Number Of Nodes In Network Research Articles

Virtual coordinate system using dominating set for GPS-free adhoc networks

Reported work on virtual coordinate assignment (VCA) schemes are iterative-based techniques which rely upon geometric projection (i.e., projecting on circle) or embedding of network topology to low-dimensional Euclidean space (like graph embedding, multidimensional scaling). The performance of existing VCA techniques is constrained by topological situations such as low density or having voids/holes, where greedy forwarding suffers due to local minima when no neighbor is found closer to the destination or low-quality routes comprised of long distance hops. Another drawback of existing VCA techniques is the requirement of thousand iterations for usable coordinate convergence. In order to overcome these drawbacks, we propose a novel virtual coordinate construction technique using graph-theoretic dominating sets. Dominating set (DS) of G is a subset of vertices such that each vertex in G is either in DS or has a neighbor in DS. We found that our virtual coordinate assignment using dominating set algorithm has an approximation ratio \(((4.8+\ln 5)opt +1.2)\), where opt is the minimum size dominating set which has the same approximation ratio as minimum dominating set problem. Our algorithm has time complexity \(\mathcal {O}(n)\) times and \(\mathcal {O}(D)\) rounds and message complexity is \(\mathcal {O}(n\log n)\), where D is the radius and n is the number of nodes in networks.

A new algorithm for finding the K shortest paths in a time-schedule network with constraints on arcs

Time-schedule network with constraints on arcs (TSNCA) means network with a list of pre-defined departure times for each arc. Compared to past research on finding the K shortest paths in TSNCA, the algorithm in this paper is suitable for networks having parallel arcs with the same direction between two nodes. A node label algorithm for finding the K shortest paths between two nodes is proposed. Temporal-arcs are put into the labels of nodes and arranged by ascending order. The number of temporal-arcs is limited to K in every label of node to improve the effectiveness of the algorithm. The complexity of this algorithm is [Formula: see text], where [Formula: see text] is the maximum number of departure times from a node, [Formula: see text] is the number of arcs in network, and [Formula: see text] is the number of nodes in network. Experiments are carried out on major part of real urban mass transit network in Beijing, China. The result proves that the algorithm is effective and practical.

Simplified node decomposition and platoon head selection: a novel algorithm for node decomposition in vehicular ad hoc networks

This paper presents a novel Simplified Node decomposition and Platoon (SNAP) head selection cluster-based routing algorithm for Vehicular Ad hoc Networks (VANET) communication. The major novelty of the paper is the two proposed algorithms through which the number of platoons and platoon heads (PH) are decided upon for increasing the network lifetime. Algorithm-1 analyze the distribution of nodes and forms a set of possible minimum number of platoons (clusters) having equal number nodes, based on Hierarchical clustering technique. The algorithm-2 partitions the given network into platoons and selects the platoon head (PH) based on coverage range of wireless node. It uses SPSS statistical software tool for platoon decomposition and also to determine the platoon head (PH). The number of iterations required for selection of PH is found to be minimum, irrespective of the number of network nodes. The platoon is controlled by its platoon head (PH) and the data transfer happens locally between the platoon nodes and its head. The platoon head alone communicates with the road side unit (RSU) and avoids multiple data link between the individual nodes of platoon to RSU. The performance of SNAP algorithm has been verified using NS-2 network simulator and found to consume less transmission energy, increased node and network lifetime which in turn increase the overall efficiency of the network.

Financial Networks as a Source of Systemic Instability

Abstract Systemic risk is a fundamental constituent of contemporary financial systems. For the past decades a growing number of abrupt upsets in financial systems could be observed. Due to previous experiences, politicians and regulators prefer to identify the offenders outside the system or to blame one of the entities inside the system. However, nowadays many disasters in anthropogenic systems cannot be perceived that way. They are often results of inappropriate interactions rather than external or internal impulses. This requires a paradigm shift in thinking about systemic risk. A component-oriented perspective should be nowadays replaced with a network-oriented view. Closer insight into the concept of systemic risk can refer to the model of the system composed of a huge number of interconnected components. In such a system, systemic risk is usually considered to have a ‘cascading’, ‘domino’ or ‘contagion’ effect, resulting from strong connections. An initial failure could have disastrous effects and cause extreme damage as the number of network nodes goes to infinity. Strongly interconnected, complex dynamic systems cannot be understood by the simple sum of their components’ properties, in contrast to loosely coupled systems. What makes the behaviour of complex financial systems particularly unpredictable is that systemic failures may occur even if everybody involved is highly skilled, highly motivated and behaving properly.

