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PL
Następne generacje sieci mobilnych 5G-Advanced, 6G stwarzają nowe możliwości zastosowań dla przemysłu, gdzie wykorzystywane są inteligentne systemy sterowania aplikacjami przetwarzające informacje w czasie rzeczywistym. Przetwarzanie brzegowe umożliwia przybliżenie aplikacji do użytkownika końcowego np. urządzeń Internetu rzeczy i jednocześnie wprowadzania wysoce rozproszoną architekturę i złożoność operacyjną. Cały ekosystem wymaga orkiestracji od końca do końca, inteligentnego zarządzania i automatyzacji z uwzględnieniem aspektów bezpieczeństwa. W tym artykule przedstawiamy model reprezentacji zasobów w różnych warstwach oraz ich wzajemne relacje dla konkretnej realizacji sieci.
EN
New generations of 5G-Advanced, 6G mobile networks are creating new application opportunities for industry with intelligent application control systems that process information in real time. Edge processing enables applications to be brought closer to the end user, e.g., Internet of Things devices, while introducing highly distributed architecture and operational complexity. The entire ecosystem requires E2E orchestration, intelligent management, and automation with security constraints.
PL
SmokeFinder to system umożliwiający stałe analizowanie obrazu pochodzącego z kamer obserwacyjnych w celu wyszukiwania dymu w lesie. W przypadku wykrycia nawet niewielkich słupków dymu do operatora systemu wysyłane są ostrzeżenia. Zaawansowane algorytmy uczenia maszynowego analizują dane zebrane z kamery obserwacyjnej w czasie rzeczywistym. W przypadku wykrycia dymu ustalana jest jego lokalizacja. Dane z wielu kamer są łączone w celu usprawnienia akcji gaśniczej. W przypadku potwierdzenia zagrożenia automatycznie zaalarmowane zostaną odpowiednie lokalne jednostki straży pożarnej.
EN
SmokeFinder is a system that allows to constantly analyze the image from surveillance cameras in order to search for smoke in the forest. If even small pillars of smoke are detected, alerts are sent to the system operator. Advanced machine learning algorithms analyze the data collected from the observation camera in real time. If smoke is detected, its location is determined. Data from multiple cameras are combined to improve firefighting. If the threat is confirmed, the relevant local fire departments will be automatically alerted.
EN
Machine learning-based classification algorithms allow communication and computing (2C) task allocation to network edge servers. This article considers poisoning of classifiable 2C data features in two scenarios: noise-like jamming and targeted data falsification. These attacks have a fatal effect on classification in the feature areas with unclear decision boundary. We propose training and noise detection using the Silhouette Score to detect and mitigate attacks. We demonstrate effectiveness of our methods.
PL
Algorytmy klasyfikacji oparte na uczeniu maszynowym umożliwiają zoptymalizowaną alokację zadań telekomunikacyjnych i obliczeniowych (2C) do serwerów brzegowych sieci. W artykule omówiono ataki zatruwające, które mają negatywny wpływ na klasyfikację zadań 2C w obszarach, w których granica decyzyjna jest niejasna. Proponujemy metodę trenowania modelu oraz wykorzystanie testu Silhouette do wykrywania i unikania ataków. Wykazujemy skuteczność tych metod wobec rozważanych ataków.
EN
The dynamically changing environment forces companies to introduce changes in production processes and the need for employees to adapt quickly to new tasks. Therefore, it is expected to implement solutions to support employees. The system that will manage the work on a manufacturing line should work in real time to support the ongoing activities and, to be implemented in SMEs, must not be expensive. The authors identified important system components and expected functionalities. The methodology of the work is based on humancentered design. A concept of a cyber-physical system is proposed. The aim of the proposed edge computing-based system is to manage the work on the manufacturing line in which certain elements communicate with each other to achieve common goals. The paper presents what the system can consist of, how information and knowledge are managed in the system, and what can be the benefits for enterprises from its implementation.
