Design Of Networked Systems Research Articles

Secure Control of Networked Control Systems Under Replay Attacks: A Switching‐Like Approach

ABSTRACTThis paper focuses on the secure control design of networked control systems (NCSs) under replay attacks. Mitigating replay attacks is a challenging task since the statistical similarity between replayed signals and real observations makes them difficult to distinguish. Given the persistence of potential attackers and the probability of individual attack failures, a novel switching‐based replay attack model is introduced. By employing this replay attack model, NCSs vulnerable to replay attacks can be transformed into a class of switching‐like systems. Within the framework of the average dwell time switching strategy and multiple Lyapunov stability theory, sufficient conditions for the mean‐square exponential stability of the underlying system are presented. Compared with the existing control schemes using the delay‐based replay attack model, the proposed secure control method effectively reduces the impact of replay attacks on the control performance. The effectiveness of the proposed approach is validated through simulation experiments carried out on two practical systems.

Reverse design of molecule‐process‐process networks: A case study from HEN‐ORC system

AbstractThe integrated design of the heat exchanger network (HEN) and organic Rankine cycle (ORC) system with new working fluids is a complex optimization problem. It involves navigating a vast design space across working fluid molecules, ORC processes, and networks. In this article, a new two‐stage reverse strategy is developed. The optimal HEN‐ORC configurations and operating conditions, and the thermodynamic properties of the hypothetical working fluid are identified by an equation of state (EOS) free HEN‐ORC model in the first stage. With two developed group contribution‐artificial neural network thermodynamic property prediction models, working fluid molecules are screened out in the second stage from a database containing more than 430,000 hydrofluoroolefins (HFOs). The presented method is employed in two cases, where new working fluids are found. The total annual cost of Case 1 is 12%–22% lower than the literature, and the power output of Case 2 is 5%–8% higher than the literature.

Design and application of interval type-2 fuzzy neural network systems optimized with hybrid algorithms

Interval type-2 fuzzy neural network systems (IT2 FNNSs) own the advantages of IT2 fuzzy logic systems (FLSs) and NN. The paper designs a kind of IT2 FNNSs for permanent magnetic drive (PMD) forecasting. For each rules of IT2 FNNSs, whose antecedents, consequents and input measurements are chosen as Gaussian IT2 membership functions (MFs). The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are adopted to tune all the parameters simultaneously. Simulation instances on the basis of data of permanent magnetic drive (PMD) torque and revolutions per minute (rpm) are adopted for testing the performances of proposed hybrid optimized IT2 FNNSs for forecasting. Convergence analysis illustrates that the IT2 FNNSs have excellent generalization capability compared with both singleton and non-singleton T1 FNNSs.

Design and application of fuzzy neural network systems optimized with hybrid algorithms

Fuzzy neural network systems (FNNSs) can incorporate the merits of fuzzy logic systems (FLSs) and neural networks (NN). This paper designs a type of non-singleton FNNSs for forecasting issues. The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are used for optimizing the parameters of antecedents, input measurements, and consequents simultaneously. Two computer simulation examples based on the data of European Network on Intelligent Technology (EUNITE) and data of west Texas intermediate (WTI) crude oil price are used for testing. Convergence analysis shows that the hybrid optimized FNNSs have very high generalization ability.

Logistics Network Design System in Eu Through Modern Modelling Techniques: Establishing Strong Transport Links to Improve Foreign Economic Relations

Logistics Network Design System in Eu Through Modern Modelling Techniques: Establishing Strong Transport Links to Improve Foreign Economic Relations

Optimization research of handwritten digit recognition algorithm based on hardware neural networks

In the modern era of digitization, the widespread application of handwritten numerical data has led to an exponential increase in data volume, necessitating efficient real-time recognition and processing systems. This project is grounded in the foundational structure of neural networks, with a specific focus on integrating these networks into hardware through Very-Large-Scale Integration (VLSI). The primary objective is to achieve faster processing speeds, lower power consumption, and real-time operations. The central emphasis of this study is the design and implementation of a hardware neural network system tailored specifically for handwritten digit recognition. Drawing from neural network theory, we explore the construction of a hardware-based handwritten digit classifier. The fundamental model employed is the single-layer perceptron neuron model. Upon receiving 28x28 pixel grayscale images, the image classifier utilizes pre-trained weights, activation functions, and a maximum selector to compare and output recognition results for numerical digits. The concrete implementation is realized using the Verilog hardware description language, coupled with algorithm optimization strategies to enhance performance and efficiency. This research endeavor aims to provide an effective hardware solution for real-time handwritten digit recognition in the digital age.

