Complex Network Topology Research Articles

SDN-Assisted Safety Message Dissemination Framework for Vehicular Critical Energy Infrastructure

The proliferation of fifth-generation (5G) networks toward vehicle-to-everything (V2X) communication has paved the way for driverless autonomous vehicles (AVs) in vehicular critical energy infrastructures (CEI). Though technological advancements improve AVs, the safety-critical messages (SCMs) still play a vital role in reducing crashes, preventing injuries, and saving lives. AVs’ high speed and complex network topology challenge disseminating SCMs with a highly successful delivery ratio and extremely low latency. Furthermore, the typical SCM dissemination schemes cause channel congestion and minimize the delivery ratio, making the systems incompatible with the AVs. Therefore, in this article, a software-defined-networking-assisted continuous clustering approach called migrating consignment region (MiCR) based on the federated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means algorithm is proposed for disseminating SCMs to the AVs via 5G V2X communication. Unlike other methods that create clusters for every instance of SCM dissemination, MiCR continuously holds moving clusters for disseminating SCMs to AVs with ultrahigh reliability and low latency. The proposed MiCR approach has been simulated under real-time highway road maps and compared with other methods. The simulation results prove the superiority of MiCR in terms of network overload, SCM delivery ratio, latency, dissemination efficiency, and collision rate compared with the existing methods.

Spatial characteristics and complexity of the urban economic network structure based on the secure Internet of Things

In the era of network economic development, the Internet of Things has become a key factor in economic growth and urban competitiveness. Inter-regional interconnection promotes economic growth, and urban economic growth provides innovative elements for regional innovation such as technology, capital, talent, and information. These elements are indispensable for the development and optimization of the innovation environment. On this basis, establish an urban economic network structure system based on the Internet of things, analyze the three relationships between the city’s industrial structure and urban economy, urban economy and urban spatial characteristics, and the Internet of Things and urban network structure systems. The system framework, the construction process and the specific content of the framework are checked. The description method is suitable for the similarity analysis of complex network topologies and the evaluation of the effect of complex network scanning algorithms. The samples generated by the evolutionary network model are used to analyze the similarity of network topologies. Several different sampling algorithms are used to compare the evolution model samples and the empirical network samples to evaluate the sample results. The experimental results confirm the practicability and effectiveness of the description method. The data of the article is downloaded in the city's commercial network module through the Internet of Things. According to the relevant information of the city's commercial logistics, calculate the ratio of each interval to the overall to measure the economic development status. According to different development situations, set up indicators to measure traffic conditions, determine the causal relationship between quantities, and observe the relationship between data and urban economic management. The urban business network system based on the Internet of Things constructed in this paper provides reports on the transformation and upgrading of urban industries, enriches the urban distribution theory and the application of the Internet of Things, and provides Internet of Things reports and foundations for organization and management, surpassing the national bourgeoisie In the economic system, the efficiency of urban economic management has increased by more than 30%.

Hardware Development and Interoperability Testing of a Multivendor-IEC-61850-Based Digital Substation

Substations are becoming increasingly reliant on international electrotechnical commission (IEC)-61850-enabled devices. However, device compatibility with these standards does not guarantee interoperability when devices are taken from different manufacturers. If interoperability of multivendor devices can be achieved, then power utilities will be in a position to implement multivendor devices in substations. The study here presents the development and testing of a digital substation test platform that incorporates devices from different manufacturers. The process bus communication and protection operation of the intelligent electronic devices (IEDs) were tested to validate device interoperability. The testbed was tested for two IED process bus communications, generic object-oriented substation event (GOOSE) and sampled measured value (SMV). The GOOSE is travelling between IED to IED with an end-to-end (ETE) delay of 2 ms and the SMV read by the IEDs are the same as the injected real-time substation inputs 220 kV and 1 kA. Three IED protection studies (overcurrent, earth fault, and overvoltage) were performed, and IED response curves were obtained. In addition, data monitoring and client–server communications were studied using installed software tools. The testbed configuration in this study has faced some real-time challenges regarding differences in device edition, device firmware, and ethernet switch due to its multivendor approach. All the mentioned configuration issues were resolved in this study with successful testing and validation of the testbed. The study of this testbed will provide solutions to the problems associated with a multivendor system faced by substation engineers and will help them in opting for multivendor installations. This system can be extended in the future by installing more multivendor devices with complex network topology and a SCADA system.

