Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure

Determining the impact of 5G-technology on manufacturing performance using a modified TOPSIS method

A digital transformation is currently taking place in society, where people and things are connected to each other and the Internet. The number of connected devices is projected to be 28 Billion in 2025, and our expectations on digitalization set new requirements of mobile communication technology. To handle the increased amount of connected devices and data generated, the next generation of mobile communication technology is under deployment: 5G-technology.The manufacturing industry follows the digital transformation, aiming for digitalized manufacturing with competitive and sustainable production systems.5G-technology meets the connectivity requirements in digitalized manufacturing, with low latency, high data rates, and high reliability. Despite these technological benefits, the question remains: Why should the manufacturing industry invest in 5G-technology? This study aims to determine the impact of 5G-technology on manufacturing performance; based on a mixed-methods approach including a modified TOPSIS method to ensure robustness of the results. The results show that 5 G-technology will mainly impact productivity, maintenance performance, and flexibility. By linking 5G-technology to the performance of the manufacturing system, instead of focusing on network performance, the benefits of using 5G-technology in manufacturing become clear, and can thus facilitate investment and deployment of 5G-technology in the manufacturing industry

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Teknoekonominen toteutettavuusanalyysi etäylläpidon liitettävyydestä tehtaissa

Maintenance activities play a major role in factory operations, as they prevent breakdowns and extend machine life. With the advances in sensor, computing and communications technology, sensor data can be increasingly exploited for real-time supervision of machine condition. However, the acquisition of the data is challenging due to proprietary technologies and interfaces applied in Industrial Networks. Therefore, sensor data is rarely utilized in other processes than automation. As the industry is heading towards a new industrial era, also referred to as Industrial Internet or Industrie 4.0, there is growing need to improve data availability for applications that can realize its potential value. In this research, the focus is on the feasibility of remote maintenance deployment in factories. The topic is approached from the connectivity viewpoint. The research is conducted by reviewing the literature, and by interviewing numerous industry experts regarding the connectivity and data exploitation in factories. These form the basis for the value network analysis, in which Value Network Configuration (VNC) method is applied, to analyze the value distribution among different actors in alternative remote connection cases. As a result of the VNC analysis, three alternative value network configurations are formed. They provide a high-level technical architecture of the remote connection implementation and discuss the accumulated value of each actor concerning remote maintenance service. The insights gained from the VNCs and literature are then employed to propose a future technical architecture for remote maintenance connectivity in factories.Huoltotoimet ovat suuressa roolissa tehtaan toiminnassa, sillä ne ehkäisevät konerikkoja ja pidentävät koneen käyttöikää. Sensori-, laskenta- ja tietoliikenneteknologian kehittymisen johdosta sensoridataa voidaan hyödyntää yhä enemmän koneen kunnon reaaliaikaiseen valvontaan. Datan saanti on kuitenkin haastavaa teollisissa verkoissa käytettyjen sovelluskohtaisten teknologioiden ja liitäntöjen takia. Sen vuoksi sensoridataa hyödynnetään harvoin muissa prosesseissa kuin automaatiossa. Teollisuuden suunnatessa kohti uutta teollista aikakautta, joka tunnetaan myös nimillä Teollinen Internet ja Teollisuus 4.0, on datan saatavuutta parannettava sovelluskohteille, jotka voivat realisoida sen potentiaalisen arvon. Tämä tutkimus tarkastelee etäylläpidon käyttöönoton toteutettavuutta tehtaissa. Aihetta lähestytään liitettävyyden näkökulmasta. Tutkimus suoritetaan tarkastelemalla kirjallisuutta sekä haastattelemalla lukuisia teollisuuden asiantuntijoita koskien liitettävyyttä ja datan hyödyntämistä tehtaissa. Nämä muodostavat perustan arvoverkkoanalyysille, jossa sovelletaan arvoverkkokonfiguraatio-menetelmää, jolla analysoidaan arvon jakautumista eri toimijoiden kesken vaihtoehtoisissa etäyhteystapauksissa. Arvoverkkokonfiguraatioanalyysin tuloksena muodostetaan kolme vaihtoehtoista arvoverkkokonfiguraatiota. Ne tarjoavat korkean tason teknisen arkkitehtuurin etäyhteyden implementaatiosta ja tarkastelevat toimijoiden kerryttämää arvoa etäylläpitopalvelun osalta. Arvoverkkokonfiguraatioista ja kirjallisuudesta saatujen näkemysten pohjalta esitellään lisäksi tulevaisuuden tekninen arkkitehtuuri etäylläpidon liitettävyydelle tehtaissa

