Variety Of Network Topologies Research Articles

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes.

As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

Distributed Supervision Strategies for Cyber-Physical Systems with Varying Network Topology

Distributed Supervision Strategies for Cyber-Physical Systems with Varying Network Topology

Beetle colony optimization algorithm‐based node clustering scheme for efficient data dissemination in vehicular ad hoc networks

SummaryVehicular ad hoc networks (VANETs) are the ultimate solution for preventing road accidents, which result in the loss of precious human life worldwide. In this context, effective communication between the vehicular nodes is essential due to the varying network topology and high vehicular mobility inherent with VANETs. Cluster‐based routing is identified to be a significant approach for achieving efficient routing and improving communication proficiency in VANETs. In this paper, a beetle colony optimization algorithm–based clustering scheme (BCOACS) is proposed for generating optimized clusters for facilitating reliable data dissemination. This BCOACS algorithm includes two vital strategies such as beetle antenna search (BAS) and swarm intelligence for attaining inter‐cluster and intra‐cluster communications. In specific, BAS strategy that includes random search attributed toward gradient direction is used for intra‐cluster communication without using the complete amount of gradient information. On the other hand, a swarm intelligence strategy that encompasses a collective approach of self‐organized and decentralized agents is used for inter‐cluster communication with the view to minimize the load on each cluster head (CH) and to extend the clusters' lifetime. The simulation outcomes of the proposed BCOACS scheme confirmed improved performance in optimizing the number of constructed clusters independent of the increase in the network grid size, transmission range, and number of vehicular nodes in the network compared to the benchmarked approaches. The results also confirmed that the proposed BCOACS scheme achieved a maximized throughput of 13.42%, with reduced delay and protocol overhead of 18.96% and 19.45%, better than the benchmarked schemes used for investigation.

Optimal single settings based relay coordination in DC microgrids for line faults

A novel time–current-rate-based inverse characteristic curve for relays in a DC microgrid is proposed in this paper. Line current rise rate is used as actuating quantity, ensuring quick line fault clearing (relay operating time is in order of a few μs). The advantage of using line current rise rate as actuating quantity is that for a line short-circuit fault, it does not vary significantly for grid-connected and islanded modes of operation and varying network topologies of DC microgrid. Consequently, using the proposed characteristic, a single set of optimal relay settings is obtained using an optimization solver in MATLAB. The obtained settings ensure reliable and selective coordination between primary and backup relays for various pole-to-ground and pole-to-pole line faults under different operating conditions with multiple sources in two different 4-bus 400V low voltage DC microgrids. The maximum relay operating time for a high resistance fault with the proposed curve is 394.9μs for the first considered low voltage DC microgrid. Simulations in the Real Time Digital Simulator and comparisons with previous schemes (one comparison on the second considered DC microgrid), conventional standard inverse, and extremely inverse curves for DC microgrid protection indicate the effectiveness of the proposed scheme.

An entropy-based scheme for protection of DC microgrids

The design of an appropriate scheme for protection of DC microgrids is still a significant challenge. This paper proposes a new method for protection of DC microgrids when subjected to various types of faults and modes of operation. The proposed method shares some features of the differential protection scheme and adopts the Shannon entropy to evaluate the "informational value" of current waveforms in the presence and absence of a fault. It uses the relative entropy of the current waveforms at the two sides of the faulty line to detect and characterize a fault. In the case of failure in the primary protection system, the entropy variations of the current waveforms are used to detect the faulty line. The main features of the proposed method include a) accuracy in a noisy environment with poor data synchronization (due to the delay in communication links) and varying network topology, b) fast operation regardless of the fault type and location, c) ability to detect high-impedance faults, and d) provision of backup protection. The efficiency of the proposed method is demonstrated by comparing the simulation results of several case studies in a typical ring DC network with those obtained using an advanced differential protection scheme.

Unique Brain Network Identification Number for Parkinson's and Healthy Individuals Using Structural MRI.

