Network Design Research Articles

Correction: Trade credit and loan in capital‑constrained supply chain network design model

Correction: Trade credit and loan in capital‑constrained supply chain network design model

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Heterometallic [2]Catenane-Crosslinked Supramolecular Networks with Improved Antibacterial Activity.

The construction of supramolecular networks with novel crosslinks is of great significance in expanding their chemical structures and exploring their advanced functions. Herein, we prepare a type of [2]catenane-cored supramolecular networks based on the crosslinking of polyethylene glycol (PEG) using a heterometallic [2]catenane unit. By adjusting the molecular weight of PEG, the solubility of the networks can be tuned and gels are formed using low molecular weight PEG. The introduction of heterometallic [2]catenane offers the networks good antibacterial properties owing to the synergistic antimicrobial activity of Pt(II) and Cu(I) ions in the [2]catenane. This study provides a simple and efficient strategy for constructing supramolecular networks with topological crosslinks as antibacterial materials, which will promote the structural design and biological applications of supramolecular networks.

The complexity of optimizing atomic congestion

Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a system-optimal set of strategies—that is, a centrally planned routing that minimizes the average cost of agents—is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem.

Volume-based branch-and-cut algorithm for large-scale fixed-charge multicommodity network design

Volume-based branch-and-cut algorithm for large-scale fixed-charge multicommodity network design

Airspace Constrained Free-Flight Analysis: Implications for Uncrewed Air Traffic Management

This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic.

Optical circuit switched three-stage twisted-folded Clos-network design model guaranteeing admissible blocking probability

Some data center networks have already started to use optical circuit switching (OCS) with potential performance benefits, including high capacity, low latency, and energy efficiency. This paper addresses a switching network design to maximize the network radix, i.e., the number of terminals connected to the network under the condition that a specified number of identical switches with the size N×N and the maximum admissible blocking probability are given. Previous work presented a two-stage twisted and folded Clos network (TF-Clos) with a blocking probability guarantee for OCS, which has a larger network radix than TF-Clos with a strict-sense non-blocking condition. Expanding the number of stages allows for enhancing the network radix. This paper proposes a model designing an OCS three-stage TF-Clos structure with a blocking probability guarantee to increase the network radix compared to the two-stage TF-Clos. We formulate the problem of obtaining the network configuration that maximizes the network radix as an optimization problem. We conduct an algorithm based on an exhaustive search to obtain a feasible solution satisfying the constraints of the optimization problem. This algorithm identifies the structure with the largest network radix in non-increasing order to avoid unnecessary searches. Numerical results show that the proposed model achieves a larger network radix than the two-stage model.

Fault diagnosis method of automobile rolling bearing based on transfer learning and improved DenseNet

Aiming at the problems caused by ignoring the time series characteristics, the scarcity of labeled data and the long diagnosis time in the fault diagnosis of one-dimensional vibration signals of automobile bearings, a new method combining improved DenseNet and transfer learning is proposed in this study. This method uses Recurrent Plot (RP) technology to convert one-dimensional vibration data into two-dimensional images to fully tap the potential value of time series. By optimizing the DenseNet network structure, the fault features are extracted effectively. Lightweight network design and MobileViT Attention mechanism are used to reduce the number of parameters and improve computing efficiency. With the help of transfer learning technology, the fault features in the source domain are transferred to the target domain, which solves the problem of cross-condition diagnosis and greatly reduces the diagnosis time. The experimental results show that the proposed method can improve the accuracy of fault identification and diagnosis efficiency, and achieve accurate classification.

Data model to enable multidimensional process mining for data farming based value stream planning in production networks

AbstractInternational industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.

Conjugated Coordination Nanosheets with Molecular Rotors for Pseudocapacitors: Nanoarchitectonics and Enhanced Performance.

