Big Data Analysis Based Network

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 14074

Special Issue Editors

Special Issue Information

Dear Colleagues,

Entities and their interactive relations are complex and diverse in various fields, and they are not only huge in number but also rich in variance, which form a variety of networks, such as electrical power networks, transportation networks, communication networks, social networks, and biological networks. Thus, the research on networks is more challenging. With the rise of big data analysis technology, advances in the perception and analysis of various networks have been more prosperous. For example, big data analysis can improve the transmission, distribution, and control of power in the electrical power networks with the increasing requirement for greater reliability, efficiency, security, and sustainability of power, which realize smart management and maintenance.

The main aim of this Special Issue is to seek high-quality submissions that highlight big data analysis and applications in networks, address recent breakthroughs in network behaviors, network embedding learning, large-scale networks, heterogeneous networks, network visualization and storage, network anomaly analysis, etc. The topics of interest include, but are not limited to, the following:

  • Big data analysis in complex networks;
  • Network behaviors mining with big data analysis;
  • Network embedding learning technology with big data analysis;
  • Large-scale or heterogeneous network applications;
  • Dynamic spatio-temporal network analysis with big data;
  • Network visualization system with big data analysis;
  • Network structure storage with big data;
  • Network anomaly analysis and detection with big data;
  • Big data applications in electrical power networks, transportation networks, communication networks, social networks, and biological networks, etc.

Prof. Dr. Xiangjie Kong
Prof. Dr. Mario Collotta
Guest Editors

Manuscript Submission Information

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Published Papers (9 papers)

