Problem In Networks Research Articles

Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks

This paper addresses the rendezvous problem in asymmetric cognitive radio networks (CRNs) by proposing a novel reinforcement learning (RL)-based channel-hopping algorithm. Traditional methods like the jump-stay (JS) algorithm, while effective, often struggle with high time-to-rendezvous (TTR) in asymmetric scenarios where secondary users (SUs) have varying channel availability. Our proposed RL-based algorithm leverages the actor-critic policy gradient method to learn optimal channel selection strategies by dynamically adapting to the environment and minimizing TTR. Extensive simulations demonstrate that the RL-based algorithm significantly reduces the expected TTR (ETTR) compared to the JS algorithm, particularly in asymmetric scenarios where M-sequence-based approaches are less effective. This suggests that RL-based approaches not only offer robustness in asymmetric environments but also provide a promising alternative in more predictable settings.

A Novel Optimization Algorithm Inspired by Egyptian Stray Dogs for Solving Multi-Objective Optimal Power Flow Problems

One of the most important issues that can significantly affect the electric power network’s ability to operate sustainably is the optimal power flow (OPF) problem. It involves reaching the most efficient operating conditions for the electrical networks while maintaining reliability and systems constraints. Solving the OPF problem in transmission networks lowers three critical expenses: operation costs, transmission losses, and voltage drops. The OPF is characterized by the nonlinearity and nonconvexity behavior due to the power flow equations, which define the relationship between power generation, load demand, and network component physical constraints. The solution space for OPF is massive and multimodal, making optimization a challenging concern that calls for advanced mathematics and computational methods. This paper introduces an innovative metaheuristic algorithm, the Egyptian Stray Dog Optimization (ESDO), inspired by the behavior of Egyptian stray dogs and used for solving both single and multi-objective optimal power flow problems concerning the transmission networks. The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. The results obtained indicate that, in comparison to other used algorithms, the suggested technique gives a significantly enhanced performance in solving the OPF problem.

Uncertainty Modeling and Stability Assessment of Minimum Spanning Trees in Network Design

The Minimum Spanning Tree (MST) problem in networks focuses on finding efficient routes, with applications in transportation, logistics, telecommunications, and more. However, catastrophes can make these networks uncertain, requiring robust computational models for decision-making. This paper introduces an uncertainty theory-based model to analyze the stability of MSTs in uncertain networks. By incorporating reliability and risk variables, we assess the robustness of uncertain MSTs (UMSTs) and address the challenge of computing link tolerances, which define the range within which network links can vary without compromising MST optimality. This study proposes computational formulations to systematically calculate these tolerances, offering a more efficient alternative to traditional re-optimization methods.

Machine Learning With Tree Tensor Networks, CP Rank Constraints, and Tensor Dropout.

Tensor networks developed in the context of condensed matter physics try to approximate order- N tensors with a reduced number of degrees of freedom that is only polynomial in N and arranged as a network of partially contracted smaller tensors. As we have recently demonstrated in the context of quantum many-body physics, computation costs can be further substantially reduced by imposing constraints on the canonical polyadic (CP) rank of the tensors in such networks. Here, we demonstrate how tree tensor networks (TTN) with CP rank constraints and tensor dropout can be used in machine learning. The approach is found to outperform other tensor-network-based methods in Fashion-MNIST image classification. A low-rank TTN classifier with branching ratio b=4 reaches a test set accuracy of 90.3% with low computation costs. Consisting of mostly linear elements, tensor network classifiers avoid the vanishing gradient problem of deep neural networks. The CP rank constraints have additional advantages: The number of parameters can be decreased and tuned more freely to control overfitting, improve generalization properties, and reduce computation costs. They allow us to employ trees with large branching ratios, substantially improving the representation power.

Revisiting the traffic flow observability problem: A matrix-based model for traffic networks with or without centroid nodes

This study introduces a graph theory-based model that addresses the link flow observability problem in traffic networks by optimizing passive sensor deployment. The model aims to determine the minimal number of sensors and their optimal placement. It constructs a virtual network and uses isomorphic graph theory to map between the original and virtual networks, ensuring consistency in nodes, links, and link directions. Two formulas are proposed to calculate the minimum number of observable links required across different networks, factoring in links, ordinary nodes, centroid nodes, and added links. Key concepts such as chords, cut sets, and loops, along with their matrices, are analyzed. A matrix-based framework is developed to consider flow conservation conditions. Results show that solving the full link flow observability problem using node flow conservation equations yields a fixed number of sensors with non-unique deployment schemes, Additionally, a resource-constrained sensor network optimization (RSNO) model is presented, employing null space projection (NSP) as an objective function to quantify the impact of budget constraints particularly under the condition if all the link flows cannot be observed. Numerical examples demonstrate the RSNO model's applications.

