Improve Data Transmission Efficiency Research Articles

Full-Reference Image Quality Assessment with Transformer and DISTS

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

Implementation and Evaluation of 5G MEC-Enabled Smart Factory

A 5G network can provide more comprehensive bandwidth connectivity for the industry 4.0 environment, which requires faster and tremendous data transmission. This study demonstrates the 5G network performance evaluation with MEC, without MEC, WiFi 6, and Ethernet networks. Usually, a 5G network engages with Multi-access Edge Computing, providing the computing functions dedicated to the users on edge nodes. The MEC network architecture presents significant facilities, a network schematic, and data transmission routers. The field test performs high-definition streaming video and heavy-traffic load testing to evaluate the performance based on different protocols by comparing throughput, latency, jitter, and packet loss rate. MEC network performance, streaming video performance, and load test evaluation results reveal that the 5G network working with MEC achieved better performance than when it was working without MEC. The MEC can improve data transmission efficiency by dedicated configuration but is only accessible with authentication from mobile network operators (MNOs). Therefore, MNOs should offer industrial private network users partial authentication for accessing MEC functionality to improve network feasibility and efficiency. In conclusion, this work illustrates the 5G network implementation and performance measurement for constructing a smart factory.

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

With the advent of the 5G era, people's demand for information and bandwidth is increasing. The signal source will mainly be high-frequency radio waves with high bandwidth. However, the coverage of high-frequency radio waves is very limited, which leads to the emergence of micro base stations. Opportunistic social networks are just suitable for this new transmission method, it is easy for users to simultaneously request content with high popularity in a short time, which will cause a heavy load on the micro base station and a high transmission delay. This paper establishes the community mechanism and proposes a user cache management and cooperative transmission mechanism (CMCT) based on edge community computing by using the predictability of users' requests for popular content. The free user equipment space is used to manage cache distribution through the edge community computing to perform cooperative transmission, thereby reducing the content delivery delay and the load of the micro base station. Simulation experiments show that the CMCT algorithm can reduce end-to-end delay and overhead, and improve data transmission efficiency.

The Application of 5G Network Technology in the Innovative Development of Physical Education

The rapid development of information technology is changing people’s work, learning, and lifestyle and bringing new development space to education. Physical education is an important component of the university education system. The goal of physical education is to increase the physical fitness of the students and promote their advancement. In the context of the recent evolution of the social economy, the fifth-generation (5G) network technology is increasingly being deployed in many fields which may assist students to actively partake in physical education and promote successful physical education development. This study proposes an innovative framework based on 5G network technology for the development of physical education. The Internet of Things (IoT) technology is used to achieve long-term data collecting and real-time data monitoring, and a unique cuckoo search firefly optimization algorithm (CSFOA) is presented to improve data transmission efficiency. The performance of the system is assessed in terms of accuracy, energy efficiency, security level, error rate, and student satisfaction, respectively. Results confirm that the proposed system has certain practical effects and has the potential to optimize the process of physical education development.

A Novel Niche Quantum Ant Colony-Based Clustering Optimization Approach for Wireless Auto Meter Reading Network

Since the low cost and high flexibility, wireless automatic meter reading network (WAMRN) is widely used by utility companies to realize automatic collection and transmission of remote energy consumption information. Considering that WAMRN is composed of several wireless communication nodes, the lifetime of the network will be affected by factors such as the changeable deployment environment and the limited energy of nodes. Thus, a novel niche quantum ant colony-based WAMRN clustering optimization method is proposed in this paper to address the problem of how to make full use of the limited energy to extend network lifetime and improve data transmission efficiency. In the proposed approach, a clustering model of WAMRN is defined; moreover, an improved Niche Quantum Ant Colony Optimization (NQACO) is proposed to optimize the model therefore to obtain an optimal clustering scheme, which can help WAMRN reduce unnecessary energy loss to achieve the purpose of extending the lifetime of the entire network. To verify the performance of the proposed method, NQACO is compared with some popular clustering methods, i.e., GA and SA, under different scenarios. The results show that under the premise of ensuring network communication, NQACO is superior to the other two methods in reducing the total energy consumption and prolonging the network lifetime.

Design of cost-effective real time tremor alerting system for patients of neurodegenerative diseases

In recent years, many people are affected by neurodegenerative disorders like Alzheimer, Parkinson, motor neuron disease, and Chorea. Brain function loss, movement problems and typically cognitive impairments occur from their influence on the central nervous system. In this situation, early symptom monitoring and assessment are essential for enhancing the tremor patient life. Here, we propose embedded system development, for early alerting of tremors by integrating heart rate and MEMS sensors. In the created system, a collection of different four sensors are linked to a development board. This device can track the geographical location of Alzheimer's patients and detect tremor/movement, as well as accidental falls. Using GSM connectivity, a remote administrator may collect data from the development board. A data compression method is constructed directly on board to improve data transmission efficiency and reduce power usage.

