Network Transmission Bandwidth Research Articles

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks.

The performance of multipath transmission control protocol (MPTCP) subflow through the enhancement mechanism of the MPTCP communication is improved. When dealing with multiple MPTCP subflows occupying the same transmission path, critical issues such as selection and optimization of multipath, and efficient scheduling of available multiple tracks are effectively addressed by incorporating the technology called software defined network (SDN) that is constructed based on four key parameters, namely, network transmission bandwidth, transmission paths, path capacity, and network latency. Besides, critical equipment such as the network physical device layer and SDN controller are integrated with the four parameters. So, the network model defines the transmission control process and data information. Considering the predetermined total network bandwidth capacity to select multiple paths, the adequate bandwidth capacity is determined by defining the data transfer rate between MPTCP terminals and MPTCP servers. However, the processing latency of the OpenFlow switch and the SDN controller is excluded. The effective network transmission paths are calculated through two rounds of path selection algorithms. Moreover, according to the demand capacity of the data transmission and the supply capacity of the required occupied network resource, a supply and demand strategy is formulated by considering the bandwidth capacity of the total network and invalid network latency factors. Then, the available network transmission path from the valid network transmission path is calculated. The shortest path calculation problem, which is the calculation and sorting of the shortest path, is transformed into a clustering, Inter-Cluster Average Classification (ICA), problem. The instruction of the OpenFlow communication flow is designed to schedule MPTCP subflows. Thus, various validation objectives, including the network model, effective network latency, effective transmission paths, supply-demand strategies, ineffective transmission paths, shortest feasible paths, and communication rules are addressed by the proposed method whose reliability, stability, and data transmission performance are validated through comparative analysis with other conventional algorithms. Found that the network latency is around 20 s, the network transmission rate is approximately 10Mbps, the network bandwidth capacity reaches around 25Mbps, the network resource utilization rate is about 75%, and the network swallowing volume is approximately 3M/s.

Event‐triggered attack detection and state estimation based on Gaussian mixture model

AbstractUnder the framework of event‐triggered transmission mechanism, the problem of attack detection and state estimation of multi‐sensor linear time‐invariant systems under static attacks is considered. First, for each transmission channel, the sensor collects measurement information according to an event‐triggered mechanism to reduce unnecessary energy consumption. Then, inspired by the clustering algorithm in machine learning, a detection mechanism based on Gaussian mixture model, which can set a confidence level for the measurement of each sensor is proposed. Finally, centralised data fusion is performed according to the results of attack detection and event‐triggered judgement to realise remote state estimation. A numerical example proves that the proposed algorithm can locate the damaged sensor, save the network transmission bandwidth under the premise of ensuring accuracy and efficiency of sensor estimation.

A lightweight encrypted deduplication scheme supporting backup

Data deduplication technology is frequently used by many cloud service providers to handle the exponential growth in data. To protect their data privacy, users frequently encrypt their data before uploading it to a cloud storage provider. Therefore, efficient encrypted deduplication technology has been widely studied. Although cloud storage provides convenience for users, it also has the problem of high data concentration. Once an accident occurs in the data center, the impact is enormous. Therefore, it is necessary to back up the ciphertext data after deduplication. In this paper, we propose an efficient encrypted data deduplication technology that supports backup. Our scheme uses a one-way hash chain to verify the legitimacy of a user’s identity. It updates the user’s ownership of the data using the user’s index table. In this paper, we use the XOR operation to re-encrypt data to obtain backup ciphertext, reducing computational complexity. In LEDB, the user can extract the key from the data label. Therefore, the user does not need to manage the key separately. In addition, LEDB supports a two-level deduplication mechanism, which can not only ensure transmission security but also reduce network transmission bandwidth. Finally, LEDB allows the user to determine the integrity of the data before decrypting the ciphertext. Security analysis and experimental results show that our scheme is secure and efficient for data encryption and decryption.

Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.

Real-time identification based shipborne network transmission optimization algorithm

Objectives This paper proposes a real-time identification based algorithm named RTDB. Methods The algorithm classifies messages by the real-time requirements of different types of them, and assigns different thresholds to them. The algorithm gain is then analyzed by combining Link 11 and Link 16, which are widely used by the US Navy. Results Theoretical analysis shows that the algorithm can bring a certain delay gain when the data rate is greater than the network transmission bandwidth or the generation rate satisfies the deterministic distribution. In this paper, NS3 is used as the simulation platform, and the gain of the proposed algorithm is simulated and analyzed, yielding the same results as the theoretical results. Conclusions The deployment cost of the proposed RTDB algorithm is small, and it can be of great significance for countries that urgently need to modernize their navies.

An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control.