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson's disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2-(i-1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person's unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33-42 years (n1 = 18, n2 = 14), C: 43-52 years (n1 = 42, n2 = 23), D: 53-62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.

A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks

The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes.

Prediction and analysis of power consumption and power loss at industrial facilities

THE PURPOSE. Conduct a study to improve the reliability of forecasting the magnitude of power consumption and power losses at an industrial enterprise.METHODS. Methods are used to determine and predict the parameters of consumption and losses of electricity at industrial facilities.RESULTS. To clarify the magnitude of electricity losses, it is proposed to use coefficients that take into account the type of load curves and show the ratio of the values of the sum of the squares of currents (powers) of the variable load curve and the values of the sum of average currents (powers), that is. the ratio of power losses during load operation according to variable and uniform schedules (Kgraph), as well as a coefficient that takes into account the topology of the circuit (Ktop). The study of radial and main circuits of networks was carried out and the losses of electricity were determined using the proposed coefficients. The values of equivalent resistances of shop circuits of networks of various topologies are calculated. The operational data of the section of the workshop network are given. It was revealed that with a constant technological process, an increase in the equivalent resistance of the network circuit is due to an increase in the resistance of the contacts of switching devices installed on the lines. The value of the estimated supply of electricity was determined using the parameter of the average value of the equivalent resistance. At the same time, the error in calculating the estimated supply in relation to the actual annual supply of electricity amounted to 2,63%. According to the retrospective values of the average equivalent resistance of the circuit, it is possible to determine the predicted value of this parameter using the average value of the coefficient of change in the equivalent resistance. These characteristics of the scheme are recommended to be used in the assessment and forecasting of losses and the estimated supply of electricity, which will increase the reliability of the predicted parameters for industrial facilities.

RELIABILITY OF SERIAL-PARALLEL NETWORKS VS RELIABILITY OF PARALLEL-SERIAL NETWORKS WITH CONSTANT NUMBERS OF SUB-NETWORKS AND UNITS

In this paper it is performed, based on dynamic models, a comparative analysis of the reliability of two types of networks: serial-parallel and parallel-serial networks when the numbers of subnets and units in each subnet are predefined, constant numbers, but also when the lifetimes of the units are independent random variables. The equations for calculating the reliability of the related networks have been deduced. These functions are deduced for the dynamic model which is less studied and it is prooved to be relevant to the static model too. All equations are demonstrated and graphically illustrated in some examples. A few examples are analyzed graphically for different values of the number of units in the subnet and the number of subnets. This paper contains four different network topology models which are also analyzed by equations and graphically. The mathematical model described and the deduced equations will serve as a basis for the subsequent analysis of the dynamic networks of various topologies and various types of random variables that describe the lifetimes of the units of the analyzed system.

A Differential Protection Scheme Based on Improved DTW Algorithm for Distribution Networks With Highly-Penetrated Distributed Generation

Differential protection has been introduced into the distribution network to address the ineffectiveness of traditional protection due to the uncertainties of power flow caused by the access of multiple inverter-interfaced distributed generation (IIDG). Designed for lower data synchronization costs, this paper proposes a novel differential protection scheme based on improved dynamic time warping (DTW) distance. The proposed algorithm can effectively alleviate the singularity caused by DTW’s excessive X-axis distortion through relaxation search and derivative estimation and minimize the impact of data synchronization errors and data loss. Based on the proposed algorithm, the protection scheme combined with the feeder terminal unit (FTU) for fast fault isolation is designed, which can make full use of the existing equipment and reduce costs. Moreover, multiple influencing factors considered in this scheme, including transition resistance, penetration rate, and output intermittency of IIDG, variable network topology, underground cables, and hybrid lines. The test results demonstrate that the proposed protection scheme can effectively isolate short-circuit faults in various scenarios.

