Dynamic Topology Research Articles

Latency‐Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

ABSTRACTSatellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency‐sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi‐domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning‐driven latency‐sensitive service function chain intelligent migration algorithm (LS‐SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency‐sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management

AbstractForests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a significant threat to this ecosystem. In recent decades, the emergence of the IoT has been characterised by the utilisation of smart sensors for real‐time data collection. IoT facilitates proactive decision‐making for forest monitoring, control, and protection through advanced data analysis techniques, including AI algorithms. This research study presents a comprehensive approach to deploying a dynamic and adaptable network topology in forest environments, aimed at optimising data transmission and enhancing system reliability. Three distinct topologies are proposed in this research study: direct transmission from nodes to gateways, cluster formation with multi‐step data transmission, and clustering with data relayed by cluster heads. A key innovation is the use of high‐powered telecommunication modules in cluster heads, enabling long‐range data transmission while considering energy efficiency through solar power. To enhance system reliability, this study incorporates a reserve routing mechanism to mitigate the impact of node or cluster head failures. Additionally, the placement of gateway nodes is optimised using meta‐heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.

Digital dynamic modeling and topology optimized design of shell face mills

This paper presents digital modeling of shell face mills in milling cylinder heads. The cutter's structural dynamics and its mode shapes are predicted using a Finite Element system. The geometries of the cutter body and inserts are imported from their Computer Aided Design (CAD) models. The insert edge is discretized into small segments to model its varying normal rake and inclination angles, which affect the cutting mechanics. The cutter is dynamically assembled with the target machine tool spindle using the receptance coupling method. A general dynamic cutting force model, which considers the varying edge geometry and inserts’ run-outs, is developed and used to predict cutting forces and chatter stability diagrams. The proposed model is experimentally verified to demonstrate the feasibility of the systematic application of physics-based digital design and analysis of tools for the mass machining of specific parts. The cutter body shape is optimized to increase the stiffness of the bending mode shape that caused chatter via topology optimization, which led to five-fold increase in the absolute stable depth of cut.

UPI-LT: Enhancing Information Propagation Predictions in Social Networks Through User Influence and Temporal Dynamics

The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model’s technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model’s enhanced predictive capabilities for information spread.

Topological dynamics of adiabatic cat states

We consider a quantum topological frequency converter, realized by coupling a qubit to two slow harmonic modes. The dynamics of such a system is the quantum analog of topological pumping. Our quantum mechanical description shows that an initial state generically evolves into a superposition of two adiabatic states. The topological nature of the coupling between the qubit and the modes splits these two components apart in energy: for each component, an energy transfer at a quantized rate occurs between the two quantum modes, in opposite directions for the two components, reminiscent of the topological pumping. We denote such a superposition of two quantum adiabatic states distinguishable through measures of the modes’ energy an adiabatic cat state. We show that the topological coupling enhances the entanglement between the qubit and the modes, and we unveil the role of the quantum or Fubini-Study metric in the characterization of this entanglement.

The endoplasmic reticulum as an active liquid network

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the timescales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.

Intensive study, tuning and modification of reactive routing approach to improve flat FANET performance in data collection scenario.

The Flying Ad-hoc Network (FANET) can be defined as the Ad-hoc network that connects unmanned aerial vehicles flying in the space with each other and with a ground base station. However, the 3D movement of these drones with higher speeds results in a network of highly dynamic topology and intermittent connections, making the standard Ad Hoc routing protocols are not suitable for FANET. The approaches followed to address this issue include designing from scratch a routing protocol specific to FANET or modifying the existing protocols. From the view point of reliability, accuracy, and time, it is preferable to base the work on a protocol standard. But before amending the standard, tuning its performance and applying it under suitable conditions may be satisfactory for the new use. Therefore, this work considers flat FANET of fully mission-controlled drones and performs an extensive parametric simulation study to determine the best conditions and parameters' values for applying the popular Ad Hoc On-demand Distance Vector (AODV) to it. After deducing the recommended operating environment (FAODVN-OE), some examples of amendments were suggested to further improve the performance. It was found that the modified FAODVN-OE achieves high performance compared to the default standard in terms of jitter and delay. It helped reduce jitter and delay by an average of 93.2% and 83.8%, respectively, while exhausting less energy; however the network experiences a 24.5% reduction in packet delivery ratio.

Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression

Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.

Stability and Safety Analysis of Connected and Automated Vehicle Platoon Considering Dynamic Communication Topology

Stability and Safety Analysis of Connected and Automated Vehicle Platoon Considering Dynamic Communication Topology

Extracting optimal fuel cell parameters using dynamic Fick’s Law algorithm with cooperative learning strategy and k-means clustering

This article introduces an enhanced stochastic search method tailored for optimizing the parameters of fuel cells (FCs), which hold significant relevance across various applications. The nonlinear nature of FCs poses a modeling challenge, prompting the proposal of an advanced Dynamic Fick’s Law Algorithm (DFLA). This improved approach incorporates a cooperative learning strategy, leveraging K-Means clustering, to derive optimal FC parameters. DFLA introduces a dynamic swarm topology by segmenting the population into subswarms, boosting diversity, and enabling broader global exploration. Simultaneously, the cooperative learning strategy fosters information exchange among subswarms, enhancing the algorithm’s ability to explore vast solution spaces and exploit diverse subswarm-derived solutions. The evaluation of DFLA utilized real-world datasets from commercial PEMFC stacks: 250-W stack, BCS 500-W, and NedStack PS6. Performance assessment relied on the Sum Squared Error (SSE), a standard evaluation metric, with comparisons drawn against established competing methods. The results underscored DFLA’s superior efficiency, showcasing improved performance metrics and convergence behaviors compared to the tested algorithms.

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications

As vehicular applications continue to evolve, the computational capabilities of individual vehicles alone are no longer sufficient to meet the increasing demands. This has led to the integration of edge computing in the Internet of Vehicles (IoV) as an essential solution. Due to the limited resources within vehicles, there is often a need to offload tasks to edge nodes. However, task offloading in IoV environments presents several challenges, including high mobility, dynamic network topology, and varying node density. Traditional offloading methods fail to effectively address these challenges. Moreover, tasks differ in importance, necessitating a mechanism for edge nodes to prioritize tasks based on their urgency. To overcome these challenges, we propose a Vehicle-to-Vehicle (V2V) fuzzy-based task offloading scheme. In this scheme, fuzzy logic plays a critical role by enabling dynamic prioritization of tasks based on their urgency and the available computational resources at edge nodes, ensuring intelligent, context-aware decision-making. The user vehicle selects an appropriate edge node using an edge selection mechanism, which guarantees prolonged connection time and sufficient computational resources. Tasks at the edge are then organized based on their latency requirements and evaluated using a fuzzy rule-based inference system. Our simulation results demonstrate improved task execution rates, reduced overall system delay, and minimized queuing delays.

Topological and Morphological Membrane Dynamics in Giant Lipid Vesicles Driven by Monoolein Cubosomes

Lipid nanoparticles have important applications as biomedical delivery platforms and broader engineering biology applications in artificial cell technologies. These emerging technologies often require changes in the shape and topology of biological or biomimetic membranes. Here we show that topologically‐active lyotropic liquid crystal nanoparticles (LCNPs) can trigger such transformations in the membranes of giant unilamellar vesicles (GUVs). Monoolein (MO) LCNPs, cubosomes with an internal nanostructure of space group Im3m incorporate into 1,2‐dioleoyl‐sn‐glycero‐3‐phosphocholine (DOPC) GUVs creating excess membrane area with stored curvature stress. Using time‐resolved fluorescence confocal and lattice light sheet microscopy, we observe and characterise various life‐like dynamic events in these GUVs, including growth, division, tubulation, membrane budding and fusion. Our results shed new light on the interactions of LCNPs with bilayer lipid membranes, providing insights relevant to how these nanoparticles might interact with cellular membranes during drug delivery and highlighting their potential as minimal triggers of topological transitions in artificial cells.

