Network Topology Changes Research Articles

FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

DTE-RR: Dynamic Topology Evolution-Based Reliable Routing in VANET

The dynamics of Vehicular Ad-hoc Network (VANET) brings tremendous challenges to reliable routing. In the existing topology evolution schemes, the link connectivity and the interaction between links have yet to be well investigated, resulting in the inability to accurately describe the evolution of routing topology. In this letter, we propose a Dynamic Topology Evolution (DTE) based reliable routing algorithm to improve transmission reliability. The DTE model is constructed to formulate the evolution of network topology. Especially, we analyze the network topology changes from the perspective of nodes and links. Meanwhile, the Dynamic Bond Percolation (DBP) model is extended to describe link interaction. Then, the DTE model is completed by deriving the state transition matrix of the network topology based on the link change using Markov theory. Finally, the DTE-based Reliable Routing (DTE-RR) algorithm is designed to optimize routing paths. The experimental results demonstrate that DTE-RR outperforms the existing protocols regarding packet delivery ratio and end-to-end delay.

Temporality-induced chaos in the Kuramoto Model

Switched dynamical systems have been extensively studied in engineering literature in the context of system control. In these systems, the dynamical laws change between different subsystems depending on the environment, a process that is known to produce emergent behaviors---notably chaos. These dynamics are analogous to those of temporal networks, in which the network topology changes over time, thereby altering the dynamics on the network. It stands to reason that temporal networks may therefore produce emergent chaos and other exotic behaviors unanticipated in static networks, yet concrete examples remain elusive. Here, we present a minimal example of a networked system in which temporality produces chaotic dynamics not possible in any static subnetwork alone. Specifically, we consider a variant of the famous Kuramoto model, in which the network topology alternates between different configurations in response to the phase dynamics. We show under certain conditions this can produce a strange attractor, and we verify the presence of chaos by analyzing its geometrical properties. Our results provide new insights on the consequences of temporality for network dynamics, and acts as a proof of concept for a novel mechanism behind generating chaotic dynamics in networks.

Vehicle Clustering and Resource Allocation Algorithm Based on Cellular Network

<p>As a special Mobile Ad-hoc Network (MANET), Vehicular Ad-hoc Network (VANET) plays a very important role in the future intelligent transportation system. In order to solve the problems of unstable communication connection, fast network topology change and low communication resource utilization caused by high vehicle mobility in VANET, a low-complexity resource allocation algorithm based on vehicle cluster is proposed. Firstly, considering the speed, position and moving direction of the vehicles, a vehicle clustering algorithm based on movement consistency is proposed to cluster the vehicles and keep the vehicle cluster stable. Secondly, a low-complexity resource allocation algorithm is proposed to improve the utilization rate of communication resources, which is constrained by the interference caused by the vehicle clusters to the cellular users. Simulation results show that the proposed algorithm has low complexity and can better maintain the stability of vehicle clusters and improve the system capacity in the common complex Internet of Vehicles (IoV) scenarios in cities.</p> <p> </p>

Evolutionary Method of Heterogeneous Combat Network Based on Link Prediction.

Currently, research on the evolution of heterogeneous combat networks (HCNs) mainly focuses on the modeling process, with little attention paid to the impact of changes in network topology on operational capabilities. Link prediction can provide a fair and unified comparison standard for network evolution mechanisms. This paper uses link prediction methods to study the evolution of HCNs. Firstly, according to the characteristics of HCNs, a link prediction index based on frequent subgraphs (LPFS) is proposed. LPFS have been demonstrated on a real combat network to be superior to 26 baseline methods. The main driving force of research on evolution is to improve the operational capabilities of combat networks. Adding the same number of nodes and edges, 100 iterative experiments demonstrate that the evolutionary method (HCNE) proposed in this paper outperforms random evolution and preferential evolution in improving the operational capabilities of combat networks. Furthermore, the new network generated after evolution is more consistent with the characteristics of a real network.

