Data Transmission Delay Research Articles

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

In conjugate symmetric data transmission, due to insufficient judgment of congestion during transmission, the amount of data is large and the transmission rate is low. In order to improve the data transmission rate, a conjugate symmetric data transmission control method based on machine learning is designed. Firstly, the data to be transmitted is tracked and determined, and then conjugate symmetric data fusion is completed according to the calculation result of the best tracking signal. According to the fusion results, the framework of the conjugate symmetric data coding system is established, and the data coding is completed. The average congestion mark value is calculated by the machine learning method, and the congestion judgment of data transmission is completed. On the basis of congestion determination, the efficient transmission control of conjugate symmetric data is realized by specifying the conjugate symmetric data transmission protocol. Experimental results show that compared with traditional control methods, this control method has the advantages of a high delivery rate, low message transmission overhead and low data transmission delay. Compared to the traditional two-way path model, the scheduling method proposed in this study increases the transmission delivery rate by 5%, while reducing the transmission cost and delay by 0.7 cost index and 1.1 min delay, respectively. In comparison with the performance of accurate error tracking equalization, the transmission delivery rate of the research method increased by 21%, and in transmission cost index and delay analysis, it also decreased by 3.1 and 2.9 minutes. Based on the above performance comparison analysis, it can be concluded that the machine learning method has more superior transmission control performance.

Securely Offloading Computation to the Edge with the Tangle Cache

The rising area of the Internet of Things (IoT) faces various issues related to the scalability and security of the data produced. Most IoT devices cannot perform complex computations on their own due to resource constraints, which require them to outsource their jobs. Due to the abundance of devices and the IoT network’s heterogeneous nature, there is a large attack surface against IoT systems. During outsourcing jobs, a major factor that affects performance is the delay in data transmission. However, the security issues such as data manipulation and leakage for edge caching have not been fully addressed in existing literature. This paper proposes a Tangle based caching model for edge services, as well as an efficient and effective scheme that will aid the offloading process between IoT devices and edge devices. The proposed model utilizes reversible integer transformation in the context of edge computing to build a secure data transfer technique that distorts the original data so that an adversary cannot access it. Experimental studies and theoretical evaluation demonstrate that our proposed scheme is promising for adoption in future IoT systems with its efficiency and security features.

Low Latency and High Data Rate (LLHD) Scheduler: A Multipath TCP Scheduler for Dynamic and Heterogeneous Networks.

The scheduler is a crucial component of the multipath transmission control protocol (MPTCP) that dictates the path that a data packet takes. Schedulers are in charge of delivering data packets in the right order to prevent delays caused by head-of-line blocking. The modern Internet is a complicated network whose characteristics change in real-time. MPTCP schedulers are supposed to understand the real-time properties of the underlying network, such as latency, path loss, and capacity, in order to make appropriate scheduling decisions. However, the present scheduler does not take into account all of these characteristics together, resulting in lower performance. We present the low latency and high data rate (LLHD) scheduler, which successfully makes scheduling decisions based on real-time information on latency, path loss, and capacity, and achieves around 25% higher throughput and 45% lower data transmission delay than Linux's default MPTCP scheduler.

LPLL-LEACH: A study of a low-power and low-delay routing protocol based on LEACH

For a series of problems, such as uneven energy consumption and long packet forwarding time of a low-energy adaptive clustering hierarchy (LEACH), an efficient routing protocol with low power and low latency based on LEACH (LPLL-LEACH) is proposed in this paper. In terms of energy, first, the optimal number of cluster heads (CHs) is calculated by quantitative analysis. Second, multiple factors are considered to redefine the threshold for electing CHs. In addition, ordinary nodes decide which cluster to join according to the cost function. Moreover, a forwarding function is utilized to determine the optimal next hop CH to forward the data. Last, the ordinary nodes are allowed to directly communicate with the base station (BS). In regard to latency, a hybrid mechanism of carrier sense multiple access and time division multiple access (CSMA_TDMA) is applied to minimize the time delay of data transmission. MATLAB is used to analyze the latency and energy of LPLL-LEACH and to compare it with LEACH, LEACH-C, ECH and EE-LEACH. The experimental results show that the time of all node deaths extended by 107%, 45%, 31.8%, and 8.8%. Similarly, the latency is greatly reduced, which improves the efficiency of data transmission.

