Dynamic Network Topology Research Articles

In tactical communication networks, highly dynamic topologies and frequent data exchanges create complex spatiotemporal dependencies among link states. However, most existing intelligent routing algorithms rely on simplified model architectures and fail to capture these spatiotemporal correlations, resulting in limited situational awareness and poor adaptability under dynamic network conditions. To address these challenges, this study proposes an intelligent path selection method—Deep Reinforcement Learning with Spatiotemporal-aware Link State Guidance Algorithm (DRLSGA). The algorithm builds upon the Proximal Policy Optimization (PPO) framework to develop an intelligent decision-making model and integrates a link state feature extraction module that combines Gated Recurrent Units (GRU) and a Graph Attention Network (GAT). This design enables the model to learn long-term temporal dependencies and spatial structural relationships from sequential link state data, thereby enhancing perception and decision-making capability. An attention mechanism is further introduced to highlight salient features within link state sequences, while an optimal routing strategy is derived through a deep reinforcement learning-based training process. Experimental results demonstrate that, compared with the existing DRL-ST algorithm, DRLSGA reduces average end-to-end latency by at least 2.07%, lowers the packet loss rate by 1.65%, and increases average throughput by up to 2.59% under high-traffic conditions. Moreover, the proposed algorithm exhibits stronger adaptability to highly dynamic network topologies.

ABSTRACT Accurately characterizing the connectedness among financial institutions is a critical prerequisite for monitoring and preventing systemic financial risk. This study focuses on the co-jump behavior of financial institutions’ equities and examines their interconnectedness within a co-jump network framework. By combining simulation modeling and empirical analysis, it investigates the early warning potential of critical transitions in co-jump network topology for systemic risk events. Simulation results reveal a stable relationship between the network’s structural tightness and overall dependence within the financial system, highlighting the importance of connectivity-based indicators in risk monitoring. Building on this, empirical analyses are conducted using data from both China’s and U.S. financial markets. The findings show that the clustering coefficient of the co-jump network exhibits strong early warning effectiveness for endogenous systemic risk events in emerging markets. By analyzing the structural dynamics of co-jump networks, this study contributes to a deeper theoretical understanding of systemic risk and yields a feasible early warning approach, particularly suited to emerging market conditions.

BACKGROUND The objective of this study was to analyse contemporary approaches, identify key challenges, and provide recommendations for optimising data exchange with regard to speed, reliability, energy efficiency, and security. MATERIALS AND METHODS This work presents a structured narrative review analysing modern methods and technologies for data transmission between a drone swarm and a ground base, enabling an assessment of the effectiveness of various approaches in ensuring stable, rapid, and secure communication under conditions of high mobility and dynamic network topology. RESULTS The research indicated that 5G provides superior bandwidth (up to 10 Gbit/s) and negligible latency (under 1 ms), although its implementation is constrained by range (up to 1 km) and the requirement for advanced infrastructure. MANETs and DTNs offer adaptability in dynamic settings but encounter latency (up to several minutes) and connection stability challenges. Wi-Fi mesh networks provide effective coverage and stability. Nonetheless, they necessitate energy consumption optimisation for autonomous operation. Principal problems encompass interference, restricted drone power, and the necessity for improved security in conveyed data. CONCLUSION Combined approaches are proposed, incorporating dynamic routing, adaptive frequency management, and blockchain integration to improve authentication and data protection. Additionally, the use of machine learning algorithms for real-time threat detection and more efficient network resource allocation is explored. The findings can be further applied to enhance wireless communication systems in unmanned networks.

Disrupted static and dynamic small-world brain network topologies in patients with schizophrenia.

Reconfiguration of dynamic brain networks in heart failure with preserved ejection fraction: Linking neurovascular coupling and cardiac dysfunction.

