Network Topology Construction Research Articles

Research on Parameter Correction of Distribution Network Model Based on CIM Model and Measurement Data

The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China’s distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction.

Construction of trail networks based on growing self-organizing maps and public GPS data

Manual creation of trail maps for hikers is time-consuming and can be inaccurate. This paper presents a new method to construct trail networks based on a growing self-organizing map (GSOM) using publicly available Global Positioning System (GPS) data. Unlike other network topology construction techniques, this approach is not dependent on sequential GPS traces. Fine-tuning multiple hyperparameters enables to customize this process based on unique features of datasets and networks. The generated maps, which are trained on public GPS data, are compared to a ground truth from Open Street Map (OSM). The performance evaluation is based on the accuracy, completeness, and topological correctness of the trail maps. The proposed approach outperforms, particularly on sparse networks without significant GPS noise.

Construction and evolutionary pattern of the coupling relationship network of regional sustainable development in China

Climate change poses a serious threat to the survival and development of humankind. Accordingly, sustainable socioeconomic transformation has become a major global concern. This study quantitatively assesses the process and speed of regional-level sustainable development in China, as well as the degree of coupling and the degree of coupling coordination among Sustainable Development Goals (SDGs). To this end, based on regional development and data monitoring in China, we construct a regional sustainable development assessment system containing 111 indicators, which combines existing sustainable development assessment indicators with the indicators of the 2030 Agenda for Sustainable Development. On that basis, using network topology construction and modal transformation, we propose a framework for analyzing the coupling relationships and evolutionary patterns of regional sustainable development. The study found that the score for SDGs at the national level increased from 50.80 in 2010 to 60.86 in 2020, showing a good development trend and the differences between provinces and regions was gradually reduced. The development speed of regional sustainable transformation shows a change from decreasing to increasing. In recent years, there is a significant change in the acceleration of regional sustainable development in China, with the vast majority of provinces having positive acceleration and only three provinces having negative acceleration. China's regional sustainable development shows that mutual coordination and promotion among provinces was higher in the eastern area than in the central region, which was higher than in the western area. The sustainable processes of each region and the coordination of sustainable development steadily increased toward a high level of coupling, all achieving coordinated development. The degree of coupling coordination and the SDG scores in each region were positively correlated with economic development, reflecting the role of economic development in the promotion of sustainability. However, this positive correlation showed a tendency of strengthening and then weakening. The correlation of the evolution patterns in the eastern–central, eastern–western, and central–western regions over time presented a trend of first weakening, then strengthening, and then weakening. Although the process of promoting sustainable development in western and central regions over time is weak synergistic consistency with that in eastern regions, overall, China's regional sustainable development presents a development model of multi-province linkage and multi-goal coordination.

The use of social network analysis in different fields

Social network analysis is a well-established research method in sociology. Some scholars also refer to this quantitative approach to graph theory as network science. As research intensified in the 1970s, it gained the attention of researchers in many other fields and has become an interdisciplinary research method. Social network analysis collects data from multiple sources and unearths new information by building network structures.The constructed network topology diagram and the calculated network properties are eventually analysed and generalised to produce further research results. The use of social network analysis (SNA) in a few different fieldstourism, society, transportation, citation, viral communication, medical education, and economic geographyis reviewed in this essay. Social network analysis is a quantitative analysis method that blends multiple disciplines. Various relationships are quantified through the construction of networks and property analysis. The paper is categorized according to the different fields of application. The different applications of social network analysis in various fields show its general and specific analytical approach.