A Priority-Aware Multichannel Adaptive Framework for the IEEE 802.15.4e-LLDN

The low-latency deterministic network (LLDN) protocol defined in the IEEE 802.15.4e amendment is intended for factory automation applications that require very low latency and large networks, such as automotive manufacturing. However, LLDN does not provide priority support to properly deal with real-time traffic or dynamic channel configuration capabilities to cope with unreliable channels. Moreover, it offers a limited scalability, as the cycle time grows linearly with the number of network nodes. This paper proposes the priority-aware multi-channel adaptive (PriMulA) framework, which introduces in the LLDN priority-aware scheduling, multichannel communication, adaptive channel selection, and channel blacklisting. PriMulA supports a higher number of network nodes than the LLDN protocol while keeping short cycle times. In addition, PriMuLA avoids deadline miss and improves the network reliability. It maintains the interoperability with LLDN standard nodes and can be implemented on commercial off-the-shelf devices. This paper presents the framework, a schedulability analysis, comparative simulations, and a proof-of-concept implementation.

Finite-Time Convergent Gossiping

Gossip algorithms are widely used in modern distributed systems, with applications ranging from sensor networks and peer-to-peer networks to mobile vehicle networks and social networks. A tremendous research effort has been devoted to analyzing and improving the asymptotic rate of convergence for gossip algorithms. In this work we study finite-time convergence of deterministic gossiping. We show that there exists a symmetric gossip algorithm that converges in finite time if and only if the number of network nodes is a power of two, while there always exists an asymmetric gossip algorithm with finite-time convergence, independent of the number of nodes. For $n=2^m$ nodes, we prove that a fastest convergence can be reached in $nm=n\log_2 n$ node updates via symmetric gossiping. On the other hand, under asymmetric gossip among $n=2^m+r$ nodes with $0\leq r<2^m$, it takes at least $mn+2r$ node updates for achieving finite-time convergence. It is also shown that the existence of finite-time convergent gossiping often imposes strong structural requirements on the underlying interaction graph. Finally, we apply our results to gossip algorithms in quantum networks, where the goal is to control the state of a quantum system via pairwise interactions. We show that finite-time convergence is never possible for such systems.

Regenerator location problem: Polyhedral study and effective branch-and-cut algorithms

In this paper, we study the regenerator location problem (RLP). This problem arises in optical networks where an optical signal can only travel a certain maximum distance (called the optical reach) before its quality deteriorates, needing regenerations by regenerators deployed at network nodes. The RLP is to determine a minimal number of network nodes for regenerator placement, such that for each node pair, there exists a path of which no sub-path without internal regenerators has a length greater than the optical reach. Starting with a set covering formulation involving an exponential number of constraints, reported and studied in Rahman (2012) and Aneja (2012), we study the facial structure of the polytope arising from this formulation, significantly extending known results. Making use of these polyhedral results, we present a new branch-and-cut (B&C) solution approach to solve the RLP to optimality. We present a series of computational experiments to evaluate two versions of the proposed B&C approach. Over 400 benchmark RLP instances, we first compare them with an available exact method for the RLP in the literature. Because of the equivalence among the RLP, the minimum connected dominating set problem (MCDSP), and the maximum leaf spanning tree problem (MLSTP), we further compare our approaches with other available exact algorithms using 47 benchmark MCDSP/MLSTP instances.

A Multiphysics Design Methodology Applied to a High-Force-Density Short-Duty Linear Actuator

This paper presents a coupled electromagnetic and thermal design methodology which addresses the problem of balancing accuracy against computation time. A case study on a short-duty permanent-magnet (PM) linear actuator is used to illustrate the approach. The proposed method employs a two-dimensional electromagnetic finite-element (FE) model coupled with a detailed thermal equivalent circuit (TEC) model which is automatically constructed and parameterized using geometric and material data. A numerical method of estimating the equivalent thermal properties of the winding amalgam is used along with published empirically derived convection and radiation heat transfer correlations. The relatively high number of network nodes and more accurate thermal material properties minimizes model calibration and allows improved temperature prediction, including winding hot spots, while maintaining a low computational cost for both steady-state and transient analyses. A comparison between experimental and theoretical actuator performance shows that the design methodology provides good accuracy electromagnetic and transient thermal performance predictions without the need for direct model calibration and can yield an optimized design within an acceptable time frame.