EN
Improper disposal of municipal sewage sludge poses a significant threat to effective environmental protection. With the continuous advancement of artificial intelligence technology and the Internet of Things (IoT), remote sensing detection technology is emerging as a promising research avenue to address this issue. However, the current state of real-time detection technology is inadequate, hindering comprehensive and stable monitoring operation. Additionally, the rational use of network resources remains suboptimal. To address this challenge, this study proposes a resource optimisation technology for the current insufficient intelligent monitoring system of urban sewage sludge. By leveraging IoT and wireless technology, water meter data can be collected with minimal earth construction compared to traditional PLC collection. This is followed by utilising Faster R-CNN to plan the network transmission of sewage remote sensing information resources. Finally, the architecture collection module’s scalability is enhanced by incorporating edge computing and reserving sensor ports to meet future plant expansion demands. The experiment demonstrates the significant potential of this technology in application and resource optimisation. In actual parameter tracking tests, the proposed method effectively monitors sewage sludge, providing policy guidance and measure optimisation for relevant authorities, ultimately contributing to pollution-free urban development.
6
Content available The performance of IIoT communication standards
EN
The requirements of Industry 4.0 determine the necessity to change thinking in the field of production development, adopted management methods and modernisation of production resources. When planning the implementation of a new production system (or retrofit), it is possible to use the RAMI 4.0 reference model, which was published in April 2015 by the VDI/VDE Society Measurement and Automatic Control. A key aspect of modern industrial systems is connectivity and trouble-free data exchange. In the case of data exchange, the basic element holding back the development of Industry 4.0 is the lack of standardisation, as well as the lack of interoperability between IIoT network nodes. Modern IIoT applications require high network throughput, low latency and reliability. In view of such guidelines, efficient communication standards and specialised equipment are required. Edge Computing is one of the most important technology trends of the 21st century that will play a key role in the IIoT market. Due to the diversity of available technologies and solutions, no universal standards have been developed to date that can be referred to when planning, building and implementing new applications. The article presents an overview of the most popular industrial communication protocols and their systematisation in terms of meet the requirements for IIoT devices.
EN
Cloud computing provides centralized computing services to the user on demand. Despite this sophisticated service, it suffers from single-point failure, which blocks the entire system. Many security operations consider this single-point failure, which demands alternate security solutions to the aforesaid problem. Blockchain technology provides a corrective measure to a single-point failure with the decentralized operation. The devices communicating in the cloud environment range from small IoT devices to large cloud data storage. The nodes should be effectively authenticated in a blockchain environment. Mutual authentication is time-efficient when the network is small. However, as the network scales, authentication is less time-efficient, and dynamic scalability is not possible with smart contract-based authentication. To address this issue, the blockchain node runs the skip graph algorithm to retrieve the registered node. The skip graph algorithm possesses scalability and decentralized nature, and retrieves a node by finding the longest prefix matching. The worst time complexity is O(log n) for maximum n nodes. This method ensures fast nodal retrieval in the mutual authentication process. The proposed search by name id algorithm through skip graph is efficient compared with the state-of-art existing work and the performance is also good compared with the existing work where the latency is reduced by 30–80%, and the power consumption is reduced by 32–50% compared to other considered approaches.
EN
Belts are widely applied in mine production for conveying ores. Understanding ore granularity, which is a crucial factor in determining the effectiveness of crushers, is vital for optimising production efficiency throughout the crushing process and ensuring the success of subsequent operations. Based on edge computing technology, an online detection method is investigated to rapidly and accurately obtain ore granularity information on high-speed conveyor belts. The detection system utilising machine vision technology is designed in this paper. The high-speed camera set above the belt is used to collect the image of the ore flow, and the collected image is input into the edge computing device. After binary, grey morphology and convex hull algorithm processing, the particle size distribution of ore is obtained by statistical analysis. Finally, a 5G router is used to output the settlement result to a cloud platform. In the GUANBAOSHAN mine of Ansteel Group, the deviation between manual screening and image particle size analysis was studied. Experimental results show that the proposed method can detect the ore granularity, ore flow width and ore flow terminal in real-time. It can provide a reference for the staff to adjust the parameters of the crushing equipment, reduce the mechanical loss and the energy consumption of the equipment, improve the efficiency of crushing operation and reduce the failure rate of the crusher.