Identification of Critical Road Links Based on Static and Dynamic Features Fusion

Traffic congestion is a significant challenge in modern cities, leading to economic losses, environmental pollution, and inconvenience for the public. Identifying critical road links in a city can assist urban traffic management in developing effective management strategies, preserving the efficiency of critical road links, and ensuring the smooth operation of urban transportation systems. However, the existing road link importance evaluation metrics mostly rely on complex network metrics and traffic metrics, which may lead to biased results. In this paper, we propose a critical road link identification framework based on the fusion of dynamic and static features. First, we propose a directed dual topological traffic network model that considers the subjectivity of road links, traffic circulation characteristics, and time-varying characteristics, which addresses the limitations of existing traffic network topology construction. Subsequently, we employ a novel graph representation learning network to learn the road link node low-dimensional embeddings. Finally, we utilize clustering algorithms to cluster each road link node and evaluate critical road links using the average importance evaluation indicator of different categories. The results of comparison experiments using real-world data demonstrate the clear superiority and effectiveness of our proposed method. Specifically, our method is able to achieve a reduction in traffic network efficiency of 70–75% when less than 25% of the road links are removed. In contrast, the other baseline methods only achieve a reduction of 50–70% when removing the same proportion of road links. These findings highlight the significant advantages of our approach in identifying the critical links.

Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint

Network topology construction plays an important role in the application of large-scale unmanned aerial vehicle (UAV) swarm. Current researches usually perform the topology construction in terms of criteria of nodes energy consumption, transmission delay and network throughput, etc. However, another important criterion, the stability of swarm network topology, which is much critical for dynamic scenarios, has not been fully considered. In this paper, a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a novel Group Trend Similarity based double-head Clustering method(GTSC) is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account. The proposed method is effective when used to perform the network topology construction for UAV swarm, which is verified by the simulation results. Furthermore, in comparison with representative algorithms, the proposed GTSC method exhibits better performance on topology duration, network throughput, end-to-end delay and energy consumption balance especially in a large-scale swarm scenarios.

A reinforcement learning-based cluster routing scheme with dynamic path planning for mutli-UAV network

Unmanned Aerial Vehicles (UAVs) with visual sensors are widely used for area mapping, management of crops and traffic, rescuing lives, and many other applications that need to cover a large area. The process of coverage can be improved with the use of efficient path planning and data transfer algorithms. Numerous research studies have been performed by concentrating on each of the aforementioned elements separately. However, to tackle the rapidly changing environmental situations, this paper proposes a cluster-based routing approach by incorporating a dynamic planning algorithm. The proposed model is composed of four modules, such as an online path planning algorithm, clustering-based network topology construction, reinforcement learning-based cluster management, and a data routing mechanism. Firstly, to maximise the coverage output, an optimal set of waypoints has been generated for all UAVs. For each UAV to complete the coverage task, it needs to completely cover its own set of waypoints. Since the environment is changing, a static path planning approach might fail to achieve complete coverage. Therefore, to drive the mission without getting stuck, a dynamic path planning approach is proposed that decides the next waypoint for a UAV based on the current waypoint. The main purpose of the algorithm is to cover all the waypoints in a polynomial amount of time. Secondly, the topology construction module consists of the initialization process, cluster head election, and cluster formation. Based on five parameters such as degree of centrality, surplus energy, link stability time, connectivity with the backbone UAV, and velocity, a set of cluster heads are selected. Then the cluster management process is performed by the optimal re-clustering policy determined using an approach of reinforcement learning called State Action Reward State Action (SARSA) in ground control station. Finally, the introduction of inter-cluster forwarders and selective flooding of route requests makes the routing scheme enhance the packet delivery ratio and reduce the delay. The result shows that the proposed work performs better than the existing result in terms of different generic performance metrics used for path planning and routing.

Fault-Tolerant Secure Routing Based on Trust Evaluation Model in Data Center Networks

With the rise of cloud computing technology, the whole information technology industry has undergone dramatic changes. Large-scale computing and the growing demand for computing power from large data have made it impossible for traditional data center network topology construction methods and network layer operation control methods with good stability to meet the requirements of technological development. The topology of a data center network is usually characterized as an undirected graph, and graph algorithms are used to implement communication patterns in complex network structures. However, the routing algorithms proposed earlier for graphs cannot be applied to all topological graphs. Based on the comprehensive interactive trust evaluation network security model, this study analyzes the advantages and disadvantages of relevant algorithms and proposes a fault-tolerant routing algorithm suitable for all networks. The algorithm can dynamically call different algorithms according to the number of current nodes and links. From the simulation results, it can be found that the algorithm not only improves the accuracy and fault tolerance but also effectively reduces the loss of time and memory.

