Switching Networks Research Articles

Controller Placement Approach with Criteria Balance in SDN networks

Software Defined Network (SDN) is an emerging technology that centralizes network control and automates network management by separating the data plane from the control plane. The control plane is a set of controllers that manage the network switches. The crucial problem of positioning these controllers is known as the Controller Placement Problem (CPP). The optimal controllers positioning is very constrained and affects the different network performance parameters, such as, controller-to-switch latency, inter-controller latency, number of hops between controller and switch and inter-controller. In this paper, we study the impact of optimizing some parameters on the others, we have developed different approaches, and each considers one or two parameters. Moreover, we propose a balanced optimization approach that considers the various network performance parameters. We formulate the problem as an Integer Linear Programming model that considers all parameters using the Lexicographic Multi-objective optimization method. By ensuring a balance between all parameters, network performance is significantly improved. The obtained results reveal that our approach is more inclusive and provides very interesting results.

Incumbent inertia, innovativeness, and performance (dis)advantages: A demand-side learning perspective

Strategies of incumbent firms have received considerable attention in marketing and across business disciplines, but findings regarding performance (dis)advantages and innovativeness are mixed. Prior studies on supply-side sources (factors internal to the firm) of incumbent inertia disadvantages are more prevalent than those on demand-side factors, which relate to a firm’s customers and may explain potential incumbent advantages. We introduce an integrated demand-side framework to incumbent inertia, recognizing how the supply- and demand-side factors interrelate through learning mechanisms. On the one hand, incumbent firms learn and develop various routines and assets that influence their product strategies, typically reflecting inertia and incremental innovation. At the same time, customers learn about products in the market, forming preferences that reflect switching costs and network externalities (demand-side factors). Although an incumbent can gain advantages from demand-side effects, this could accelerate the onset of supply-side disadvantages. We present a set of research propositions that specify critical effects, and examine implications for incumbent strategies.

Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis

BackgroundInternal capsule strokes can lead to multidomain cognitive impairments, and while the brain showcases adaptability post-stroke, the interplay between this adaptability and multidomain cognitive function remains elusive. In addition, although most studies indicate that adaptive changes after stroke manifest as brain functional network abnormalities, the changes in dynamic topological features, especially dynamic community structure, required for adaptive adjustment in stroke patients are not yet clear. MethodsResting-state fMRI data were acquired from patients with infarct in left-sided (n = 46, CI_L) and right-sided (n = 45, CI_R) internal capsule and 81 healthy controls (HC). Dynamic network matrices were generated using a sliding-window approach. These matrices were partitioned into communities utilizing a multilayer community detection algorithm. The network switching rate were employed as a key metric to characterize the differences of dynamic community structure between stroke and HC groups, and the correlations between these significant intergroup differences and cognitive performance scores was calculated. ResultsCompared to HC, CI_L patients exhibited an increase in network switching rates across all networks in the whole brain. However, these increases were largely unbeneficial for cognitive recovery; CI_R group show a more limited range of increased network switching rates and a more limited negative correlations with cognitive performance. ConclusionsThis study elucidates that following a stroke, the brain continuously adapts its dynamic community structure in an effort to address multidomain cognitive impairments, but this adaptation is often ineffective, unorganized, and disordered. Moreover, these findings elucidate the lateralized effects of stroke lesions on dynamic reorganization.

Quantization-Dependent Dynamic Event-Triggered Control for Networked Switched Systems Under Denial-of-Service Attacks

Quantization-Dependent Dynamic Event-Triggered Control for Networked Switched Systems Under Denial-of-Service Attacks