ABSTRACTVehicular Fog Computing (VFC) presents a promising paradigm to reduce latency and energy usage through utilization of nearby edge resources by vehicles. Yet, efficient and scalable resource management is still a significant challenge particularly due to dynamic network topologies, resource, and high Quality of Service (QoS) requirements. Traditional metaheuristic methods such as GA and PSO are limited in convergence speed and solution quality under such restrictions. This research introduces Enhanced NSGA‐II+, a cutting‐edge multi‐objective evolutionary model enhancing NSGA‐II and NSGA‐III through dynamic population adaptation, Pareto‐front‐leveraged selection, and premature convergence prevention. Experimental comparisons in both common and ultra‐dense vehicular settings with up to 1000 vehicles and 2000 tasks show that NSGA‐II+ outperforms baseline algorithms by far, reducing average delay by 72.55% (compared to NSGA‐II) and 71.75% (vs. NSGA‐III), and energy cost by 70.96% (compared to NSGA‐II) and 70.75% (compared to NSGA‐III). This reinforces how NSGA‐II+ addresses both dynamic topologies and resource heterogeneity. Its strong exploration‐exploitation trade‐off and flexibility render it an appealing solution for real‐time, energy‐efficient deployment in smart transportation systems.

Urban mobility insights: A dataset for exploring network topology and city dynamics

The development of Unmanned Aerial Vehicle (UAV) networks has revolutionized several sectors, such as emergencies, environmental monitoring, and intelligent agriculture. UAV networks suggest significant benefits in real-time information gathering, remote surveillance, and automation. Yet, achieving optimal communication efficiency in UAV networks is still a major challenge with dynamic network topology, energy constraints, and interference from environmental variables like weather conditions and signal obstructions. Preserving untainted and reliable connectivity for UAV networks is essential for their effective deployment in mission-critical scenarios. The study provides a Hybrid Snow Ablation Shark Nose Optimizer (HSASNO) method to enhance communication performance in UAV networks with focus on performance parameters including latency, energy efficiency, and throughput.The suggested method makes use of nature-based and bio-based optimization methods to adaptively modify the network parameters so as to guarantee adaptive and auto-optimizing communication by UAVs. HSASNO aims to optimize UAV path planning, power transmission management, and resource planning, which will result in a remarkable network performance and stability improvement.Simulations were conducted in diverse scenarios, such as different densities of UAVs, movement styles, and weather conditions, to estimate the robustness of the suggested method. Results show that there is considerable enhancement in network performance with reduced latency, better energy efficiency, and increased data throughput in comparison to conventional schemes. In particular, the HSASNO scheme recorded less energy consumption (24.40), less end-to-end delay (0.98), and increased throughput (0.992), which shows its superiority. The outcomes reflect the effectiveness of meta-heuristic methods in overcoming the inherent complexity of UAV communication in networks. The system as proposed guarantees efficient and reliable communication and flexibility with respect to changes in climatic and operational environments, hence highly suitable for real-world applications, including disaster response, environmental surveillance, and smart transportation systems.

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments.

Therefore, Vehicular Ad-hoc Networks (VANETs) are the fundamental infrastructure required for the deployment of Intelligent Transportation Systems (ITS), which will facilitate enhanced safety, efficiency and comfort by interconnecting vehicles. Trust establishment is first and foremost for the smooth operation of VANETs, with trust appearing as necessary in the case for safe dissemination and preventive measures against malicious behaviours for nodes making use of inter-communication within the network. This paper provides a comprehensive summary of the existing trust-based models in VANETs, and takes an in-depth look into associated issues. We go through entity-centric, data-centric and hybrid models providing their strengths and limitations. This is backed by the statistics which indicate that implementing emerging technologies such as block chain and machine learning can greatly improve the reliability mechanisms. Moreover, they overcome the limitations suggested by trust models including dynamic network topology, scalability and problems related to privacy and security etc. This systematic review will facilitate the insight of researchers regarding the state-of-the-art research in trust and also highlight necessary enhancements in order to produce stable and efficient models for trust in VANETs. This highlights the need for interdisciplinary teamwork in integrating cryptographic techniques, machine learning solutions, and informative messages to develop resilient trust systems against adversarial activities on vehicular communication networks.

Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. Building on this foundation, an attention-based Actor-Critic framework makes joint offloading decisions by intelligently selecting the optimal destination and collaboratively determining the ratios for offloading and resource allocation. A multi-objective reward function, designed to minimize task latency and to alleviate link congestion, guides the entire learning process. Comprehensive simulation experiments and ablation studies show that, compared to traditional heuristic algorithms and standard deep reinforcement learning methods, GAPO significantly reduces average task completion latency and substantially decreases backbone link congestion. In conclusion, by deeply integrating the state-aware capabilities of GNNs with the decision-making abilities of DRL, GAPO provides an efficient, adaptive, and congestion-aware solution to the resource management problems in dynamic VEC environments.