SOCIAL NETWORK AND THE DIFFUSION OF INVESTMENT BELIEFS: THEORETICAL EXPERIMENT AND THE CASES OF GAMESTOP SAGA

It is critical to understand how investment beliefs are transmitted across a community and affect individuals' investment decisions, given the proliferation of online social networks. This study proposes a novel approach to capture the cognitive effects (dissonance and exposure), which outperforms previous social contagion models in terms of expressive power. The cognitive model was analyzed across a variety of network topologies and communications patterns. It is found that the cognitive diffusion models that account for the difference in belief scores between previous and new beliefs performed as expected. This study establishes a framework under which researchers studying financial behaviors and social contagion in finance could collaborate to better understand individual investments' decisions.

A Coordination Scheme for a Combined Protection System Considering Dynamic Behavior and Wind DGs Fault Ride-Through Constraints

• A proposed design of a hierarchical protection scheme is presented. • Protective devices are optimally coordinated based on fault current transients. • Hybrid superconducting fault current limiters are optimally allocated to keep coordination under synchronous and inverter-based wind DGs. • Ride-through capability of wind DGs is integrated into the fault current limiter sizing problem. • Changes of network topology and variation of detected fault current are considered. A combined protection system has multiple types of devices such as directional overcurrent relays, reclosers, and fuses to protect distinct areas in a DG-incorporated network. An approach for setting all protective devices to realize complete coordination of adjacent ones taking transient responses into account is presented. The participation of synchronous generator-based and/or inverter-based wind DGs can degrade protection coordination. So, a scheme for sustaining coordination by optimal hybrid superconducting fault current limiters is proposed. The scheme can cope with variation of network topology; and handles the change of fault current level perceived by the protection device under a given topology. Moreover, instead of the conventional steady-state models, more realistic dynamic models of relays, reclosers, DGs, and fault current limiter are used. As a serious operation problem, undesired tripping of wind DGs under fault condition can occur due to the sag of terminal voltage below certain limits defined by national and international standards. Therefore, the proposed scheme prevents this undesired tripping of wind DGs by integrating its ride-through capability constraints into the fault current limiter allocation problem. The proposed approach is applied to the IEEE 9-bus and 30-bus systems. Results confirm the efficacy of the proposed method.

Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm

Abstract **This paper presents a novel fast algorithm for feedforward neural networks training. It is based on the Recursive Least Squares (RLS) method commonly used for designing adaptive filters. Besides, it utilizes two techniques of linear algebra, namely the orthogonal transformation method, called the Givens Rotations (GR), and the QR decomposition, creating the GQR (symbolically we write GR + QR = GQR) procedure for solving the normal equations in the weight update process. In this paper, a novel approach to the GQR algorithm is presented. The main idea revolves around reducing the computational cost of a single rotation by eliminating the square root calculation and reducing the number of multiplications. The proposed modification is based on the scaled version of the Givens rotations, denoted as SGQR. This modification is expected to bring a significant training time reduction comparing to the classic GQR algorithm. The paper begins with the introduction and the classic Givens rotation description. Then, the scaled rotation and its usage in the QR decomposition is discussed. The main section of the article presents the neural network training algorithm which utilizes scaled Givens rotations and QR decomposition in the weight update process. Next, the experiment results of the proposed algorithm are presented and discussed. The experiment utilizes several benchmarks combined with neural networks of various topologies. It is shown that the proposed algorithm outperforms several other commonly used methods, including well known Adam optimizer.

Disconnection-Aware Attack Detection and Isolation With Separation-Based Detector Reconfiguration

This study addresses incident handling during an adverse event for dynamical networked control systems. Incident handling can be divided into five steps: detection, analysis, containment, eradication, and recovery. For networked control systems, the containment step can be conducted through physical disconnection of an attacked subsystem. In accordance with the disconnection, the equipped attack detection unit should be reconfigured to maintain its detection capability. In particular, separating the detection subunit associated with the disconnected subsystem is considered as a specific reconfiguration scheme in this study. This article poses the problem of disconnection-aware attack detection and isolation with the separation-based detector reconfiguration. The objective is to design an attack detection unit that preserves its detection and isolation capability even under any possible disconnection and separation. The difficulty arises from network topology variation caused by disconnection that can possibly lead to stability loss of the distributed observer inside the attack detection unit. A solution is proposed based on an existing controller design technique referred to as retrofit control. Furthermore, an application to low-voltage power distribution networks with distributed generation is exhibited. Numerical examples evidence the practical use of the proposed method through a benchmark distribution network.