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Research

19 pages, 18668 KiB  
Article
A Post-Evaluation System for Smart Grids Based on Microservice Framework and Big Data Analysis
Electronics 2023, 12(7), 1647; https://doi.org/10.3390/electronics12071647 - 30 Mar 2023
Cited by 1 | Viewed by 1000
Abstract
Wind energy, as a clean energy source, has been experiencing rapid development in recent years. However, there is often a significant difference between the designed electricity generation capacity and the actual electricity generation capacity during the construction of wind farms, making it difficult [...] Read more.
Wind energy, as a clean energy source, has been experiencing rapid development in recent years. However, there is often a significant difference between the designed electricity generation capacity and the actual electricity generation capacity during the construction of wind farms, making it difficult to assess the economic benefits of wind farms. Therefore, the development post-evaluation technology is required to support the renovation of old wind farms. In addition, traditional data analysis techniques are unable to handle and analyze massive data in a timely manner. Therefore, big data technology must be developed to improve processing efficiency. To address these issues and meet actual business needs, this paper designs an intelligent grid electricity generation post-evaluation platform for wind farms based on a microservice framework and big data analysis technology. The platform evaluates the operating status of wind farms by analyzing their operational and design data and visualizes relevant big data information. It provides technical support and improvement solutions for wind farm renovation and new wind farm construction. The platform has been tested and proven to meet the requirements for processing and analyzing massive data, post-evaluating electricity generation, and visualization. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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21 pages, 2247 KiB  
Article
HIT-GCN: Spatial-Temporal Graph Convolutional Network Embedded with Heterogeneous Information of Road Network for Traffic Forecasting
Electronics 2023, 12(6), 1306; https://doi.org/10.3390/electronics12061306 - 09 Mar 2023
Cited by 1 | Viewed by 1361
Abstract
In road networks, attribute information carried by road segment nodes, such as weather and points of interest (POI), exhibit strong heterogeneity and often involve one-to-many or many-to-one relationships. However, research on such heterogeneity in traffic prediction is relatively limited. Our research examines how [...] Read more.
In road networks, attribute information carried by road segment nodes, such as weather and points of interest (POI), exhibit strong heterogeneity and often involve one-to-many or many-to-one relationships. However, research on such heterogeneity in traffic prediction is relatively limited. Our research examines how varying the network propagation pattern based on the degree of node-to-node heterogeneity of information affects the model prediction performance. Specifically, at the node level, we use knowledge embedding to generate knowledge vectors that quantify the heterogeneity among the attribute information of a node. At the road network level, we calculate a homogeneity adjacency matrix that captures both the topological structure of the road network and the similarity of node heterogeneity. This adjacency matrix assigns different weights to neighbors based on their homogeneity, guiding the propagation of graph convolutional networks (GCN). Finally, we separate the representation of propagation into self-representation and neighbor representation to extract multi-attribute information, including self, homogeneity, and heterogeneity. Experiments on real datasets demonstrate that the incorporation of our homogeneity adjacency matrix leads to a significant improvement in the accuracy of short-term and long-term prediction compared with previous work on homogeneous and single-dimensional information. Furthermore, our approach maintains its performance advantage over baseline models under different embedding dimensions and parameter settings. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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30 pages, 54337 KiB  
Article
Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes
Electronics 2023, 12(6), 1287; https://doi.org/10.3390/electronics12061287 - 08 Mar 2023
Cited by 14 | Viewed by 1944
Abstract
Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves [...] Read more.
Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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18 pages, 1325 KiB  
Article
A Big Data Platform for International Academic Conferences Based on Microservice Framework
Electronics 2023, 12(5), 1182; https://doi.org/10.3390/electronics12051182 - 01 Mar 2023
Viewed by 1380
Abstract
In the era of the information explosion, big data are always around us. Academic big data are defined as a large amount of data generated in the life cycle of all academic activities, which usually contains a large amount of academic information. Academic [...] Read more.
In the era of the information explosion, big data are always around us. Academic big data are defined as a large amount of data generated in the life cycle of all academic activities, which usually contains a large amount of academic information. Academic conferences can effectively promote academic exchanges among scholars. In recent years, academic conferences in various fields have been held around the world. However, with the increase in the number of academic conferences, the quality of conferences and the efficiency of hosting and participating in conferences are uneven. In today’s fast-paced life, high-quality and efficient academic conferences have become the first choice of scholars. In this paper, a conference recommendation method based on a big data analysis of users’ interests and preferences is proposed to help users choose high-quality academic conferences and to help organizers reduce conference costs and improve the conference operation efficiency. The method first divides the research fields of user-related academic conferences into three categories: the fields that users are interested in, the fields that users attend, and the research fields that users follow up. Then, the weights of these three categories are set, and the importance of each category recommendation related to the user is calculated. Finally, the conference recommendation index is calculated and several conferences with a high recommendation value are recommended to users. The experimental results show that the proposed conference recommendation method provides a convenient and fast service to conference participants and conference organizers. The developed big data platform can significantly improve the operation and participation efficiency of academic conferences, reduce the costs, and give full play to the role and value of academic conferences. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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11 pages, 2216 KiB  
Article
