Network Transmission Efficiency Research Articles

A survey of transmission control protocol variants

TCP (Transmission Control Protocol), is a reliable connection oriented end-to-end protocol. It contains within itself, mechanisms for ensuring reliability by requiring the receiver to acknowledge the segments that it receives. The network is not perfect and a small percentage of packets are lost enroute, either due to network error or due to the fact that there is congestion in the network and the routers are dropping packets. TCP ensures reliability by starting a timer whenever it sends a segment. If it does not receive an acknowledgement from the receiver within the ‘time-out’ interval then it retransmits the segment. In this paper a review of various TCP is carried out. There are a number of TCP variants for application in the management of network efficiency in terms of network congestion and transmission efficiency. These variants include: - TCP Tahoe, TCP Reno, TCP New Reno, TCP Vegas, TCP SACK, TCP FACK, TCP Asym, TCP RBP, Full TCP and TCP CUBIC. Therefore, the main objective of this paper is to study the tcp types on the network performance variances. All have different features and advantages but with maximal throughput as main objective, which are termed as the clones of TCP, have been incorporated into TCP/IP protocol for handling congestion efficiently in different network scenarios.

Enabling efficient routing for traffic engineering in SDN with Deep Reinforcement Learning

Traffic Engineering (TE) is applied to optimize network transmission efficiency by managing the routing of complicated traffic. Emerging Deep Reinforcement Learning (DRL) and Software-Defined Networking (SDN) provide flexible ability for traffic management and congestion control. However, existing methods either cannot reroute the network-wide traffic in an accurate way or encounter too much calculation latency due to the inference of mathematical optimization techniques. In this paper, we propose a flexible and link-reconfigurable TE solution called EfficientTE that effectively adjusts the traffic routing based on real-time traffic demands. By analyzing the characteristics of network topology and traffic, EfficientTE selects a few links that are critical for congestion in the network. Then, we propose the idea of virtual capacity that helps the DRL algorithm adjust to different link bandwidths. Based on the traffic demand and topology information collected by the SDN controller, the DRL algorithm is used to dynamically adjust the virtual capacity of the critical links to reshape the network. To ensure network performance with low disturbance, we selectively reroute the Top-K critical flows using the weighted K-shortest path algorithm, while forwarding the major flows with default rerouting. Experiments show that EfficientTE optimizes maximum link utilization and outperforms existing TE solutions by improving the load-balancing performance ratio by at least 6.13%, 18.78%, 16.20%, and 22.81% respectively in four network topologies.

A Novel Approach to Reduce Video Traffic Based on Understanding User Demand and D2D Communication in 5G Networks

Mobile network customers today prefer video news updates than text. However, the conventional cellular base stations may have challenges in simultaneously managing video transmission for all users, particularly in extensive network deployments. To address the aforementioned challenge discussed in this article, a proposed solution is presented. To lessen the burden of video traffic, we intend to improve the efficiency of D2D multicast network transmission in cellular D2D communications. A scalable video multicast strategy that accounts for user demand and makes use of device-to device (D2D) communication is offered as a means to this end. In the first step, we analyze the users' interests by looking at things like their video viewing habits and criteria like video popularity. The purpose of this research is to ascertain whether or not users are inclined to share movies, which would increase the number of viewers who receive multicast clusters. In the next stage, an algorithm for selecting cluster heads will be developed, taking into account criteria including channel quality, social metrics, and the requirements for video quality. In comparison to similar systems without multiplexing capacity, the proposed method exhibits superiority in three distinct aspects. The initial modification exhibits a surge ranging from 70% to 375 %. Furthermore, the overall growth rate of the entire system ranges from 32% to 64%. Furthermore, it is worth noting that there has been a significant rise of 90% in the number of network recipients, indicating a substantial boost in customer satisfaction.