The performance effect due to varying network topologies on a software defined network employing the k-shortest path

Packet routing has always been an issue that affects network performance. The traditional protocol is known to work with local information and thus not able to produce a global optimal decision. In the software-defined network (SDN), its centralized controller obtains unified access to the entire network topology information and has the ability to partially solve traditional network packet forwarding problems. SDN controller uses the Dijkstra algorithm to find the shortest path calculated from the source node to the destination node. However, constraints such as the need to bypass the node which has a high rate of failure exist to prevent the Dijkstra algorithm from meeting this demand. In practice, we need not only consider the shortest path but also consider the second short path, the third short path, and so on. K-shortest paths algorithm discovers a set of paths ordered in the most optimal, optimal, and suboptimal, has a very wide application is integrated into SDN controller to handle routing functionality. In this study, instead of only the number of hops, bandwidth and delay are adhered to within the k-shortest path to select the final route. Under this circumstance, different topologies are examined in response to network bandwidth and packet delay. The experiment shows that tree topology is best suited for improving bandwidth while simple topology for reducing delay.

Supervised vector machine learning with brown boost energy efficient data delivery in MANET

A mobile ad hoc network (MANET) is an auto-configuring, infrastructure-free network in which nodes move independently in any direction. Owing to the fact that a MANET necessitates minimum cost for the design of networks, infrastructure construction, and management, fault tolerance by possessing a distributed control, its popularity has risen in the recent few years. However, it is a laborious and cumbersome process of MANET for ensuring consistent communication between nodes because of unstable network topology. MANET necessitates a genuine data delivery method that redesigns the variable network topology for ensuring consistent communication. Several methods were proposed in the literature, however, causing a significant time and energy during data delivery. To address these issues, we plan to develop a data delivery method called, Supervised Vector Machine with BrownBoost Classification (SVM-BBC) for efficient data delivery with minimal time consumption and higher data delivery rate. In the SVM-BBC method, distinct numbers of mobile nodes are considered as training data samples. Moreover, Support Vector Machine (SVM) classifiers are considered weak classifiers. Here Supervised SVM is employed for classifying neighboring nodes with better link quality and lesser energy consumption. Next, weak learners outputs are aggregated to obtain strong classifier output results, therefore ensuring efficient data delivery. Here, aggregation is carried out by means of BrownBoost Node Classification to produce a higher data delivery rate during data delivery. Experimental evaluation is performed on various factors, namely energy consumption, data delivery rate,data delivery time, data drop rate, and throughput with varying numbers of mobile nodes.

Deep learning-based transient stability assessment framework for large-scale modern power system

When severe disturbance occurs in power system, lack of efficacious information about transient stability state is a key challenge for power network operator. Especially for the system operating in boundary of security constraints due to economic reasons, it becomes more prominent. With the extensive deployment of phasor measurement units (PMUs), abundant historical data of power system have been collected and the data driven method based on machine learning model has become important tool to solve transient stability assessment (TSA) problem. However, the implementation of data-driven TSA method is difficult due to the characteristics of huge feature number and variable network topology of large-scale modern power system. In order to address this issue, a novel deep learning-based online TSA framework is proposed in this article. Firstly, parallel convolution algorithms are employed to address redundant input features. secondly, a novel machine learning model, deep forest is employed to train a TSA model. Some improvements are implemented for better assessment performance: 1) The internal feature transmission of deep forest is adjusted for higher assessment accuracy. 2) A fast update scheme is proposed based on active learning technique and graded strategy. 3) The cost-based method is employed to address class-imbalance training data. The test results on three power systems show that the proposed TSA framework has advantages in prediction accuracy, training speed, and update time. It is suitable for application of large-scale modern power system.