Analysis on rules of acupoint compatibility and application characteristics in acupuncture treatment of migraine based on complex network technique

To analyze the compatible rules of acupoints in the treatment of migraine with acupuncture and moxibustion based on complex network technique so as to provide a reference for composing best acupoint recipes. Clinical papers about acupuncture treatment of migraine published from January of 2010 to October of 2020 were searched from Chinese and English databases including CNKI, Wanfang, VIP and Pubmed by using keywords of acupuncture, moxibustion and migraine. According to our inclusive and exclusive criteria, the collected papers were screened out to set up a database. The SPSS Modeler 18.0 software was used to analyze the association rules of acupoints, and the Gephi 0.9.2 software was used to conduct a complex network analysis about the compatibility rules of acupoints and their application characteristics of moxibustion and needling methods in the treatment of migraine. In the end, 572 articles meeting the conclusive criteria were selected, and a total of 1 091 acupoint prescriptions for the treatment of migraine were extracted, including 230 acupoints with a total appea-rance frequency of 7 902. Those acupoints most frequently used were mainly attributed to the Gallbladder Meridian, with Fengchi (GB20) being the highest in the cumulative frequency. The association rule analysis showed that the highest supporting degree acupoints was GB20-Taiyang(EX-HN5). The highest levels of confidence were EX-HN5-Touwei(ST8), GB20-Baihui(GV20), and GB20-ST8. The analysis of complex network topology showed that a total of 17 acupoints including GB20, EX-HN5, Shuaigu(GB8), Taichong(LR3), GV20, Ashi point, etc. were the core node acupoints for acupuncture treatment of migraine. In the treatment of migraine, the acupoints selected are mainly distributed in the head region. When the specific acupoints chosen, the five Shu acupoints, Yuan (source) acupoints and the confluent acupoints of eight meridians are frequently used. In terms of composing acupoint recipes, the acupoints in the local and distant part of the body are often used in combination. These outcomes may have a certain guiding significance for clinical treatment of migraine.

Rational Selection of Rail Transit Emergency Site Using Complex Network Topology and Genetic Algorithm

Rail transit network is a convenient and reliable transportation mode in urban areas. With every surge in transportation demand, the network undergoes operational changes like restructuring and making of multiline tracks. Urban rail transit network is a complex network and needs proper emergency services and controlling mechanisms. Unexpected mishaps in rail transit will cause more hazard and panic than the other transportation systems. Therefore, a reliable emergency service station is required to ensure the safety and security of passengers. In the proposed method, the basic statistical properties of complex network topology are considered to construct a P-center site selection model for urban rail transit emergency service stations. The P-center site selection model is solved by genetic algorithm. Validity and reasonableness of the model are demonstrated by implementing it in the Hangzhou emergency service stations for rail transportation. The results show that the P-center site selection model based on statistical properties of complex network topologies can better solve the urban rail transit emergency service station site selection problems. The model minimizes the number of emergency service stations while satisfying the optimal objective function and reduces the construction cost of emergency service stations. The approach has a significant effect on improving system reliability and reducing the risk of emergencies.

A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks

Recently, Deep Spiking Neural Network (DSNN) has emerged as a promising neuromorphic approach for various AI-based applications, such as image classification, speech recognition, robotic control etc. on edge computing platforms. However, the state-of-the-art offline training algorithms for DSNNs are facing two major challenges. Firstly, many timesteps are required to reach comparable accuracy with traditional frame-based DNNs algorithms. Secondly, extensive memory requirements for weight storage make it impossible to store all the weights on-chip for DSNNs with many layers. Thus the inference process requires continue access to expensive off-chip memory, ultimately leading to performance degradation in terms of throughput and power consumption. In this work, we propose a hardware-friendly training approach for DSNN that allows the weights to be constrained to ternary format, hence reducing the memory footprints and the energy consumption. Software simulations on MNIST and CIFAR10 datasets have shown that our training approach could reach an accuracy of 97% for MNIST (3-layer fully connected networks) and 89.71% for CIFAR10 (VGG16). To demonstrate the energy efficiency of our approach, we have proposed a neural processing module to implement our trained DSNN. When implemented as a fixed, 3-layers fully-connected system, the system has reached at energy efficiency of 74nJ/image with a classification accuracy of 97% for MNIST dataset. We have also considered a scalable design to support more complex network topologies when we integrate the neural processing module with a 3D Network-on-Chip.