Ethernet networks with deterministic delays

The work is devoted to a new technology that provides low and predictable data transmission delays in industrial Ethernet networks. The traffic is divided into critical and non-critical to delays. The features of TSN functioning are considered. The operating principle and architecture of such networks are considered. It is shown that cost reduction can be achieved by having only one network that meets the requirements of deterministic networking and is compatible with modern Ethernet solutions. Examples are given and it is shown that deterministic networks can be used to create hard real-time systems. This is especially true for automatic regulation and control systems that include feedback circuits. The key features of TSN that provide guaranteed message delivery times are traffic transmission time synchronization and scheduling. The TSN switch is presented as two subsystems, one of which performs standard Ethernet functions, and the second provides the TSN control logic. An example of cyclic traffic control according to the schedule is considered. The prospects of using TSN technology in industrial automation control networks are shown.

Task Scheduling Strategy for Industrial Control Network based on Cloud-Edge-End Fusion Framework

Industrial tasks with traditional cloud architecture have many disadvantages, which are mainly longer delay of data transmission and difficulty in software deployment. In view of the above disadvantages, this paper used containers to solve the problems for industrial software deployment and upgrade. This paper uses a cloud-edge-end framework to deploy tasks with high delay on the edge of the network or even on the device side, so as to reduce the delay of data transmission and computing pressure. At the same time, the task scheduling strategy based on the particle swarm optimization algorithm is used to search for the node with the minimum task delay among multiple nodes to realize the task allocation optimization. Finally, through the comparison experiment, it is concluded that the task scheduling strategy for industrial control networks based on a cloud-edge-end fusion framework can better reduce the task execution delay and reduce the difficulty of software deployment.

Link-State Aware Hybrid Routing in the Terrestrial–Satellite Integrated Network

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

Design and Validation of Lifetime Extension Low Latency MAC Protocol (LELLMAC) for Wireless Sensor Networks Using a Hybrid Algorithm

As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid algorithm to improve the energy efficiency of sensor networks with nodes that are regularly placed. Every node in this protocol has three operating modes, which are sleep mode, receive mode, and send mode. Every node enters a periodic sleep state in order to conserve energy, and after waking up, it waits for communication. During the sleep mode, the nodes turn off their radios in order to reduce the amount of energy they consume while not in use. As an added feature, this article offers a channel access mechanism in which the sensors can send data based on the Logical Link Decision (LLD) algorithm and receive data based on the adaptive reception method. It is meant to select acceptable intermediary nodes in order to identify the path from the source to the destination and to minimize data transmission delays among the nodes in the network scenario. Aside from that, both simulation and analytical findings are used to examine the activity of the suggested MAC, and the created models are evaluated depending on their performance. With regard to energy consumption, latency, throughput, and power efficiency, the result demonstrates that the suggested MAC protocol outperforms the corresponding set of rules. The extensive simulation and analytical analysis showed that the energy consumption of the proposed LELLMAC protocol is reduced by 22% and 76.9% the end-to-end latency is 84.7% and 87.4% percent shorter, and the throughput is 60.3% and 70.5% higher than the existing techniques when the number of node is 10 and 100 respectively.