Semantic-Driven Efficient Service Network Towards Smart Healthcare System in Intelligent Fabric

The advantages of intelligent fabrics enable health service systems more efficient and safe to perceive and transmit, where massive sensitive data related to health and behavior are collected through the multi-source flexible sensor network. However, due to its complex network structure, health service systems face the challenge of low-power data transmission and processing in intelligent fabric space. Recently, Deep learning-powered semantic communication (Deep-SC) has emerged as a promising communication paradigm. To tackle the above problem, we introduce the concept of semantic cognition into the cyber-physical system of health service and present the Deep-SC based network service framework for the healthcare system, where physical layer blocks are merged into the traditional communication system to jointly optimize the transceiver in communication. Since semantic encoding and decoding require additional knowledge, consider the introduction of the semantic knowledge base in the system to formulate the joint optimization problem of service offloading and bandwidth allocation in the proposed network service framework, with the goals of reliable semantic communication and efficient network transmission. Furthermore, an online algorithm that optimally adapts service offloading and subcarrier allocation decisions to the time-varying channel conditions is required. Finally, we use a reinforcement learning-based decision algorithm to solve combinatorial optimization problems, which further reduces the computing complexity. Experimental results show that the proposed framework achieves significant performance improvements in system energy consumption and reliable transmission, compared with traditional communication strategies.

An intelligent routing algorithm for energy prediction of 6G-powered wireless sensor networks

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling.

Long-term effects of childhood trauma subtypes on adult brain function.

Childhood trauma is prevalent in our society, whereas little is known about why and how different forms of early traumatic experiences exert long-term effects on differential brain function in adulthood. This study aimed to scale experience-specific neural correlates of distinct subtypes of childhood trauma. We used resting-state functional magnetic resonance imaging data from 216 adults with different degrees of childhood trauma. Graph analysis was combined with the Childhood Trauma Questionnaire to inspect the impact of distinct subtypes of childhood trauma on the brain. We found that childhood trauma experiences have a detrimental effect on depression and anxiety behavior. On one hand, childhood neglect scores were positively associated with network transmission efficiency of regions involved in cognitive and executive functions, such as temporal lobe, insular cortex, and parahippocampal gyrus. On the other hand, childhood abuse scores were tightly linked to functional reorganizations of regions mediated by immature ego defense system and damaged emotion representation. Specifically, the abuse scores were positively associated with network transmission efficiency of the visual, auditory, linguistic, and motor cortex. Isolated communications in temporal cortex and supplementary motor cortex were related to emotional and physical abuse scores. Our data showed the differential associations of abusive and neglectful experiences with brain function in adulthood. These experience-specific associations could underpin potentially differential risks of specific forms of psychiatric sequelae in adulthood. More attentions to maltreated children and timely psychological treatment are needed to reduce the incidence of psychosis.

Simulative study of high speed light modulated measured sources for high optical network transmission efficiency

Abstract This study reported the performance evaluation and simulation of passive optical networks for high transmission rates up to 10 Gb/s for possible distance up to 50 km. Laser rate equations, directly modulated laser measured and vertical cavity surface emitting laser (VCSEL) are employed for the measurement of the network performance. Different bit sequences are suggested to show the network performance parameters with various proposed optical sources. Optical and electrical powers are measured for 50 km network reach. In addition to max. Q factor is also measured for various optical sources and bits sequence variations. Laser rate equations are employed in this network to upgrade the Q factor criteria for maximum network reach of 100 km in the downstream direction.

A Survey of Traffic Shaping Technology in Internet of Things

In the era of the Internet of Everything, the burst of traffic will bring many problems. Traffic shaping, as a common means to limit burst traffic rate, achieves “peak and valley reduction” to smooth the output rate, avoid network congestion, guarantee the quality of service (QoS) and improve the overall network transmission efficiency. This paper first explains the basic concept of traffic shaping, the related algorithms in traffic shaping, and compares the differences between traffic shaping and traffic policing. It then describes the research on traffic shaping in software-defined networks and the results from the combination of the two. This is followed by an introduction to relatively new technology for industry, time-sensitive networks, and an exploration of the functions of various types of traffic shapers in time-sensitive networks. This is followed by a description of case studies of traffic shaping in IoT scenarios and an overview of these traffic shaping schemes, summarizing the usefulness of the shaping schemes. Finally, the future of traffic shaping is explored.

