Network System Research Articles

Controllability of Multilayer Networked Sampled-Data Systems.

The controllability analysis of networked systems is challenging due to their high dimensionality and complex structure. The influence of sampling on network controllability is rarely studied, making it an important topic to explore. In this article, the state controllability of multilayer networked sampled-data systems is studied, considering the deep network structure, multidimensional node dynamics, various inner couplings, and sampling patterns. Necessary and/or sufficient controllability conditions are proposed and validated by numerical and practical examples, requiring less computation than the classic Kalman criterion. Single-rate and multirate sampling patterns are analyzed, showing that adjusting the sampling rate of local channels can affect the controllability of the overall system. It is shown that the pathological sampling of single-node systems can be eliminated by an appropriate design of interlayer structures and inner couplings. In the case of systems with drive-response mode, the overall system may not lose controllability even when the response layer is uncontrollable. The results demonstrate that mutually coupled factors collectively affect the controllability of the multilayer networked sampled-data system.

Optimal output feedback control for networked control systems with dual multi‐memoryless channels

Optimal output feedback control for networked control systems with dual multi‐memoryless channels

Switching LPV approach for analysis and control of TCP-based cyber-physical systems under DoS attack

Cyber-physical systems (CPSs) integrate controllers, sensors, actuators, and communication networks. Tight integration with communication networks makes CPSs vulnerable to cyberattacks. In this paper, the impact of denial of service (DoS) attacks on the stability of cyber physical systems is investigated, with a focus on the transmission control protocol (TCP). A sufficient stability condition is extracted in linear matrix inequality (LMI) form. The TCP-CPS under DoS attack is modeled as a switching linear parameter-varying (LPV) time-delay system using the Markov jump model. Subsequently, a parameter-dependent stabilizing controller is designed for CPSs under DoS attacks, taking into account network parameters. Finally, the efficiency and feasibility of the findings are demonstrated through a well-known case study in the networked control systems literature.

Resilience optimization of bus-metro double-layer network against extreme weather events

The resilience of bus and metro systems to extreme weather events is a critical concern in urban planning, given their growing complexity and interconnectivity. Traditional studies often simplify or overlook the interdependency between different transportation modes, focusing on recovery strategies for a single mode to enhance system resilience. This study proposes an integrated resilience assessment framework for bus and metro systems, conceptualized as a Bus-Metro Double-Layer Network (B-M DLN). The framework considers both network structure and system function to accurately evaluate the B-M DLN resilience. A resilience optimization model for B-M DLN based on Genetic Algorithm (GA) is established to suggest the optimal recovery sequence of damaged stations, emphasizing the importance of station repair time, node strength, and node degree in recovery prioritization. Through a case analysis of Xi’an City, China, the B-M DLN shows significantly enhanced resilience when applying the optimal recovery strategy, especially in large-scale failure scenarios

Dynamic Bandwidth Allocation for Collaborative Multi-Robot Systems Based on Task Execution Measures

Multi-robot systems (MRSs) is a growing field of research that focuses on the collaboration of multiple robots to achieve a common global objective. Managing these systems poses several challenges, including coordination, task allocation, and communication. Among these challenges, a major area of focus is devising an effective communication scheme that ensures robots’ cooperation and adapts to varying conditions during task execution. In this paper, we develop a novel communication management framework tailored for MRSs, specifically addressing dynamic bandwidth distribution in networked teleoperated robotic systems. The algorithm is combined with semi-autonomous formation control based on the Artificial Potential Fields (APF) algorithm, which allows each individual robot to avoid local obstacles autonomously and tries to maintain a desired formation with its neighbors, while the operator is in charge of high-level control only. Common Dynamic Bandwidth Allocation (DBA) algorithms allocate bandwidth to different units based on network conditions and requirements. On the other hand, our proposed DBA scheme dynamically distributes the available bandwidth on communication streams based on factors related to task execution and system performance. In specific, bandwidth is allocated in a way that adapts to changes occurring in the system’s environment and its internal state, including the effect of the autonomous action taken by the path planner on the MRS and the performance of the controller of each individual robot. By addressing the limitations of existing approaches through shaping the communication behavior of the MRS based on performance measures, our proposed algorithm offers a promising solution for improving the performance and efficiency of MRSs. The proposed scheme is tested through simulations on a group of six unmanned aerial vehicles (UAVs) in the Robot Operating System (ROS)-Gazebo simulation environment. The obtained results show the scheme’s capability for enhancing the robotic system’s performance while significantly reducing bandwidth consumption. Experimental testing on two mobile robots further demonstrates the effectiveness of the proposed scheme.

