5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0

Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among PLs in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the E2E mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of TSN against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.

Operationelle in-situ Messungen von Luftschadstoffen und Klimagasen im nationalen und internationalen Kontext

<p>Im Unterschied zu Forschungsinfrastrukturen in anderen Disziplinen, zeichnen sich Forschungsinfrastrukturen für Umweltbeobachtungen in der Regel durch langfristige Messungen zahlreicher Parameter mit verschiedenen Instrumenten an unterschiedlichen Orten aus. Bodengestützte, atmosphärische Beobachtungen von Luftschadstoffen und Klimagasen können unterschiedliche Ziele verfolgen, wie zum Beispiel die Überwachung regulatorischer Massnahmen und die Einhaltung von Grenzwerten, die wissenschaftliche Untersuchung von Variabilitäten und Trends, die Validierung von Modellrechnungen und Satellitenbeobachtungen oder die Früherkennung von neu auftretenden Substanzen. Die Qualitätskontrolle und Qualitätssicherung müssen nicht nur dem dezentralen Charakter der Beobachtungen Rechnung tragen, sondern auch sicherstellen, dass die der Fragestellung angepassten Datenqualitätsziele erreicht werden. Zusätzlich müssen Beobachtungen, die Teil von mehreren Messnetzen und Infrastrukturen sind, verschiedene Kriterien erfüllen, z.B. im Hinblick auf das Normal der Rückführbarkeit, die Präzision, aber auch bezüglich Dokumentation und Bereitstellung der Resultate in Datenbanken.</p> <p>Die Präsentation gibt einen Überblick über die langfristigen Luftqualitätsmessungen in der Schweiz im Rahmen des Nationalen Beobachtungsnetzes für Luftfremdstoffe (NABEL), ihre Einbettung in das European Monitoring and Evaluation Programme (EMEP), die Kooperation mit den europäischen Forschungsinfrastrukturen ICOS (Integrated Carbon Observation System) und ACTRIS (Aerosols, Clouds, and Trace gases Research Infrastructure Network), und die Zusammenarbeit in globalen Aktivitäten wie dem Advanced Global Atmospheric Gases Experiment (AGAGE) zur kontinuierlichen Messung von klimawirksamen und ozonabbauenden Substanzen und dem von der Weltorganisation für Meteorologie (WMO) koordinierten Global Atmosphere Watch (GAW) Programm.</p>

Understanding Traffic Patterns of Covid-19 IoC in Huge Academic Backbone Network SINET

Recently, APT (Advanced Persistent Threats) groups are using the COVID-19 pandemic as part of their cyber operations. In response to cyber threat actors, IoCs (Indicators of Compromise) are being provided to help us take some countermeasures. In this paper, we analyse how the coronavirus-based cyber attack unfolded on the academic infrastructure network SINET (The Science Information Network) based on the passive measurement with IoC. SINET is Japan's academic information infrastructure network. To extract and analyze the traffic patterns of the COVID-19 attacker group, we implemented a data flow pipeline for handling huge session traffic data observed on SINET. The data flow pipeline provides three functions: (1) identification the direction of the traffic, (2) filtering the port numbers, and (3) generation of the time series data. From the output of our pipeline, it is clear that the attacker's traffic can be broken down into several patterns. To name a few, we have witnessed (1) huge burstiness (port 25: FTP and high port applications), (3) diurnal patterns (port 443: SSL), and (3) periodic patterns with low amplitude (port 25: SMTP) We can conclude that some unveiled patterns by our pipeline are informative to handling security operations of the academic backbone network. Particularly, we have found burstiness of high port and unknown applications with the number of session data ranging from 10,000 to 35,000. For understanding the traffic patterns on SINET, our data flow pipeline can utilize any IoC based on the list of IP address for traffic ingress/egress identification and port filtering.

The Power of Limit

This project analyses the field of current geographic political partitions offering an interdisciplinary evaluation able to describe the space in the borders as ‘narrative beginning’, as ‘contact infrastructure’ that crosses territories where inhabitants are neither citizens nor refugees but only ‘border people’. At the end, cognitive horizons able to breach the Wall, going beyond the political-territorial divisions which have always existed in a world which is a sort of more or less fortified bulwark able to suggest ‘border worlds’ that are ‘city’, ‘border land’. We observe a porous border with a rhizomatic trend that reformulates a synergistic relationship between the individual and the territory in an antinomic game of actions and reactions. Appears an idea of multiplicity in which the ‘rhizome-like’ structure becomes decentralized configurations where each part can be connected to another without go through specific points, as the infrastructure network or even the virtual system of global contacts. So, the space in the borders results in a new map of the delocalized space that increasingly requires of a design thinking that, on the basis of critiques of data, variables and statistics, sometimes becomes ‘hard’ and sometimes ‘elastic’, sometimes ‘insurmountable’ and sometimes ‘flexible’ and that finds an answer in the connivance between opposites and in the territorial synergy.