The Impact Of Douyin (Chinese TikTok) On The Socialization Of Chinese Youth

The more efficient network transmission rate and increasingly popular hardware technology brought by 5G technology have driven the rapid development of online HD short videos. As one of the most popular online short video mediums in China, Douyin, its international version known as TikTok, has a large youth user base, and its influence on youth growth has attracted widespread attention from families, society, and academia. Therefore, it is important to study the influence of Douyin on youth socialization and propose a media environment that is conducive to building a healthy socialization environment for youth.

DTC-HSR: Distributed Topology Control and Hierarchical Self-Routing for Bluetooth Load Balancing Networks

In this article, a distributed topology control approach with hierarchical self-routing (DTC-HSR) is presented for Bluetooth low-energy (BLE) networks. First, the conventional star piconet is replaced by the designed mesh-ring subnet with better throughput and lower delay. To achieve the goal of load balancing design, two phases, including the leader selection and the topology construction are executed in the proposed approach. In the leader selection phase, each master node discovers its adjacent slave nodes to determine a leader master as a coordinator. In the topology construction phase, the local mesh-ring subnet is first formed and then the global mesh-ring subnets are interconnected into the desired DTC-HSR topology. To form the local mesh-ring subnet, each leader master computes the desired number of piconets with even link connectivity and distributes the piconet connection information for each node to form a mesh-ring subnet. In addition, each master node connects with the other local mesh-ring subnets via its associated bridge nodes, including slave nodes, intrabridges, and interbridges to create the definitive DTC-HSR scatternet. Afterward, a hierarchical self-routing strategy is jointly employed for the DTC-HSR to efficiently deliver routed packets through different mesh-ring subnets. Simulation results demonstrate that the DTC-HSR topology with the even connectivity feature outperforms the dual-ring tree (DRT) and cluster-based mesh (CBM) approaches in terms of network transmission and energy efficiency performances. The DTC-HSR configuration thus achieves efficient topology construction and hierarchical self-routing for load balancing in BLE networks.

Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster

With the gradual networking of inter-city relations and the increase in acute impact and chronic stress, the measurement of the resilience of urban network structures is particularly prominent. Based on the construction of the urban network by passenger train trips in the Yellow River Basin, this paper analyzes and assesses the characteristics of the structural resilience of the urban network, and probes into the network resilience and urban response under the circumstances of node failure and line failure in Zhengzhou. The main conclusions are as follows: (1) The urban network in the Yellow River Basin was clearly hierarchical, with a significant spatial distribution of “low in the north and high in the south”, and the overall characteristics of “robustness” in small areas and “fragility” in large areas. The network connection forms were diversified and open. The network transmission efficiency was high, and the edge cities depended on the core cities with prominent characteristics, and the risk load of regional core cities rose. (2) The network structure was “robust” as it maintained high operational efficiency and connectivity under random attacks. Under deliberate attacks, the city network operated efficiently with a small increase in connectivity before the 60% threshold, and after the threshold, the overall network started to split into many sub-networks, and the network fragmentation gradually increased until the network collapsed. (3) Zhengzhou node failure and line failure states in the Yellow River Basin urban network were resilient, in the sense that when suffering important nodes and lines going down it could still maintain good network operation efficiency, and the core nodes in the impact of natural disasters could adapt to the destructive nature of the network through the urban network structure self-regulation.

I2M: Intelligent Information Management for Rendering IoE Services in Society 5.0

In this paper, we propose an intelligent information management scheme – I2M – an two level delay-tolerant profile matching scheme for provisioning Internet-of-Everything (IoE) services in a Society 5.0. The primary aim of Society 5.0 is to provide seamless real-time IoE services using artificial intelligence (AI) and next-generation networks (NGNs). In this context, we introduce Information-Centric Satellite Network (ICSN), which is expected to enable efficient in-network caching, content delivery, and large area coverage in Society 5.0. The existing works on ICSNs suggest caching the information, requested by the user, in the satellites. However, the satellites are storage-constraint in nature. Therefore, storing information in such satellites, in the long run, may not be a suitable solution. Moreover, the existing solutions increase the delay while a cache-miss occurs in ICSN, which is unacceptable in providing real-time IoE services in Society 5.0. On the other hand, data requested by the increased number of users results in reduced network lifetime and transmission efficiency for Society 5.0 without having a suitable load balancing mechanism in the existing ICSN. To address these issues, we design I2M, which is capable of managing information in a Society 5.0. In I2M, we introduce a tabular data structure to store, update, and replace in-network information in the resource-constrained satellites. Using this tabular data structure, I2M performs a 2-level profile matching scheme, which helps to efficiently access the in-network information and reduce the delay for cache-miss in satellites. Additionally, to increase the transmission efficiency and network lifetime, we apply a long-shot term memory (LSTM)-based intelligent load balancing mechanism in I2M. The extensive simulation results show that I2M outperforms as compared to the existing schemes for ICSNs, in terms of energy consumption, packet loss, and delay. We observe that I2M is capable of reducing delay by 35-55%, energy consumption by 34-54%, and packet loss by 36-53%.