Physics to system-level modeling of silicon-organic-hybrid nanophotonic devices

The continuous growth in data volume has sparked interest in silicon-organic-hybrid (SOH) nanophotonic devices integrated into silicon photonic integrated circuits (PICs). SOH devices offer improved speed and energy efficiency compared to silicon photonics devices. However, a comprehensive and accurate modeling methodology of SOH devices, such as modulators corroborating experimental results, is lacking. While some preliminary modeling approaches for SOH devices exist, their reliance on theoretical and numerical methodologies, along with a lack of compatibility with electronic design automation (EDA), hinders their seamless and rapid integration with silicon PICs. Here, we develop a phenomenological, building-block-based SOH PICs simulation methodology that spans from the physics to the system level, offering high accuracy, comprehensiveness, and EDA-style compatibility. Our model is also readily integrable and scalable, lending itself to the design of large-scale silicon PICs. Our proposed modeling methodology is agnostic and compatible with any photonics-electronics co-simulation software. We validate this methodology by comparing the characteristics of experimentally demonstrated SOH microring modulators (MRMs) and Mach Zehnder modulators with those obtained through simulation, demonstrating its ability to model various modulator topologies. We also show our methodology's ease and speed in modeling large-scale systems. As an illustrative example, we use our methodology to design and study a 3-channel SOH MRM-based wavelength-division (de)multiplexer, a widely used component in various applications, including neuromorphic computing, data center interconnects, communications, sensing, and switching networks. Our modeling approach is also compatible with other materials exhibiting the Pockels and Kerr effects. To our knowledge, this represents the first comprehensive physics-to-system-level EDA-compatible simulation methodology for SOH modulators.

Detection of Drowsy Drivers in Video Sequences via LSTM with CNN Features

Fatigued driving is one of the main causes of traffic accidents. This paper developed a method for sleep drive detection that makes use of the convolutional neural network (CNN), recurrent neural network (RNN), short-term neural network (LSTM), cascade, and cam switching algorithms. Using CNN, the algorithm initially extracts the driver's face traits. The RNN that predicts the driver's degree of tiredness is then fed these features. The technology tracks the driver's eyes and eyelids using algorithms called Cascades and Cam shifts. The accuracy of the sleepiness prediction is increased further with the usage of this data. Using datasets of video recordings of attentive and somnolent drivers, the system was assessed. The outcomes demonstrated how well the algorithm could identify sleepy drivers. This system may be able to stop sleepy driving-related accidents. Ten thousand videos of awake and sleepy drivers were used to train the algorithm. Drivers can be observed in real time by the system. If the system detects that the driver is sleepy, it has the ability to sound an alarm. The technology can be used to stop driving when sleepy from causing accidents. Keywords: Convolution neural network, Recurrent neural networks, long short-term memory, eye tracking, face detection, drowsy driver detection, deep learning, computer vision, machine learning, and deep learning.

СПРОЩЕНА ІМІТАЦІЙНА МОДЕЛЬ НАВАНТАЖЕННЯ В ЗАМКНЕНІЙ МЕРЕЖІ КОМУТАЦІЇ ПАКЕТІВ

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a dis-cretized description of the load states of the network nodes and corresponds to the repre-sentation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially se-lected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (en-sures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained re-sults are generalized, but attention is focused on the example of satellite packet switching networks.

Event-Based Fault Detection for Networked Switched Systems Subject to Hybrid Attacks

Event-Based Fault Detection for Networked Switched Systems Subject to Hybrid Attacks

SIMPLIFIED SIMULATION MODEL OF LOAD IN A CLOSED PACKET SWITCHING NETWORK

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a discretized description of the load states of the network nodes and corresponds to the representation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially selected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (ensures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained results are generalized, but attention is focused on the example of satellite packet switching networks.

Optimized diagnosis of local anomalies in charge and discharge of solar cell capacitors

BackgroundWith the increasingly serious environmental pollution and natural environment damage, renewable energy such as solar cells have gradually become the key to change this situation. Therefore, the local abnormal diagnosis of the charge and discharge of solar cell capacitors is particularly important. ObjectiveTo extend the life of ultracapacitors by resolving the issue of their low detection rate and enhancing the capacity to recognize fault diagnosis factors. A novel approach to charging and discharging, as well as the diagnosis of local anomalies, is put forth, utilizing switching networks. MethodsBy controlling the capacitors of multiple solar cells and supercapacitors to work together, it is possible to compensate for the shortage of photovoltaic power. The performance of fault diagnosis is optimized by combining principal component analysis and binary K-means clustering, which completes the fault diagnosis of capacitors. ResultsThe experimental results show that the research method can increase the maximum output power of photovoltaic by 32.9% under multi-layer shadows. In the charging state of the training set, the number of abnormal capacitors is 6, and the number of normal capacitors is 12, and both of them are in accordance with the preset value. The number of abnormal capacitors and normal capacitors in the discharge state is the same as that in the charging state, which is also 6 and 12. ConclusionThe research method can effectively address the issue of unbalanced energy storage battery packs and minimize the impact of local shadows on photovoltaic systems. In comparison to fuzzy C-means clustering, this method requires fewer iterations, enables faster fault diagnosis, and produces more accurate clustering results. It can provide technical support for diagnosing local abnormalities in the charging and discharging of solar cell capacitors.