Memory Enabled Bumblebee-Based Dynamic Spectrum Access for Platooning Environments

In this paper, we propose a novel memory-enabled non-uniform sampling-based bumblebee foraging algorithm (MEB) designed for optimal channel selection in a distributed Vehicular Dynamic Spectrum Access (VDSA) framework employed in a platoon operating environment. Given how bumblebee behavioral models are designed to support adaptation in complex and highly time-varying environments, these models can be employed by connected vehicles to enable their operation within a dynamically changing network topology and support their selection of optimal channels possessing low levels of congestion to achieve high throughput. As a result, the proposed VDSA-based optimal channel selection employs fundamental concepts from the bumblebee foraging model. In the proposed approach, the Channel Busy Ratio (CBR) of all channels is computed and stored in memory to be accessed by the MEB algorithm to make the necessary channel switching decisions. Two averaging techniques, Sliding Window Average (SWA) and Exponentially Weighted Moving Average (EWMA), are employed to leverage past samples and are evaluated against the no-memory case. Due to the high variability of the environment (e.g., high velocities, changing density of vehicles on the road), we propose to calculate the CBR by employing non-uniform channel sampling allocations as well as evaluate it using both simplified numerical and realistic Vehicle-to-Vehicle (V2V) computer simulations. The numerical simulation results show that gains in the probability of the best channel selection can be achieved relative to a uniform sampling allocation approach. By utilizing memory, we observe an additional increase in the channel selection performance. Similarly, we see an increase in the probability of successful reception when utilizing the bumblebee algorithm via a system-level simulator.

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

BackgroundUnderstanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce.ResultsOur results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors.ConclusionsThe utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.

Functional brain networks reflect spatial and temporal autocorrelation.

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

PERFORMANCE ANALYSIS OF ROUTING PROTOCOL IN MOBILE AD-HOC NETWORKS

Routing in a mobile ad hoc network (MANET) is a difficult task due to the constantly changing network topology and the absence of a fixed infrastructure. In such a scenario, mobile hosts can act as both hosts and routers, forwarding packets for other mobile nodes in the network. Routing protocols used in MANETs must be able to adapt to frequent changes in topology, while minimizing the impact on wireless resources. The AODV, DSR, ZRP and DYMO protocol are specifically designed for mobile nodes in wireless multihop ad hoc networks. It is capable of adapting to the changing network topology and determining unicast routes between nodes within the network. This paper presents a comparative analysis of commonly used routing protocols in terms of key performance metrics, including Broadcast sent packets, CTS packets sent, Packet drops due to re-transmission,RTS re-transmission the timeout , Unicast packet received ,RTS packets sent,Packet due to ACK timeout, ACK packet sent, Unicast sent packet and Broadcast Packet Received. The study's findings demonstrate that the routing protocols' performance is influenced by the network's size, node density, and mobility patterns. These routing protocols showing the simulation performance using random waypoint model on qualnet simulator.

A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks

In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively. This representative knowledge-graph model may also consider the dynamics involved in the evolution of the network (i.e., dynamic networks), in addition to a classic static representation (i.e., static networks). Bioinformatics solutions for network analysis allow knowledge extraction from the features related to a single network of interest or by comparing networks of different species. For instance, we may align a network related to a well known species to a more complex one in order to find a match able to support new hypotheses or studies. Therefore, the network alignment is crucial for transferring the knowledge between species, usually from simplest (e.g., rat) to more complex (e.g., human). Methods: In this paper, we present Dynamic Network Alignment based on Temporal Embedding (DANTE), a novel method for pairwise alignment of dynamic networks that applies the temporal embedding to investigate the topological similarities between the two input dynamic networks. The main idea of DANTE is to consider the evolution of interactions and the changes in network topology. Briefly, the proposed solution builds a similarity matrix by integrating the tensors computed via the embedding process and, subsequently, it aligns the pairs of nodes by performing its own iterative maximization function. Results: The performed experiments have reported promising results in terms of precision and accuracy, as well as good robustness as the number of nodes and time points increases. The proposed solution showed an optimal trade-off between sensitivity and specificity on the alignments produced on several noisy versions of the dynamic yeast network, by improving by ∼18.8% (with a maximum of 20.6%) the Area Under the Receiver Operating Characteristic (ROC) Curve (i.e., AUC or AUROC), compared to two well known methods: DYNAMAGNA++ and DYNAWAVE. From the point of view of quality, DANTE outperformed these by ∼91% as nodes increase and by ∼75% as the number of time points increases. Furthermore, a ∼23.73% improvement in terms of node correctness was reported with our solution on real dynamic networks.