Insights of machine learning-based threat identification schemes in advanced network system

An advanced network system (ANS) is characterized by extensive communication features that can support a sophisticated collaborative network structure. This is essential to hosting various forms of upcoming modernized and innovative applications. Security is one of the rising concerns associated with ANS deployment. It is also noted that machine learning is one of the preferred cost-effective ways to optimize the security strength and address various ongoing security problems in ANS; however, it is still unknown about its overall effectivity scale. Hence, this paper contributes to a systematic review of existing variants of machine learning approaches to deal with threat identification in ANS. As ANS is a generalized form, this discussion considers the impact of existing machine learning approaches on its practical use cases. The paper also contributes towards critical gap analysis and highlights the study's potential learning outcome.

DeepFeature: Guiding adversarial testing for deep neural network systems using robust features

DeepFeature: Guiding adversarial testing for deep neural network systems using robust features

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

Optimal dispatching of distributed photovoltaic system based on operating costs

Abstract This article investigates the method of distributed photovoltaic optimal dispatching method. The goal is to minimize the operating costs of the distribution network system by coordinating electric heating and distributed energy storage when adjusting the demand-side resources of the distribution network. The analysis is conducted on the operating costs of the distribution network system under different penetration rates of photovoltaics to further promote the accommodation capacity of distributed photovoltaics. The research results show that when the penetration rate of photovoltaics is less than or equal to 90%, only electric heating needs to participate in the accommodation capacity of distributed photovoltaics; when the photovoltaic penetration rate exceeds or equals 100%, both distributed energy storage and electric heating need to participate in the accommodation capacity of distributed photovoltaics. As the penetration rate of photovoltaics gradually increases, the total operating costs and purchased electricity costs show a downward trend. When the penetration rate of photovoltaics increases from 70% to 100%, the network loss costs decrease first, but when the penetration rate reaches 110%, photovoltaics cannot be completely integrated, leading to an increase in network loss costs and dispatching costs. This method fully utilizes the flexible adjusting capabilities of electric heating loads and distributed energy storage, promotes the accommodation capacity of distributed photovoltaics, and improves the economic efficiency of distribution network operation.

Softwarized and containerized microservices-based network management analysis with MSN

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

Predefined-time synchronization of time-varying delay fractional-order Cohen-Grossberg neural network based on memristor

This paper delves into the synchronization dynamics of fractional-order memristor Cohen-Grossberg neural network systems with time-varying delays at predefined times (PTS-MFCGNNs). Firstly, leveraging the concept of predefined-time stability, we devise a fractional-order controller, establish sufficient conditions for predefined-time synchronization, and achieve synchronization within the Cohen-Grossberg drive–response system. Secondly, building upon these findings, we scrutinize the synchronization dynamics within the time domain of the PTS-MFCGNNs system. Finally, we validate our theoretical framework through numerical simulations and engage in a comprehensive discussion on predefined-time synchronization within the PTS-MFCGNNs system.

Effect of wedge duration and electromagnetic noise on spiral wave dynamics

This paper focuses particularly on the influence of wedge duration on spiral wave formation and the regulation of electromagnetic noise. The motion stability or periodicity of a constructed regular neuronal network system is revealed by applying the master stability function method. The effect of wedge duration and electromagnetic noise on spiral wave dynamics is quantified using defined metrics, and explained by bifurcation of neuronal activity and differentiation of neuronal populations. Research results are as follows: (1) The appearing wave head rotates and evolves into a spiral pattern due to the potential difference between neurons, which is determined by wedge duration. (2) Whether it is homogeneous or heterogeneous, electromagnetic noise can effectively regulate the evolution of spiral waves. (3) Noise excitation significantly suppresses the network firing activity and alters the electric field distribution, leading to the narrowing of spiral arm and the drift of wave head. This study not only demonstrates the importance of wedge duration for spiral wave formation, but also provides guidance for stochastically regulating the spiral wave evolution.

