Journal of High Speed Networks - Volume 27, issue 3

Authors: Souri, Alireza | Chen, Mu-Yen

Article Type: Editorial

DOI: 10.3233/JHS-210660

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 203-204, 2021

Authors: Niu, Xin | Jiang, Jingjing

Article Type: Research Article

Abstract: Multimedia is inconvenient to use, difficult to maintain, and redundant in data storage. In order to solve the above problems and apply cloud storage to the integration of university teaching resources, this paper designs a virtualized cloud storage platform for university multimedia classrooms. The platform has many advantages, such as reducing the initial investment in multimedia classrooms, simplifying management tasks, making maximum use of actual resources and easy access to resources. Experiments and analysis show the feasibility and effectiveness of the platform. Aiming at the problems of the single-node repair algorithm of the existing multimedia cloud storage system, the limited …domain is large, the codec complexity is high, the disk I/O (Input/Output) cost is high, the storage overhead and the repair bandwidth are unbalanced, and a network coding-based approach is proposed. Multimedia cloud storage. System single node repair algorithm. The algorithm stores the grouped multimedia file data in groups in the system, and performs XOR (exclusive OR) on the data in the group on the GF(2) finite field. When some nodes fail, the new node only needs to be connected. Two to three non-faulty nodes in the same group can accurately repair the data in the failed node. Theoretical analysis and simulation results show that the algorithm can reduce the complexity and repair of the codec, and reduce the disk I/O overhead. In this case, the storage cost of the algorithm is consistent with the storage cost based on the minimum storage regeneration code algorithm, and the repair bandwidth cost is close to the minimum bandwidth regeneration code algorithm. Show more

Keywords: Multimedia, cloud storage, network coding, single node repair, Internet

DOI: 10.3233/JHS-210661

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 205-214, 2021

Authors: Yan, Xiaohong | Zhao, Zhigang | Liu, Yongqiang

Article Type: Research Article

Abstract: As the need of power supply is tremendously increasing in modern society, the stableness and reliability of the power delivery system are the two essential factors that ensure the power supply safety. With the quick expansion of electricity infrastructures, the failures of power transmission system are becoming more frequent, leading to economic loss and high risk of maintenance work under hazardous conditions. The existing automatic power line inspection utilizes advanced convolutional neural network (CNN) to improve the inspection efficiency, emerging as one promising solution. But the needed computational complexity is high since CNN inference demands large amount of multiplication-and-accumulation operations. …In this paper, we alleviate this problem by utilizing the heterogeneous computing techniques to design a real-time on-site inspection system. Firstly, the required computational complexity of CNN inference is reduced using FFT-based convolution algorithms, speeding up the inference. Then we utilize the region of interest (ROI) extrapolation to predict the object detection bounding boxes without CNN inference, thus saving computing power. Finally, a heterogeneous computing architecture is presented to accommodate the requirements of proposed algorithms. According to the experiment results, the proposed design significantly improves the frame rate of CNN-based inspection visual system applied to power line inspection. The processing frame rate is also drastically improved. Moreover, the precision loss is negligible which means our proposed schemes are applicable for real application scenarios. Show more

Keywords: Power transmission inspection, continuous vision, fast convolution, neural network

DOI: 10.3233/JHS-210662

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 215-224, 2021

Authors: Ju, Xiaotao

Article Type: Research Article

Abstract: This research was conducted to enhance the energy performance of wireless sensor networks (WSN) and improve the performance of end-to-end delay and packet receiving rate of network operation. In this study, the low-energy data collection routing algorithm and adaptive environment sensing method in WSN were mainly examined. The node centrality, energy surplus, and node temperature were calculated for cluster head selection to reduce the energy consumption of nodes and improve the reliability of network data. The research results have shown that the parameter setting guided by the theoretical analysis makes each node selfishly achieve the maximum expected benefit while the …whole network runs reliably, and the energy consumption is reduced by the selfishness of the node. As a result, the proposed algorithm can effectively reduce the network energy consumption and increase the network life cycle of wireless sensor networks. It can be seen that the machine learning methods such as support vector machine are used to model and analyze the state of the sensing node, and to obtain more accurate wireless channel availability judgment based on the historical state data, thereby adaptively adjusting the working duty ratio and reducing the invalidity data sent. Show more

Keywords: Wireless sensor network, energy efficient, routing protocol, node centrality

DOI: 10.3233/JHS-210663

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 225-235, 2021

Authors: Huang, Cong | Huang, Ying

Article Type: Research Article

Abstract: At present, China’s traffic signal control machine has a low level of intelligence and a single control strategy. It cannot make corresponding control according to the actual traffic situation, and its ability to direct traffic flow in a reasonable and orderly manner is low. In order to understand the urban rail transit signal and control system, we analyzed the requirements of the train dispatching subsystem, designed the overall architecture of the system from the perspective of function realization and architecture, and constructed the wireless sensor network of the system, which is the best for other experts. In this paper, combined …with the research of related technologies of the Internet of Things (IoT), an intelligent traffic signal control machine is designed, and the traffic signal control effects under different algorithms are compared, and the relevant rail transit conditions are statistically studied. Studies have proved that sensors based on IoT technology can effectively improve the intensity and control effect of urban rail transit signals. Compared with other algorithm technologies, the overall score of the sensor algorithm is higher than other algorithms, and the ratio is about 30% higher. This article realizes the maintenance function of various data on the system simulation operation terminal, and builds the overall framework of the system; realize the main functions of the train dispatching operation terminal, including the realization of train dispatching functions such as station map, manual route arrangement, automatic route triggering, station deduction and station jump settings, and log report generation. This shows that the sensor algorithm under the IoT has a great promotion effect on the urban rail transit signal and control system. Show more