An Intelligent Intrusion Detection System for 5G-Enabled Internet of Vehicles

The deployment of 5G technology has drawn attention to different computer-based scenarios. It is useful in the context of Smart Cities, the Internet of Things (IoT), and Edge Computing, among other systems. With the high number of connected vehicles, providing network security solutions for the Internet of Vehicles (IoV) is not a trivial process due to its decentralized management structure and heterogeneous characteristics (e.g., connection time, and high-frequency changes in network topology due to high mobility, among others). Machine learning (ML) algorithms have the potential to extract patterns to cover security requirements better and to detect/classify malicious behavior in a network. Based on this, in this work we propose an Intrusion Detection System (IDS) for detecting Flooding attacks in vehicular scenarios. We also simulate 5G-enabled vehicular scenarios using the Network Simulator 3 (NS-3). We generate four datasets considering different numbers of nodes, attackers, and mobility patterns extracted from Simulation of Urban MObility (SUMO). Furthermore, our conducted tests show that the proposed IDS achieved F1 scores of 1.00 and 0.98 using decision trees and random forests, respectively. This means that it was able to properly classify the Flooding attack in the 5G vehicular environment considered.

Traffic-aware efficient consistency update in NFV-enabled software defined networking

In network function virtualization(NFV)-enabled software defined networks, the controller needs to frequently update the flow forwarding rules in the data plane to adapt to dynamic changes in network topologies or service requests. However, inconsistent rule updates may lead to blackholes, loops, transient congestion or policy violations (e.g., packets do not traverse designated network functions in a specific order), resulting in service interruption and throughput degradation. Therefore, this paper proposes an effective rule consistent update mechanism to avoid the above four problems simultaneously, while improving network throughput and satisfying user requests. Specifically, we first build three effective models to avoid blackholes, loops, and policy violations. Then, considering that network function nodes may change the sizes of their processed flows, we build a congestion avoidance model based on traffic changes to avoid congestion, which can reduce unnecessary rule update delays and packet loss. Subsequently, we prove that the consistent update problem constructed above is NP-hard, and then design an effective heuristic rule consistent update algorithm to obtain the rule update sequence that can simultaneously avoid blackholes, loops, congestion, and policy violations. Extensive trace-driven simulation results show that compared with the existing update methods, our proposed method can improve the success rate by up to 20.6% and reduce the maximum link utilization by up to 7.5%.

Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach

In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

Proximal policy optimization through a deep reinforcement learning framework for remedial action schemes of VSC-HVDC

A proximal policy optimization (PPO)-based back-to-back VSC-HVDC emergency control strategy based on multi-agent deep reinforcement learning (DRL) approach is proposed for use in an energy management system (EMS). In this scheme, an advanced DRL algorithm is proposed by implementing both PPO and a communication neural network for large power systems. The PPO modeled as intelligent agents with objective functions have shown a higher convergence performance than have existing DRL algorithms. Further, the model was demonstrated to effectively address voltage variances caused by the high penetration of renewable energy sources. By implementing PPO, the learning procedure is stabilized and made robust to continuous changes in network topology. To escalate the effectiveness of the proposed algorithm, a comprehensive case studies were conducted on an standard test systems and Korean power system considering variations in load and PV generation and a weak centralized communication environment. The results indicate that outstanding control performance and autonomously regulated bus voltage and line flows, thereby validating the effectiveness of the method.

Smart optimization in 802.11p media access control protocol for vehicular ad hoc network

<span lang="EN-US">The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.</span>

Intra- and interbrain synchrony and hyperbrain network dynamics of a guitarist quartet and its audience during a concert.