The proliferation of sophisticated email-borne malware necessitates advanced modeling techniques to predict and mitigate cyber threats. While prior research established foundational lattice-based models for virus propagation via email, contemporary attacks exploit multi-vector infiltration (e.g., malicious links, macros, and embedded scripts) and evade traditional detection. This paper presents a novel hybrid model combining agent-based deterministic simulations with machine learning-driven defense adaptations to quantify malware spread in heterogeneous organizational networks. Key innovations include: (1) a dynamic network topology incorporating hierarchical user roles and device diversity (desktop), (2) probabilistic infection pathways aligned with real-world phishing metrics (Verizon DBIR), and (3) an adaptive detection module trained on behavioral anomalies i.e, email burst rates, attachment types. Simulations demonstrate a 40–62% improvement in outbreak containment compared to classical models, with false positives reduced by 28% through ML-augmented filtering. The framework bridges theoretical epidemiology and practical cybersecurity, offering actionable insights for IT policy design.

Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies.

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

ABSTRACTThe rapid development of heterogeneous vehicular networks (HetVNETs) has transformed the transportation industry by enabling vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication. However, as traffic load increases, these networks face severe congestion, leading to unreliable and insecure communication. Congestion control in HetVNETs is challenging due to dynamic topologies, multiple communication protocols, and varying traffic intensities. Traditional congestion control techniques struggle to address these issues, necessitating an intelligent mechanism to detect and prevent data congestion in advance. This research introduces the enhanced heterogeneous vehicular networks with intelligent congestion avoidance mechanism via regularized Q‐value‐based graph generalized neural network transformer (RQ‐GGNN‐ArJ). The proposed hybrid framework integrates a graph‐based generalized neural network for modeling dynamic network topologies, a regularized Q‐value transformer for adaptive dedicated short‐range communications (DSRC) transmission power control to ensure real‐time congestion mitigation, and artificial jelly‐driven adaptive optimization (ArJ‐AO) for fine‐tuning weight parameters and loss functions. These components collectively form a highly efficient congestion avoidance mechanism with real‐time decision‐making capabilities. The proposed system achieves remarkable performance, with 99.8% prediction accuracy in identifying congestion patterns, a 99.6% reduction in packet loss, a 99.7% improvement in communication reliability, and 99.3% resource utilization. Therefore, the RQ‐GGNN‐ArJ framework establishes a new benchmark for intelligent congestion management in HetVNETs.

Wireless Body Area Networks (WBANs) have gained significant attention due to their widespread applications in healthcare monitoring, sports performance tracking, military communication, and remote patient care. These networks consist of wearable or implanted sensor nodes continuously collecting and transmitting physiological data, requiring an efficient and reliable communication framework. However, the unique challenges of WBANs, such as limited energy resources, dynamic network topology, and high sensitivity to node temperature, necessitate specialized routing strategies. Traditional routing protocols, which often prioritize shortest-path selection, tend to create traffic congestion and overheating in specific nodes, leading to early network failures and reduced overall performance. To address these issues, this paper proposes an intelligent, temperature-aware, and reliability-based routing approach that enhances the overall efficiency and stability of WBANs. The proposed method operates in two phases: (1) network setup and intelligent path selection and (2) dynamic traffic management and hotspot avoidance. In the first phase, sensor nodes exchange vital network status information, including residual energy, node temperature, link reliability, and delay, to build an optimized network topology. Instead of relying solely on shortest-path routing, a multi-criteria decision-making algorithm is employed to select the most efficient paths, prioritizing those that balance energy consumption, temperature regulation, and communication stability. This prevents excessive energy depletion in specific nodes and avoids forming traffic bottlenecks. The system continuously monitors real-time network conditions in the second phase, dynamically rerouting traffic away from overheated or energy-depleted nodes. This ensures that critical sensor data is reliably delivered while extending the network’s lifetime. Simulation results demonstrate the superiority of the proposed approach compared to existing methods. The proposed method improves throughput by 13% and reduces end-to-end delay by 10%. Additionally, it achieves a 25% reduction in energy consumption. The proposed method also significantly reduces the normalized routing load by 30%.

This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments.