Separable Reversible Data Hiding in Encrypted AMBTC Images Using Hamming Code

Data hiding is a field widely used in copyright, annotation, and secret communication for digital content, and has been continuously studied for more than 20 years. In general, data hiding uses the original image as a cover image to hide data, but recently, the research area has expanded to research on improving the security and privacy protection of image contents by encrypting the image. This research is called separable reversible data hiding in an encrypted image (SRDH-EI). In this paper, we proposed a more efficient SRDH-EI method based on AMBTC. AMBTC can guarantee very good network transmission efficiency for applications that do not require a particularly high image quality because the compression time is short and calculation is simple compared to other existing compression methods. The SRDH-EI method presented here divides AMBTC into non-overlapping 4×4 blocks and then performs image encryption on them. Thereafter, data can be hidden using a Hamming code for each block. The proposed method has an advantage in that the quality of the cover image and the hiding capacity can be adjusted by appropriately using the value T of the difference between the two quantization levels. The experimental results proved the efficiency and superiority of our proposed model.

Design of Game Data Acquisition System Based on Artificial Intelligence Algorithm

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient.

Adaptive Hurst-Sensitive Active Queue Management.

An Active Queue Management (AQM) mechanism, recommended by the Internet Engineering Task Force (IETF), increases the efficiency of network transmission. An example of this type of algorithm can be the Random Early Detection (RED) algorithm. The behavior of the RED algorithm strictly depends on the correct selection of its parameters. This selection may be performed automatically depending on the network conditions. The mechanisms that adjust their parameters to the network conditions are called the adaptive ones. The example can be the Adaptive RED (ARED) mechanism, which adjusts its parameters taking into consideration the traffic intensity. In our paper, we propose to use an additional traffic parameter to adjust the AQM parameters—degree of self-similarity—expressed using the Hurst parameter. In our study, we propose the modifications of the well-known AQM algorithms: ARED and fractional order and the algorithms based on neural networks that are used to automatically adjust the AQM parameters using the traffic intensity and its degree of self-similarity. We use the Fluid Flow approximation and the discrete event simulation to evaluate the behavior of queues controlled by the proposed adaptive AQM mechanisms and compare the results with those obtained with their basic counterparts. In our experiments, we analyzed the average queue occupancies and packet delays in the communication node. The obtained results show that considering the degree of self-similarity of network traffic in the process of AQM parameters determination enabled us to decrease the average queue occupancy and the number of rejected packets, as well as to reduce the transmission latency.

Identification Method of Vulnerable Lines in Power Grid Based on Comprehensive Sensitivity of Branches

This paper proposes a method of vulnerable lines identification in power grid based on comprehensive sensitivity of branches in order to effectively prevent large-scale cascading faults. Firstly, this paper improves the power transmission distribution factor, and consideres the influence of load rate of the branch. The sensitivity index of the branch power flow is proposed to reflect the sensitivity of the branch to the power flow transfer. Then, this paper modify the branch breaking distribution factor, and the branch breaking sensitivity is constructed to reflect the branch breaking vulnerability from the two aspects. Entropy weight method is used to allocate the weights of the two indexes to screen out the vulnerable lines in the system.The identification results are compared with related documents, and finally simulates by the IEEE39 system This paper uses three attack methods to attack, and the network transmission efficiency is used to reflect the changes of the attack method to the system. The results prove the feasibility and rationality of the method.