Playing music in a concert represents a multilevel interaction between musicians and the audience, where interbrain synchronization might play an essential role. Here, we simultaneously recorded electroencephalographs (EEGs) from the brains of eight people during a concert: a quartet of professional guitarists and four participants in the audience. Using phase synchronization analyses between EEG signals within and between brains, we constructed hyperbrain networks, comprising synchronized brain activity across the eight brains, and analyzed them using a graph-theoretical approach. We found that strengths within and between brains in the delta band were higher in the quartet than in the public. Within-brain strengths were higher and between-brain strengths were lower in the music than in the applause condition, both particularly in the quartet group. These changes in coupling strength were accompanied by corresponding changes in the hyperbrain network topology, which were also frequency-specific. Moreover, the network topology and the dynamical structure of guitar sounds showed specific guitar-brain, guitar-guitar, and brain-brain directional associations, indicating multilevel dynamics with upward and downward causation. Finally, the hyperbrain networks exhibit modular structures that were more stable during music performance than during applause. Our findings illustrate complex hyperbrain network interactions in a quartet and its audience during a concert.

End-to-end learning of user equilibrium with implicit neural networks

This paper intends to transform the transportation network equilibrium modeling paradigm via an “end-to-end” framework that directly learns travel choice preferences and the equilibrium state from multi-day link flow observations. The centerpiece of the proposed framework is to use deep neural networks to represent travelers’ route choice preferences and then encapsulate the neural networks in a variational inequality that prescribes the user equilibrium flow distribution. The proposed neural network architecture ensures the existence of equilibrium and accommodates future changes in road network topology. The variational inequality is then embedded as an implicit layer in a learning framework, which takes the context features (e.g., road network and traveler characteristics) as input and outputs the user equilibrium flow distribution. By comparing computed equilibrium flows with observed flows, the neural networks can be trained. The proposed end-to-end framework is demonstrated and validated using synthesized data for the Sioux Falls network.

Network analysis reveals abnormal functional brain circuitry in anxious dogs.

Anxiety is a common disease within human psychiatric disorders and has also been described as a frequently neuropsychiatric problem in dogs. Human neuroimaging studies showed abnormal functional brain networks might be involved in anxiety. In this study, we expected similar changes in network topology are also present in dogs. We performed resting-state functional MRI on 25 healthy dogs and 13 patients. The generic Canine Behavioral Assessment & Research Questionnaire was used to evaluate anxiety symptoms. We constructed functional brain networks and used graph theory to compare the differences between two groups. No significant differences in global network topology were found. However, focusing on the anxiety circuit, global efficiency and local efficiency were significantly higher, and characteristic path length was significantly lower in the amygdala in patients. We detected higher connectivity between amygdala-hippocampus, amygdala-mesencephalon, amygdala-thalamus, frontal lobe-hippocampus, frontal lobe-thalamus, and hippocampus-thalamus, all part of the anxiety circuit. Moreover, correlations between network metrics and anxiety symptoms were significant. Altered network measures in the amygdala were correlated with stranger-directed fear and excitability; altered degree in the hippocampus was related to attachment/attention seeking, trainability, and touch sensitivity; abnormal frontal lobe function was related to chasing and familiar dog aggression; attachment/attention seeking was correlated with functional connectivity between amygdala-hippocampus and amygdala-thalamus; familiar dog aggression was related to global network topology change. These findings may shed light on the aberrant topological organization of functional brain networks underlying anxiety in dogs.

Stress-stress correlations reveal force chains in gels.

We investigate the spatial correlations of microscopic stresses in soft particulate gels using 2D and 3D numerical simulations. We use a recently developed theoretical framework predicting the analytical form of stress-stress correlations in amorphous assemblies of athermal grains that acquire rigidity under an external load. These correlations exhibit a pinch-point singularity in Fourier space. This leads to long-range correlations and strong anisotropy in real space, which are at the origin of force-chains in granular solids. Our analysis of the model particulate gels at low particle volume fractions demonstrates that stress-stress correlations in these soft materials have characteristics very similar to those in granular solids and can be used to identify force chains. We show that the stress-stress correlations can distinguish floppy from rigid gel networks and that the intensity patterns reflect changes in shear moduli and network topology, due to the emergence of rigid structures during solidification.