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

A QoE Evaluation Method for RT-HDMV Based on Multipath Relay Service

Multipath diversity leads to a possible higher performance for real-time high definition video, especially for medical video transmission, which would improve the stability of multiple transmission paths in the symmetrical state, and avoid the potential losses of imaging information in the communication process. Most of the previous works are always based on the single-path end-to-end transmission, although the service had been demonstrated that it is unable to meet the rigorous demand for the RT-HDMV. In the paper, a multipath relay service based on the QoE (quality of experience) evaluation method is proposed for the RT-HDMV (real-time high definition medical video). The method eliminates several of the limitations in the existing methods for some conventional single-path transmission. It can fully utilize the finite network resources and transmission bandwidth to meet the users’ demands of the RT-HDMV to get a better score of the QoE. We use a four-stage framework to evaluate the QoE, which consists of constructing the multipath relay transmission for the RT-HDMV, calculating the weights of diversified QoS parameters in the multipath, designing the load distribution strategy by the mapping between the QoS (quality of service) and QoE, and redefining the rule of the QoE evaluation. Many experiments show that the proposed design scheme achieves weighting of the transmission sub-paths and computes the QoE score. Compared with the state-of-art methods in the single path transmission scene, our framework mainly gains the excellent performance for the RT-HDMV.

Investigation on static routing and resource assignment of elastic all-optical switched intra-datacenter networks

In this paper, we explore the issue of static routing and spectrum/IT resource assignment (RSIA) of elastic all-optical switched intra-datacenter networks (intra-DCNs) by proposing anycast- and manycast-based integer linear programming (ILP) models. The objective is to jointly optimize the DCN resources, i.e., network transmission bandwidth and IT resources, under different situations. First, for given service-request matrices with unknown network transmission bandwidth and IT resources, we propose anycast and manycast ILP models to minimize the maximum numbers of required network and IT resources to accommodate all the service requests. For anycast RSIA issue, we proposed two different ILP models that are based on node-arc and link-path methods, respectively. Node-arc based manycast ILP model is also proposed for the first time to our knowledge. Second, for given network transmission bandwidth and IT resources and known service-request matrices, we propose node-arc based anycast ILP models to maximize the total number of successfully served service requests. To evaluate the efficiency of anycast and manycast models, all proposed ILP models are evaluated and compared with unicast ILP models. Simulation results show that anycast and manycast ILP models perform much better in efficiently using DCN resources and successfully accommodating more service requests when compared to unicast ILP models under the same network conditions.

Based on the Correlation of the File Dynamic Replication Strategy in Multi-Tier Data Grid

The data grid is one of the infrastructures for data and storage resource management. Data replication can improve data access performance, fault tolerance, and reduce network transmission bandwidth. As the site storage space is limited in the grid, how to effectively use grid resources become an important challenge. This paper introduces a dynamic grid replication algorithm based on popularity support and confidence (BPSC). Through the algorithm, data and its associated copy can be placed on a suitable site, with reduced access latency. Through Optorsim, simulation results show that the algorithm can provide better performance compared with other algorithms Grid computing is an important branch of distributed computing. Grid computing constructs a virtual computer system through using a different location of the large number of heterogeneous resources (CPU and storage), provides the model to solve the problem of large scale computing and storage. The data grid is mainly the grid for data storage management. The data grid is defined as an effective combination of data and computing resources of distributed systems by Data Grid Group. The large-scale data- intensive applications, such as bioinformatics, high energy physics, space information generated vast amounts of data, these data may be TB scale, even up to the scale of PB. It is not practical to maintain large scale data at each site, and provide computing resources for visitors. At the same time, it will lead to increased access latency through a centralized way to storing and accessing these data from each site. Data replication technology is an effective method to reduce access latency in the data grid system. Data replication is one of the basic methods of distributed systems to improve the efficiency of system access, maintenance, system high availability. Data replication is to store data in the appropriate site for users to effective access to massive data. Data replication can reduce data access latency, reduce network bandwidth consumption, avoid congestion, adjust the load balancing in server-side, improve system reliability. Many data replication algorithm can reduce data access latency, improve system reliability, but which data will be copied, when the data will be copied at, where the data will be copied are the basic means of determining data replication algorithm. In this paper, we introduced a real-time dynamic data replication algorithm. The algorithm is based on the popularity of file dynamic to replicate data, and consider the storage space limitations of the site. Through optorsim simulation verified the algorithm. This paper is organized as follows: Section 2 is about related works on replication in data grids. In Section 3, system model and component in system are presented. The file

Implementation and Application of QoS in Power Ethernet Switch

Smart grid based on Ethernet technology is the development direction of power transmission network, power Ethernet carried different data communication service, which have different needs for network transmission time delay and bandwidth guarantee, which is reflected in the quality of service of network QoS (Quality of Service) requirements, in power Ethernet, QoS which can provided differentiated services (Diff-Serv) is essential. This paper outlines QoS implementation, QoS feature and related technologies in the power Ethernet switch, and analyses the flow characteristics of digital substation network, and proposes classification and scheduling scheme of service traffic based on network nodes, and elaborates an important effect that QoS has in ensuring substation communication network transmission of high quality service.