The influence of risks on the outturn cost of ICT infrastructure network projects

Purpose The internet of things (IoT) is becoming an increasingly inescapable part of society. IoT paradigm cannot function without the networking infrastructure. High-speed data networks are essential to enable the IoT future. Thus, the purpose of this study is on the identification of risks that influence the development, installation and operation of information and communication technology (ICT) infrastructure network project cost outcomes. So far, there has been little attention has been paid to risks problems in these types of IoT enabling projects. Design/methodology/approach This research follows a quantitative analysis approach. Data for this study were collected by a survey from 209 professionals. Multiple regression analysis was used to model the relationship between risks and outturn cost of infrastructure needed to enable the operation of IoT technologies. Findings The main risk factors that were identified were planning and development, people and management, operations, technology and hardware. Research limitations/implications This research has expanded the existing literature by documenting and clustering ICT infrastructure network project risks into themes, and has developed a scale (risk statements) for measuring such risks. Further, the research has advanced the understanding by identifying the most likely risks that will contribute to the overrun of these projects. Originality/value This research establishes a reliable regression method for the assessment of the risks that influence the development, installation and operation of ICT infrastructure network projects outturn cost. No other research has measured or studied the risks in this type of project.

The Measurement of Logistics Industry Efficiency Based on the Construction of Tobacco Transport Logistics Infrastructure

With the increasingly fierce market competition, China’s tobacco industry has been severely tested. At the same time, according to the latest report of global Logistics Performance Index (LPI), there is a significant difference between China’s LPI and other developed countries, indicating that China’s logistics has low efficiency, high input and low output. How to improve the service level and operation efficiency of tobacco enterprises by strengthening the construction of logistics infrastructure network is an urgent problem for tobacco enterprises to solve. Therefore, DEA model and Malmquist index model are adopted in this paper to measure the logistics efficiency of Chinese tobacco enterprises from the aspect of logistics infrastructure network construction. This paper analyzes the state of logistics efficiency and the reasons of low efficiency in some economic regions and puts forward countermeasures and suggestions to improve the logistics efficiency of tobacco enterprises based on the construction of logistics infrastructure network.

G-CAS: Greedy Algorithm-Based Security Event Correlation System for Critical Infrastructure Network

The attacks on the critical infrastructure network have increased sharply, and the strict management measures of the critical infrastructure network have caused its correlation analysis technology for security events to be relatively backward; this makes the critical infrastructure network’s security situation more severe. Currently, there is no common correlation analysis technology for the critical infrastructure network, and most technologies focus on expanding the dimension of data analysis, but with less attention to the optimization of analysis performance. The analysis performance does not meet the practical environment, and real-time analysis is even more impossible; as a result, the efficiency of security threat detection is greatly declined. To solve this issue, we propose the greedy tree algorithm, a correlation analysis approach based on the greedy algorithm, which optimizes event analysis steps and significantly improves the performance, so the real-time correlation analysis can be realized. We first verify the performance of the algorithm through formalization, and then the G-CAS (Greedy Correlation Analysis System) is implemented based on this algorithm and is applied in a real critical infrastructure network, which outperformed the current mainstream products.

Using Car to Infrastructure Communication to Accelerate Learning in Route Choice

The task of choosing a route to move from A to B is not trivial, as road networks in metropolitan areas tend to be over crowded. It is important to adapt on the fly to the traffic situation. One way to help road users (driver or autonomous vehicles for that matter) is by using modern communication technologies.In particular, there are reasons to believe that the use of communication between the infrastructure (network), and the demand (vehicles) will be a reality in the near future. In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users can accelerate their learning processes regarding route choice by using reinforcement learning (RL). The kernel of our method is a two way communication, where road users communicate their rewards to the infrastructure, which, in turn, aggregate this information locally and pass it to other users, in order to accelerate their learning tasks. We employ a microscopic simulator in order to compare this method with two others (one based on RL without communication and a classical iterative method for traffic assignment). Experimental results using a grid and a simplification of a real-world network show that our method outperforms both.