Adaptive leader‐following formation for high‐order nonlinear multi‐agent systems with an uncertain leader system over switching networks

AbstractIn this article, we study the leader‐following formation problem for high‐order nonlinear multi‐agent systems with an uncertain leader system over switching networks. We first divide the uncertain leader system into two types and then design adaptive distributed observers for them respectively. Then, based on our designed observers, we synthesize two adaptive distributed control laws respectively to solve our problem, forcing the errors between the states of the followers and that of the leader to the prescribed time‐varying desired formation vectors. Finally, our main result is illustrated by two examples.

Simulation Studies of 3-Stage Clos Switching Network with Prioritization Mechanism Used in Flexible Wavelength-Division Multiplexing Network Nodes

Given the escalating need for greater bandwidth, there is a growing interest in implementing mechanisms that ensure reliable and optimal service levels for specific traffic classes, especially during periods of heavy network traffic. One such mechanism is the prioritization mechanism, where certain portions of resources are exclusively allocated to predefined services. In the context of contemporary flexible WDM networks that employ advanced data transmission techniques, efforts have been made to create a simulation program capable of assessing the traffic characteristics of nodes within flexible WDM networks. This article presents a simulator of flexible WDM nodes with an implemented mechanism for prioritizing call classes. The simulator allows us to determine the loss probability for individual traffic classes in a switching network with point-to-point selection. In the simulator, the Clos structure was adopted as the structure of the flexible WDM network node due to its popularity in many studies and applications.

Interruptible Scheduling of Partially Re-Configurable Optical Switching in Data Center Networks

Interruptible Scheduling of Partially Re-Configurable Optical Switching in Data Center Networks

Mode reconfigurable OAM helical array antenna for higher mode generation

AbstractIn this paper, an orbital angular momentum (OAM) mode reconfigurable antenna based on a uniform circular array (UCA) is presented. The phase superposition method is presented to overcome the limitation of the number of array units on the mode order in the conventional four‐element UCA antenna, which is limited to generating first‐order OAM beams. The unit phase is introduced by a bifilar helical antenna in this method, and the unit phase is superimposed with the feed phase of the UCA to enable the generation of 3rd‐order OAM beams from the four‐element UCA. At the same time, the DC signal‐controlled feed network assigns three distinct feed phases to the UCA, which realizes the reconfigurability of the three OAM beams. To achieve mode reconfigurability, 20 p‐i‐n diodes were integrated as switches in the feed network for controlling the ON/OFF status of p‐i‐n diodes in the feed network using DC signals. Finally, simulated and measured results show that three OAM beams (lz = −1, −2, and −3) have been generated in the frequency band from 10.6 to 11.8 GHz.

Resilient Event-Triggered Filtering for Networked Switched T-S Fuzzy Systems Under Denial-of-Service Attacks

Resilient Event-Triggered Filtering for Networked Switched T-S Fuzzy Systems Under Denial-of-Service Attacks

Analysis of the most probable exit path in the synthetic gene network with genetic toggle