PL
W artykule przedstawiono docelową architekturę systemu obliczeń na brzegu sieci, która została opracowana, zaimplementowana i wdrożona w ramach projektu SyMEC. W szczególności przedstawiono główne elementy opracowanego systemu, podstawowe realizowane procesy dotyczące zarządzania cyklem życia oferowanych aplikacji i usług MEC, a także doświadczenia wynikające z implementacji prototypu systemu SyMEC i jego wdrożenia w krajowej sieci badawczej PL-LAB 2020. W podsumowaniu przedstawiono kierunki dalszego rozwoju system SyMEC.
EN
The article presents the final design of the edge computing system developed by the SyMEC project. We describe the main elements of the developed system, the fundamental processes related to the lifecycle management of the MEC applications and services, and the experiences coming from the system deployment in the PL-LAB 2020 research network. The summary presents further research directions.
10
Content available remote An Edge Computing Collaboration Solution for Internet of Vehicles
EN
The advent of 5th generation communication systems (5G) in the early 21st century has realized real-time Internet of Things applications. 5G has capable of providing network services with extremely-high throughput and extremely low delay and allows a huge device number to connect together based on Internet infrastructure, forming the Internet of Things (IoT). In recent years, IoT has been applied in a variety of fields serving humans, such as smart cities, smart agriculture, e-healthcare, smart education, military, and IoT ecosystems. One of the main challenges of IoT applications is computing solutions to reduce service response times. In this study, we propose an Edge Computing Collaboration Solution for the Internet of Vehicles (IoV). Our solution proposes a small database that allows edge computing servers of IoVs to store each other's information. When the mobile end-users move to the new edge servers' managed coverage, properties related to the EC service are exchanged between the edge servers. The results have shown that our proposed solution improves significantly service response time, by up to 10-20\%, compared to the existing solutions
EN
In machine tools, existing solutions for process monitoring and condition monitoring rely on additional sensors or the machine control system as data sources. For a higher level of autonomy, it becomes necessary to combine several data sources, which may be within or outside of the machine. Another requirement for autonomy is additional computing power, which may be hosted on edge devices or in the cloud. A seamless and modular architecture, where sensors are integrated in smart machine components or smart sensors, which are in turn connected to edge devices and cloud platforms, provides a good basis for the incremental realisation of autonomy in all phases of the machine life cycle.
PL
W niniejszej pracy zaproponowano algorytm zarządzający zadaniami obliczeniowymi w sieci typu mgła minimalizujący całkowitą zużywaną energię przy ograniczeniu maksymalnego dopuszczalnego opóźnienia ich transmisji (w sieci przewodowej i bezprzewodowej) i realizacji. Zdefiniowany problem optymalizacyjny uwzględnia wykonania zadań obliczeniowych w urządzeniu końcowym bądź w węźle sieci wyższego rzędu. Optymalne rozwiązanie zostało wyznaczone dla określonej alokacji zasobów transmisyjnych i obliczeniowych oraz częstotliwości taktowania procesora realizującego obliczenia. Wyniki symulacji pokazują efektywność proponowanej metody.
EN
In this paper, the algorithm of computations offloading is proposed, which minimizes the total energy consumption required for the (wireless and wired) transmission and processing of computational tasks in a fog network with the maximum overall delay constraint. The defined optimization problem takes the options of either local or distant (in a fog network node) tasks processing into account. The optimal solution has been obtained for the transmission- and computational resources allocation and for the determined processor clock frequency. The simulation results show the effectiveness of the proposed method.