A blockchain-based information management system for academic institutions: a case study of international students’ workflow

PurposeThis study aims to explore the impact of applying a blockchain-based information system in the international students’ administrative workflow in the context of a public research university in the USA.Design/methodology/approachSpecifically, a prototype of the blockchain-based information system was developed and tested. The system has the potential advantages of being more efficient, more transparent, faster and safer.FindingsThe results show that digital registration could increase student registration efficiency with a blockchain-based application.Research limitations/implicationsThere are also some limitations of this study. As an early exploration of the use of blockchain technology in higher education, this study could be further extended in several aspects: different institutions (e.g. private vs public school) may have different operational strategies towards international student affairs. The different administrative processes should be discussed in the context of different institutions, and thus the proposed system should be tested and evaluated based on the requirements of different institutions. Use experience and feedback from different users such as university administrators and students should be considered and investigated with respect to the necessary improvement of the proposed system. The extensibility and scalability of the system should be assessed to accommodate any potential modifications of the administrative process.Originality/valueThe findings show that a blockchain-based information management system has the potential to improve data transmission efficiency and protect information privacy.

Satellite-Assisted Cell-Free Massive MIMO Systems with Multi-Group Multicast.

In this paper, we use satellite-assisted and multi-group multicast mechanisms to relieve ground traffic pressure and improve data transmission efficiency of cell-free massive MIMO systems. We propose to estimate channel state information (CSI) by common pilot scheme. Given the estimated CSI, we derive the closed-form expressions of achievable rate with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding. The correctness of the closed-form expressions is verified through simulations. The results show that with the help of satellite and multicast, the average system spectrum efficiency (SE) can be significantly improved.

Mode-dependent guaranteed cost event-triggered synchronization for singular semi-markov jump neural networks with time delays

This paper addresses the drive-response synchronization problem for singular semi-Markov jump neural networks with time delays. Furthermore, the mode-dependent event-triggered scheme is applied to improve data transmission efficiency. Sufficient criterion is derived by a designed mode-dependent Lyapunov–Krasovskii functional for synchronization error system. Then, synchronization controller gains matrix and event-triggered weight matrix are co-designed simultaneously assisted by linear matrix inequality, such that proposed guaranteed cost performance can be achieved in the mean-square sense. Finally, the usefulness of our synchronization design is verified by numerical simulations.

Research on Biomechanical Simulation and Simulation of Badminton Splitting and Hanging Action Based on Edge Computing

Sports biomechanics refers to the science of the laws of mechanical motion produced in the process of biological movement. Its essence is to systematically and digitally reconstruct the fundamental attributes and characteristics of motion. At present, the research of sports biomechanics mainly focuses on the theoretical research of basic aspects and lacks the new technology of sports biomechanics digital simulation innovation and data measurement. This article takes the badminton chopping action as the research object and carries out biomechanical simulation and simulation research with the help of edge computing and genetic algorithm. First of all, this paper constructs a badminton chopping and hanging action system framework based on edge computing, so as to facilitate simulation and improve data transmission efficiency. Secondly, genetic algorithm is used in biomechanics simulation and simulation optimization and data analysis process. System testing and simulation verify the excellent performance of the biomechanical simulation of badminton chopping and hanging action established in this paper. The research will provide a reference for the academic circles to explore the field of sports biomechanics.

MP-CREDIT: Multi-path credit for high-speed data center transports

In high-speed interconnection networks, credit-based flow control is widely deployed at the link layer to ensure data lossless. In this case, each credit schedules data on a one-hop fixed link. When employing credit in higher layers, e.g. transport layer, credit needs to be transmitted through multiple hops to realize end-to-end scheduling. In credit-based transports, each credit is transmitted to the data sender through a credit path, in return, each data packet is sent to the data receiver through a data path. In modern high-speed data centers that deploy topologies with multiple equivalent paths, the multi-hop credit can be routed into diverse paths. For the credit-based transports that require strong path-consistency between the data path and the credit path, properly routing credits becomes significant to improve data transmission efficiency. However, existing solutions lack consideration for exploiting multiple paths, this limits them to be single-path transports and affects their performance.To optimize the end-to-end latency and fully utilize the network resources, this paper presents MP-CREDIT, the first multi-path credit protocol designed for strong path-consistency transports. MP-CREDIT leverages credit spray to uniformly balance the traffic on multiple paths, and employs sequence-free feedback control to remedy credit out-of-order. Besides, we develop path probing for MP-CREDIT to handle path failures. Compared to state-of-the-art credit-based transport, MP-CREDIT reduces average flow completion times to 0.53x in the realistic workloads, greatly improves the throughput to 1.28x with permutation traffic, and achieves much better fairness in asymmetry network.

