Dynamic Topology Research Articles

Adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game

Autonomous cooperation of unmanned swarms is the research focus on "new combat forces" and "disruptive technologies" in military fields. The mechanism design is the fundamental way to realize autonomous cooperation. Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level, an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game is designed. This paper analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarms, including the emergence of swarm intelligence, information network construction and collaborative mechanism design. Then, based on the framework, the adaptive dynamic reconfiguration mechanism is discussed in detail from two aspects: topology dynamics and strategy dynamics. Next, the unmanned swarms' community network is designed, and the network characteristics are analyzed. Moreover, the mechanism characteristics are analyzed by numerical simulation, focusing on the impact of key parameters, such as cost, benefit coefficient and adjustment rate on the level of swarm cooperation. Finally, the conclusion is made, which is expected to provide a theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.

Intelligent Terahertz Medium Access Control (MAC) for Highly Dynamic Airborne Networks

The Terahertz (THz) link provides over 100 Gbps of data transmission rate. However, it is extremely vulnerable to obstacles and requires very narrow (highly directional) beams for highly focused energy radiation. The main task of THz medium access control (MAC) protocol is not channel access collision avoidance; instead, it is to coordinate all the one-hop neighbors for scheduled, antenna-aligned line-of-sight communications. This study aims to build an intelligent MAC scheme for highly mobile Terahertz airborne networks (TANs). Our TAN MAC design has three features as follows: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Spatio-temporal TAN state learning:</i> We will build a predictive network state estimation model through deep learning and generative adversarial network (GAN). Based on the predicted node/link status, all the one-hop neighbors can prepare well for the antenna alignment and THz channel scheduling. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accurate, two-level MAC operation control:</i> we propose to use nested deep reinforcement learning (DRL) with outer/inner policy loops for high-/low-level action determination: The outer loop determines the high-level, coarse actions (such as antenna codebook selection); the inner loop determines the low-level, fine actions (such as individual beam control) under the selected high-level action. (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TAN-specific, comprehensive MAC behavior control:</i> Based on the above deep neighbor adaptation (DNA) model, we design a complete TAN MAC protocol that considers the routing context and dynamic network topology. Our simulations demonstrate the smooth, high-rate THz communications in MAC layer with resilient RF links under high node mobility.

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear technique to reveal the dynamics of the brain neural system. Recently, the topological data analysis (TDA) scheme has been used to explore the properties of space, which provides a powerful tool to think over the phase space. In this work, we proposed a topological EEG nonlinear dynamics analysis approach using the phase space reconstruction (PSR) technique to convert EEG time series into phase space, and the persistent homology tool explores the topological properties of the phase space. We perform the topological analysis of EEG signals in different rhythm bands to build emotion feature vectors, which shows high distinguishing ability. We evaluate the approach with two well-known benchmark datasets, the DEAP and DREAMER datasets. The recognition results achieved accuracies of 99.37% and 99.35% in arousal and valence classification tasks with DEAP, and 99.96%, 99.93%, and 99.95% in arousal, valence, and dominance classifications tasks with DREAMER, respectively. The performances are supposed to be outperformed current state-of-art approaches in DREAMER (improved by 1% to 10% depends on temporal length), while comparable to other related works evaluated in DEAP. The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.

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.

Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks.

Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL-SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends "hello" packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL-SR can perceive network information faster and converge faster. Additionally, FRL-SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL-SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.

Heterogeneous Local Dynamics in Mussel-Inspired Elastomers

Materials with increased mechanical strength and toughness bearing mussel-inspired iron–catechol complexes and network architectures exhibit several dynamic features, e.g., high and broad glass-“transition” temperature, not explored so far. By combining differential scanning calorimetry and dielectric spectroscopy, the latter as a function of temperature and pressure, we have explored the increase in the glass temperature, Tg, and the concomitant increase in the breath of Tg in bioinspired networks bearing one (covalent) or two (covalent and coordination) types of cross-links. Cross-linked networks experience heterogeneous segmental dynamics that are responsible for the broad Tg range observed in the thermal measurements. The two distinct dynamics reflect the relaxation of segments in the vicinity versus more distant cross-linked units. The various topologies are shown to have different fragilities and tend to form stronger glasses with increasing network topology. In addition, the ionic conductivity is influenced by the segmental dynamics and the increased Tg in the networks. These features (reduced specific heat step, Δcp, at Tg and “strong” dynamic behavior) are also found in permanently cross-linked polymers with dynamic network topology known as vitrimers. The similarities in the two network systems are discussed.

Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks

Satellite network is considered a prominent architecture for future space-air-ground integrated networks, where the relay satellite network (RSN) plays an essential role in data transmission. However, the relay satellite network faces practical challenges. First, due to the dynamic network topology and limited resources such as storage capacity, antennas, and bandwidth, optimizing data transmission and resource allocation in a resource-constrained stochastic system is challenging. Second, satellites intermittently enter the eclipse zone, which leads to an unstable energy supply. Therefore, optimal energy management is required to balance energy supply and consumption. In this paper, we proposed an online optimal control algorithm, based on Lyapunov stability theory, to optimize data transmission and energy management jointly. Furthermore, the performance of our proposal is analyzed comprehensively, including the reasonable bounds for data and energy and the optimality of our proposed algorithm. Finally, extensive performance evaluation simulations are performed based on real-world satellite parameters. The simulation results indicate that the proposed method can achieve long-term network stability and energy sustainability. Moreover, the proposed algorithm shows an improvement of 9.6% and 20.3% in system utility compared to the non-optimized transmission method and non-optimized energy management algorithm, respectively.

A Novel Method of Enhancing Security Solutions and Energy Efficiency of IoT Protocols

Mobile Ad-hoc Networks (MANET’s) are wireless networks that are capable of operating without any fixed infrastructure. MANET routing protocols must adhere to strict secrecy, integrity, availability and non-repudiation criteria. In MANETs, attacks are roughly categorised into two types: active and passive. An active attack attempts to modify or remove data being transferred across a network. On the other hand, passive attack does not modify or erase the data being sent over the network. The majority of routing protocols for MANETs were built with little regard for security and are therefore susceptible to a variety of assaults. Routing technologies such as AODV and dynamic source routing are quite common. Both however are susceptible to a variety of network layer attacks, including black holes, wormholes, rushing, byzantine, information disclosure. The mobility of the nodes and the open architecture in which the nodes are free to join or leave the network keep changing the topology of the network. The routing in such scenarios becomes a challenging task since it has to take into account the constraints of resources of mobile devices. In this an analysis of these protocols indicates that, though proactive routing protocols maintain a route to every destination and have low latency, they suffer from high routing overheads and inability to keep up with the dynamic topology in a large sized network. The reactive routing protocols in contrast have low routing overheads, better throughput and higher packet delivery ratio. AODVACO-PSO-DHKE Methodology boosts throughput by 10% while reducing routing overhead by 7%, latency by 8% and energy consumption by 5%. To avoid nodes always being on, a duty cycle procedure that's also paired with the hybrid method is used ACO-FDR PSO is applied to a 100-node network and NS-3 is used to measure various metrics such as throughput, latency, overhead, energy consumption and packet delivery ratio.

Cooperative caching strategy based mobile vehicle social‐aware in internet of vehicles

AbstractThe dramatic growth of smart in‐vehicle applications in the Internet of Vehicles and the increasing quality of experience for vehicle users have put a huge traffic load on the mobile core network. Mobile edge caching has been proposed to place content at the edge of the network to serve users in close proximity to them. However, the dynamic topology of existing edge networks and the inherent storage limitations of edge devices pose a serious challenge to vehicular edge caching. Therefore, in this article, we propose a cooperative caching strategy based on mobile prediction and social awareness that allows collaborative content decision making among edge devices. Specifically, the long short‐term memory network is used to predict vehicle trajectories, social relationships are computed using content similarity and contact rates among vehicle users to select vehicles that can serve as caching nodes, and deep reinforcement learning is employed to achieve the final caching decision. Simulation results show that the caching strategy proposed in this article achieves up to 7.7%, 24.2%, and 27.3% gains, respectively, in terms of content acquirement delay compared to the reinforcement learning algorithm, the algorithm without considering mobility, and the non‐cooperative algorithm.

A review on machine learning based routing protocols for delay tolerant networks

Nowadays, Delay Tolerant Network plays an important role in improving the communication between the network nodes. Applications of Delay Tolerant Network are disaster recovery, vehicular communication, sensor networks, interplanetary networks, and communication in remote and rural areas. Routing is one of the important tasks for enhancing the energy effectiveness of data transmission among the mobile nodes under network congestion and dynamic topology. Machine Learning-based routing algorithms are used for improving network communication in Delay Tolerant Networks. Its objective is to reduce the delay, minimize the overhead, reduce energy consumption, improve throughput, minimize packet loss, and efficient data transmission. This paper presents a comprehensive review of routing algorithms using machine learning for Delay Tolerant Networks.