Visualization Program Design for Complex Piping Systems in Marine Engine Simulation Systems

This study is dedicated to the development of an advanced ship piping network programming tool to address the challenges faced by traditional text-based design and computation methods when dealing with complex and large-data-volume piping systems, such as burdensome programming tasks, high error rates, and difficulty in troubleshooting faults. Leveraging Microsoft’s WPF technology and the C# language, combined with Excel as a data input platform, this tool provides an intuitive graphical user interface, allowing users to intuitively build and analyze ship piping network models by dragging and dropping controls. The tool not only simplifies the design process of complex piping systems but also significantly improves efficiency and accuracy through automated data processing and calculations. It supports user customization of key pipeline characteristics, such as maximum flow and direction, further enhancing the applicability and accuracy of the piping network model. In addition, with optimized interaction design and data management methods, the tool significantly reduces the learning difficulty for users, while improving the reliability of design and efficiency of troubleshooting. The results of this study show the tool not only technically outperforms traditional methods but also provides a new efficient, intuitive, and user-friendly tool for the teaching and engineering applications of ship piping networks, paving a new path for the design and optimization of ship piping network systems, with significant practical application value and theoretical significance. Looking forward, this tool is expected to play a broader role in the instruction and industrial practices associated with ship piping networks, moving the field toward more efficient and intelligent development.

Differential Entropy-Based Fault-Detection Mechanism for Power-Constrained Networked Control Systems.

In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach, we then discuss the finite-time approximation of the power constraints for the relevant control loop signals. The network under study is formed by two additive white Gaussian noise (AWGN) channels located on the direct and feedback paths of the closed control loop. The finite-time approximation of the controller output signal allows us to estimate its differential entropy, which is used in our proposed fault-detection mechanism. After fault detection, we propose a fault-identification mechanism that is capable of correctly discriminating faults. Finally, we discuss the extension of the contributions developed here to future research directions, such as fault recovery and control resilience.

Enhancing Cognitive Radio Network Design with New Energy Detection versus Pilot and Radio-Based Techniques

This study aimed to enhance the energy efficiency (EE) and accuracy of the Cognitive Radio Network (CRN)system design by using a unique energy detection approach, contrasting it with the conventional Pilot andRadio Based Detection Technique. A model was developed and processed in Python, using a network datasetfor initial exploration, sourced from the UCI Machine Learning Repository. Statistically, with a confidenceinterval of 95% and sample size of 140, the energy detection's precision was assessed. In evaluating spectrumallocation, the conventional technique had a slightly higher accuracy. However, our proposed energy detectionmethod achieved an impressive 95.2713% accuracy. Surprisingly, it processed in just 4 seconds, half the timetaken by the conventional method. The results confirm the new method's superiority in energy efficiency.

A multi-factor evolutionary algorithm for solving the multi-tasking robust optimization problem on networked systems

Realistic networks constantly face complex environments and are vulnerable to external disturbances that can damage their structure and disrupt information flow. These challenges give rise to two critical optimization problems: network robustness optimization and robust influence maximization. These problems hold significant theoretical and practical importance, garnering increased attention in recent research. Models and optimizers have been developed to tackle related typological design and seed determination tasks, which provide rational candidates for practical scenarios. However, existing approaches have limitations. Firstly, while structural disturbances significantly impact network robustness, their effects on information propagation remain understudied. The quest for robust seed determination in the face of topological failures remains unresolved. Secondly, both network robustness optimization and robust influence maximization focus on leveraging structural network information, but the potential synergy between solutions for these problems has been overlooked in current research. There is a pressing need for highly efficient optimizers and versatile candidate solutions capable of withstanding various scenarios comprehensively. In response to these limitations, this study introduces multi-task optimization theory to address the network correlation optimization problem described above. We first systematically analyze the correlation between the two problems, demonstrating a positive relationship through experimental results. Building on this insight, we propose MFEA-Net, a multifactor evolutionary algorithm equipped with problem-specific operators. MFEA-Net concurrently tackles the challenges of network robustness optimization and robust influence maximization, considering multiple optimization scenarios simultaneously. It aims to uncover synergistic insights across different tasks during the optimization process. Our experimental results indicate that MFEA-Net surpasses existing methods in terms of performance. This research bridges a gap in the fields of robust influence maximization and network robust cooperative optimization, offering valuable guidance for addressing real-world network system design and optimization challenges.