PL
Przedstawiono architekturę systemu, umożliwiającego realizację obliczeń na brzegu sieci SyMEC, który jest obecnie opracowywany w ramach projektu PoIR pt. System Mec dla wspierania zaawansowanych aplikacji w środowisku sieci przewodowych i bezprzewodowych 3G/4G/5G. Proponowana architektura systemu jest wzorowana na architekturze rekomendowanej przez eTSI, ale zawiera też elementy, które nie są wzmiankowane w powoływanej architekturze. Takimi elementami są: repozytorium do przechowywania obrazów aplikacji oraz system zarządzania. Naczelną myślą przewodnią przy projektowaniu architektury systemu SyMEC było założenie, że jest on ekosystemem, w którym rozróżnia się styki ze „światem zewnętrznym”, nazywane stykami zewnętrznymi i styki pomiędzy elementami systemu, które traktuje się jako styki wewnętrzne. Przyjęto, że styki zewnętrzne powinny być zgodne ze stykami zalecanymi przez eTSI, zaś styki wewnętrzne niekoniecznie.
EN
The paper presents the architecture of the SyMEC system, which is currently developed in the PoIR project entitled „The Mec system enabling advanced applications in wired and wireless 3G/4G/5G networks”. The proposed architecture follows the eTSI concept but includes new elements such as (1) repository and (2) management system. The SyMEC system is designed as ecosystem, which has external interfaces (following eTSI standards) and internal interfaces (not necessary following standards).
EN
Several use cases from the areas of manufacturing and process industry, require highly accurate sensor data. As sensors always have some degree of uncertainty, methods are needed to increase their reliability. The common approach is to regularly calibrate the devices to enable traceability according to national standards and Syst\`eme international (SI) units - which follows costly processes. However, sensor networks can also be represented as Cyber Physical Systems (CPS) and a single sensor can have a digital representation (Digital Twin) to use its data further on. To propagate uncertainty in a reliable way in the network, we present a system architecture to communicate measurement uncertainties in sensor networks utilizing the concept of Asset Administration Shells alongside methods from the domain of Organic Computing. The presented approach contains methods for uncertainty propagation as well as concepts from the Machine Learning domain that combine the need for an accurate uncertainty estimation. The mathematical description of the metrological uncertainty of fused or propagated values can be seen as a first step towards the development of a harmonized approach for uncertainty in distributed CPS in the context of Industrie 4.0. In this paper, we present basic use cases, conceptual ideas and an agenda of how to proceed further on.
PL
W artykule skupiono się na efektywnych metodach transmisji, obliczeń i sterowania w sieciach typu mgła (fog). Sieci te proponowane są jako lepsze rozwiązanie dla przyszłych sieci teleinformatycznych oraz Internetu rzeczy niż oparte na chmurze obliczeniowej czy też na całkowicie rozproszonym działaniu sieci i jej zarządzaniu. Przyczyną są ich lepsze możliwości spełnienia wymagań stawianych przed przyszłymi sieciami masowej komunikacji urządzeń, obejmujących m.in. ultrawysoką niezawodność, ultraniskie opóźnienie (poniżej 1 ms), niski pobór mocy i ciągłość oferowanych usług. Możliwości sieci teleinformatycznych o architekturze typu mgła wynikają z tego, że realizują one zadania związane z transmisją, sterowaniem i przetwarzaniem danych w sposób elastyczny, tj. rozproszony, scentralizowany lub pośredni w lokalnych centrach sterowania siecią, przechowywania danych i obliczeń. Jest to szczególnie istotne dla krytycznych zastosowań Internetu rzeczy i wymagających pod względem niezawodności, opóźnień i efektywności energetycznej.
EN
This paper focuses on effective communication, computing and control methods in fog networks. A fog network is considered as a better architectural solution for future teleinformatic networks and the Internet of Things (IoT) than the cloud-based or edge-computing architectures. This is because it has functionalities that allow for fulfilling the requirements stated for the future massive-communication networks, such as ultra-high reliability, ultra-low delay (below 1 ms), low power consumption and pervasive connectivity. The fog network architecture allows for flexible processing of tasks related to communication, computing and control of the network, i.e., either in the centralized (using cloud), distributed (using edge devices) or intermediate manner. It is particularly important for mission-critical and energy-efficient applications of IoT.
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