Network topology generation based on eigenvector centrality with real‐time guarantee

AbstractThe real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case.

Construction of network topology and geographical vulnerability for telecommunication network

Telecommunications network is vulnerable to large-scale infrastructure failures caused by physical attacks or natural disasters. Academics view this failure as region failure, which is geographically related and highly localized. Several simplified models have been previously proposed, resulting in an inaccurate characterization of network activity. In this paper, we propose a probability model with full consideration of the actual situation, which gives a more realistic picture of network behavior. In particular, the disaster takes the form of a randomly placed disk in a plane. The research aims to find out disaster locations that can cause significant damage to network performance, i.e., vulnerable areas of the network. The topology of vulnerable areas can be redesigned to improve the network’s disaster survivability. Moreover, only a few special cases need to be judged for the sake of achieving the aim despite disasters occur randomly across the plane. Another primary contribution is our attempt to construct a proper network topology by using clusters as units. Finally, numerical experiments conducted on this network topology demonstrate the applicability of the methodology in realistic scenarios. The work in this paper provides guidance for real-world problems and offers a way to figure them.

Hierarchical Distribution Network Topology Formulation and Dimensionality Reduction Using Homeomorphism Transformation

The scales of the power distribution networks in real-world power grids expand quickly while the network structures are becoming more and more complex. The power grid companies analyze the power distribution networks in different business scenarios with different topology models. In this work, we propose a hierarchical graph model to describe the medium-voltage distribution network (which is a typical power distribution network in power grids) based on homeomorphic transformation. The hierarchical graph model preserves the basic network topology described by the traditional Common Information Model (CIM). Firstly, the nodes in the distribution network topology are classified according to graph theory. Secondly, three typical business scenarios of distribution network topology analysis are summarized, and the original model is simplified by progressive dimensionality reduction method to meet the analysis requirements of different scenarios, the simplified method consists of three abstract levels: critical path, core path and minimal path, and can effectively reduce the space complexity of the model while maintaining the topological properties. Thirdly, a multi-level distribution network topology construction and mapping method based on the graph database is proposed. It is used to realize the rapid conversion and traceability between different levels of topology. Finally, a practical distribution network in a county is used as an example to verify the effectiveness of the proposed method in the aspects such as topology rendering and path searching. The evaluation indicates that the proposed model can visualize the distribution network intuitively. The model can also speed up the visualization and path searching significantly.

A Regional Network Topology Construction Algorithm Based On Sampling Measurement

Network topology construction is one of the basic means to study the target network. Whether it is the discovery of vital nodes, IP location or network characteristics mining, a complete and accurate network topology is needed. In this paper, a regional network topology construction algorithm based on sampling measurement is proposed to solve the problem of high time cost and resource waste of existing algorithms. Firstly, multiple vantage points located inside and outside the target area are selected to form a probing set. Then the IPes of the target area is extracted based on sampling to form the target IP set. Finally, the probing set is used to probe the target IP set periodically. The network topology of the target region was constructed based on the results of a 40-day internet measurement and compared with the existing database. The experimental results show that the coverage rate of routing nodes in the target area is improved by more than 90% compared with the measured data provided by CAIDA (Center for Applied Internet Data Analysis) and by more than 10% compared with the measured data provided by IPIP.