Heterogeneous platform-aware workload feature recognition for edge intelligence

With the advent of the Internet of Everything, the combination of AI (Artificial Intelligence) and edge computing has become a new research hotspot, and edge intelligence has emerged, which enables network edge devices to analyze data through AI algorithms. Since the edge computing environment is more complex and variable than cloud computing, there are many issues in building edge intelligence, such as lack of quantitative evaluation criteria, heterogeneous computing platforms, complex network topologies, and changing user requirements. To analyze the performance of edge intelligence workloads running on heterogeneous hardware platform, we target machine learning workloads in edge intelligence and analyze the impact of algorithm model complexity, edge data characteristics and heterogeneous platform differences in edge intelligence in terms of relative performance. By analyzing the machine learning workload in edge intelligence, we find that the inference time and memory usage of a model can be predicted based on the amount of computation and number of parameters of the model. Moreover, image complexity, edge data network features, and batch size all affect the performance of edge intelligence workloads. Furthermore, the upper limit of model performance on the same computing platform is limited by hardware resources. And finally, the model performance of a platform depends on its own computing power and bandwidth.

Selection of spatial modes in an ensemble of non-locally coupled chaotic maps

Purpose of this work is to determine regularities of formation of spatial structures in an ensemble of chaotic systems with non-local diffusion couplings and to study how these structures depend on the wave response of the digital filter formed by the ensemble couplings structure. Methods. The study was carried out by numerical simulation of an ensemble of logistic maps, calculation of its typical oscillatory regimes and their spectral analysis. The network was considered as a digital filter with a frequency response depending on the coupling parameters. Correlation between the spatial spectra and the amplitude-frequency response of the coupling filter and the mutual coherence of oscillations when the coupling parameters change were considered. Results. The system of couplings between chaotic maps behaves like a wave filter with selective properties, allowing spatial modes with certain wavelengths to exist and suppressing others. The selection of spatial modes is based on the wave characteristic of the coupling filter, the type of which is determined by the radius and the magnitude of couplings. At strong coupling the wave characteristics for ensembles with local and non-local couplings are qualitatively different, therefore they demonstrate essencially different behavior. Discussion. Using spectral methods for dynamics analysis systems with complex network topologies seems to be a promising approach, especially for research of synchronization and multistability in ensembles of chaotic oscillators and maps. The found regularities generalize the results known for ensembles of maps with local couplings. They also can be applied to ensembles of self-sustained oscillators.

Modeling Complex Networks Based on Deep Reinforcement Learning

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

Identifying Partial Topology of Complex Networks with Stochastic Perturbations and Time Delay

Identifying Partial Topology of Complex Networks with Stochastic Perturbations and Time Delay

Деякі підходи підвищення ефективності функціонування інформаційно-комунікаційної системи Державної прикордонної служби України

The article proposes an approach to the development of a methodology for increasing the efficiency of the functioning of special purpose electronic communications systems, especially the electronic communications system of the State Border Guard Service of Ukraine. The State Border Service of Ukraine is a subject of integrated border management. Information and communication systems, in particular, the information and communication system of border control are actively used in the operational and service activities of the State Border Guard Service to ensure the performance of the assigned functions. The technical basis for the transmission of information in the information and communication system is the system of electronic communications. The electronic communications system of the State Border Guard Service of Ukraine is built in the form of a complex network topology of the “star” type and uses the Transmission Control Protocol/Internet protocol (TCP/IP) stack for data transmission and, mainly, static routing, therefore, it does not provide the automatic recovery in the event of a violation of the existing route. Thus, for a certain time, a separate segment of the electronic communications system does not transmit information, as a result of which violations may occur at the state border. In the existing methods for evaluating the effectiveness of the functioning of the electronic communications system, such indicators as performance, stability, reliability of transmission, safety of data transmission are used. But those methods do not take into account the indicator of the dependence of tasks on the network. The study proposes to develop a methodology that would take into account the specified indicator of operational and service activity dependence in the system of indicators of the effectiveness of the electronic communications system. After entering into the system of efficiency indicators of the electronic communications system for special purposes, the operational and service activity efficiency indicator is proposed to calculate the updated efficiency of the electronic communications system when applying one or another routing protocol, network topology options and choose the best one. Key words: State Border Guard Service of Ukraine; integrated information and communication system “Hart”; information and communication system; operational and service activity; electronic communications system.