High-level pseudocapacitive materials require incorporations of significant redox regions into conductive and penetrable skeletons to enable the creation of devices capable of delivering high power for extended periods. Coordination nanosheets (CNs) are appealing materials for their high natural electrical conductivities, huge explicit surface regions, and semi-one-layered adjusted pore clusters. Thus, rational design of ligands and topological networks with desired electronic structure is required for the advancement in this field. Herein, we report three novel conjugated CNs (RV-10-M, M=Zn, Ni, and Co), by utilizing the full conjugation of the terpyridine-attached flexible tetraphenylethylene (TPE) units as the molecular rotors at the center. We prepare binder-free transparent nanosheets supported on Ni-foam with outstanding pseudocapacitive properties via a hydrothermal route followed by facile exfoliation. Among three CNs, the high surface area of RV-10-Co facilitates fast transport of ions and electrons and could achieve a high specific capacity of 670.8 C/g (1677 F/g) at 1 A/g current density. Besides, the corresponding flexible RV-10-Co possesses a maximum energy density of 37.26 Wh kg-1 at a power density of 171 W kg-1 and 70 % capacitance retention even after 1000 cycles.

Improving losses & accuracy through design of deep convolutional generative adversarial network (DCGAN) for plant disease detection tasks

Improving losses & accuracy through design of deep convolutional generative adversarial network (DCGAN) for plant disease detection tasks

SFDiff: Diffusion model with sufficient spatial‐Fourier frequency information interaction for low‐light image enhancement

AbstractDiffusion models are increasingly applied in low‐light image enhancement tasks due to their exceptional capability to model data distributions, but most current methods focus only on the original pixel space and neglect the potential of Fourier frequency information. In this article, SFDiff is proposed, a novel low‐light image enhancement method that integrates Fourier frequency information into the diffusion process. Specifically, Fourier transforms are applied at both the image and feature levels to separately enhance the amplitude and phase components, which restores global illumination degradation and positional information. Then a Spatial‐Frequency Fusion (SFF) block is used to fully integrate and interact with the information across spatial and frequency domains. Since illumination degradation is primarily manifested in the amplitude component, a loss function based on maximum likelihood learning is employed to constrain the amplitude component at each step of the sampling process, ensuring that the reverse process maintains an optimal trajectory. Owing to the streamlined network design and the fact that the Fourier transform requires no extra parameters, SFDiff achieves a reduction in parameters of over compared to several state‐of‐the‐art (SOTA) diffusion models and delivers high‐quality enhancement results on multiple real‐world datasets. The code is available at https://github.com/MrWan001/SFDiff.

Design of A Novel Generative Adversarial Network for Outlier Prediction with AMBO Algorithm

Design of A Novel Generative Adversarial Network for Outlier Prediction with AMBO Algorithm

Joint Sensing, Communications, and Computing Design for 6G URLLC Service-Oriented MEC Networks

Joint Sensing, Communications, and Computing Design for 6G URLLC Service-Oriented MEC Networks

Solving large-scale instances of the urban transit routing problem with a parallel artificial bee colony-hill climbing optimization algorithm

Swarm Intelligence simulates the collective behavior of decentralized and self-organized swarms. One of the main relevant methods is the Artificial Bee Colony (ABC) algorithm which simulates the foraging behavior of bee swarms in a colony to produce efficient solutions to various problems. The Urban Transit Routing Problem (UTRP) involves finding an efficient set of routes in a transit network to satisfy travel demand subject to operational and budget constraints. It is a complex, NP-Hard problem, in which otherwise correct solutions can be rejected because of impracticability. In this study, a hybrid algorithm consisting of a parallel ABC and Hill Climbing was used to find quality solutions to the UTRP. Thorough comparative results on Mandl’s well-known instance and Mumford’s large-scale instances demonstrate that the proposed algorithm outperforms existing techniques, achieving high levels of direct trip coverage in small computational times. Remarkably, when applied to the most extensive benchmark comprising over 6 million trips and 60 bus routes, the proposed algorithm demonstrates an impressive 11 % enhancement in direct coverage over the previously best-reported results, allowing the design of real-world bus networks in under 3 hours.