Dynamic Network Topology Portrait for Digital Twin Optical Network

Digital twin (DT) as one promising technology has been introduced in optical networks, whose role is to achieve interactive functions and intelligent control by building a bridge between physical space and digital space. In this paper, a multifactor-associated network topology portrait (NTP) scheme is proposed to draw a dynamic, real-time, and comprehensive topology representation for DT optical network (DTON). Multiple parameters from nodes and links are jointly evaluated, including link distance, link loss, optical signal-to-noise ratio, bandwidth utilization rate, load capacity, node failure rate, and link failure rate, involving both network layer and physical layer. Based on the NTP, the performance of dynamic routing computation combined with six different routing algorithms is studied, Here, the time for routing computation is sacrificed a little bit in exchange for significant improvements in outage probability, outage latency, and the number of service requests.

A Novel Method of Enhancing Security Solutions and Energy Efficiency of IoT Protocols

Mobile Ad-hoc Networks (MANET’s) are wireless networks that are capable of operating without any fixed infrastructure. MANET routing protocols must adhere to strict secrecy, integrity, availability and non-repudiation criteria. In MANETs, attacks are roughly categorised into two types: active and passive. An active attack attempts to modify or remove data being transferred across a network. On the other hand, passive attack does not modify or erase the data being sent over the network. The majority of routing protocols for MANETs were built with little regard for security and are therefore susceptible to a variety of assaults. Routing technologies such as AODV and dynamic source routing are quite common. Both however are susceptible to a variety of network layer attacks, including black holes, wormholes, rushing, byzantine, information disclosure. The mobility of the nodes and the open architecture in which the nodes are free to join or leave the network keep changing the topology of the network. The routing in such scenarios becomes a challenging task since it has to take into account the constraints of resources of mobile devices. In this an analysis of these protocols indicates that, though proactive routing protocols maintain a route to every destination and have low latency, they suffer from high routing overheads and inability to keep up with the dynamic topology in a large sized network. The reactive routing protocols in contrast have low routing overheads, better throughput and higher packet delivery ratio. AODVACO-PSO-DHKE Methodology boosts throughput by 10% while reducing routing overhead by 7%, latency by 8% and energy consumption by 5%. To avoid nodes always being on, a duty cycle procedure that's also paired with the hybrid method is used ACO-FDR PSO is applied to a 100-node network and NS-3 is used to measure various metrics such as throughput, latency, overhead, energy consumption and packet delivery ratio.

Feature Cluster-Based Secure Data Transmission Method for Social Internet of Vehicles

Due to the high mobility of vehicles and the high dynamics of SIoV network topology, the communication between users will be frequently interrupted, thus affecting the service quality of users. In addition, due to the open nature of the SIoV wireless channel, any user can broadcast messages in the system. However, unreliable users pose serious security threats to other users on the network. In order to solve these problems, we propose a feature cluster-based secure data transmission method (FC-SDTM) to ensure safe and stable data transmission between vehicles. This method creates feature clusters according to the feature similarity of users, which provides the stability of communication between users and improves users’ reliability in the cluster. Second, consortium blockchains store the transmission data sent by the sender in the cluster for the receiver to verify, further ensuring the security of intra-cluster communication. Finally, the random number key reduces the running time of the proposed method and solves the security problem caused by cluster topology updates. The experimental results demonstrate that this method can reduce the system running time and the message exposure rate, while also improving transmission accuracy.

VANET Secure Reputation Evaluation &amp; Management Model Based on Double Layer Blockchain

Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it is essential to consider how to protect the security of the data itself. Existing studies have evaluated the reliability of interactive data through reputation quantification, but there are still some issues in the design of secure reputation management schemes, such as its low efficiency, poor security, and unreliable management. Aiming at the above-mentioned issues, in this paper we propose an effective VANET model with a secure reputation based on a blockchain, and it is called the double-layer blockchain-based reputation evaluation &amp; management model (DBREMM). In the DBREMM, we design a reputation management model based on two parallel blockchains that work collaboratively, and these are called the event chain and reputation chain. A complete set of reputation evaluation schemes is presented. Our schemes can reduce observation errors and improve evaluation reliability during trust computation by using direct trust calculation based on the multi-factor Bayesian inference. Additionally, we propose an indirect trust calculation based on the historical accumulated reputation value with an attenuation factor, and a secure a reputation fusion scheme based on the number threshold with the fluctuation factor, which can reduce the possibility of attacks, such as collusive attacks and false information injection. Theoretical analysis and extensive simulation experiments reflect the DBREMM’s security algorithm effectiveness, accuracy, and ability to resist several attacks.