Research on big data storage and compression method for dual core intelligent meter

With the rapid integration and development of cloud computing, big data and smart grid, not only the type and accuracy requirements of the collected data are increasing, but also the storage depth is getting higher and higher. In hardware, the dual port RAM design is used to improve the real-time performance of reading and writing, digital filter is used for preprocessing in software, and “Equivalent Normalization (EN)” is used to improve storage depth. These schemes save hardware storage space and improve data transmission efficiency in practical application. So the advantages of dual-core intelligent power meter will be further exploited and brought into play.

An Efficient and Secure Data Transmission Mechanism for Internet of Vehicles Considering Privacy Protection in Fog Computing Environment

Aiming at the problems for existing data transmission mechanisms in Internet of vehicles, such as real-time performance, high efficiency of computing tasks, vehicle data privacy, etc., this paper proposes an efficient and secure data transmission mechanism for Internet of vehicles considering privacy protection in fog computing environment. Firstly, this paper proposes a system model and main message attack model for Internet of vehicles in fog computing environment, and designs a data transmission system based on privacy protection to improve data transmission efficiency in Internet of vehicles and protect the privacy information of vehicle users. Then, the task allocation and data aggregation mechanism of privacy protection is proposed in the crowd-sensing model based on the assistance of fog nodes, and the location prediction method based on social infection theory is used to predict the vehicle location for better allocating network resources. Finally, for the selfishness of end users in Internet of vehicles, selfish node incentive mechanism based on Robin Steiner bargaining game model is proposed to encourage selfish nodes to perform data transmission and reduce time delay. Operational network environment simulation software is used to carry out experiments. The experimental results show that when malicious nodes and selfish nodes exist in the Internet of Vehicles, the proposed method has stronger competitiveness in resisting attacks and improving the efficiency of message transmission compared with other methods, which can achieve efficient and secure data transmission.

Energy-Efficient QoS-Aware Intelligent Hybrid Clustered Routing Protocol for Wireless Sensor Networks

A wireless sensor network consists of numerous low-power microsensor devices that can be deployed in a geographical area for remote sensing, surveillance, control, and monitoring applications. The advancements of wireless devices in terms of user-friendly interface, size, and deployment cost have given rise to many smart applications of wireless sensor networks (WSNs). However, certain issues like energy efficiency, long lifetime, and communication reliability restrict their large scale utilization. In WSNs, the cluster-based routing protocols assist nodes to collect, aggregate, and forward sensed data from event regions towards the sink node through minimum cost links. A clustering method helps to improve data transmission efficiency by dividing the sensor nodes into small groups. However, improper cluster head (CH) selection may affect the network lifetime, average network energy, and other quality of service (QoS) parameters. In this paper, a multiobjective clustering strategy is proposed to optimize the energy consumption, network lifetime, network throughput, and network delay. A fitness function has been formulated for heterogenous and homogenous wireless sensor networks. This fitness function is utilized to select an optimum CH for energy minimization and load balancing of cluster heads. A new hybrid clustered routing protocol is proposed based on fitness function. The simulation results conclude that the proposed protocol achieves better efficiency in increasing the network lifetime by 63%, 26%, and 10% compared with three well-known heterogeneous protocols: DEEC, EDDEEC, and ATEER, respectively. The proposed strategy also attains better network stability than a homogenous LEACH protocol.

Intermediate model based efficient and integrated multidisciplinary simulation data visualization for simulation information reuse

The efficient and integrated visualization and reuse of multidisciplinary simulation data are imperative for the development of complex products. However, it is not a trivial task for designers to efficiently acquire, view and then reuse simulation data to improve the product design process. The challenge is that simulation data are always too huge to be transferred and retrieved quickly and they tend to be heterogeneous and tool-specific due to the lack of a uniform representation. In this study, an approach of intermediate model based efficient and integrated visualization of multidisciplinary simulation data for simulation information reuse is proposed to address the above issues. Firstly, the intermediate model based integrated model framework is designed to support the uniform modeling and integrated visualization of multidisciplinary simulation data. Then, the intermediate mesh model is constructed based on the hybrid mesh size (HMS) field to achieve the uniform representation of multidisciplinary simulation data with high-fidelity. Thirdly, a series of strategies such as coarse filtering, fine filtering for incremental transmission and data compression are proposed to improve data transmission efficiency. Moreover, a simulation model descriptor (SMD) based similarity assessment method is developed to support the efficient retrieval of simulation models for reuse. Finally, several experiments are conducted to demonstrate the feasibility and effectiveness of the proposed model and methods.