Rapid data collection and processing in dense urban edge computing networks with drone assistance

In the edge computing network systems for the Internet of Things (IoT), there is growing attention to utilizing drones for collecting data and maintaining the freshness of data processing. This study focuses on analyzing the problems related to trajectory planning and task scheduling in a single drone-assisted edge computing network within a dense, three-dimensional urban environment. We first design an edge computing network architecture and establish an air-to-ground channel model between the drone and ground mobile devices to address the blockage caused by buildings in urban environments. Subsequently, to provide effective edge computing services, we structure the problem as a Partially Observable Markov Decision Process (POMDP) and introduce an optimization framework based on reinforcement learning. This improves data timeliness and reduces energy consumption.

How does the supply chain market respond to policy shocks? Evidence from solar photovoltaic sectors in China

Solar photovoltaic (PV) power policy implementation represents a pivotal strategy in addressing the challenges posed by global warming and climate change. This research endeavors to assess the effects of such policies on stock returns within the solar PV industry chain spanning the years 2013–2023. Employing contagion tests alongside the network analysis, our study delves into the intricate dynamics of policy impact. Our findings underscore the notably stronger influence exerted by opinion-based policies on policy contagion within industry chain sectors, eclipsing that of notification-based policies. Moreover, policies directed towards downstream sectors demonstrate a more pronounced effect on the mid- and downstream segments, relative to their impact on upstream components. Among the nine policies scrutinized, the 2017 notice pertaining to feed-in tariffs emerges as the most influential, driving and receiving policy shocks within the solar PV network system, while the 2022 notice aimed at coordinating the development of the PV industry supply chain exhibits comparatively weaker pervasive impact.

A Review of Traffic Flow Prediction Models in 5G Using Machine Learning Techniques

Abstract: Wireless technology has advanced significantly since its origin and is now an essential aspect of our daily life. The advancement of wireless communication from the first generation (1G) to the fifth generation (5G) of technology has been revolutionary. An extensive summary of the development of wireless communication from 1G to 5G has been given in this paper. Cellular networks are heading towards becoming more diverse, broadband, integrated, and intelligent networks with the introduction of 5G networks. The goals of 5G wireless technology are to provide more users with more consistent user experiences, ultralow latency, vast network capacity, faster multi-Gbps peak data speeds and increased reliability. While the resources needed for computation and communication are also growing with the maturity of 5G technology .At the same time, cellular traffic has increased dramatically due to the widespread use of smart devices. Cellular traffic prediction is a crucial component of the resource management system for cellular networks but it confronts many difficulties due to strict standards for accuracy and dependability. Among the most important issues is how to enhance the predictive performance of Mobile data traffic. This review describes the need of traffic forecasting in cellular network in 5G technology. A study of different models for network analysis and traffic prediction by different researchers is presented in this paper. The distinctiveness and guidelines of earlier research for traffic prediction in 5G are examined. To determine the distinctive qualities of each method used for traffic prediction in mobile network, a thorough analysis of the most popular techniques using Machine learning for predictive analysis are discussed.

Machine Learning Approaches for Network Intrusion Detection: An Evaluation of their Efficacy in Bolstering Security

Abstract: In today's digital environment, securing networked systems is critical due to the increasing sophistication and frequency of cyberattacks. Network Intrusion Detection Systems (NIDS) are essential for detecting and addressing unauthorized and harmful activities within a network. NIDS function by continuously monitoring network traffic and analyzing data packets for signs of suspicious behavior or known attack patterns. This paper provides an in-depth examination of NIDS, focusing on the application of three machine learning algorithms—K Neighbors Classifier, Logistic Regression, and Random Forest Classifier—to create a robust model for network intrusion detection using the NSL-KDD dataset. Our findings highlight the superior performance of the Random Forest Classifier, which achieved an accuracy of 99.31%, proving its effectiveness in differentiating between normal and malicious traffic. The analysis of ICMP, TCP, and UDP protocols reveals unique attack patterns, underscoring the need for protocol-specific security measures. Additionally, the study emphasizes the importance of integrating NIDS with other security systems for a multi-layered defense strategy and the crucial role of skilled personnel in managing and interpreting NIDS alerts. The results advocate for ongoing innovation and adaptation in NIDS technologies and strategies to effectively counter evolving cyber threats