Spin motive force by the momentum-space Berry phase in magnetic Weyl semimetals

We show that the magnetic precession of ferromagnetic moments in a noncentrosymmetric magnetic Weyl semimetal induces an electric current through a mechanism analogous to the adiabatic charge pumping. The current is a consequence of a Berry phase in the momentum space resulting from the circular motion of Weyl nodes induced by the precession. This mechanism resembles the Faraday effect, namely, induced magnetic field by circular electric current. The circular motion of Weyl nodes induces magnetic charge current in the momentum space, which results in a Berry phase that describes the adiabatic pump. Experimentally, this phenomenon is similar to spin motive force, which is an electric current induced by magnetic precision in the presence of the spatial gradient of magnetization. However, unlike the conventional spin motive force, this current occurs without a magnetization gradient. The result demonstrates a nontrivial interplay between the topological electronic state and magnetic dynamics.

Global Topological Synchronization on Simplicial and Cell Complexes.

Topological signals, i.e., dynamical variables defined on nodes, links, triangles, etc. of higher-order networks, are attracting increasing attention. However, the investigation of their collective phenomena is only at its infancy. Here we combine topology and nonlinear dynamics to determine the conditions for global synchronization of topological signals defined on simplicial or cell complexes. On simplicial complexes we show that topological obstruction impedes odd dimensional signals to globally synchronize. On the other hand, we show that cell complexes can overcome topological obstruction and in some structures signals of any dimension can achieve global synchronization.

Connectivity Preservation and Collision Avoidance in Multi-Agent Systems Using Model Predictive Control

This paper presents an innovative predictive control scheme based on a potential field in order to maintain the connectivity of a flock of agents in a leader-follower configuration with dynamic topology. We consider a group of agents navigating in an environment with obstacles towards their target. The followers avoid collisions with each other and obstacles without using any communication links. It is also required to maintain connectivity with the leader, which is unidentified to the followers. The potential field is dynamically updated by introducing time-varying weighted links between the followers to preserve connectivity as we assume only the leader knows the target position. The values of these weights are adjusted continuously according to agents' trajectories by which the critical neighbours of each agent are determined. The superior performance of the proposed predictive controller for navigation of agents to quickly reach their target is demonstrated comparatively by simulation.

Constructed complex motions and chaos.

Constructed motions and dynamic topology are new trends in solving nonlinear systems or system interactions. In nonlinear engineering, it is significant to achieve specific complex motions to satisfy expected dynamical behaviors (e.g., nonlinear motions, singularities, bifurcations, chaos, etc.), and complex motion application and control. To achieve such expected motions and global dynamical behaviors, mapping dynamics, constructed networks, random/discontinuous dynamic theorems, etc., are applied to quantitatively determine the complex motions. These theories adopt the symbolic dynamic abstracts and topological structures with nonlinear dynamics to investigate constructed complex motions to satisfy expected dynamical behaviors.

INFLUENCE WITH TRAFFIC RATE AND BEACON TIME FOR JAMMING ATTACK ON 802.11B IN MOBILE AD HOC NETWORKS

MANETs have distinctive characteristics like dynamic topology, wireless radio medium, restricted resources and lack of centralized administration; as a result, they're liable to differing types of attacks in several layers of protocol stack. Every node in a Manet is capable of acting as a router. The need for a secure Manet networks is powerfully tied to the protection and privacy options. This Jamming attacks are one in every of them. These occur by transmittal continuous radio ways in which to inhibit the transmission among sender and receiver. These attacks have an effect on the network by decreasing the network performance with buffer size, beacon amount varied as certained result on mobile ad-hoc network degrade the performance with Jamming attack. This work includes a network with high Mobility IEEE 802.11b with improved AODV (Ad hoc On Demand Distance Vector) routing protocol parameters. FTP with high rate is being generated within the network. For the Simulation purpose we tend to utilized OPNET (Optimized Network Engineering Tool) MODELER 14.5 is employed for simulation. The performance of network is measured with relation to the QoS parameters.

Time–Space Dynamic Incentives Topology Equilibrium Control for Mechanical Vibration Wireless Sensor Networks

Time–Space Dynamic Incentives Topology Equilibrium Control for Mechanical Vibration Wireless Sensor Networks