Traffic aware dynamic load distribution in the Data Plane of SDN using Genetic Algorithm: A case study on NSF network

For a particular topology, with the best controller and switch positions SDN occasionally exhibits subpar performance, resulting in annoyance-inducing delays and unsatisfied users. Increased traffic volume can lead to congestion, which raises the burden on specific switches and controllers and lengthens the delay of data transmission. Additionally, the failure of specific nodes or links might result in an anomalous rise in the traffic load on specific switches and controllers. The conventional switch migration and congestion control techniques might not be sufficient to deliver the best throughput and capacity for frequent and unpredictable network traffic variations. Also, certain switches/controllers may experience heavy traffic load as compared to other switches/controllers. Under such adverse situations, the performance of the network becomes extremely poor. In light of these factors, this paper suggests an effective approach based on Genetic Algorithm for traffic load distribution in the Data Plane of SDN, which effectively reroutes packet flows (when such an adverse situation arises) in order to avoid abnormal load on switches/links and maintain acceptable network performance with the available resources. The proposed approach was applied to the NSF network and a case study was performed. The proposed approach was evaluated and found to be quite effective in reducing abnormal loads on switches/links as it is found that there can be a decrease in the average load by 396 units at the cost of increasing the average latency by 1.29 ms only.

Certificateless multi-source signcryption with lattice

Network coding can improve network transmission performance and reduce data transmission delay. However, the nodes in network are vulnerable to pollution attacks. Previous network coding schemes are based on difficult problems in number theory and cannot resist the quantum computing attacks. Hence, certificateless multi-source signcryption with lattice (LCL-MSSC) is devised for network coding, it uses common public key to verify the combination ciphertext from different source node users. Based on learning with errors and inhomogeneous small integer solution problems, LCL-MSSC is unforgeable under the adaptive chosen-message attacks and indistinguishable under the adaptive chosen-ciphertext attacks; in addition, it can resist the pollution attacks and quantum computing attacks. Efficient LCL-MSSC has broad application prospect in UAV wireless communication network, mobile wireless network, wireless sensor network or cyberspace security.

무선 통신 기반 해수식 기화기 운영 상태 모니터링 시스템 개발

An open rack vaporizer is a facility that vaporizes liquefied natural gas using sea water. When a vaporization efficiency of the open rack vaporizer decreases, liquefied natural gas can leak, which can cause great damage to the facility. Operators have to monitor the operation state of the facility in real-time to prevent the accident. However, operators have visited the site and have checked the state by looking at the value of sensors installed in the open rack vaporizer through indicators. For the safe operation of the open rack vaporizer, a monitoring system is needed to monitor the operation state of the open rack vaporizer in real-time without the need for operators to visit the site. In this paper, we developed a long term evolution based monitoring system to monitor the operation state of the open rack vaporizer. The developed system can monitor the real-time operation state of the open rack vaporizer at a control center far from the facility. For the system development, data transmission infrastructure using long term evolution was built. Afterwards a software was developed to monitor the operation state of the open rack vaporizer in real-time using the transmitted data. Finally, performance evaluation was conducted to confirm that the developed system operated successfully without data transmission delay or data missing.

Roadside infrastructure deployment scheme based on Internet of Vehicles information service demand

AbstractRoadside unit (RSU) plays an important role in communication relay and data processing in the Internet of Vehicles. The rational deployment of roadside units directly affects the operational efficiency and system robustness of the Internet of Vehicles. This article proposes an RSU deployment scheme based on vehicular transmission demand. First, the mobility model of vehicles is mined through system communication transmission requirements to determine the total delay of vehicle communication transmission tasks. The delay includes two parts, namely, the vehicle data transmission delay and the channel service delay. Second, the optimization objective of the model is established, and some parameters such as inhibition distance coefficient are defined as constraint conditions. Finally, a evolutionary feature‐based RSU deployment algorithm, ICA‐EFRDA, preprocessed by a greedy algorithm is proposed to implement RSU deployment. The deployment results show that the proposed scheme has lower delay and higher coverage time ratio than the EFRDA scheme in both urban and suburban road networks. Under limited cost constraints, the system decides to place a limited number of RSUs in low‐traffic density areas of the suburban road network to improve performance. When the traffic density is high, the increased placement of RSUs in the urban road network has a more obvious effect on improving the service capability of the system. Under different deployment cost constraints, ICA‐EFRDA outperforms the comparison schemes in terms of the contacts per trip, contact probability, and packet delivery ratio.

