Network Topology Changes Research Articles

μPMU Placement Considering the Distribution of Observation Redundancy and Topology Changes in Active Distribution Networks

μPMU Placement Considering the Distribution of Observation Redundancy and Topology Changes in Active Distribution Networks

Research on AdHoc network routing algorithm for link stability prediction

Abstract This study aims to improve network communication efficiency and reliability by researching routing algorithms for predicting link stability in AdHo networks. AdHoc network is a self-organizing, decentralized network structure in which nodes establish temporary connections through wireless communication, making the network resilient and flexible. However, due to the continuous changes in network topology and the instability of wireless links, routing problems in AdHoc networks have become particularly complex. This article proposes a backup path routing algorithm LS-BPR based on link stability prediction. This mechanism takes the path with the minimum delay as the main path, uses the cost function as the standard for selecting backup paths, and then uses the ARIMA model to predict links with good link stability as backup paths. We simulate and compare LS-BPRAODV and AODV protocols using the QualNet simulation platform. The simulation results show that the LS-BPRAODV, with the addition of a prediction mechanism, improves network performance, reduces the number of source node routing discoveries, reduces the number of route breaks, significantly reduces the packet loss rate and information reception delay in the network, and improves the average throughput of the network. This study provides new theoretical and empirical support for the routing algorithm for link stability prediction in AdHo networks, which is of great significance for promoting the development of wireless self-organizing networks.

Sampling and active learning methods for network reliability estimation using K-terminal spanning tree

Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated.

Markov-reward based estimation of the idle-time in vehicular networks to improve multimetric routing protocols

Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as Tidle. This current study focuses on modeling and analyzing the influence of Tidle on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute Tidle. This equation provides a quick evaluation of Tidle, facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our Tidle evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our Tidle metric. Simulation results demonstrate that integrating Tidle as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

Analyzing Data Transmission Reliability in Mobile Ad-Hoc Networks under Dynamic Scenarios

Mobile ad hoc networks (MANETs) face inherent challenges in maintaining reliable data transmission because of their dynamic and unpredictable nature. This research conducts a comprehensive reliability analysis of data transmission in MANETs, emphasizing the influence of dynamic conditions such as node mobility, changing network topologies, and fluctuating channel conditions. Through the utilization of mathematical models and simulations, the study assesses the overall reliability of data transmission, considering scenarios with diverse node densities, mobility patterns, and network sizes. Existing routing protocols, error correction mechanisms, and adaptive transmission strategies are examined for their effectiveness in reducing the impact of dynamic conditions on reliability. The research not only examines the current state of protocols but also explores potential enhancements and introduces novel approaches to enhance data transmission reliability under dynamic conditions. The findings offer a nuanced understanding of the challenges and opportunities related to reliable communication in MANETs. This research provides crucial insights for designing resilient communication systems in dynamic and mobile network scenarios, offering valuable guidance to researchers, network designers, and practitioners involved in optimizing the performance of MANETs in real-world applications.

Brain network topological changes in inflammatory bowel disease: an exploratory study.

Although the aetio-pathogenesis of inflammatory bowel diseases (IBD) is not entirely clear, the interaction between genetic and adverse environmental factors may induce an intestinal dysbiosis, resulting in chronic inflammation having effects on the large-scale brain network. Here, we hypothesized inflammation-related changes in brain topology of IBD patients, regardless of the clinical form [ulcerative colitis (UC) or Crohn's disease (CD)]. To test this hypothesis, we analysed source-reconstructed magnetoencephalography (MEG) signals in 25 IBD patients (15 males, 10 females; mean age ± SD, 42.28 ± 13.15; mean education ± SD, 14.36 ± 3.58) and 28 healthy controls (HC) (16 males, 12 females; mean age ± SD, 45.18 ± 12.26; mean education ± SD, 16.25 ± 2.59), evaluating the brain topology. The betweenness centrality (BC) of the left hippocampus was higher in patients as compared with controls, in the gamma frequency band. It indicates how much a brain region is involved in the flow of information through the brain network. Furthermore, the comparison among UC, CD and HC showed statistically significant differences between UC and HC and between CD and HC, but not between the two clinical forms. Our results demonstrated that these topological changes were not dependent on the specific clinical form, but due to the inflammatory process itself. Broader future studies involving panels of inflammatory factors and metabolomic analyses on biological samples could help to monitor the brain involvement in IBD and to clarify the clinical impact.