Secure memory adaptive event‐triggered filter design of nonlinear network system under hybrid network attacks with application to tunnel diode circuit

AbstractThis article investigates the problem of secure memory adaptive event‐triggered filter design for nonlinear network systems under hybrid network attacks. First, the introduction of memory adaptive event‐triggered mechanism and numerical quantization is employed to enhance system performance, optimize network channel utilization, and prevent network congestion. Second, we consider the impact of deceptive attacks and DoS attacks occurring in the network channel on data transmission, which form the hybrid network attacks studied in this article. Subsequently, based on the established error model, we derive sufficient conditions for the mean‐square exponential stability of the system under a given performance index using Lyapunov–Krasovskii function and linear matrix inequality (LMI) techniques. Furthermore, the filter parameters are determined using the LMI method. Finally, we illustrate the feasibility and generality of the conclusions through numerical simulation examples and a tunnel diode circuit system.

A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization

The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling.

SIM+: A comprehensive implementation-agnostic information model assisting AI-driven optimization for beyond 5G networks

Forthcoming beyond 5G (B5G) network systems are expected to introduce a new era in the mobile networks, supporting new and challenging use cases and applications. This new era will provide ubiquitous connection of up to billions of devices that are estimated to generate vast amounts of data, paving the way towards a data-driven network system design. In such network design, Artificial Intelligence (AI) will act as a key enabler with regard to network system automation and optimizations. One of the biggest challenges for AI in such complex and dynamic network environments, is the interoperability in terms of seamless exchange and analysis of information between two or more systems. Technological interoperability requires that the format in which data is generated or stored is standardized and that interconnected systems are aware of the nature of transmitted information and are able to analyze it. This paper proposes a novel, comprehensive Information Model (IM), named as SIM+, that extends all across the network, describing different network entities, attributes, capabilities and operations in an implementation-agnostic and standardized way towards assisting AI-driven optimizations. Seven key abstraction layers have been identified in an attempt to hide infrastructure complexities. The adaptability of the proposed SIM+ is showcased through its application in two state-of-the-art AI-driven solutions.

Design and use of a wireless temperature measurement network system integrating artificial intelligence and blockchain in electrical power engineering.

This work aims to investigate the potential fire hazard stemming from the overheating of power equipment. The advent of the artificial intelligence era has facilitated the fusion of blockchain and Internet of Things (IoT) technologies. This work delves into the technical standards for IoT equipment monitoring and smart grid communication, and the IoT environment of power grid equipment. This work introduces a temperature monitoring network tailored for IoT wireless power equipment suitable for the power environment, and conducts system debugging in the power laboratory. The findings affirm that the temperature out-of-limit alarm testing has met the required criteria, confirming the system's ability to issue timely warnings when temperatures breach a predefined threshold, effectively avoiding high-temperature misfires. This work fully harnesses the secure and user-friendly operation of smart blockchain and the wireless sensing technology of the IoT to realize online monitoring and remote temperature measurement of the power system. It can effectively prevent equipment from overheating and damage, and promote the development of equipment condition monitoring technology in electric power engineering.

Application of IoT technology in cyber security prevention system

Abstract In the process of gradually expanding the scale of computer networks and the design of network systems becoming more and more complex, people pay more and more attention to the construction of network security protection systems. Starting from the blockchain encryption technology, the article establishes the authentication and access management key based on the elliptic curve encryption algorithm and combines the maximum entropy model with the hidden Markov model to construct the MEMM for intrusion detection of network security. Based on the effective signal-to-noise ratio model of the network channel, an adaptive channel selection strategy based on the UCB algorithm is proposed. The IoT security prevention system is built based on IoT technology, and each functional module of the system is designed. The system’s authentication security, network intrusion detection, adaptive channel selection, and concurrency performance were tested after the design was completed. The encryption operation time of the ECC algorithm was improved by 41.53% compared to the RSA algorithm, the average time of the MEMM network intrusion detection was 41.54ms, and the false alarm rate of the intrusion detection was kept below 16.5%. The average packet collection rate of the nodes in the adaptive channel selection algorithm is 90.98%. The maximum system throughput is up to 62.19MB, and the extreme difference in data volume between different nodes is only 38 entries. Constructing a network security prevention system based on IoT technology and combining multiple encryption techniques can ensure the secure transmission of network data.