Optimizing Grover's Algorithm for Routing in Quantum Wireless Communication Networks

This paper presents a novel approach to optimizing Grover’s algorithm for addressing the routing problem in quantum wireless communication networks. We begin by conducting a comprehensive analysis of key quantum communication technologies, including quantum teleportation and quantum entanglement swapping, and subsequently construct a quantum wireless communication network model grounded in these principles. Building on this foundation, we design and implement a multi-level quantum wireless network information transmission mechanism, incorporating quantum channels for identity authentication to ensure secure communication. Focusing on the routing challenges in quantum wireless communication networks, we propose innovative applications and optimizations of Grover’s quantum search algorithm. Specifically, we introduce an improved algorithm that efficiently searches for routing paths with maximal metrics within a limited number of hops. This enhancement not only significantly increases routing efficiency but also mitigates the risk of quantum channel disconnection caused by the depletion of entangled quantum pairs, thereby improving the overall success rate of communication. Simulation and experimental results demonstrate that the proposed optimization algorithm substantially enhances the routing performance of quantum wireless communication networks while maintaining robust security measures. This study offers a new solution to the routing challenges in quantum wireless communication networks and significantly optimizes Grover’s algorithm, contributing to the development of more efficient and reliable quantum communication networks.

Learning Activities in Mathematics Education: Application of Problem-Based Learning in Power Dominating Set for Electricity Network Optimization Problems

Learning activities in mathematics education play an important role in developing students' analytical and problem-solving skills, especially in applying mathematical concepts to real-world problems. This research aims to apply the Problem-Based Learning (PBL) method to the topic of Power Dominating Set to optimize the electricity network. Power Dominating Set is a concept in graph theory that can be used to minimize the number of control points in the electricity network, thus optimizing the use of resources and increasing the efficiency of the network. By using PBL, students are expected to understand the concept of Power Dominating Set both abstractly and in a practical context relevant to the real world. This research was conducted using an experimental method with two groups of students, namely the experimental group using the PBL method and the control group using the conventional learning method. The results showed that the group that learned with PBL had a deeper understanding and better analytical skills in applying Power Dominating Set to electricity network optimization problems. Discussion of the results showed that the PBL approach is not only effective in improving the understanding of abstract mathematical concepts, but also capable of enhancing students' skills in identifying and solving real-world electricity network problems. Thus, the application of PBL in mathematics education can be an effective alternative to connect abstract mathematical concepts with practical applications, especially in electricity network optimization.

RECC: Joint Congestion Control Based on RTT and ECN for High-speed RDMA Networks

Together with the construction of RDMA networks for data center applications, the RDMA-coupled DCQCN dominates the RDMA Congestion Control (CC). However, DCQCN suffers severe performance problems in high-speed RDMA networks with modern high-performance distributed applications such as machine learning training. This paper presents RECC, inspired by both the latest emerging programmability of RDMA NICs (RNICs) and limitations in existing RDMA congestion control mechanisms. RECC comprehensively leverages RTT and ECN events from RNICs to handle congestion timely and precisely, along with a History-aware Burst Smooth mechanism to avoid wrong rate decisions under various traffic patterns. We implement RECC completely based on commercial RNICs without any modifications to switches, RDMA protocol stack, and applications. The results of microbenchmark testbed experiments and real Machine Learning (ML) workload experiments with hundreds of 200G RNICs show that RECC can significantly reduce network tail latency and pause duration by up to 64.4% and 95%, respectively, compared with DCQCN. In addition, large-scale simulations with realistic workloads demonstrate that RECC achieves comparable performance with HPCC.