Adaptive complex network topology with fitness distance correlation framework for particle swarm optimization

The particle swarm optimization algorithm is an effective tool to solve various optimization problems due to the small number of parameters and the simple learning strategy. However, the updated strategy from the basic PSO mainly aims to learn the global optimal particles, and it often leads to premature convergence with poor solution accuracy. An adaptive complex network topology with a fitness distance correlation for the particle swarm optimization algorithm is proposed (CNAPSO). Using the CNAPSO algorithm, it is concluded that different network topologies have different degrees of dispersion in the process of particle swarm optimization search. Therefore, the adaptive strategy with the fitness distance correlation proposes to effectively balance the global exploration and local exploitation capabilities, which is the particle swarm adaptive network neighborhood topology. The neighborhood topology construction strategy with a complex network is used to construct the neighborhood topology for each particle. Therefore, the local optimal particles in the neighborhood participate in the search process of particle swarm optimization and eliminate the situation of only learning the global optimal particles. Moreover, it improves the solution accuracy of the particle swarm optimization algorithm. In addition, to avoid the particle swarm falling into premature convergence, this study introduces a random drift strategy to make the particles drift slightly and reduces the risk of premature convergence. The experimental results on twenty-four benchmark functions show that CNAPSO has great improvements in the accuracy of the solution and the speed of convergence compared with the six representative PSO algorithms.

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

The network topology is the basis of studying the overall performance and dynamic evolution mechanism of network. In regards to the study on the topology of multi-layer complex networks, the current studies fail to consider the heterogeneity of edges and nodes in multi-layer complex networks, and such methods are insufficient when they are used to analyze the static characteristics of topology. Therefore, this paper based on the theoretical basis of the multi-layer complex network, establishes a two-layer public transit complex network model where urban rail transit and bus transit cooperate with each other, and by optimizing this two-layer public transit complex network model, further proposes an integrated public transit network model. According to the method of analyzing the static statistical characteristics of complex networks, and considering the heterogeneity of nodes and edges in multi-layer network, this​ paper gives the evaluation indicators of static statistical characteristics of multi-layer public transit complex networks. Taking the two-layer public transit complex network structure in Lanzhou as an example, this paper finds out the main factors affecting its network stability and efficiency by analyzing the physical structure characteristics of this two-layer public transit complex network in Lanzhou where urban rail transit and bus transit cooperate with each other, and then puts forward the network optimization suggestions from the point of view of optimizing key nodes by using the method of identifying key nodes with centrality. In this study, the coupling among heterogeneous public transit network layers and the static structure characteristics of multi-layer public transit complex network are studied from the perspective of network structure, to assist the traffic management department in finding out the problems of the road network for the management and improvement of the performance and efficiency of the network. This has a profound influence on the development planning of urban public transit system.

Deciphering the generating rules and functionalities of complex networks

Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

Motif‐based embedding label propagation algorithm for community detection

Community detection can exhibit the aggregation behavior of complex networks. Network motifs are the fundamental building blocks which can reveal the higher-order structure of complex networks. Label propagation algorithm has the advantage of approximately linear time complexity, unfortunately, the randomness of label update is a major but unsolved issue. For these reasons, this paper proposes a novel community detection method, named motif-based embedding label propagation algorithm (MELPA). First, complex network topology is reconstructed by merging higher-order topology with lower-order connectivity features, where higher-order topology is captured by mining network motifs. Second, We design a label propagation characteristic model according to nodes influence, then a new label update rule is formulated based on reconstructed weighted network, the rule integrates frequency among neighbor labels, influence of nodes, propagation characteristics and closeness of nodes to update the node label, the purpose is to overcome the randomness of label selection and identify a better and more stable community structure. Finally, extensive experiments on synthetic networks and real-world complex networks are conducted to verify the effectiveness of MELPA, especially for the complex networks with unobvious community structure, MELPA will get unexpected results.

Partial synchronization in the second-order Kuramoto model: An auxiliary system method.