Chosen Plaintext Attack on Single Pixel Imaging Encryption via Neural Differential Cryptanalysis

AbstractSingle pixel imaging (SPI) shows great potential in encryption by its indirect imaging mechanism. However, there appears to be room for further exploration in the corresponding cryptanalysis. Current studies primarily rely on straightforward end‐to‐end cryptanalysis of plain‐ciphertext pairs, ignoring the fundamental SPI optical path. As a result, the effectiveness of most attacks depends on the training data and the design of network, triggering low certainty and confidence. In this study, an alternative model is proposed to attack multiple SPI encrypting methods based on chosen plaintext attack framework, where arbitrary plaintexts can be encrypted as ciphertexts for cryptanalysis. In terms of the basic SPI setup, it is found that no matter how complicated the patterns are encrypted, the linear relationship between encrypted patterns and intensity always maintain. Thus, specifically, the ciphertext is first differentialized to derive encrypted patterns. By further reconstructing the pixel correlation of these derived patterns, deep learning is employed to correct them. Ultimately, the cracked patterns are used to decrypt plaintexts by conventional correlation. The experiments demonstrate that this method possesses a certain degree of reusability in the SPI encryption with linear propagating characteristic, like pattern‐encrypting class, demonstrating potential for the indirect optical encryption.

Zero-cost upgrade to a multi-fiber network with partial lane-change capabilities

Growing capacity requirements are leading to the deployment of multiple fibers in each optical network link. Even though deploying state-of-the-art multi-fiber network architectures with stacked and independent fiber layers simplifies network design and control, spectrum can be used more efficiently if the optical-network nodes allow fiber layers to be interconnected, i.e., if the so-called lane change is enabled. Unfortunately, lane change in high-degree optical nodes requires wavelength selective switches (WSSs) with a high number of ports, which is prohibitively costly or even unfeasible with current WSS technology. Instead, lane change in low-degree optical nodes can be enabled at no extra cost, using WSS ports that are otherwise left empty. In this study, we describe our proposal for a multi-fiber network with partial lane-change capabilities and perform a simulative study to identify the advantages of this architecture, as well as discuss the emerging resource allocation challenges associated with it. We demonstrate that, by enabling lane change in degree-2 nodes, we can increase network throughput by 3% and restore 5%–8% more traffic in the case of single- and double-link failures at no additional equipment cost.

Synchronization Optimization of Pipeline Layout and Pipe Diameter Selection in a Drip Irrigation Network System Based on the Jaya Algorithm

To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical model of synchronization optimal design of pipe layout and pipe diameter selection in a drip irrigation network system with constraints on pipe diameter, flow velocity, and pipe pressure is established. Using an irrigation district in Xinjiang, China, as an example, the Jaya algorithm optimization design program was run independently 50 times, and the relative deviation of each optimization result from the optimal solution was calculated. The results show that the annual cost per unit area o is reduced to 635.99 RMB/hm2, a 25.34% reduction compared to the original engineering program, and the investment-saving effect is obvious. The relative deviation is controlled within 3%, which shows that the algorithm has stable convergence performance and can meet the requirements of actual engineering design. The Jaya algorithm eliminates the need for parameter tuning, and it excels in cost savings, algorithm stability, and computational accuracy, making it an effective method for the single-objective optimization design of drip irrigation networks.

RyuGuard—Combining Ryu and machine learning for proactive DDoS defense in software‐defined networks

SummarySoftware defined network (SDN) is an experimental network design utilized by software companies, academia, and healthcare systems to provide adequate resource utilization, data management, superior network control, and administration. However, these networks face substantial risks, especially from distributed denial of service (DDoS) attacks, requiring robust cybersecurity measures. This article proposes RyuGuard, an intrusion detection and prevention system (IDPS) enhanced with machine learning (ML) capabilities, specifically designed to protect SDNs from DDoS attacks. A DDoS‐specific dataset was collected in the SDN environment through feature extraction from normal and malicious traffic. The evaluation of the dataset with the ML classifiers demonstrates that the decision tree (DT) was the most effective model, with a low false alarm rate (FAR), achieving an accuracy of 99.9%, and rapid execution time, which ensures timely detection and response, suitable for real‐time implementation. RyuGuard, with DT deployment and utilizing the programmability feature of SDN, is designed to predict and prevent the DDoS attack from the ongoing traffic of SDN. Compared with the other existing models, the presented IDPS, RyuGuard, enables early attack prediction, preventing the full impact of DDoS within the network while maintaining sustained throughput and performance with low CPU utilization.

Intelligent agricultural robotic detection system for greenhouse tomato leaf diseases using soft computing techniques and deep learning

The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.