Academic literature on the topic 'Multicasting (Computer networks) Electronic data processing Data transmission systems'

With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of high transmission delay and limited bandwidth, in this paper, we propose a novel three-layer network architecture model which combines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational scheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning tree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we divide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by using the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the data transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data communication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing layer. Simulation shows that the CENAM has better performance in data processing delay than traditional cloud computing.

The multicast communication concept offers a scalable and efficient method for many classes of applications; however, its potential remains largely unexploited when it comes to link-layer multicasting in wireless local area networks. The fundamental lacking feature for this is a transmission rate control mechanism that offers higher transmission performance and lower channel utilization, while ensuring the reliability of wireless multicast transmissions. This is much harder to achieve in a scalable manner for multicast when compared with unicast transmissions, which employs explicit acknowledgment mechanisms for rate control. This article introduces EWRiM, a reliable multicast transmission rate control protocol for IEEE 802.11 networks. It adapts the transmission rate sampling concept to multicast through an aggregated receiver feedback scheme and combines it with a sliding window forward error correction (FEC) mechanism for ensuring reliability at the link layer. An inherent novelty of EWRiM is the close interaction of its FEC and transmission rate selection components to address the performance-reliability tradeoff in multicast communications. The performance of EWRiM was tested in three scenarios with intrinsically different traffic patterns; namely, music streaming scenario, large data frame delivery scenario, and an IoT scenario with frequent distribution of small data packets. Evaluation results demonstrate that the proposed approach adapts well to all of these realistic multicast traffic scenarios and provides significant improvements over the legacy multicast- and unicast-based transmissions.

With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as high network delay and insufficient cache space. Thus, we propose an opportunistic social network routing algorithm based on user-adaptive data transmission. The algorithm will combine the similarity factor, communication factor, and transmission factor of the nodes in the opportunistic social network and use information entropy theory to adaptively assign the weights of decision feature attributes in response to network changes. Also, edge nodes are effectively used, and the nodes are divided into multiple communities to reconstruct the community structure. The simulation results show that the algorithm demonstrates good performance in improving the information transmission’s success rate, reducing network delay, and caching overhead.

The regularities showing the development of information exchange in the virtual space have been examined, electronic document management in the organization using information systems has been considered. It has been shown that the intellectualization of information systems is based on a powerful mathematical apparatus that provides work with texts by computer programs, based on the creation of models that include patterns necessary for the design of modern information search systems, as well as systems for automatic classification and analysis of texts.A document flow model based on a number of mathematical regularities, numerical methods of multivariate integration, the theory of mixed Queuing networks has been proposed. This model allows you to evaluate the multivariate integration of devices of electronic document management systems (EDMS), contributes to the formation of statistical data on document processing, provides with minimal time costs the formation of information flows in electronic document management systems, has a convenient user interface for object interaction programs. This model, based on the definition of a set of options for data transmission channels, a set of options for data collection equipment EDMS, a set of options for data processing equipment, a set of end devices of the EDMS communication environment, allows you to analyse the functionality of electronic document management systems.The proposed EDMS model shows that a number of functions of complex document management in an organization can be automated and optimized based on mathematical methods, which will reduce the time of document preparation, its routing and processing. Document management of the organization, using this EDMS, ensures optimization of information flows in the organization, contributes to the formation of important management decisions, taking into account all risk factors.

Next-generation networks are data-driven by design but face uncertainty due to various changing user group patterns and the hybrid nature of infrastructures running these systems. Meanwhile, the amount of data gathered in the computer system is increasing. How to classify and process the massive data to reduce the amount of data transmission in the network is a very worthy problem. Recent research uses deep learning to propose solutions for these and related issues. However, deep learning faces problems like overfitting that may undermine the effectiveness of its applications in solving different network problems. This paper considers the overfitting problem of convolutional neural network (CNN) models in practical applications. An algorithm for maximum pooling dropout and weight attenuation is proposed to avoid overfitting. First, design the maximum value pooling dropout in the pooling layer of the model to sparse the neurons and then introduce the regularization based on weight attenuation to reduce the complexity of the model when the gradient of the loss function is calculated by backpropagation. Theoretical analysis and experiments show that the proposed method can effectively avoid overfitting and can reduce the error rate of data set classification by more than 10% on average than other methods. The proposed method can improve the quality of different deep learning-based solutions designed for data management and processing in next-generation networks.