In this paper, the most probable exit path is applied to understand the switching of a bistable synthetic gene network with genetic toggle in Escherichia coli from a new perspective. It is worth mentioning that the most probable exit path is a deterministic indicator for researching stochastic system, and its role is analogous to the phase diagram and time history diagram of deterministic system. Firstly, the large deviation theory proves that when a dynamic system has a stable equilibrium point, the cumulative effect of its long-term action can make its sample trajectory reach another region with a probability of 1 from the attractive region under weak random disturbance. The concept of the most probable exit path is given by defining Freidlin-Wentzell action functional. Moreover, considering the noise intensity, Onsager-Machlup action functional is defined to obtain the most probable exit path of the system subject to random disturbance. Based on the Onsager-Machlup action functional, it can be observed that the impact of two types of noise intensities on the most probable exit path for the transition of the system. In addition, based on vector field decomposition, energy landscape is used to analyze the global stability of the system. Finally, this article intuitively demonstrates the switching process of the system with genetic toggle. The analysis of Onsager-Machlup action functional reveals that two independent noises have opposite effects on the system switching. The increase in the intensity of the second Gaussian noise is conductive to more accurately observing the most probable exit path of the system. According to the energy landscape, the reduction of noise intensity is not conductive to switching of the system with genetic toggle.

Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to study the embedding representation to solve the incompleteness of knowledge graphs (KGs). Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been widely used in KGC tasks by capturing neighbor information of entities. However, Both GCNs and GATs based KGC models have their limitations, and the best method is to analyze the neighbors of each entity (pre-validating), while this process is prohibitively expensive. Furthermore, the representation quality of the embeddings can affect the aggregation of neighbor information (message passing). To address the above limitations, we propose a novel knowledge graph completion model with mixed geometry message and trainable convolutional attention network named MGTCA. Concretely, the mixed geometry message function generates rich neighbor message by integrating spatially information in the hyperbolic space, hypersphere space and Euclidean space jointly. To complete the autonomous switching of graph neural networks (GNNs) and eliminate the necessity of pre-validating the local structure of KGs, a trainable convolutional attention network is proposed by comprising three types of GNNs in one trainable formulation. Furthermore, a mixed geometry scoring function is proposed, which calculates scores of triples by novel prediction function and similarity function based on different geometric spaces. Extensive experiments on three standard datasets confirm the effectiveness of our innovations, and the performance of MGTCA is significantly improved compared to the state-of-the-art approaches.

Microwave Imaging System Based on Signal Analysis in a Planar Environment for Detection of Abdominal Aortic Aneurysms.

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors.

A fractional stochastic SPEIQR epidemic model in switching network for COVID-19

The rapid spread of COVID-19 globally is the cause of panic and billions of dollars in losses and some public policies have been implemented to contain the spread of the virus. It is highly significant to establish mathematical models to describe the spread of the pandemic. In this paper, a fractional stochastic susceptible–protected–exposed–infected–quarantined–recovered (SPEIQR) epidemic model in switching network is proposed to investigate COVID-19 and other diseases in the future, in which the switching connections in the propagation network of viruses based on a Bernoulli random variable are given to represent the policy change during COVID-19. The effects of environmental fluctuations are also considered by introducing the multiplicative noise terms into the disease transmission coefficient to obtain a more realistic mathematical model. Furthermore, the Riemann–Liouville fractional derivative is used to describe the dynamics of disease transmission process which is dependent on the history information of the stochastic system. Some qualitative properties of the model are derived and some sufficient conditions are obtained to ensure the local extinction with probability one of the disease. Finally, we use the epidemic data of three countries (Italy, Germany and Switzerland) and three cities in China (Hubei, Hunan and Henan) from January 22 to July 20 to demonstrate the validity of the derived results and the availability of the proposed models. This work provides some theoretical results to deeply understand the dynamics of COVID-19 and regulate the disease dynamics.

Design of fault tolerant algorithm for network on chip router using field programmable gate array

<p class="Abstract">Many internet protocol (IP) modules are present in contemporary system on chips (SoCs). This could provide an issue with interconnection among different IP modules, which would limit the system's ability to scale. Traditional bus-based SoC architectures have a connectivity bottleneck, and network on chip (NoC) has evolved as an embedded switching network to address this issue. The interconnections between various cores or IP modules on a chip have a significant impact on communication and chip performance in terms of power, area latency and throughput. Also, designing a reliable fault tolerant NoC became a significant concern. In fault tolerant NoC it becomes critical to identify faulty node and dynamically reroute the packets keeping minimum latency. This study provides an insight into a domain of NoC, with intention of understanding fault tolerant approach based on the XY routing algorithm for 4×4 mesh architecture. The fault tolerant NoC design is synthesized on Field programmable gate array (FPGA).</p>