Partial synchronization emerges in an oscillator network when the network splits into clusters of coherent and incoherent oscillators. Here, we analyze the stability of partial synchronization in the second-order finite-dimensional Kuramoto model of heterogeneous oscillators with inertia. Toward this goal, we develop an auxiliary system method that is based on the analysis of a two-dimensional piecewise-smooth system whose trajectories govern oscillating dynamics of phase differences between oscillators in the coherent cluster. Through a qualitative bifurcation analysis of the auxiliary system, we derive explicit bounds that relate the maximum natural frequency mismatch, inertia, and the network size that can support stable partial synchronization. In particular, we predict threshold-like stability loss of partial synchronization caused by increasing inertia. Our auxiliary system method is potentially applicable to cluster synchronization with multiple coherent clusters and more complex network topology.

Network reconstruction based on generalized splay state

Complex network is the simplest abstraction of the interaction of the components of the systems. Reconstructing the full topology of the network from data is a focal problem for understanding and controlling the dynamics behavior of the systems. Based on the characteristics of generalized periodic splay state of weakly coupled systems, that is, when the system is in the generalized periodic splay state, the nodes which are connected directly usually have different phases, and the phases of the nodes which are not connected directly are usually random, we develop an efficient approach to reconstruct the topology of complex network. With the most simplified form of Logistic map as the node dynamics of the coupled systems, we calculate the matrix of the phase correlation, and reconstruct the topological structure of the networks according to the matrix. It is found that this method can reconstruct the topological structure of the complex network with sparse connection perfectly.

AI-based network topology optimization system

Existing network topology planning does not fully consider the increasing network traffic and problem of uneven link capacity utilization, resulting in lower resource utilization and unnecessary investments in network construction. The AI-based network topology optimization system introduced in this paper builds a Long Short-Term Memory (LSTM) model for time series traffic forecasting, which uses NetworkX, a Python library, for graph analysis, dynamically optimizes the network topology by edge deletion or addition based on traffic over nodes, and ensures network load balancing when node traffic increases, mainly introducing the LSTM forecasting model building process, parameter optimization strategy, and network topology optimization in some detail. As it effectively enhances resource utilization, this system is vital to the optimization of complex network topology. The end of this paper looks forward to the future development of artificial intelligence, and suggests the possibility of how to cooperate with operator networks and how to establish cross-border ecological development.

A framework for deriving dispatching rules of integrated urban drainage systems

The urban drainage system (UDS) with the integration of new and old, green and gray infrastructures has become an underlying trend, but its sewage dispatch is getting more and more difficult owing to complex network topology, much uncertainty introduced by the lack of empirical data or monitoring system, and consideration of multiple objectives. We propose a novel framework for deriving dispatching rules that allocate the wastewater inflow according to the comprehensive capacity of treatment facilities. The main innovative point of the framework is using the Prophet algorithm to handle sewage volume uncertainty introduced by rainfall. Moreover, we construct a multi-objective optimization model of operating costs and surface water quality and apply it in multiple scenarios. Lastly, we build a decision tree to mine dispatching rules and threshold identification. Taking the Pingshan River Basin, we plot dispatching diagrams and discover the threshold of creating the risk to surface water quality. When the sewage volume reached 18.0 × 105 m³ in winter, water quality exceeded limits. Other than that, the wastewater spatiotemporal allocation placed receiving water quality and operating cost in a competitive relationship. An application of dispatching rules shows that they fill a gap in the scientific and reasonable dispatch of the UDS.

Discussion on the Security Mechanism of Energy Internet in 5G network

With the coming of 5G network, it is possible for energy Internet to achieve high bandwidth and low delay interconnection. At the same time, it also brings new security problems. The core elements of 5G - virtualization, cloud migration, network slicing, Internet of Things, 5G gateway and even related software development may become security vulnerabilities. Aiming at the possible security problems in the application of 5G in the energy Internet, including the overall security architecture, industry security subdivision, Internet of Things security operation and maintenance, and the application of new security mechanisms, this paper expounds the relevant concepts and threats. This paper puts forward that in security architecture, we should make full use of security mechanisms such as logical isolation and access control to achieve in-depth defense; in industry segmentation, we should have customized security capability response for different application scenarios of different industries; in the security operation and maintenance of the Internet of Things, we should aim at the complex network environment and topology of mass terminals. In the new security mechanism, it should be combined with large data analysis and AI intelligent processing to enhance the ability of security early warning. Finally, combined with the application scenario of the energy internet, this paper proposes a comprehensive solution to the energy Internet Security in the 5G.