Sensor-cloud is a developing technology and popular paradigm for various applications. It integrates wireless sensor into a cloud computing environment. On the one hand, the cloud offers extensive data storage and analytical and processing capabilities not available in sensor nodes. On the other hand, data distribution (such as time synchronization and configuration files) is always an important topic in such sensor-cloud systems, which leads to a rapid increase in energy consumption by sensors. In this paper, we aim to reduce the energy consumption of data dissemination in sensor-cloud systems and study the optimization of energy consumption with time-varying channel quality when multiple nodes use the same channel to transmit data. Suppose that there is a certain probability that the nodes send data for competing channel. And then, they decide to distribute data in terms of channel quality for saving energy after getting the channel successfully whether or not. Firstly, we construct the maximization problem of average energy efficiency for distributing data with delay demand. Then, this maximization problem transferred an optimal stopping problem which generates the optimal stopping rule. At last, the thresholds of the optimal transmission rate in each period are solved by using the optimal stopping theory, and the optimal energy efficiency for data distribution is achieved. Simulation results indicate that the strategy proposed in this paper can to some extent improve average energy efficiency and delivery ratio and enhance energy optimization effect and network performance compared with other strategies.

With the development of modern science and technology as well as the steady advancement of urbanization, intelligent networks have emerged and are replacing traditional networks with the identity of next-generation networks. And information security is one of the most important research directions in the intelligent network construction. In order to resist the threat of privacy leakage during the data transmission of intelligent terminal, an original four-layer fog computing system which is suitable for intelligent network data collection, transmission, and processing structure is established in the paper. With the help of the Paillier algorithm for encryption and fine-grained aggregation, the fine-grained aggregated data as coefficients are embed in the cloud node, and Horner’s rule is conformed for unary polynomials, which further aggregates to reduce the amount of transmitted data, so that communication overhead is reduced as well. Meanwhile, the resolvability of Horner’s rules allows EPSI to finally obtain the subregional information plain text, and it is summed up to obtain cloud-level information data. Therefore, the comparative analysis of simulation experiments with other algorithms proves that the rational optimization of the research content in this paper plays a higher security role.

With the popularization of 5G communications, the scale of social networks has grown rapidly, and the types of messages have become increasingly complex. The rapid increases in the number of access nodes and the amount of data have put a greater burden on the transmission of information in the networks. However, when transferring data from a large number of users, the performance of traditional opportunistic network routing algorithms is insufficient, which often leads to problems such as high energy consumption, network congestion, and data packet loss. The way in which to improve this transmission environment has become a difficult task. Therefore, in order to ensure the effective transmission of data and reduce network congestion, this paper proposed a link prediction model based on triangular relationships in opportunistic social networks (LPMBT). In the topological structure of the social network, the algorithm scores links based on the frequency of use and selects the optimal relay node based on the score. It can also efficiently track the target node and reconstruct the sub-community. The simulation experimental results showed that the algorithm had excellent performance, effectively reduced overhead, reduced the end-to-end delay, and greatly improved the data transfer rate in the opportunistic network.

In the digital network era, people have higher requirements for physical fitness. In the future, physical fitness requires not only good fitness equipment and fitness environment but also more convenient and intelligent health management, service guidance, social entertainment, and other refined fitness services. The innovation of sports and fitness equipment for the digital network era will definitely depend on the development of information technology and network technology. Based on the cutting-edge Internet of Things technology, this thesis focuses on the development and application of a new generation of digital fitness equipment adapted to future development, advocating the new concept of seamless integration of fitness exercise and information services through human-oriented systematic design thinking and providing implementable solutions to realize the science, convenience, and life of public fitness. This thesis uses modern science and technology, especially the Internet of Things (IoT) technology, to fully meet the diversified fitness needs of the fitness crowd as the guide; IoT digital fitness equipment design and application research was newly generated, using a variety of research methods to explore the functional design and application of IoT fitness equipment; the goal is to create a more intelligent and three-dimensional IoT fitness service model in the future. Through the application research of intelligent devices in IoT fitness equipment, the realization of the functions of identity identification, environment perception, and data transmission of IoT fitness equipment is made faster. Intelligent devices can become the interaction channel between fitness service personnel, fitness equipment, and fitness users and also reduce the development cost of IoT fitness equipment. The construction of an IoT fitness cloud service platform and data management system integrates the application of IoT, cloud computing, mobile communication, and other technologies to make IoT fitness service supply remote, real-time, and diversified. While providing convenient and value-added fitness services for fitness people, it also brings sustainable development space for the health service industry.