Топологические резервы «сплющенных» системных сетей

A method of modification the topology of double-hop system network type of Flattened Batterfly is considered. The method ensures diminution of component switch sizes and as a consequence of that feature decrease in hardware complexity and power consuming, preserving number of network nodes (processors), network diameter and functional characteristics. In case of retain the original component switch size the method gives a possibility to enhance number of network nodes dramatically with preservation of network diameter.

Multicriteria Breadcrumbs Scoping: Multiple Constraint-Based Distribution Methods of In-Network Guidance Information in Content-Oriented Network

Lately, network usage has been dominated by content distribution, and hence, content oriented network (CON) has attracted much attention. In CON, however, we encounter a scalability problem regarding content acquisition, particularly in large-scale networks composed of multiple domains, because the total amount of content is much larger than the number of network nodes. CON requires a more scalable routing scheme than the traditional IP routing. We have proposed a breadcrumbs-based architecture to establish highly scalable content routing for the content-oriented paradigm. In this paper, we consider another novel approach for controlling guidance (routing) information. We propose a breadcrumbs-scoping (BC-scoping) framework that manages the distribution scope of guidance information according to some criteria. First, we propose BC-scoping on domain, which limits the distribution scope of guidance information within domains according to a hierarchical network structure. It promotes intradomain content acquisition and prevents the occurrence of cross-domain BC trails, because intradomain content acquisition is preferred, both technically and economically. Second, we consider the use of multiple criteria simultaneously with this framework when the criteria are independent of each other. Specifically, we propose BC-scoping on domain and popularity as a practical example of BC-scoping on multiple-criteria. For both of the proposed methods, we show the effectiveness through extensive computer simulation.

Parameter identification and synchronization for uncertain network group with different structures

Abstract We design a novel synchronization technique to research the synchronization of network group constituted of uncertain networks with different structures. Based on Lyapunov theorem, the selection principles of the control inputs and the parameter identification laws of the networks are determined, and synchronization conditions of the network group are obtained. Some numerical simulations are provided to verify the correctness and effectiveness of the synchronization technique. We find that the network number, the number of network nodes and network connections indeed will not affect the stability of synchronization of network group.

A Bioinspired Fair Resource-Allocation Algorithm for TDMA-Based Distributed Sensor Networks for IoT

Many studies on distributed resource-allocation algorithms have been conducted recently because of the increasing number of network nodes and the rapidly changing network environments in the Internet of Things (IoT). In this paper, we propose the multihop DESYNC algorithm, which is a bioinspired Time Division Multiple Access- (TDMA-) based distributed resource-allocation scheme for distributed sensor networks. We define a detailed frame structure for the proposed multihop DESYNC algorithm and a firing message, which acts as a reference for resource allocation. In addition, operating procedures for resource allocation and collision detection avoidance under multihop DESYNC are explained. Simulations show that multihop DESYNC effectively resolves the hidden-node problem and that it fairly shares resources among nearby nodes in multihop networks. Moreover, it achieves better performance than the CSMA/CA algorithm in terms of channel reuse gain and average throughput.

Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks.

Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (∼50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies.

Probabilistic query generation and fuzzy c‐means clustering for energy‐efficient operation in wireless sensor networks

SummaryDepending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution‐based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c‐means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A Research Paper: Comparison of LEACH and EEP Protocol

A mobile ad hoc network (MANET) is a collection of autonomous nodes or terminals which communicate with each other by forming a multi-hop radio network without the aid of any established infrastructure or centralized administration such as a base station. It is composed of tiny wireless sensor nodes which are mobile and dynamic nature. Due to mobility, when nodes re communicate to each other, they easily loss their energy or we can say the network life time will reduce by time. To maintain the energy efficiency of any node or network, some protocols are developed. The size of network depends on the total number of nodes in network. When size of network expands it means numbers of nodes are increases. This paper presents how life time of network can be increased by increasing number of nodes using EEP protocol. This work presents the improvement over LEACH protocol.

