A Statistical Analysis for the Accessibility of Electronic Data Delivery System of the Central Bank of the Turkish Republic

The study aims to reveal the most effective factors on the accessibility statistics of the Electronic Data Delivery System (EDDS) of The Central Bank of The Turkish Republic. Besides, another aim is to reveal the effect of the exchange rate on the access statistics of EDDS exchange rate data. For this purpose, a stepwise regression model was used to find the most effective factors on accessibility statistics. According to the results of stepwise regression analysis, it was revealed that 9 out of 26 variables significantly affected the EDDS access statistics. Engle-Granger cointegration test was chosen as the method to examine the relationship between exchange rate and EDDS access statistics. It has been revealed that there is a long-run equilibrium relationship between the EURO/TRY exchange rate and the access statistics of EDDS exchange rate data.

Exploring National Library of Scotland datasets with Jupyter Notebooks

The National Library of Scotland’s Digital Scholarship Service has been releasing collections as data on its data-delivery platform, the Data Foundry, since September 2019. Following the COVID-19 lockdown, this service experienced significantly higher traffic, as library users increasingly made use of online resources. To ensure that as many users as possible were able to explore the datasets on the Data Foundry, the Library invested in a Digital Research Intern post, with a remit to provide introductory analysis of the Data Foundry collections using Jupyter Notebooks. This article provides a case study of this project, explaining the Library’s work to date around its new Digital Scholarship Service and releasing datasets on the Data Foundry; the reasoning behind the decision to begin to provide Jupyter Notebooks; the Notebooks themselves and what types of analysis they contain, as well as the challenges faced in creating them; and the publication and impact of the Notebooks.

ROUTE SELECTION METHOD FOR HIGH-DENSITY WIRELESS IOT NETWORK

Приводятся результаты исследования зависимости скорости передачи данных (ПД) от параметров маршрута в беспроводной сети интернета вещей. Предложен метод выбора количества и позиций узлов для организации маршрута в сети. Метод учитывает влияние расстояния между узлами маршрута и их количества на задержку доставки данных и скорость ПД по маршруту. Метод позволяет получить выигрыш в достижимой скорости ПД по сравнению с методами выбора маршрута по критерию количества транзитных узлов. The article presents theresults ofthe studyof thedependence of the data transfer rate on the route parameters in wireless Internet of Things (loT) networks. A method for choosing the number and positions of nodes for organizing a route in the loT network is proposed. The method takes into account the influence of the distance between the nodes of the route and their number on the delay in data delivery and the speed of data transmission along the route. The method allows one to obtain a gain in the attainable data transfer rate in comparison with methods of choosing a route based on the criterion of the number of transit nodes.

THE TRANSPORT LAYER OF THE ISO/OSI MODEL IN COM­PUTER NETWORKS

Background. The transport layer is designed to deliver data without errors, losses and duplication in the order in which they were transmitted. It provides data transfer between two applications with the required level of reliability. Transport layer protocols, which guarantee reliable data delivery, establish a virtual connection before data exchange and resend segments in case of loss or damage. The aim of the study was to determine the role of transport security protocols in computer networks. Materials and methods. To achieve the goal, the study used statistical analysis and a systematic approach. Results. TCP provides reliable message transmission through the formation of logical connections, while allowing peers on the sending computer and the receiving computer to support data exchange in duplex mode. It also has the ability to seamlessly send a byte stream generated on one of the computers to any other computer connected to the network.In addition, TCP controls the connection load, UDP does not control anything but the integrity of the received datagrams. Conclusion. The difference between TCP and UDP is the so-called "delivery guarantee". TCP requires a response from the client to whom the data packet is delivered, confirmation of delivery, and for this he needs a pre-established connection. TCP is also considered reliable, unlike UDP, which is called "unreliable datagram protocol". TCP eliminates data loss, duplication and shuffling of packets, delays, UDP allows all this, and it does not need a connection to work., as a result of which the data is transferred on UDP, should manage received, even with losses.

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one.