Development of a monitoring system for disassembled towers with internal suspension poles

The traditional construction monitoring methods of suspended pole-mounted decomposed towers are mostly manual monitoring. The monitoring personnel has multiple blind spots, and the possibility of misjudgment based on personal experience is relatively large. It is difficult to ensure the construction safety of the suspended pole decomposing tower. For this reason, combined with the current power Internet of Things technology, this paper develops an intelligent monitoring system for suspended pole-mounted decomposing towers. According to the construction technology and its safety requirements of inner suspension derrick for transmission tower erection in sections, this system is classified into intellisense layer, wireless transport layer and information integration layer. According to the physical characteristics of the seven major risk points of the inner suspension pole group tower, the intellisense layer developed corresponding sensing equipment to obtain risk information. In the wireless transport layer, the ZigBee and 4G communication technologies are selected to interconnect self-constituted LAN and 4G wide area networks, to complete on-site data interaction and long-distance transmission. In the information integration layer, the force of cable, the inclination and height of derrick, and the distance between derrick and tower are determined. The system has been verified by the 500 kV delivery project of Fujian Zhouning Pumped Storage Power Station. The average error of critical monitoring point data is 4.14%, and the average data transmission delays in the far and near fields of the system are 18 ms and 176 ms.

Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks.

The 5G cellular network comes with a promise to provide a very high data rate at low latency, which is becoming critical for advancing technologies. Mobile operators are currently deploying the 5G cellular network worldwide. However, because of limited coverage and high susceptibility of the 5G network to obstacles, handoffs from 5G to 4G and vice versa frequently occur, especially when the user equipment (UE) is moving. These handoffs often cause significant delays in data transmission due to packet losses and retransmissions. A promising solution can be to use both 4G and 5G networks simultaneously, which can solve this problem and yield a better throughput. Multipath transmission control protocol (TCP) is an effective solution for this problem, but it requires significant performance evaluation before practical deployment. In this study, we implement an MPTCP testbed based on NS3-DCE that enables to test the performance of MPTCP schedulers and congestion control algorithms (CCAs) in both 3GPP and non-3GPP networks. Through extensive simulation experiments in a scenario where a UE simultaneously utilizes both 4G and 5G networks, we found that blocking estimation (BLEST) scheduler implemented with balanced linked adaptation (BALIA) CCA can produce the highest throughput and lowest delay. Finally, we showed how received signal to interference and noise ratio (SINR), congestion window, throughput, and packet losses are interconnected.

A Data-Driven Packet Routing Algorithm for an Unmanned Aerial Vehicle Swarm: A Multi-Agent Reinforcement Learning Approach

Routing decisions made by unmanned aerial vehicle (UAV) swarms are affected by complex and dynamically changing topologies. A centralized routing algorithm imposes the entire computational burden on one module, and the high data dimensionality renders computation burdensome. In this letter, we develop a multi-agent reinforcement learning-based routing algorithm for a UAV swarm. The UAVs are trained in a data-driven manner to make distributed routing decisions. Factors that include channel quality, UAV movement, UAV overhead, and the extent of neighbor variation are incorporated into link quality assessment. Long short-term memory is used to improve the Actor and Critic networks, and more information on temporal continuity is added to facilitate adaptation to the dynamically changing environment. Simulations show that the proposed routing algorithm reduces data transmission delay and enhances the transmission rate compared with traditional routing algorithms.

Construction of Landscape Ecological Planning Evaluation Model Based on Sensor Network