Hybrid Clustering Approach (SG-MFOA) using Multipath Cross-Layer Design in MANET Network

Mobile Ad-hoc Networks (MANETs) are used in a wide range of applications because of their unique capabilities. The routing in MANET is regarded as a main challenge on account of its permanent movement and nodes' randomness that can cause a continual change of network topology, thus finding correct paths among the nodes is the fundamental task of routing protocols. There is a rise in protocols that depend on cross-layer communication across several layers to increase the MANET performance. In this manuscript, a Hybrid Clustering Approach (SG-MFOA) using a Multipath Cross-Layer Design in a MANET Network is proposed. Here, multiple routes are selected for data packet transmission with the help of hybrid routing. Furthermore, a cross-layer metric is acquired depending upon Expected Transmission Time (ETT), Residual Energy and Load Balancing Factor. It is necessary to select a Cluster Head (CH) depending on this trade for proficient routing. So, the cluster head is optimally chosen to utilize SG-MFOA. Thus, the multipath route selection is performed by the multi-objective functions containing bandwidth, congestion delay, transmission delay and to ensure the prolonging of network lifetime, queue delay at each connection Access Category from the available routes to a destination. When compared with the existing methods, like a proficient self-attention-based conditional variational auto-encoder generative adversarial networks-based multipath cross-layer design routing paradigm for MANET (SCVAGAN-MCDR-MANET), Energy Aware Cross Layer Routing Protocol to Increase Route Reliability in MANETs (EACLRP-RR-MANET), Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector Routing Protocol for MANET (CLAF-DVRP-MANET), respectively.

Improved MPR Selection Algorithm-Based WS-OLSR Routing Protocol

Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WSOLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.

Exploring the effectiveness of service migration strategies for virtual network embedding

Network slicing, a key component of post-5G networks, has brought virtual network embedding (VNE) to the forefront of networking research. However, existing VNE approaches are limited by their consideration of static network topologies, failing to account for the dynamic nature of real-world networks. This limitation becomes particularly problematic in the face of link failures or topology modifications, rendering these approaches ineffective. To address this, we propose a new service placement strategy that remains effective even when the network topology changes. Furthermore, we introduce several service migration strategies and thoroughly investigate their effectiveness. Our results demonstrate the adaptability of our proposed strategy, which leverages graph neural networks, in handling link failures without necessitating relearning. This adaptability underlines the potential of our approach to significantly enhance the robustness and flexibility of service migration in VNE, thereby contributing to the evolution of network slicing in post-5G networks.

An Information-Theoretic Approach to Analyze Irregularity of Graph Signals and Network Topological Changes Based on Bubble Entropy

This work generalizes the recently introduced bubble entropy (BE) algorithm for univariate time series to graph and network analysis. In this paper, we introduce a modified method, called bubble entropy for graph signals ([Formula: see text], as an invaluable tool for detecting the irregularity of signals defined on graphs. Our algorithm is based on using the adjacency matrix to combine the signal values with the topology of the graph. Experiments on both synthetic and real data demonstrate the availability of the proposed measures in detecting the irregularity of graph signals and identifying topological changes in graphs.

Effect of Peptide-Polymer Host-Guest Electrostatic Interactions on Self-Assembling Peptide Hydrogels Structural and Mechanical Properties and Polymer Diffusivity.

Peptide-based supramolecular hydrogels are an attractive class of soft materials for biomedical applications when biocompatibility is a key requirement as they exploit the physical self-assembly of short self-assembling peptides avoiding the need for chemical cross-linking. Based on the knowledge developed through our previous work, we designed two novel peptides, E(FKFE)2 and K(FEFK)2, that form transparent hydrogels at pH 7. We characterized the phase behavior of these peptides and showed the clear link that exists between the charge carried by the peptides and the physical state of the samples. We subsequently demonstrate the cytocompatibility of the hydrogel and its suitability for 3D cell culture using 3T3 fibroblasts and human mesenchymal stem cells. We then loaded the hydrogels with two polymers, poly-l-lysine and dextran. When polymer and peptide fibers carry opposite charges, the size of the elemental fibril formed decreases, while the overall level of fiber aggregation and fiber bundle formation increases. This overall network topology change, and increase in cross-link stability and density, leads to an overall increase in the hydrogel mechanical properties and stability, i.e., resistance to swelling when placed in excess media. Finally, we investigate the diffusion of the polymers out of the hydrogels and show how electrostatic interactions can be used to control the release of large molecules. The work clearly shows how polymers can be used to tailor the properties of peptide hydrogels through guided intermolecular interactions and demonstrates the potential of these new soft hydrogels for use in the biomedical field in particular for delivery or large molecular payloads and cells as well as scaffolds for 3D cell culture.

COMPUTATIONAL INTELLIGENCE BASED ROUTING SOLUTIONS FOR MOBILE AD HOC NETWORKS

Mobile Ad Hoc Networks (MANETs) refers to a wireless network where nodes dynamically establish and maintain connections with each other, directly without the need for a centralized infrastructure. MANETs are characterized by their decentralized nature, presenting challenges for routing protocols concerning rapid data transmission, minimized delay and Quality of Service (QoS) provisioning. Traditional routing protocols may struggle to perform optimally in MANETs due to frequent changes in network topology and resource constraints. The development of intelligent routing protocols tailored to the specific characteristics of MANETs is vital for dynamic and resource-constrained environments. Computational intelligence (CI) techniques have gained attention for designing intelligent routing protocols for MANETs. This paper provides a comprehensive overview of majorly used CI methodologies in MANETs. The paper reviews existing research on intelligent routing protocols based on CI techniques in MANETs and identifies future research directions in this field to address the unique challenges of MANETs and improve network performance and reliability.