SDMob: SDN-Based Mobility Management for IoT Networks

Internet-of-Things (IoT) applications are envisaged to evolve to support mobility of devices while providing quality of service in the system. To keep the connectivity of the constrained nodes upon topological changes, it is of vital importance to enhance the standard protocol stack, including the Routing Protocol for Lossy Low-power Networks (RPL), with accurate and real-time control decisions. We argue that devising a centralized mobility management solution based on a lightweight Software Defined Networking (SDN) controller provides seamless handoff with reasonable communication overhead. A centralized controller can exploit its global view of the network, computation capacity, and flexibility, to predict and significantly improve the responsiveness of the network. This approach requires the controller to be fed with the required input and to get involved in the distributed operation of the standard RPL. We present SDMob, which is a lightweight SDN-based mobility management architecture that integrates an external controller within a constrained IoT network. SDMob lifts the burden of computation-intensive filtering algorithms away from the resource-constrained nodes to achieve seamless handoffs upon nodes’ mobility. The current work extends our previous work, by supporting multiple mobile nodes, networks with a high density of anchors, and varying hop-distance from the controller, as well as harsh and realistic mobility patterns. Through analytical modeling and simulations, we show that SDMob outperforms the baseline RPL and the state-of-the-art ARMOR in terms of packet delivery ratio and end-to-end delay, with an adjustable and tolerable overhead. With SDMob, the network provides close to 100% packet delivery ratio (PDR) for a limited number of mobile nodes, and maintains sub-meter accuracy in localization under random mobility patterns and varying network topologies.

An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

Many precious lives are lost world-wide due to road accidents. To counter this issue, the ultimate solution is Vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and varying network topology in VANETs, efficient communication among the vehicular nodes is of extreme importance. To enhance communication proficiency in VANETs, clustering is a renowned procedure. Therefore, a clustering algorithm based on Moth-Flame Optimization (MFO), titled AMONET, is projected which can effectively work in high mobility nodes scenario of VANETs. AMONET is based on bio inspired procedure and creates optimized clusters for reliable and efficient communication. Our algorithm is assessed experimentally with well-known procedures such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi Objective Particle Swarm Optimization (MOPSO). Several experiments are conducted to measure the comparative efficiency of these procedures. The average cumulative results for all the grid sizes are 27.1% for AMONET while 36.3% for ACO, 54.9% for CLPSO and 58.7% for MOPSO. The results signify that AMONET produces near ideal results, covers the entire network and generates least number of clusters. It is an efficient technique to accomplish vehicular clustering with the purpose of improving the network’s overall performance and consequently reducing the routing cost of the vehicular network.

Mitigating biological epidemic on heterogeneous social networks

Recent Covid-19 pandemic has demonstrated the need of efficient epidemic outbreak management. We study the optimal control problem of minimizing the fraction of infected population by applying vaccination and treatment control strategies, while at the same time minimizing the cost of implementing them. We model the epidemic using the degree based Susceptible–Infected–Recovered (SIR) compartmental model. We study the impact of varying network topologies on the optimal epidemic management strategies and present results for the Erdős–Rényi, scale free, and real world networks. For efficient computational modeling we form groups of groups of degree classes, and apply separate vaccination and treatment control signals to each group. This allows us to identify the degree classes that play a significant role in mitigating the epidemic for a given network topology. We compare the optimal control strategy with non optimal strategies (constant control and no control) and study the effect of various model parameters on the system. We identify which strategy (vaccination/treatment) plays a significant role in controlling the epidemic on different network topologies. We also study the effect of the cost of vaccination and treatment controls on the resource allocation. We find that the optimal strategy achieves significant improvements over the non optimal heuristics for all networks studied in this paper. Our results may be of interest to governments and healthcare authorities for devising effective vaccination and treatment campaigns during an epidemic outbreak.