From Data to Decision Support in Manufacturing

Digitalization is changing society, industry, and how business is done. For new companies that are more or less born digital, there is the opportunity to use and benefit from the capabilities offered by the new digital technologies, of which data-driven decision-making forms a crucial part. The manufacturing industry is facing the Fourth Industrial Revolution, but most manufacturing organizations are lagging behind in their digital transformation. This is due to the technical and organizational challenges they are experiencing. Based on this current state description and existing gap, the vision, aim, and research questions of this thesis are: Vision - future manufacturing organization to be driven by fact-based decision support based on data rather than of relying mainly on intuition and experience.Aim - to show manufacturing organizations the applicability of digital technologies in digitalizing manufacturing system data to support decision-making and how data sharing may be achieved.Research Question 1. How do manufacturing system lifecycle decisions influence the requirements of data collection towards interoperability? Research Question 2. What makes interoperability standardization applicable to sharing data in a manufacturing system’s lifecycle?This research is applied, addressing real-world problems in manufacturing. For this reason, the main objective is to solve the problem at hand, and data collection methods will be selected that can help address it. This can best be explained by taking a pragmatic worldview and using mixed methods approach that combines quantitative and qualitative methods. The research upon which this thesis is based draws on the results of three research projects involving the active participation of manufacturing companies. The data collection methods included experiments, interviews (focus group and semi-structured), technical development, literature review, and so on. The results are divided into three areas: 1) connected factory, 2) standard representation of machine model data, and 3) the digital twin in smart manufacturing. Connected factory addresses the question of how a mobile connectivity solution, 5G, may be used in a factory setting and demonstrates how the connectivity solution should be planned and how new data from a connected machine may support an operator in decision-making. The standard representation of machine model data demonstrates how an international standard may be used more widely to support the sharing and reuse of information. The digital twin in smart manufacturing investigates the reasons why there are so few real-world examples of this. The findings reveal that a manufacturing system’s lifecycle impacts data requirements, including a need for data accuracy in design, speed of data in operation (to allow operators to act upon events), and availability of historical data in maintenance (for finding root causes and planning). The volume of data was identified as important to all lifecycles. The applicability of standards was found to depend on: 1) the technology providers’ willingness to adapt standards, 2) enforcement by OEMs and larger actors further down a supply chain, 3) the development of standards that consider the user, and 4) when standards are required for infrastructure reasons. Based on the results and findings obtained, it may be stated that it is important to determine what actual manufacturing problem should be addressed by digital technology. There is a tendency to view this change from the perspective of what the digital technology might solve (a technology push), rather than setting aside the solution and focusing on what problem should be solved (a technology pull). This work also reveals the importance of the collaboration between industry and academia making progress in the digital transformation of manufacturing. Proofs-of-concept and demonstrators of how digital technologies might be used are also important tools in helping industry in how they can address a digital transformation

Reference Scenarios and Key Performance Indicators for 5G Ultra-dense Networks

The so-called 5G will revolutionize the way we live, and work. In order to demonstrate the profound changes, we can expect to experience within the next 5 to 10 years, we present use cases for the planned research within the TeamUp5G project. Some use cases are strongly linked to the network layer and aim at developing solutions capable of optimizing the main promising benefits of 5G: extremely low latency and extremely high bandwidth (e.g., handle video streams, traffic congestion, user profiles), in the most efficient way possible. Other use cases focus on commercial applications that make use of middleware applications to enhance their performance. The latter fall into two main areas: real-time virtual reality and live video streaming, which are extremely demanding in terms of latency and bandwidth to provide an acceptable QoE/QoS to multiple users. The use cases presented are built assuming that 5G is essential for their support with appropriate QoE/QoS. Key performance indicators and their range of variation are also identified.info:eu-repo/semantics/acceptedVersio