Визначення оптимального значення опору резистора заземлення нейтралі в мережах 20 кВ

At present, there is an objective need for a solution in the power system of Ukraine problems of improving the principles of construction of network topology by voltage classes and types of performance on based on scientifically sound technical solutions using modern methods and technologies. The article is devoted to the study of the optimal value of the resistance of the neutral grounding resistor in 20 kV networks. Distribution electric networks of Ukraine with a voltage of 6 (10) kV are included in the final link systems of providing consumers with electric energy. They are in direct interaction both with the consumer, and regional and main electric networks. Therefore, the state and the operation of 6 (10) kV electrical distribution networks has a significant impact on performance reliability, quality and efficiency of the integrated power system of Ukraine. Transition from voltage level 6 (10) kV on 20 kV is an actual and priority step to increase the efficiency of operation distribution network and a significant reduction in electricity losses. The paper presents the types of resistors and options for their installation in the neutral voltage network 20 kV. The relationship between the resistance of the neutral grounding resistor and the single-phase current is established short circuit, which directly affects the types of relay protections, as well as their number. Simulation of the 20 kV network operation mode was performed, based on the results of which the optimal one was chosen the value of the resistance of the neutral grounding resistor in the presented network, which is achieved sufficient sensitivity of relay protection devices at the lowest short-circuit current. This one was chosen for the study neutral grounding method, because resistive grounding has the following advantages: no arcs high-frequency overvoltages and multiple damages in the network; no need to disconnect the first single-phase earth fault; reducing the likelihood of impressions of staff and outsiders at single-phase short circuit; almost complete exclusion of the possibility of transition of single-phase circuit to ground in multiphase; simple implementation of sensitive and selective relay protection single-phase ground fault.

Neural Network Topology Construction and Classroom Interaction Benchmark Graph Based on Big Data Analysis

With the rapid development of artificial intelligence and deep learning in recent years, many universities have put forward the goal of achieving digitalization, intelligent, and education informatization on campus. Throughout the lecture and learning process, the classroom status is an important reference factor to assess students’ acceptance of the course and the quality of lectures. However, at present, classroom status analysis is mainly conducted manually, which can distract teachers’ attention, so it is of great research significance to find a method that can improve the efficiency of classroom status analysis. In this paper, we choose an offline method to analyze the status of a classroom video recording in terms of students’ behavior and attendance in terms of frames, in which student behavior is identified by an improved target detection algorithm and attendance is analyzed by face recognition. By analyzing the structure of the neural network model, an improved neural network model is proposed for its characteristics of a large number of parameters and poor detection of small targets in the basic network. The backbone network is replaced by the improved neural network, and the depth‐separable convolutional network is used to reduce the network parameters and increase the computation speed. The information in the deeper feature map is fused upward into the shallow layer to improve the accuracy of small target recognition. Finally, the optimization algorithm is incorporated into the network to optimize the network model and accelerate the model convergence speed. In addition, this paper incorporates the improved behavior recognition method and face recognition method into the system to realize the analysis of the offline classroom status. The system is divided into a teacher side and a management side, where the teacher side is responsible for uploading course recordings and the management side is responsible for randomly analyzing students’ status and attendance at any time, and the combination of the two forms a convenient and comprehensive classroom status analysis system platform. Users can upload classroom videos through the instructor interface and can view the classroom status analysis results of a course at any time by searching randomly in the administration. In this paper, the classroom status is mainly judged by the recognition of students’ behaviors.

A Novel Two-Mode QoS-Aware Mobile Charger Scheduling Method for Achieving Sustainable Wireless Sensor Networks

For developing a sustainable wireless sensor network (WSN), wireless energy charging and mobile data collection are two promising techniques for enhancing energy efficiency and achieving semi-permanent operation time of WSNs. Currently, many energy-aware methods have been developed by adopting these two technologies. However, joint methods that can combine the advantages of both are still lacking. Technically, by reducing the transmission energy consumption of individual nodes while wirelessly charging low power nodes, the joint method can better extend the operating time of the nodes in the system, improving the overall system life. Therefore, we design a two-mode QoS-aware mobile chargers (MCs) scheduling method for implementing both energy charging and data collection tasks simultaneously. The proposed scheme is comprised of two parts: 1) A new clustering algorithm is designed for addressing the trade-off problem regarding the delay and load balancing of sensors, and the network topology construction. 2) Two heuristic MC scheduling algorithms are introduced for facing the different delay requirements of systems, i.e., the single-path scheduling scheme (SPSS) and the multiple-path scheduling scheme (MPSS). The simulation results show that our proposed method outperforms the existing state-of-the-art approaches in the control group regarding the average delay and the charging utility.