Dense Vehicle Counting Estimation via a Synergism Attention Network
Electronics 2022, 11(22), 3792; https://doi.org/10.3390/electronics11223792 - 18 Nov 2022
Cited by 2 | Viewed by 1038
Abstract
Along with rising traffic jams, accurate counting of vehicles in surveillance images is becoming increasingly difficult. Current counting methods based on density maps have achieved tremendous improvement due to the prosperity of convolution neural networks. However, as highly overlapping and sophisticated large-scale variation [...] Read more.
Along with rising traffic jams, accurate counting of vehicles in surveillance images is becoming increasingly difficult. Current counting methods based on density maps have achieved tremendous improvement due to the prosperity of convolution neural networks. However, as highly overlapping and sophisticated large-scale variation phenomena often appear within dense images, neither traditional CNN methods nor fixed-size self-attention transformer methods can implement exquisite counting. To relieve these issues, in this paper, we propose a novel vehicle counting approach, namely the synergism attention network (SAN), by unifying the benefits of transformers and convolutions to perform dense counting assignments effectively. Specifically, a pyramid framework is designed to adaptively utilize the multi-level features for better fitting in counting tasks. In addition, a synergism transformer (SyT) block is customized, where a dual-transformer structure is equipped to capture global attention and location-aware information. Finally, a Location Attention Cumulation (LAC) module is also presented to explore the more efficient and meaningful weighting regions. Extensive experiments demonstrate that our model is very competitive and reached new state-of-the-art performance on TRANCOS datasets. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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9 pages, 2690 KiB  
Article
A Generative Learning Steganalysis Network against the Problem of Training-Images-Shortage
Electronics 2022, 11(20), 3331; https://doi.org/10.3390/electronics11203331 - 16 Oct 2022
Viewed by 1142
Abstract
In recent years, several steganalysis neural networks have been proposed and achieved satisfactory performances. However, these deep learning methods all encounter the problem of Training-Images-Shortage (TIS). In most cases, it is difficult for steganalyses to obtain enough signals about steganography from a game [...] Read more.
In recent years, several steganalysis neural networks have been proposed and achieved satisfactory performances. However, these deep learning methods all encounter the problem of Training-Images-Shortage (TIS). In most cases, it is difficult for steganalyses to obtain enough signals about steganography from a game opponent. In order to solve the problem of TIS for steganalysis, we propose a novel steganalysis method based on generative learning and deep residual convolutional neural networks. Comparative experiments show that the proposed architecture can achieve promising performance in response to spatial domain steganalysis despite a lack of training images. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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22 pages, 3312 KiB  
Article
Behavior Analysis Using Enhanced Fuzzy Clustering and Deep Learning
Electronics 2022, 11(19), 3172; https://doi.org/10.3390/electronics11193172 - 02 Oct 2022
Cited by 3 | Viewed by 1723
Abstract
Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in [...] Read more.
Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers’ previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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16 pages, 4529 KiB  
Article
Dynamic Multi-View Coupled Graph Convolution Network for Urban Travel Demand Forecasting
Electronics 2022, 11(16), 2620; https://doi.org/10.3390/electronics11162620 - 21 Aug 2022
Cited by 3 | Viewed by 1466
Abstract
Accurate urban travel demand forecasting can help organize traffic flow, improve traffic utilization, reduce passenger waiting time, etc. It plays an important role in intelligent transportation systems. Most of the existing research methods construct static graphs from a single perspective or two perspectives, [...] Read more.
Accurate urban travel demand forecasting can help organize traffic flow, improve traffic utilization, reduce passenger waiting time, etc. It plays an important role in intelligent transportation systems. Most of the existing research methods construct static graphs from a single perspective or two perspectives, without considering the dynamic impact of time changes and various factors on traffic demand. Moreover, travel demand is also affected by regional functions such as weather, etc. To address these issues, we propose an urban travel demand prediction framework based on dynamic multi-view coupled graph convolution (DMV-GCN). Specifically, we dynamically construct demand similarity graphs based on node features to model the dynamic correlation of demand. Then we combine it with the predefined geographic similarity graph, functional similarity graph, and road similarity graph. We use coupled graph convolution network and gated recurrent units (GRU), to model the spatio-temporal correlation in traffic. We conduct extensive experiments over two large real-world datasets. The results verify the superior performance of our proposed approach for the urban travel demand forecasting task. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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14 pages, 4841 KiB  
Article
Gravity-Law Based Critical Bots Identification in Large-Scale Heterogeneous Bot Infection Network
Electronics 2022, 11(11), 1771; https://doi.org/10.3390/electronics11111771 - 02 Jun 2022
Viewed by 1270
Abstract
The explosive growth of botnets has posed an unprecedented potent threat to the internet. It calls for more efficient ways to screen influential bots, and thus precisely bring the whole botnet down beforehand. In this paper, we propose a gravity-based critical bots identification [...] Read more.
The explosive growth of botnets has posed an unprecedented potent threat to the internet. It calls for more efficient ways to screen influential bots, and thus precisely bring the whole botnet down beforehand. In this paper, we propose a gravity-based critical bots identification scheme to assess the influence of bots in a large-scale botnet infection. Specifically, we first model the propagation of the botnet as a Heterogeneous Bot Infection Network (HBIN). An improved SEIR model is embedded into HBIN to extract both heterogeneous spatial and temporal dependencies. Within built-up HBIN, we elaborate a gravity-based influential bots identification algorithm where intrinsic influence and infection diffusion influence are specifically designed to disclose significant bots traits. Experimental results based on large-scale sample collections from the implemented prototype system demonstrate the promising performance of our scheme, comparing it with other state-of-the-art baselines. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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