A Swarm Intelligence Technique for Link Monitoring Problems in Wireless Ad Hoc Networks

A Swarm Intelligence Technique for Link Monitoring Problems in Wireless Ad Hoc Networks

Joint Design and Pricing Problem for Symbiotic Bioethanol Supply Chain Network: Model and Resolution Approach

To fight climate change, the Province of Quebec, Canada, has set targets to reduce greenhouse gas emissions by reducing fossil fuel consumption and integrating biofuel content into gasoline and diesel fuel. Motivated by a real-world case study, this paper presents a novel distributed decision model for designing a symbiotic supply chain network and supporting pricing decisions. A distributed decision-making problem is formulated as a game theoretic approach considering a Stackelberg–Nash equilibrium. A novel mathematical model is proposed to support the decisions of four actors: corn farms, processing depots, pig farms, and biorefineries. In addition to the configuration of a biofuel-based industrial symbiosis, the model offers the possibility of setting purchase prices and supply levels for biomass (corn stover supplied by farms), as well as determining sales prices and production levels for the main product (the cellulosic sugar used for the bioethanol production) and a coproduct (pig feed sold to pig farmers). A three-step optimization process involving the user is proposed to address the computational challenges posed by large design problem instances. The case study of the Province of Quebec is used to evaluate the performance of the proposed resolution approach.

Predefined-time modified function projective synchronization of memristor-based multidirectional associative memory neural networks with time-varying delay

This paper is concerned with the predefined-time modified function projective synchronization problem of memristor-based multidirectional associative memory neural networks (MMAMNNs) with time-varying delay. Firstly, a new predefined-time stability theorem is proposed, which imposes more relaxed and effective conditions on the Lyapunov-Krasovskii function (LKF). Secondly, by designing a new feedback control strategy, sufficient conditions for ensuring the predefined-time modified function projection synchronization between master and slave systems are obtained. In addition, by changing the projection factor, the results of this paper can be flexibly extended to various synchronization types, such as complete synchronization, anti-synchronization, and proportional synchronization. Finally, the correctness of the theory is verified through numerical simulations.

Furutsu-Novikov–like Cross-Correlation–Response Relations for Systems Driven by Shot Noise

We consider a dynamic system that is driven by an intensity-modulated Poisson process with intensity Λ(t)=λ(t)+ϵν(t). We derive an exact relation between the input-output cross-correlation in the spontaneous state (ϵ=0) and the linear response to the modulation (ϵ>0). If ϵ is sufficiently small, linear-response theory captures the full response. The relation can be regarded as a variant of the Furutsu-Novikov theorem for the case of shot noise. As we show, the relation is still valid in the presence of additional independent noise. Furthermore, we derive an extension to Cox-process input, which provides an instance of colored shot noise. We discuss applications to particle detection and to neuroscience. Using the new relation, we obtain a fluctuation-response relation for a leaky integrate-and-fire neuron. We also show how the new relation can be used in a remote control problem in a recurrent neural network. The relations are numerically tested for both stationary and nonstationary dynamics. Lastly, extensions to marked Poisson processes and to higher-order statistics are presented. Published by the American Physical Society 2024

Delay-induced consensus of fractional-order complex networks via intermittent sampled position control

This paper investigates the consensus problem of fractional-order complex networks via intermittent sampled position control. A delay-induced consensus protocol with intermittent sampled position for the fractional-order system is proposed, where only the current and delayed sampled positions are used. Meanwhile, the controllers operate only for a period of time in each sampling interval, which decrease the working time and update rates of controllers. Successively, by discussing four situations involving delay, sampling period, and communication width, necessary and sufficient consensus criteria are presented. It is interesting to find the delay has a positive impact on consensus of fractional-order complex networks, since consensus cannot be achieved without delay under the proposed protocol. Finally, simulation examples are presented to illustrate the theoretical analysis.

Quantum Binary Improved Artificial Bee Colony Algorithm to Solve the Spanning Tree Construction Problem in Vehicular Ad Hoc Network

Quantum Binary Improved Artificial Bee Colony Algorithm to Solve the Spanning Tree Construction Problem in Vehicular Ad Hoc Network

An active queue management method for multiple bottleneck networks based on distributed average tracking

The active queue management (AQM) is a key technology for the information infrastructure and the Internet. The AQM for multiple bottleneck networks is especially challenging than that for single-bottleneck networks because the stability analysis of multiple bottleneck networks is more complex. In this paper, the active queue management problem for multiple bottleneck networks is considered. A novel random early detection algorithm is designed based on distributed average tracking technology, which is used to estimate the global queue length and improve the network's performance. Sufficient conditions for global asymptotic stability of the closed-loop active queue management system are presented, and some simulation results with NS-2 are given. Compared with the RED algorithm, simulation results show that the presented algorithm has smaller oscillations at routers, which highlights its effectiveness.

Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization

Vehicle-to-Vehicle (V2V) communication can not only provide unrestricted inter-vehicle information transmission, but also improve spectrum utilization efficiency. However, it also brings uncontrollable co-channel interference, which can not guarantee the quality of service of V2V communication. In this paper, we propose an intelligent resource allocation scheme for V2V communication to improve vehicle connectivity. To enhance cooperation among vehicles and avoid excessive co-channel interference between them, we propose an asynchronous resource allocation method where vehicles choose to send or not to send data based on observed environmental information to ensure stable overall performance. Furthermore, we present a novel resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization (HAPPO) to solve the resource allocation problem in asynchronous vehicular networks. The HAPPO algorithm calculates the global advantage function when each agent makes an action during the training process to ensure that the action taken contributes to the overall performance improvement. Our proposed approach improves the robustness of V2V communication by reducing co-channel interference while maintaining stable overall performance. Simulation results show that the proposed approach can effectively improve the V2V communication connectivity and reduce the packet loss rate compared with the existing methods.

Hub port location and routing for a single-hub feeder network: Effect of liner shipping network connectivity

For the design of feeder lines within a region, previous studies assume that the hub port is given in advance. It is necessary to investigate locating hub port together with designing feeder lines for a region. This paper proposes a hub port location and routing problem for a single-hub feeder network, which addresses feeder line design and hub port location. The proposed problem also considers liner shipping network connectivity (the connection between feeder lines and main lines via container transshipment at hub ports). It is described by a mixed integer linear program, which is solved by a genetic algorithm devised. Numerical experiments show that: i) the devised algorithm can efficiently solve our model; ii) due to considering liner shipping network connectivity, the optimal solution of hub port location and feeder line design becomes more rational, as compared with that without considering liner shipping network connectivity.

B cell epitope prediction by capturing spatial clustering property of the epitopes using graph attention network

Knowledge of B cell epitopes is critical to vaccine design, diagnostics, and therapeutics. As experimental validation for epitopes is time-consuming and costly, many in silico tools have been developed to computationally predict the B cell epitopes. While most methods show poor performance, deep learning methods in recent years have shown promising results. We developed a method called EpiGraph that outperformed previous methods, including those that showed a significant improvement in performance in recent years. Our model’s performance can be attributed to the following factors: (1) a combination of structure and sequence feature embeddings obtained from pretrained ESM-IF1 and ESM-2 models could capture the structural and evolutionary features of B cell epitopes, (2) a graph attention network could learn the spatial proximity of B cell epitopes with high graph homophily, and (3) residual connections in the model framework mitigate the over-smoothing problem in the graph neural network. Our model achieved the highest performance on an independent benchmark dataset. The results were also consistent on a different dataset. The datasets and source codes are available at https://github.com/sj584/EpiGraph. A user-friendly web server is freely available at http://epigraph.kaist.ac.kr.

An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization

The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics.

Side-Scan Sonar Image Generation Under Zero and Few Samples for Underwater Target Detection

The acquisition of side-scan sonar (SSS) images is complex, expensive, and time-consuming, making it difficult and sometimes impossible to obtain rich image data. Therefore, we propose a novel image generation algorithm to solve the problem of insufficient training datasets for SSS-based target detection. For zero-sample detection, we proposed a two-step style transfer approach. The ray tracing method was first used to obtain an optically rendered image of the target. Subsequently, UA-CycleGAN, which combines U-net, soft attention, and HSV loss, was proposed for generating high-quality SSS images. A one-stage image-generation approach was proposed for few-sample detection. The proposed ADA-StyleGAN3 incorporates an adaptive discriminator augmentation strategy into StyleGAN3 to solve the overfitting problem of the generative adversarial network caused by insufficient training data. ADA-StyleGAN3 generated high-quality and diverse SSS images. In simulation experiments, the proposed image-generation algorithm was evaluated subjectively and objectively. We also compared the proposed algorithm with other classical methods to demonstrate its advantages. In addition, we applied the generated images to a downstream target detection task, and the detection results further demonstrated the effectiveness of the image generation algorithm. Finally, the generalizability of the proposed algorithm was verified using a public dataset.