A peripheral component interconnect express-based scalable and highly integrated pulsed spectrometer for solution state dynamic nuclear polarization

High sensitivity, high data rates, fast pulses, and accurate synchronization all represent challenges for modern nuclear magnetic resonance spectrometers, which make any expansion or adaptation of these devices to new techniques and experiments difficult. Here, we present a Peripheral Component Interconnect Express (PCIe)-based highly integrated distributed digital architecture pulsed spectrometer that is implemented with electron and nucleus double resonances and is scalable specifically for broad dynamic nuclear polarization (DNP) enhancement applications, including DNP-magnetic resonance spectroscopy/imaging (DNP-MRS/MRI). The distributed modularized architecture can implement more transceiver channels flexibly to meet a variety of MRS/MRI instrumentation needs. The proposed PCIe bus with high data rates can significantly improve data transmission efficiency and communication reliability and allow precise control of pulse sequences. An external high speed double data rate memory chip is used to store acquired data and pulse sequence elements, which greatly accelerates the execution of the pulse sequence, reduces the TR (time of repetition) interval, and improves the accuracy of TR in imaging sequences. Using clock phase-shift technology, we can produce digital pulses accurately with high timing resolution of 1 ns and narrow widths of 4 ns to control the microwave pulses required by pulsed DNP and ensure overall system synchronization. The proposed spectrometer is proved to be both feasible and reliable by observation of a maximum signal enhancement factor of approximately -170 for (1)H, and a high quality water image was successfully obtained by DNP-enhanced spin-echo (1)H MRI at 0.35 T.

Performance modeling of data transmission by using random linear network coding in DTNs

SummaryIn Delay Tolerant Networks (DTNs), mobile nodes exploit encounter chances to transmit data via the established opportunistic link between encounter nodes. The transmission capacity between two encounter nodes is constrained by the duration of opportunistic links. How to transmit data efficiently is one of the fundamental and most important issues in DTNs. Recently, Random Linear Network Coding (RLNC) is proposed as a promising approach to improve data transmission efficiency in DTNs. To facilitate the development of deployment of RLNC based routing protocols, it is imperative to quantify the performance gain brought by RLNC in a rigorous and systematic way. In this paper, we develop an analytical model to evaluate the data transmission performance of RLNC based epidemic routing in DTNs. In the model, we consider that multiple unicast communication sessions compete for limited \capacity. Numerical results validate the effectiveness of our analytical model and demonstrate the significant performance improvement for data transmission in DTNs brought by RLNC. Our work of this paper provides a general tool for performance evaluation and useful guidelines for designing RLNC based routing protocols in DTNs. Copyright © 2015 John Wiley & Sons, Ltd.

Efficient Data Collection for Large-Scale Mobile Monitoring Applications

Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other's sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement.

Accelerating data distribution in P2P live stream with data reservation mechanism

PurposeThe purpose of this paper is to improve data transmission efficiency in a P2P system.Design/methodology/approachThis mechanism does not require changing existing mesh topology, and only necessary peers' evaluation mechanisms need to be introduced.FindingsThis paper presents a data reservation mechanism so that it can transmit data via the push and pull model on the P2P live stream system with the mesh topology. The experiment result of establishing a simulation environment reveals that the above mentioned is an effective way of reducing the data transmission delay by 40 per cent.Originality/valueTo improve data transmission efficiency in a P2P system, this paper presents a data reservation mechanism so that it can transmit data via the push and pull model on the P2P live stream system with the mesh topology.

Virtual position-based ring routing protocol for wireless sensor network

A virtual position-based ring routing protocol was presented. This protocol can serve as the infrastructure of a large-scale and data-oriented service in wireless sensor network. This protocol used distributed Hash table to manage data. The idea of using virtual position lead to the benefit that less redundant routing information was generated, and thus the routing paths were optimized. This benefit also reduced nodes' working load and improve data transmission efficiency. Detailed simulation and comparison with relative protocol show that this protocol achieves good efficiency and scalability.