A physical explanation for an unusually long-duration slow slip event in the Nankai Trough

The Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) and the Long Term Borehole Monitoring System (LTBMS), installed above the source region of the 1944 Tonankai earthquake, revealed that crustal deformation is driven by slow slip events (SSEs) in the shallower extension of megathrust earthquakes. However, there are unresolved questions about (A) the duration of the SSE in February 2012, which was longer than expected for SSEs with similar magnitudes, and (B) the relationship of the spatial distribution of fault slip between the SSEs in February and December 2012 under the condition of drilling disturbance. To clarify these questions, we re-analyzed the pore/seafloor pressure data associated with the SSEs. Our refined fault models show that the SSE in February had a significantly slower propagation speed and a longer duration than others, while the SSE in December was comparable to others. We interpret that the difference in duration and propagation speed is related to external and internal stress perturbations, respectively. Using the ocean modeling JCOPE (Japan Coastal Ocean Predictability Experiment), we identified that the decrease and subsequent increase in seafloor pressure due to the passage of the Kuroshio meander coincided with the latter part of the longer duration of the SSE in February and its termination, respectively. This suggests that the Kuroshio meander might affect the duration of SSEs. Our refined fault model also indicates that the amount of shear stress accumulation was small before the occurrence of the SSE in February, which triggered the slow propagation of aseismic slip based on a rate- and state-dependent friction law. These results imply that we need to consider the variety of SSEs from the viewpoint of stress perturbation due to not only interaction between fault segments but also external forces from oceanographic phenomena.

A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic.

This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic.

AI-based rock strength assessment from tunnel face images using hybrid neural networks

In geological engineering and related fields, accurately and quickly identifying lithology and assessing rock strength are crucial for ensuring structural safety and optimizing design. Traditional rock strength assessment methods mainly rely on field sampling and laboratory tests, such as uniaxial compressive strength (UCS) tests and velocity tests. Although these methods provide relatively accurate rock strength data, they are complex, time-consuming, and unable to reflect real-time changes in field conditions. Therefore, this study proposes a new method based on artificial intelligence and neural networks to improve the efficiency and accuracy of rock strength assessments. This research utilizes a Transformer + UNet hybrid model for lithology identification and an optimized ResNet-18 model for determining rock weathering degrees, thereby correcting the strength of the tunnel face surrounding rock. Experimental results show that the Transformer + UNet hybrid model achieves an accuracy of 95.57% in lithology identification tasks, while the optimized ResNet model achieves an accuracy of 96.13% in rock weathering degree determination. Additionally, the average relative error in tunnel face strength detection results is only 9.33%, validating the feasibility and effectiveness of this method in practical engineering applications. The multi-model neural network system developed in this study significantly enhances prediction accuracy and efficiency, providing robust scientific decision support for tunnel construction, thereby improving construction safety and economy.

Design and implementation of a scalable correlator based on ROACH2 + GPU cluster for tianlai 96-dual-polarization antenna array

The digital correlator is one of the most crucial data processing components of a radio telescope array. With the scale of radio interferometeric array growing, many efforts have been devoted to developing a cost-effective and scalable correlator in the field of radio astronomy. In this paper, a 192-input digital correlator with six CASPER ROACH2 boards and seven GPU servers has been deployed as the digital signal processing system for Tianlai cylinder pathfinder located in Hongliuxia observatory. The correlator consists of 192 input signals (96 dual-polarization), 125-MHz bandwidth, and full-Stokes output. The correlator inherits the advantages of the CASPER system, for example, low cost, high performance, modular scalability, and a heterogeneous computing architecture. With a rapidly deployable ROACH2 digital sampling system, a commercially expandable 10 Gigabit switching network system, and a flexible upgradable GPU computing system, the correlator forms a low-cost and easily-upgradable system, poised to support scalable large-scale interferometeric array in the future.