networks for mobile fronthaul. The fronthaul requirements used to evaluate and investigate this network are maximum End to End (E2E) latency, Packet Loss Ratio (PLR) and Packet Delay Variation (PDV). The simulated results and numerical analysis confirm that the PDV and PLR of High Priority (HP) traffic in Ethernet network meet the requirements of mobile fronthaul using CPRI. However, the PDV of HP traffic meets the fronthaul network when the number of nodes in the Ethernet network is at most four. For Ethernet network, the number of nodes in the network limits the maximum separation distance between BBU and RRH (link length); for increasing the number of nodes, the link length decreases. Consequently, Radio over Ethernet (RoE) traffic should receive the priority and Quality of Service (QoS) HP can provide. On the other hand, Low Priority (LP) classes are not sensitive to QoS metrics and should be used for transporting time insensitive applications and services.</p>

On the rate of successful transmissions in finite slotted Aloha MANETs

Mobile ad hoc networks (MANETs) with Aloha MAC protocol (A-MANETs) represent a class of important and attractive networks. This paper studies the Rate of Successful Transmissions (RST), a performance index that measures the average amount of successful transmissions, in a practical A-MANET with finite number of nodes and a class of “uniform-type” mobility models. We first develop a new theoretical framework for the temporal modeling of the concerned finite A-MANET, and prove that as time evolves the network converges in distribution to a Binomial point process. With the help of the convergence property of the network and stochastic geometry theory, we then provide our analysis on the RST. In particular, we show that although it is highly cumbersome (if not impossible) to derive the exact expression for the RST of the concerned network, it is possible to have very efficient approximations to the RST under the typical nearest neighbor/receiver transmission policy, which are accurate up to an additive asymptotic error vanishing exponentially with the number of network nodes. Finally, we provide extensive simulation/numerical results to illustrate the efficiency of our RST modeling and our theoretical findings.

Precipitation and the robustness of a plant and flower-visiting insect network in a xeric ecosystem

Complex networks of species interactions are integral to the stability of ecological communities. The responses of these networks to extreme environmental fluctuations are key to understanding the evolution of these networks and their roles in stabilizing ecological communities. From 2008 to 2009 we observed the effects of extreme differences in annual precipitation on a network of plants and flower-visiting insects in a xeric grassland. Significant decreases in plant and insect diversity in the dry year were mirrored by decreases in the number of network nodes and links. However, the relative density of network connections increased in the dry year and other important topological features, such as nestedness and modularity, were largely unchanged. The resilience of the network to node (plants or insect species) loss was high in both years. Though bees tend to dominate pollination communities in drier climes, we did not observe a shift in the relative richness of bee species in the dry year. Our results indicate that well-linked, drought-resistant plants are important to community resilience during ecologically challenging periods and that networks of plants and flowering-visiting insects may be robust to extreme climatic change, at least over short periods, as long as those critical, resilient hubs are protected.