Keywords: Internet of Things, rail transit, signal control, wireless sensors

DOI: 10.3233/JHS-210664

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 237-250, 2021

Authors: Cai, Guanqun

Article Type: Research Article

Abstract: In order to extract value from data, data mining and data software technology are widely used in the industry. This study mainly discusses the precise mining of location data in communication field based on big data. Signaling preprocessing layer mainly obtains signaling message through acquisition module, filters FISU message in signaling message, judges abnormal message frame, and stamp it with time stamp, which provides effective data source for next processing. Signaling access layer mainly completes the function of signaling link access, mainly using high resistance jumper technology, time slot convergence technology, optical access technology and 155mdxc conversion technology to access …2 m link and 155 m link respectively. The signaling collection module must collect directly or via a link through DXC in order to reach the front-end data collection machine and access the signaling collection module of the front-end machine. The Signaling Collection Module also completes some of the message processing work. The presentation layer is the window of human-computer interaction of the whole system, which presents to users with friendly interface and perfect functions. The main goal of real-time big data analysis is to obtain signaling data sent by signaling acquisition system, and screen out the effective information in signaling data according to monitoring conditions, and then analyze the final real-time monitoring results. Geographic information module provides visual map control for the regional monitoring big data analysis module. The difficulty of system development can be reduced by using the existing WebGIS map toolkit. When the call from the Customs Bureau of Unicom in different cities is called into the mobile gateway Bureau, the call is rejected by the mobile customs bureau. The call time is 0 seconds, of which the interception success rate is up to 90% within 1 s. This research is of great significance for the better development and maintenance of signaling network and monitoring system. Show more

Keywords: Big data, communication field, signaling acquisition module, real-time monitoring, WebGIS map toolkit

DOI: 10.3233/JHS-210665

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 251-264, 2021

Authors: Li, Liuxing

Article Type: Research Article

Abstract: The robust control network for nonlinear large-scale systems with parametric uncertainties also considers the uncertain robust stabilization problem for controlled networks. In heterogeneous populations, hybrid regression models are the most important statistical analysis tools. To aim of the study is to conduct a more in-depth analysis of the existing completive robust control networks relying on biased temporal logic. Compared with the symmetric distribution, the skewed distribution can obtain accurate and effective information. Therefore, a time-series logic model under skewed distribution is proposed. The temporal logic under skew state is applied to describe the normative language of fuzzy systems. Firstly, the …mixed nonlinear regression model under skewed distribution data is introduced to test whether the temporal logic formula can be realized under the skew state. Secondly, through the method of reduction, the control flow interval logic CFITL is studied, and the time series logic sequence is used to describe the measurement output loss. The sufficient conditions for the control network system to satisfy the exponential stability and H ∞ performance index are given. The linear matrix inequality obtains the completeness control network to be designed, and the effectiveness of the proposed method is verified by stochastic simulation experiments. Finally, the method is verified to be practical and feasible based on actual data. The maximum recognition rates of nearest neighbor classification, nearest subspace classification and biased distribution temporal logic classification reached 0.9019, 0.9622 and 0.9304, respectively. Show more

Keywords: Skewed distribution, temporal logic, robust, the internet, control flow

DOI: 10.3233/JHS-210666

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 265-278, 2021

Authors: Chen, Jie | Chen, Yukun | Lin, Jiaxin

Article Type: Research Article

Abstract: The purpose is to minimize color overflow and color patch generation in intelligent images and promote the application of the Internet of Things (IoT) intelligent image-positioning studio classroom in English teaching. Here, the Convolutional Neural Network (CNN) algorithm is introduced to extract and classify features for intelligent images. Then, the extracted features can position images in real-time. Afterward, the performance of the CNN algorithm is verified through training. Subsequently, two classes in senior high school are selected for experiments, and the influences of IoT intelligent image-positioning studio classroom on students’ performance in the experimental class and control class are analyzed …and compared. The results show that the introduction of the CNN algorithm can optimize the intelligent image, accelerate the image classification, reduce color overflow, brighten edge color, and reduce color patches, facilitating intelligent image editing and dissemination. The feasibility analysis proves the effectiveness of the IoT intelligent image-positioning studio classroom, which is in line with students’ language learning rules and interests and can involve students in classroom activities and encourage self-learning. Meanwhile, interaction and cooperation can help students master learning strategies efficiently. The experimental class taught with the IoT intelligent positioning studio has made significant progress in academic performance, especially, in the post-test. In short, the CNN algorithm can promote IoT technologies and is feasible in English teaching. Show more

Keywords: Studio classroom, Real-time positioning, English teaching, Deep learning, Convolutional Neural Network, IoT

DOI: 10.3233/JHS-210667

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 279-289, 2021

Authors: Hu, Wanxin | Cheng, Fen

Article Type: Research Article

Abstract: With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to …be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%. Show more

Keywords: Big data, urban subway traffic mode, behavioral entropy, congestion

DOI: 10.3233/JHS-210668

Citation: Journal of High Speed Networks, vol. 27, no. 3, pp. 291-304, 2021