Physical topology optimization of infrastructure health monitoring sensor network for high-speed rail

With the rapid development of high-speed rail (HSR) systems, the security and safety of these huge systems are becoming the primary concerns for passengers. HSR infrastructure plays an important role in HSR systems, making the maintenance of security and safety of the HSR infrastructure especially important. Meanwhile, sensor network technologies allow the realization of real-time and all-weather monitoring of HSR infrastructure. This paper analyzes the application requirements and characteristics of infrastructure health monitoring sensor network (IHMSN) through construction of a three-layer IHMSN which is composed of end devices, repeater points, and access points. The physical topology optimization goal of IHMSN is to set the optimal number of network nodes (namely, minimum cost) as well as the best physical connections. Given types and amount of the end devices, a multiple knapsack model is established which converts the physical topology optimization problems into multiple knapsack problems. Based on the different needs of practical application, three different cases (basis case, adding devices case and weight-based case) are proposed, and the corresponding models are built. Some artificial intelligence algorithms and a traditional dynamic programming algorithm are presented to solve the problems. In addition, a general algorithmic finite state machine is proposed to describe the solving process. After comparing these algorithms in execution time, memory, and optimal results, the genetic algorithm and particle swarm optimization algorithm stand out when used to solve the basic case as well as the extended cases. The numerical results show that these proposed models and algorithms can effectively solve the physical topology optimization problem of IHMSN for HSR systems. Moreover, these methods can effectively reduce network costs and provide a theoretical basis for network communication link optimization.

멀티홉 네트워크에서 생체모방 기반 자원할당 기법

Recently, researches on resource allocation algorithms operating in a distributed way are widely conducted because of the increasing number of network nodes and the rapidly changing the network environment. In this paper, we propose Multi-Hop DESYNC(MH DESYNC), that is bio-inspired TDMA-based resource allocation scheme operating in a distributed manner in multi-hop networks. In this paper, we define a frame structure for the proposed MH DESYNC algorithm and firing message structure which is a reference for resource allocation and propose the related operating procedures. We show that MH DSYNC can resolve the hidden-node problem effectively and verify that each node shares resources fairly among its neighboring nodes. Through simulation evaluations, it is shown that MH DESYNC algorithm works well in a multi-hop networks. Furthermore, results show that MH DESYNC algorithm achieves better performance than CSMA/CA algorithm in terms of throughput.

BASCO: a toolbox for task-related functional connectivity.

BASCO (BetA Series COrrelation) is a user-friendly MATLAB toolbox with a graphical user interface (GUI) which allows investigating functional connectivity in event-related functional magnetic resonance imaging (fMRI) data. Connectivity analyses extend and compliment univariate activation analyses since the actual interaction between brain regions involved in a task can be explored. BASCO supports seed-based functional connectivity as well as brain network analyses. Although there are a multitude of advanced toolboxes for investigating resting-state functional connectivity, BASCO is the first toolbox for evaluating task-related whole-brain functional connectivity employing a large number of network nodes. Thus, BASCO allows investigating task-specific rather than resting-state networks. Here, we summarize the main features of the toolbox and describe the methods and algorithms.

Rapid identifying high-influence nodes in complex networks**Project supported by the National Natural Science Foundation of China (Grant Nos. 61374180 and 61373136), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project, China (Grant No. 12YJAZH120), and the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. RLD201212).

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method (RIM) to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible–infected–recovered (SIR) model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness, and eigenvector centrality methods.

Control of Large-Scale Boolean Networks via Network Aggregation

A major challenge to solve problems in control of Boolean networks is that the computational cost increases exponentially when the number of nodes in the network increases. We consider the problem of controllability and stabilizability of Boolean control networks, address the increasing cost problem by partitioning the network graph into several subnetworks, and analyze the subnetworks separately. Easily verifiable necessary conditions for controllability and stabilizability are proposed for a general aggregation structure. For acyclic aggregation, we develop a sufficient condition for stabilizability. It dramatically reduces the computational complexity if the number of nodes in each block of the acyclic aggregation is small enough compared with the number of nodes in the entire Boolean network.