Topological and Morphological Membrane Dynamics in Giant Lipid Vesicles Driven by Monoolein Cubosomes.

Lipid nanoparticles have important applications as biomedical delivery platforms and broader engineering biology applications in artificial cell technologies. These emerging technologies often require changes in the shape and topology of biological or biomimetic membranes. Here we show that topologically-active lyotropic liquid crystal nanoparticles (LCNPs) can trigger such transformations in the membranes of giant unilamellar vesicles (GUVs). Monoolein (MO) LCNPs, cubosomes with an internal nanostructure of space group Im3m incorporate into 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) GUVs creating excess membrane area with stored curvature stress. Using time-resolved fluorescence confocal and lattice light sheet microscopy, we observe and characterise various life-like dynamic events in these GUVs, including growth, division, tubulation, membrane budding and fusion. Our results shed new light on the interactions of LCNPs with bilayer lipid membranes, providing insights relevant to how these nanoparticles might interact with cellular membranes during drug delivery and highlighting their potential as minimal triggers of topological transitions in artificial cells.

Roles of network topology in the relaxation dynamics of simple chemical reaction network models

Understanding the relationship between the structure of chemical reaction networks and their reaction dynamics is essential for unveiling the design principles of living organisms. However, while some network-structural features are known to relate to the steady-state characteristics of chemical reaction networks, mathematical frameworks describing the links between out-of-steady-state dynamics and network structure are still underdeveloped. Here, we characterize the out-of-steady-state behavior of a class of artificial chemical reaction networks consisting of the ligation and splitting reactions of polymers. Within this class, we examine minimal networks that can convert a given set of sources (e.g., nutrients) to a specified set of targets (e.g., biomass precursors). By exploring the dynamics of the models with a simple setup, we find three distinct types of relaxation dynamics after perturbation from a steady-state: exponential-, power-law-, and plateau-dominated. We computationally show that we can predict this out-of-steady-state dynamical behavior from just three features computed from the network’s stoichiometric matrix, namely, (1) the rank gap, determining the existence of a steady-state; (2) the left null-space, being related to conserved quantities in the dynamics; and (3) the stoichiometric cone, dictating the range of achievable chemical concentrations. We further demonstrate that these three quantities relates to the type of relaxation dynamics of combinations of our minimal networks, larger networks with many redundant pathways, and a real example of a metabolic network. The relationship between the topological features of reaction networks and the relaxation dynamics presented here are useful clues for understanding the design of metabolic reaction networks as well as industrially useful chemical production pathways.

Role of Multiple Intermolecular H-Bonding Interactions in Molecular Cluster of Hydroxyl-Functionalized Imidazolium Ionic Liquid: An Experimental, Topological, and Molecular Dynamics Study

Role of Multiple Intermolecular H-Bonding Interactions in Molecular Cluster of Hydroxyl-Functionalized Imidazolium Ionic Liquid: An Experimental, Topological, and Molecular Dynamics Study

A novel method for concurrent dynamic topology optimization of hierarchical hybrid structures

This paper proposes a feature-decoupled method for concurrent dynamic topology optimization of the Hierarchical Hybrid Structure (HHS) to minimize the steady-state dynamic response. First, a novel single-variable uniform multiphase material interpolation model is established based on the Gaussian function and normalization method, which achieves the decoupled description of the macroscopic topology, substructure topology, and the spatial distribution of the substructures for HHS. Second, by combining the extended multiscale finite element method (EMsFEM), which overcomes the limitations of the scale separation assumption and periodic boundary conditions in HHS response analysis, a concurrent dynamic topology optimization mathematical formulation for HHS is constructed. Finally, the sensitivity scheme is established based on the adjoint method, and the MMA algorithm was employed to update the model. Numerical examples verify the correctness and feasibility of the proposed method, demonstrate its advantages in solving HHS concurrent topology optimization problem compared to traditional methods, and explore the impact of the number of substructure types on the optimization results of HHS.