A Spatial Clustering and Matching Game–Based Multihop Routing Algorithm for Heterogeneous UAVs

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in military and civilian applications, necessitating efficient and reliable communication networks for cooperative operations. However, heterogeneous UAVs pose significant challenges for data transmission within and outside the cluster due to their high mobility, rapid topology changes, and limited node energy. For the routing planning problem of transmitting data from autonomous, heterogeneous UAVs to the cloud, existing methods struggle to accommodate the heterogeneity of UAVs and the communication bandwidth requirements, given the NP‐hard nature of the problem. To address this issue, this paper first constructs an exact model based on mixed‐integer linear programming to describe the heterogeneous UAVs, comprehensively searches the solution space, and generates a reasonable routing scheme for the heterogeneous UAVs, guiding the cluster communication network system design. Secondly, a heuristic algorithm is proposed that leverages density–based spatial clustering and matching game theory. The algorithm calculates relay nodes based on local density and relative distance. It generates an approximate optimal data transmission path for each UAV through a multilevel matching process, effectively reducing communication delay within the cluster. Finally, the paper conducts simulation experiments in randomly generated regional scenarios to validate the efficiency and effectiveness of the proposed method.

A Modular Information Fusion Model for Urban Landscape Architecture Based on Virtual Network Design System

Urban landscape design is considered to be an important factor to improve the quality of urban life, which is of great significance to ecological environment protection and urban sustainable development. This paper introduces a modular urban landscape design information fusion model based on virtual network design system, aiming at improving the efficiency and quality of landscape design by using cutting-edge information technology, and develops an information fusion model to integrate geographic information system (GIS), building information modeling (BIM), virtual reality/augmented reality (VR/AR) and Internet of Things (IoT) technologies to achieve effective data management and design optimization. The experimental results show that the modular design method can significantly shorten the design process cycle and improve the design quality, and the internal rate of return reaches 28.1%-39.7%, which reflects the economic benefits and project success. Finally, the advantages and limitations of this model are discussed, and some thoughts on the future landscape design are put forward.

Economic aspects of project decision-makingat the stage of designing the utility network

The efficiency of the economic functioning of any facility largely depends on the ability of managers of various levels to carefully prepare and justify the decisions made. The conditions of the market (competitive) economy put forward serious. In solving the problem of increasing the economic efficiency of the construction of engineering networks, a special place belongs to design, in the field of which scientific and technical achievements are implemented, which determine the technical and economic level of construction. The article examines the theoretical and methodological principles of designing engineering networks, which are aimed at improving the procedural and instrumental basis for making project decisions and justifying measures to optimize the management of engineering networks in the conditions of dynamic changes in construction projects requirements for the quality, timeliness, completeness, reliability of economic information, depth of analysis of economic indicators. The interaction of the construction of new utility networks and the economy is very important for the fulfillment of the tasks of the economic strategy, which are related to capital construction. Current problems of creating a modern design automation system (CAD) of engineering networks are considered in order to ensure the effective use of the resources of the target product, which, even at the design stage, allows to take into account the possibility of expanding or reconstructing the system in case of the addition of new consumers of the target product. network The paper proposes a new approach to the design of modern systems of automated design, which allows the use of CAD to solve the problems of external and internal design of network systems. The tasks that are solved at the stages of external and internal design are formulated and specified. The key task of external design is to find out the fundamental possibility of creating a network according to the desired properties that satisfy the imposed restrictions. Internal design includes the synthesis and analysis of the network being designed. It is shown that the CAD of engineering networks can be represented as a set of methodological, informational, software, technical and organizational support, and a general diagram of the interconnection of CAD tools at the stages of engineering network design and operation has been developed and researched.

Joint energy and spectral optimization in Heterogeneous Vehicular Network

With the latest developments in both the automotive and communications industries, especially concerning the emerging 5G networks, IoV, and the adoption of Vehicle-to-Everything (V2X) connectivity, there has been a shift towards the establishment of Heterogeneous Vehicular Networks (HetVNets) environments. The rapid growth of data traffic and the drastic expansion of heterogeneous network infrastructure have resulted in a significant increase in energy consumption within wireless communication systems. Balancing energy efficiency and spectral efficiency has become a major challenge in Heterogeneous Vehicular networks, particularly concerning energy optimization, making the design of network systems considerably more challenging. Therefore, this paper attempts to optimize the energy utilized for each packet transmission, considering its stochastic nature and the optimized control parameters of two meta-heuristic algorithms-Particle Swarm Optimization and Artificial Bee Colony Optimization. The optimization process is executed using the Particle Bee Colony Swarm algorithm. Subsequently, a comparison is made with other proposed algorithms, namely LDOD, FO, RO, and MATO, in terms of energy efficiency and spectral efficiency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating a 30.32% increase in spectral efficiency and 73.25% increase in energy efficiency.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.