Design of multiband matching ladders without mutual coupling using parametric representation of Brune functions

In this study, a semianalytical method for the design of mutual coupling free multiband matching networks is introduced. A new parametric representation of Brune functions is used for the construction of multiband ladder network topologies without mutual induction. The method involves the use of Fujisawa's constraints for low pass ladders having finite transmission zeros, in a parametric representation of driving point impedance function resulting in mutual inductance free Brune sections. The developed parametric representation is incorporated with Real Frequency Techniques to design matching networks with a plurality of pass bands. Several illustrative design examples are presented to validate the method.

Topology Verification Enabled Intrusion Detection for In-Vehicle CAN-FD Networks

Controller area network with flexible data rate (CAN-FD) is a widely used communication protocol for in-vehicle sensing and control. However, due to the lack of inherent security mechanisms, unauthorized devices could access the CAN-FD by embedding external intruding devices (XIDs) to in-vehicle networks. Malicious intrusion into CAN-FD can expose a compromised vehicle to significantly increased safety, security, and privacy related risks. To enhance the security of CAN-FD networks, a novel intrusion detection method based on verification of network topology is proposed, where XIDs can be reliably detected through a simple random walk based network topology construction and subsequent verification. When an intrusion is detected, a secure mode would be triggered to further protect the network from being attacked. Simulation results indicate that multiple XIDs can be accurately detected, while the increment in the number of XIDs from 1 to 8 can lead to the convergence time increasing from 48 to 102 steps in the powertrain subnet and from 189 to 416 steps in the body subnet.

Cluster tree topology construction method based on PSO algorithm to prolong the lifetime of ZigBee wireless sensor networks

For wireless sensor networks (WSNs) based on ZigBee technology, the network topology plays an important role for improving the energy efficiency and the network lifetime. An appropriate construction method of network topology should be designed for saving the energy of the battery in each network node so as to prolong the lifetime of ZigBee WSNs. In this paper, a novel cluster tree topology construction method based on particle swarm optimization (PSO) algorithm is proposed. In order to transform the network topology construction problem into an energy consumption optimization problem, an evaluation function reflecting the network energy consumption is designed, and the network topology is mapped into a particle population individual suitable for the PSO algorithm. In order to prolong the network lifetime as long as possible, two network topology reconstruction methods with the PSO algorithm based on fixed and variable energy thresholds are further proposed, respectively. The simulation results show that the proposed methods can prolong the lifetime of ZigBee WSNs effectively.

Asynchronous Local Construction of Bounded-Degree Network Topologies Using Only Neighborhood Information

We consider the ad-hoc networks consisting of $n$ wireless nodes that are located on the plane. Any two given nodes are called neighbors if they are located within a certain distance (communication range) from one another. A given node can be directly connected to any one of its neighbors, and picks its connections according to a unique topology control algorithm that is available at every node. Given that each node knows only the indices (unique identification numbers) of its one and two-hop neighbors, we identify an algorithm that preserves connectivity and can operate without the need of any synchronization among nodes. Moreover, the algorithm results in a sparse graph with at most $5n$ edges and a maximum node degree of 10. Existing algorithms with the same promises further require neighbor distance and/or direction information at each node. We also evaluate the performance of our algorithm for random networks. In this case, our algorithm provides an asymptotically connected network with $n(1+o(1))$ edges with a degree less than or equal to 6 for $1-o(1)$ fraction of the nodes. We also introduce another asynchronous connectivity-preserving algorithm that can provide an upper bound as well as a lower bound on node degrees.

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.