Using a single bit to stabilise an n-dimensional quantised non-linear feedforward system

This study investigates the global asmptotic stabilisability of an n-dimensional quantised feedforward non-linear system. The system’s dynamics are assumed to be locally Lipschitz. So the ranges of the state variables are tightly related to the parameters of the quantisation procedure, that is, there exists coupling between quantisation and control. To save network transmission bandwidth, less quantisation bits (per sample) are preferred. In De Persis’ paper, n bits (per sample) are shown to be enough to stabilise the n-dimensional system by taking a time-invariant quantiser, which assigns 1 bit to each state variable (dimension). We design a time-varying quantiser and can globally stabilise the system with only 1 bit, instead of n bits. That single bit is assigned to the most ‘important’ state variable at each sampling instant, which yields the time-varying coupling among the quantisation errors of state variables besides the coupling between quantisation and control. Owing to the well-designed structure of our quantiser, we can place exponentially converging upper bounds on the quantisation errors of state variables and well handle both types of couplings, so that the global stability of the system is guaranteed with 1 bit. An example is used to demonstrate the effectiveness of the proposed quantiser.

Introduction to the special issue on multimedia intelligent services and technologies

The rapid development of digital media technologies enables the emergence of novel media content types for eCommerce, eEducation, and digital entertainment. On the other hand, the advances in communication and microelectronics have led to a transition from traditional personal computer-centric to more intuitive human-centric information access modes and the embedment of computer systems throughout the natural environment. This type of computation is generally known as Pervasive or Ubiquitous Computing. It can also be referred to as ambient intelligence, or talking about media environments as ambient media. It allows a person to use a variety of devices and sensor networks seamlessly embedded throughout our daily life, such as personal digital assistant (PDA), smart electronics, sensors, as well as personal computer, to access those media contents. The combination of these two trends (emergence of media and pervasive computing) holds the potential of providing a user with seamless and ubiquitous access to rich and dynamic multimedia resources from anywhere and anytime. With the emergence of ubiquitous and pervasive computation, distributed devices embedded in the natural human environment are getting more and more intelligent. This enables more advanced multimedia services far beyond simple video streaming and communication services. These services are currently on the brink of emergence, and include, e.g., personalized media environments, smart homes, semantic locative media, and context aware computer systems. There are several new technical challenges for effective and appropriate content delivery to pervasive user-terminals. First, different users may have different needs. Nowadays, multimedia content is absolutely overabundant, while the portion of which a user really desired is rather small. Smart personalization techniques, where content is aggregated, either delivered in push and/or pull schemes are required. Second, pervasive end devices usually have various capabilities (e.g., screen size, color depth, video/audio codec, memory, CPU speed, and electrical power), and network connection and transmission bandwidth might be changing dynamically. Therefore, intelligent multimedia services are expected for efficient storage, search, filtering, adaptation, and presentation of media content, to deliver a ubiquitous and personalized media experience to end users. Multimedia intelligent services and technologies have attracted increasing interest in both industry and academia over the last decade. Submissions to this special issue come from an open call for papers as well as from selected papers presented at the 6th International Conference on Ubiquitous Intelligence and Computing (UIC-09) held at Brisbane, Australia, July 7–9, 2009. A large number of reviewers assisted us in the review process. In order to ensure high reviewing Z. Yu (&) School of Computer Science, Northwestern Polytechnical University, Xi’an, China e-mail: zhiwenyu@nwpu.edu.cn

Making filters smart in distributed data stream environments

Monitoring aggregate queries in real-time over distributed streaming environments appears to be a great challenge not only because of the huge data volume and high rate, but also because of the limitation of the network transmission bandwidth. Consequently, ensuring qualified approximate results with economical network consumption becomes one of the most important goals in such scenarios. In this paper, we study how to monitor aggregate queries continuously over distributed environments efficiently by disposing numerous filters at remote sites, in order to transmit only a small part of incoming data to the query site and therefore save the network resource significantly. We also show how to adjust the parameters of a filter continuously when the incoming data distribution at the corresponding remote site changes. Analysis and extensive experimental results demonstrate that our approach outperforms the existing work.

Real-time multidepth stream compression

The goal of tele-immersion has long been to enable people at remote locations to share a sense of presence. A tele-immersion system acquires the 3D representation of a collaborator's environment remotely and sends it over the network where it is rendered in the user's environment. Acquisition, reconstruction, transmission, and rendering all have to be done in real-time to create a sense of presence. With added commodity hardware resources, parallelism can increase the acquisition volume and reconstruction data quality while maintaining real-time performance. However, this is not as easy for rendering since all of the data need to be combined into a single display.In this article, we present an algorithm to compress data from such 3D environments in real-time to solve this imbalance. We present a compression algorithm which scales comparably to the acquisition and reconstruction, reduces network transmission bandwidth, and reduces the rendering requirement for real-time performance. This is achieved by exploiting the coherence in the 3D environment data and removing them in real-time. We have tested the algorithm using a static office data set as well as a dynamic scene, the results of which are presented in the article.