ABSTRACTWireless Sensor Networks (WSNs) have transformed data transmission methodologies by merging with 5G technology to provide ultra‐reliable, low‐latency, and energy‐efficient data transfers. Nonetheless, owing to the intricacies involved in attaining dynamic network topologies, constrained resource management, and scalability, there is a want for improved routing methodologies to optimize 5G‐enabled wireless sensor networks. This study introduces the “Nadam‐Swarm based Adaptive Routing Protocol using Graph Equivariant Network for Seamless Data Transmission in 5G‐Connected Wireless Sensor Networks” (NR‐GE‐BiSO) as a proficient solution for efficient data transmission. The protocol utilizes a multi‐tiered approach: the Nadam‐based Random Search Algorithm (NR‐SA) dynamically allocates clustering head nodes to balance the load depending on the residual energy and traffic density of the nodes inside the network. Graph Equivariant Quantum Neural Networks (GE‐QNN) provide a Wireless Sensor Network (WSN) structural graph to identify optimal routing pathways based on variations within the WSN, facilitating effective data delivery with minimal power consumption. The Bipolar Swarm Optimizer (BiSO) enhanced the routing process by determining the shortest, most energy‐efficient routes with minimal latency and energy expenditure. Simulation results validate the efficacy of NR‐GE‐BiSO, achieving metrics: 99.92% throughput and a 99.88% packet delivery ratio with 99.01% reduction of routing overhead outperforming the existing methods. The findings indicated that the protocol facilitates energy‐efficient, scalable, and reliable communication. By integrating 5G capabilities with advanced routing algorithms, NR‐GE‐BiSO achieves a heightened degree of wireless sensor network efficiency, enabling innovative applications in smart cities, industrial IoT, and environmental domains.

Vehicular Ad Hoc Networks (VANETs) serve as critical platforms for inter-vehicle communication within constrained ranges, facilitating information exchange. However, the inherent challenge of dynamic network topology poses persistent disruptions, hindering safety and emergency information exchange. An alternative generalised statistical model of the channel is proposed to capture the varying transmission range of the vehicle node. The generalised model framework uses simple wireless fading channel models (Weibull, Nakagami-m, Rayleigh, and lognormal) and the large vehicle obstructions to model the transmission range. This approach simplifies analysis of connection of vehicular nodes in environments were communication links are very unstable from obstructions from large vehicles and varying speeds. The connectivity probability is computed for two traffic models—free-flow and synchronized Gaussian unitary ensemble (GUE)—to simulate vehicle dynamics within a multi-lane road, enhancing the accuracy of VANET modeling. Results show that indeed the dynamic range distribution is impacted at shorter inter-vehicle distances and vehicle connectivity probability is lower with many obstructing vehicles. These findings offer valuable insights into the overall effects of parameters like path loss exponents and vehicle density on connectivity probability, thus providing knowledge on optimizing VANETs in diverse traffic scenarios.

The bioactive components of Astragalus membranaceus and Salvia miltiorrhiza improved cardiac and renal function in chronic heart failure (CHF) and chronic kidney disease (CKD), respectively. However, the common regulating molecular mechanisms remain unclear. The aim of this study was to investigate these mechanisms using bioinformatics, network topology, and molecular dynamics simulation techniques. The active components and target sites of A. membranaceus and S. miltiorrhiza were obtained from the Traditional Chinese Medicine Systems Pharmacology database. The targets of CKD and CHF were obtained from various databases for a protein-protein interaction analysis. The Gene Ontology (GO) function and Kyotoencyclopedia of genes and genomes (KEGG) pathway enrichment of intersection targets were analyzed by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Molecular docking and dynamic simulations were conducted on the core ingredients and targets. The diagnostic efficiency of the key targets was evaluated by using receiver-operating characteristic (ROC) curves. A total of 70 active ingredients and 158 common targets were found. The top five core targets were AKT1, STAT3, TP53, MAPK1, and RELA. The GO enrichment analysis included apoptosis and oxidative stress. The KEGG pathway enrichment results indicated that the drug pair regulated the AGE-receptor for AGE signaling pathway, fluid shear stress and atherosclerosis, and the IL-17 signaling pathway. Molecular docking and dynamic simulations confirmed that the core ingredients had good affinity and stability with the key targets. The ROC curves confirmed the accuracy of every key target for identifying CKD and CHF and demonstrated that combining them improves diagnosis. The combination of A. membranaceus and S. miltiorrhiza proved effective for the treatment of CKD and CHF through various components, targets, and mechanisms. Moreover, it may predict the diagnostic value of key targets, providing a reference for clinical diagnostic applications.