Research on multi-path network balancing method for cloud computing

In the era of big data, the data type and volume of the internet are growing exponentially. Users have higher requirements for the network, and network transmission is facing new challenges. The multi-path transmission technology based on TCP is one of the commonly used high-performance networks, but the improvement is still limited by the defects of the protocols such as queue head-of-line blocking and SYN flooding attacks. In order to further improve the efficiency and reliability of network transmission, this paper studies the multi-path network load balancing technology of SCTP, designs an application download program based on its multi-homed characteristics and CMT parallel transmission characteristics, and compares it with the existing TCP transmission technology through experiments. The result indicates that using the transmission by the parallel multi-path technique combining SCTP and CMT can improve the network performance by 70% and increase transmission reliability by 12.6%.

Design and Research of M2M Message Transfer Mechanism of Looms for Information Transmission

Machine-to-machine (M2M) communication is a key implementation technology for future Industrial Internet of Things (IIoT) applications. The requirements for flexibility, efficiency, and crossplatform compatibility in intermodule communication between the connected machines raise challenges for the M2M messaging mechanism. For the loom message transmission mechanism, it is necessary to have certain flexibility and meet certain M2M communication requirements to understand each other. In this paper, we analyze and study the data model, data exchange language, MQTT protocol and logo resolution technology of looms to address the current problems of low data transmission efficiency, poor flexibility and lack of flexibility in the network.By establishing the data model of looms and classifying them by topic, selecting JSON format as the transmission format of data, and using MQTT protocol to realize data transmission between loom networks, an MQTT-based messaging mechanism is established.The data involved in the communication of the loom equipment are grouped according to the communication requirements, and only the loom data required by the application are subscribed. At the same time, the hardware and software parts of the communication module are designed with ESP32 as the core, so that it can complete the functions of loom network communication, data analysis, format conversion and so on. In addition, the real-time requirements of the loom network communication and the network requirements for future intelligent manufacturing must be met. This paper uses the BP neural network to intelligently optimize the loom network, and the existing problems related to performance indices are analyzed. The original loom network is optimized by integrating the RSSI, QoS requirements, transmission priority, real-time communication and data transmission volume of nodes. In the meantime, the particle swarm optimization algorithm is introduced to optimize the BP neural network to avoid the problem that it easily can fall into the local optimal solution and effectively improving the network optimization efficiency. Finally, the feasibility of this method is proven by using the experimental platform, which can solve the problems of low network transmission efficiency and difficult network optimization of looms and provide an effective technical example of loom information transmission and network optimization for industry.

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

Aiming at the problem of uneven clustering and the unreasonable energy consumption of LEACH protocol in the perception layer of IoT-based microgrids of static nodes; in this paper, we propose a stationary-node energy-based routing protocol (SERP). First, we select a dynamic cluster radius for clustering to meet the actual needs of the network during clustering. Then, to solve the problem that the number of cluster heads is difficult to determine, a dynamic optimal cluster head ratio is adopted. The dynamic optimal cluster head ratio can be obtained by minimizing the total energy consumption of cluster formation and the stable transmission phase, which can improve the efficiency of network transmission. Finally, by setting the residual energy factor and distance factor to improve the calculation of the cluster head election threshold, the energy load of the network is more uniform, and the location of the cluster head is more reasonable. Compared with the LEACH protocol and the HEED protocol, the simulation results show that the SERP protocol can effectively prolong the lifetime of the whole network.

Passivity-based filtering for networked semi-Markov robotic manipulators with mode-dependent quantization and event-triggered communication

In this article, the networked filtering problem for a class of robotic manipulators with semi-Markov type parameters is investigated under the passivity framework. In particular, the mode-dependent quantization and event-triggered communication scheme are both proposed for increasing the network transmission efficiency. Sufficient stability conditions are first derived by choosing mode-dependent Lyapunov–Krasovskii functionals. Then, the mode-dependent filter gains and the event-triggering parameters are further designed with the help of matrix convex optimization. In the end, a simulation example is provided such that the effectiveness of the proposed filtering method can be well demonstrated.