The effect of NaF and ZnO on regulating the network topological structure of CsPbBr3 quantum dots-embedded sodium borosilicate glass for energy-efficient dust indicator

All-inorganic CsPbX3 (X = Cl, Br, I) perovskite quantum dots (QDs) exhibit substantial potential in optoelectronics, rendering them valuable materials for next-generation lighting. However, the high cost and substantial usage of Cs present challenges. This study investigates the impact of NaF and/or ZnO modifiers on the network topological structure of CsPbBr3 QDs-embedded sodium borosilicate (SBS) glass. The incorporation of NaF and ZnO not only reduces Cs consumption and energy requirements during heat treatment but also enhances the stability and optical properties of CsPbBr3 QDs. Through a combination of experimental analysis and molecular dynamics (MD) simulations, we explore the changes in the SBS glass network topology induced by these modifiers. The results demonstrate that the addition of these modifiers effectively alters the topological structure of SBS glass, providing outstanding water and heat resistance for the embedded CsPbBr3 QDs. This suggests that the NaF and ZnO co-modified CsPbBr3 QDs-embedded SBS glass, with improved stability and optical characteristics, exhibits great promise for applications in optoelectronics.

An Efficient Fault-Tolerant Vehicular Data Forwarding Scheme for Timely and Reliable Data Transmission

With the increasing demands of various applications on vehicles, such as road condition sensing, traffic management, location-based services, and others both academic researchers and automotive industries pay much attention to Vehicular Networks. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the service requests from a large number of vehicular users. Further, network faults disrupt communication among vehicles, which affects the reliability and timely data delivery in vehicular networks. In this paper, for timely and reliable data transmission, an efficient data forwarding scheme for vehicular networks is proposed. Also, the paper addresses three types of network faults along with the data forwarding issue by selecting an optimal route with a higher probability of connectivity and lower experienced delay. The proposed scheme detects the faults dynamically and takes necessary actions to recover from the fault. Extensive simulation has been performed to evaluate the proposed scheme. Compared to the existing AODV- D approach and SRCP scheme, the proposed scheme attains better performance in terms of packet delivery ratio, end-to-end delay, and control overhead.

ER-OCN: Toward efficient network routing in ocean city based on deep reinforcement learning

Smart ocean cities are crucial to future city development, and offshore networks are an essential part of smart ocean cities. Nowadays, communication in offshore networks is becoming increasingly important. However, the offshore networks are highly heterogeneous, with low reliability, narrow bandwidth, and dramatic dynamics. Offshore networks are more challenging to manage than traditional terrestrial networks, and the standard quality of service routing faces significant challenges. Currently, artificial intelligence provides a way to meet the requirements. This paper proposes ER-OCN (Efficient Routing in Ocean City Networks), a two-stage routing optimization scheme driven by artificial intelligence to cope with the complex offshore networks environment. The first stage will calculate the flow paths based on Lagrangian relaxation. Besides, the ocean environment is under dramatic dynamics, leading to unpredictable network topology or link bandwidth changes. Thus, the second stage deployed a deep reinforcement learning algorithm to monitor the environment in real-time and update the routing strategy when needed. We conducted experiments based on actual network topology and traffic data sets of offshore platforms’ network systems. The results prove that our scheme improves communication cost performance by 36.61% compared to traditional methods.

Distributed MA‐IDDPG‐OLSR based stable routing protocol for unmanned aerial vehicle ad‐hoc network

AbstractIn unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Learning-assisted optimization for transmission switching

The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.

Application of Fuzzy Computing for VANET Routing

In the realm of vehicular ad hoc networks (VANETs), routing plays a pivotal role in ensuring efficient communication among vehicles and infrastructure. Traditional routing protocols often struggle to adapt to the dynamic and uncertain environment of VANETs, characterized by rapidly changing network topologies and unpredictable traffic conditions. In any case, the without a doubt solid and stunning nature of vehicular conditions presents superb difficulties for controlling shows, including unusual straightforwardness, changing association geologies, and dynamic traffic conditions [1, 2]. By incorporating fuzzy logic into routing algorithms, vehicles can make more informed decisions regarding route selection, considering factors such as traffic congestion, road conditions, and vehicle mobility patterns. This paper provides an overview of the key concepts of fuzzy computing and discusses its potential applications in VANET routing. Furthermore, it explores existing research efforts in this area, highlighting the benefits and challenges associated with fuzzy based routing approaches. Through a comprehensive review of the literature, this paper identifies current trends and future directions for leveraging fuzzy computing in VANET routing, aiming to improve the reliability, scalability, and efficiency of communication in vehicular networks.

A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than other methods.