The application of wireless sensor network (WSN) technology promotes the modernization of forestry. WSN application technology in forest areas is an important research topic for the sustainable development of forestry in China and is also a research hotspot for forestry ecological monitoring at present. The application of wireless sensor networks in forest areas, first of all, solves the problem of limited energy supply and low-latency data transmission in the forest environment. Due to the large area of the forest environment and uneven tree density, dynamic changes in forest height, easy to block the signal, and other characteristics, the forest environment is prone to node energy depletion fast, the network life cycle is short, and data transmission delays large dilemma. Second, the application of wireless sensor networks is usually centered on maximum data acquisition, but the contradiction between high data acquisition rate and limited energy supply is inevitable, so it is necessary to construct a maximum data acquisition rate model with limited energy supply as a constraint to guarantee the optimal acquisition conditions for wireless sensor networks. In this paper, from the application of wireless sensor network technology in forest environment, the research on the application of wireless sensor network in forestry is carried out around the analysis of energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision-making fire danger warning process, so as to provide a solution to the overall problem of forest fire warning based on rechargeable wireless sensor network. In this paper, we analyze the dynamic replenishment of energy in rechargeable wireless sensor networks and propose an energy-based transmission control protocol that effectively improves data transmission efficiency. In the rechargeable wireless sensor network, the network E2E (end-to-end) average delay time is calculated based on the number of nodes on the data transmission link. The research idea of this paper starts from the application technology of wireless sensor network in the forest environment, and the design of the energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision fire danger warning process are realized vertically.

Analisis Manajemen Bandwith Menggunakan Metode Classfulqueing Disciplines di Rumah Sakit Permata Cirebon

Permata Hospital is a company engaged in the health sector with a hospital layout condition consisting of 1 building with 4 floors. Due to the absence of bandwidth management, large-scale bandwidth is required to be able to download or upload data evenly. The current problem at Permata Hospital is the absence of bandwidth management which results in non-optimal performance of data traffic. data transmission delays because each client is free to access without any speed limits. To overcome this problem, it can be proposed the Classful Disciplines method contained in the HTB (Hiarachical Token Bucket) which is able to handle the distribution of bandwidth for each class in the parent and child groups, so that each user who enters has his own bandwidth distribution. The purpose of this research is to optimize the distribution of bandwidth on the proxy. The results obtained from this study are expected to be able to contribute and further optimize the mikrotik in Permata Hospital to make it even better. Based on the results of the calculation of the data obtained, with a sig. (2-tailed) value of 0.004 <? (significant level of 0.05). then it can be said that Ho is rejected and Ha is accepted. Based on these results, it can be stated that ""Bandwidth Management Analysis Using Classful Queuing Disciplines Method at Permata Cirebon Hospital " can improve internet services for internet users.

Telematics Collaborative Resource Allocation Algorithm Based on Cloud Sidecar

This paper provides an in-depth study and analysis of a distributed allocation algorithm for collaborative resources for cloud-edge-vehicle-based Telematics. The approach starts from the emerging application of urban environmental monitoring based on vehicular networking, with an integrated design of data sensing detection and transmission, and collaborative monitoring of vehicle swarm intelligence based on urban air quality collection to avoid redundancy of information and communication overload. A hybrid routing method with minimal delay for reliable data transmission is proposed. The power adjustment algorithm divides the channel into 3 states. When the CBR is less than 0.5, the channel is in an idle state, and when the CBR is greater than 0.5 and less than 0.8, the channel is in an active state. The algorithm designs redundancy strategies based on coding mechanisms to improve the reliability of data transmission, combines coding mechanisms with routing design, incorporates routing switching ideas, and performs probability-based routing decisions to minimize the delay. In straight-line road sections, a fuzzy logic prediction-based vehicle adaptive connectivity clustering routing algorithm is proposed to reduce the communication overhead during vehicle collaboration and ensure high network connectivity; at intersections, a probability-based minimum delay routing decision algorithm is proposed to reduce the information transmission delay. Experiments show that the proposed method effectively improves the efficiency of data-aware collection and transmission, and increases the reliability of transmission. With the explosive growth of video services, the problem of intelligent transmission of DASH-based video streams has become another research hotspot in mobile edge networks. Based on the edge container cloud architecture of vehicular networking, the resource constraints of microservices when deployed in the edge cloud platform were analyzed, and a multi-objective optimization model for microservice resource scheduling was established with the comprehensive performance objectives of shortest microservice invocation distance, highest resource utilization of physical machine clusters, and ensuring load balancing as much as possible.

Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.

Deep Learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems

The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.