In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.

This thesis addresses software design within the field of Information and Communications Technology for Development (ICTD). Specifically, it makes a case for the design and development of software which is custom-made for the context of marginalised rural areas (MRAs). One of the main aims of any ICTD project is sustainability and such sustainability is particularly difficult in MRAs because of the high costs of projects located there. Most literature on ICTD projects focuses on other factors, such as management, regulations, social and community issues when discussing this issue. Technical matters are often down-played or ignored entirely. This thesis argues that MRAs exhibit unique technical characteristics and that by understanding these characteristics, one can possibly design more cost-effective software. One specific characteristic is described and addressed in this thesis – a characteristic we describe here for the first time and call a network island. Further analysis of the literature generates a picture of a distributed network of access nodes (DANs) within such network islands, which are connected by high speed networks and are able to share resources and stimulate usage of technology by offering a wide range of services. This thesis attempts to design a fitting middleware platform for such a context, which would achieve the following aims: i) allow software developers to create solutions for the context more efficiently (correctly, rapidly); ii) stimulate product managers and business owners to create innovative software products more easily (cost-effectively). A given in the context of this thesis is that the software should use free/libre open source software (FLOSS) – good arguments do also exist for the use of FLOSS. A review of useful FLOSS frameworks is undertaken and several of these are examined in an applied part of the thesis, to see how useful they may be. They form the basis for a walking skeleton implementation of the proposed middleware. The Spring framework is the basis for experiments, along with Spring-Webservices, JMX and PHP 5’s web service capabilities. This thesis builds on three years of work at the Siyakhula Living Lab (SLL), an experimental testbed in a MRA in the Mbashe district of the Eastern Cape of South Africa. Several existing products are deployed at the SLL in the fields of eCommerce, eGovernment and eLearning. Requirements specifications are engineered from a variety of sources, including interviews, mailing lists, the author’s experience as a supervisor at the SLL, and a review of the existing SLL products. Future products are also investigated, as the thesis considers current trends in ICTD. Use cases are also derived and listed. Most of the use cases are concerned with management functions of DANs that can be automated, so that operators of DANs can focus on their core business and not on technology. Using the UML Components methodology, the thesis then proceeds to design a middleware component architecture that is derived from the requirements specification. The process proceeds step-by-step, so that the reader can follow how business rules, operations and interfaces are derived from the use cases. Ultimately, the business rules, interfaces and operations are related to business logic, system interfaces and operations that are situated in specific components. The components in turn are derived from the business information model, that is derived from the business concepts that were initially used to describe the context for the requirements engineering. In this way, a logical method for software design is applied to the problem domain to methodically derive a software design for a middleware solution. The thesis tests the design by considering possible weaknesses in the design. The network aspect is tested by interpolating from formal assumptions about the nature of the context. The data access layer is also identified as a possible bottleneck. We suggest the use of fast indexing methods instead of relational databases to maintain flexibility and efficiency of the data layer. Lessons learned from the exercise are discussed, within the context of the author’s experience in software development teams, as well as in ICTD projects. This synthesis of information leads to warnings about the psychology of middleware development. We note that the ICTD domain is a particularly difficult one with regards to software development as business requirements are not usually clearly formulated and developers do not have the requisite domain knowledge. In conclusion, the core arguments of the thesis are recounted in a bullet form, to lay bare the reasoning behind this work. Novel aspects of the work are also highlighted. They include the description of a network island, and aspects of the DAN middleware requirements engineering and design. Future steps for work based on this thesis are mapped out and open problems relating to this research are touched upon.

The wide acceptance of applying computer technology in medical imaging system for manipulation, display, and analysis contribute better improvement in achieving diagnostic confidence and accuracy on predicting diseases. Therefore, the need for biomedical image analysis to diagnose a particular type of disease or disorder by combining diverse images of human organs is a major challenge in most of the biomedical application systems. This chapter contributes an overview on the nature of biomedical images in electronic form facilitating computer processing and analysis of data. This describes the different types of images in the context of information gathering, screening, diagnosis, monitor, therapy, and control and evaluation. The characterization and digitization of the image content is important in the analysis and design of image transmission.