An integrated optimization method for the damper placements, shape and size of truss structures

This work extends the application of the approximation concept in dynamic topology optimization for truss structures. The corresponding problem contains both continuous and discrete variables, encompassing damper placements, truss shape and cross-sectional area. First, the utilization of a branched multipoint approximation (BMA) function establishes sequential approximation problems that also involve such mixed variables, marking its innovative application in dynamic optimization. Then, a modified genetic algorithm (GA) is used to optimize discrete variables. To enhance convergence stationarity, the concept of ‘adjacent individuals’ is proposed to control the degree of difference in topology configuration between initial population members and the current optimal. Additionally, adjustments to the sequence-based crossover process ensure compliance with damper quantity constraints. Examples demonstrate that the introduction of adjacent individuals is beneficial to convergence stationarity, and the shape change of the truss increases the mode damping ratio by more than 20%. The created optimization platform can also tackle other mixed variables optimization problems.

NDN multicast over wireless networks: A survey on fundamentals, challenges, and open issues

Wireless devices have shown remarkable growth in data demand in the last decade due to the proliferation of smart devices and the emergence of bandwidth-hungry applications. This increasing data traffic demand is overcrowding the radio frequency spectrum, leading wireless multicast schemes to become a popular research topic again, as they provide efficient data dissemination. NDN supports multicast communication by design. It is a networked system formed by named entities that adopt a communication model focusing on the content rather than its location. The architecture follows a receiver-driven communication model through which content consumers retrieve data through semantically meaningful names instead of specific destinations. Its properties are essential for multicast communication on ad hoc networks, as its features provide enhanced support for dynamic topologies, decentralized control, and the self-organization of participant nodes that communicate without needing a pre-existing network infrastructure. However, despite the wireless medium being broadcast by nature, NDN multicast is still challenging in wireless scenarios, especially in ad hoc environments, due to the node’s high mobility, link instability, constant handovers, and data transmission over a shared medium. Hence, this survey discusses the benefits of NDN for mobile scenarios through an in-depth analysis of NDN multicast features, focusing on fundamentals, challenges, and open issues when applied to wireless networking.

A geometric framework for asymptoticity and expansivity in topological dynamics

In this paper we develop a geometric framework to address asymptoticity and nonexpansivity in topological dynamics when the acting group is second countable and locally compact. As an application, we show extensions of Schwartzman’s theorem in this context. Also, we get new results when the acting group is Z d {\mathbb Z}^d : any half-space of R d \mathbb {R}^d contains a vector defining a (oriented) nonexpansive direction in the sense of Boyle and Lind. Finally, we deduce rigidity properties of distal Cantor systems.

Minimal zero entropy subshifts can be unrestricted along any sparse set

Abstract We present a streamlined proof of a result essentially presented by the author in [Some counterexamples in topological dynamics. Ergod. Th. & Dynam. Sys.28(4) (2008), 1291–1322], namely that for every set $S = \{s_1, s_2, \ldots \} \subset \mathbb {N}$ of zero Banach density and finite set A, there exists a minimal zero-entropy subshift $(X, \sigma )$ so that for every sequence $u \in A^{\mathbb {Z}}$ , there is $x_u \in X$ with $x_u(s_n) = u(n)$ for all $n \in \mathbb {N}$ . Informally, minimal deterministic sequences can achieve completely arbitrary behavior upon restriction to a set of zero Banach density. As a corollary, this provides counterexamples to the polynomial Sarnak conjecture reported by Eisner [A polynomial version of Sarnak’s conjecture. C. R. Math. Acad. Sci. Paris353(7) (2015), 569–572] which are significantly more general than some recently provided by Kanigowski, Lemańczyk and Radziwiłł [Prime number theorem for analytic skew products. Ann. of Math. (2)199 (2024), 591–705] and by Lian and Shi [A counter-example for polynomial version of Sarnak’s conjecture. Adv. Math.384 (2021), Paper no. 107765] and shows that no similar result can hold under only the assumptions of minimality and zero entropy.