CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario
"> Figure 1
<p>Evaluation in a 6G scenario for IoMT microservices. This figure shows the schematic representation of the evaluation scheme following the complex approach for IoMT services as dynamical resources organized at the edge level in a multilevel architecture of IoMT systems. The edge intelligence level is intended as a complex evolutionary strategical approach for the distributed resources’ organization, both in terms of evaluation modules (*) and of IoMT integration layer (**).</p> "> Figure 2
<p>CoKnowEMe. The figure describes the architectural model for evaluation Qos of IoMT microservices and shows the modular structure, the concepts taken into account for the evaluation with their attributes and sub-attributes.</p> "> Figure 3
<p>Complex Approaches for Microservices and their Evaluation. The figure shows and details the methodology of applying the evaluation procedure to a microservice S. At the left, we shed light on the complex approach for both the microservice dynamic construction and the evaluation scheme applied on it. At the right the step-by-step methodology is detailed.</p> "> Figure 4
<p>Selected Categories and classification for each level of the evaluation model. The synthesis and the procedure of the literature survey conducted and detailed in the <a href="#app1-futureinternet-13-00177" class="html-app">supplementary information document</a>, in order to select, classify and assess several attributes and sub-attributes for each level of the proposed evaluation model.</p> "> Figure 5
<p>Evaluation of the individual levels. The figure shows the different outputs of the different individual levels and the role of the use context in the definition of the attributes’ weights.</p> "> Figure 6
<p>Overall Evaluation. The figure puts in evidence how the different individual levels of the proposed architectural model can be chained to provide an overall evaluation.The figure also takes into account the role of the use context in the definition of the attributes’ weights.</p> "> Figure 7
<p>Degree distribution of three different complex topologies of the networked structure of attributes. The figure show the degree distribution <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>K</mi> <mo>)</mo> </mrow> </semantics></math> in function of the degree <span class="html-italic">k</span>. In (<b>a</b>) we consider a population size <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, in (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, in (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>. In the three plots we shed lights on three different topologies: Scale-Free network (SF) (red line), Erdős–Rényi networks (ER)(blue line), Small-World networks (SW) (green line).</p> "> Figure 8
<p>QoS in function of the population size.The figure displays the trend of QoS considering two different structural networks, the scale-free (red line) and the small-world (blue line), assumed as topology for both the microservice and evaluation graphs. The inset shows the clustering coefficient versus the population size in both cases.</p> ">
Abstract
:1. Introduction
1.1. Telecommunication Evolution and Status of 5G Deployment
1.2. Beyond 5G Networks towards 6G Revolution
1.3. 6G as a Key Enabler for Internet of Medical Things
1.4. A Complex and Dynamical Approach for a Context-Based Evaluation Model
Reference | Year | Keywords |
---|---|---|
[13] | 2020 | Emergency Service, Healthcare, 5G Communications, 6G Communications, Wireless Communications, Internet of Things, Internet of Everything, Vehicular Technology, Drones, Mobile Hospital, Hospital-to-Home Services, Fire Control, Accidental Services, Natural Disaster. |
[9] | 2020 | 6G mobile communication, 5G mobile communication, Reliability, Wireless networks, Internet of Things, Intelligent sensors. |
[10] | 2020 | Complex systems, complex networks, networked complex system, 5G, 6G, wireless communications, wireless networks, mobile communication networks, modelling |
[11] | 2019 | 6G mobile communication, 5G mobile communication, Market research, Wireless communication, Sensors, Wireless sensor networks. |
[12] | 2020 | 6G, wireless communications, terahertz band, intelligent communication environments, pervasive artificial intelligence, network automation, all-spectrum reconfigurable transceivers, ambient backscatter communications, cell-free massive MIMO, Internet of NanoThings, Internet of BioNanoThings, quantum communications. |
[8] | 2020 | 5G, 6G, artificial intelligence, automation, beyond 5G, data rate, massive connectivity, virtual reality, terahertz. |
[16] | 2020 | Computer Science, Distributed, Parallel, Cluster Computing, Artificial Intelligence, Networking and Internet Architecture. |
[17] | 2020 | Self-learning edge intelligence, technological framework, seamless integration, communication networks, mobile edge computing, key missing components, edge-native AI, self-supervised generative adversarial nets, potential performance improvement, automatic data learning, edge computing networks, key research problems, edge-native artificial intelligence, communication network, wireless 6G cellular systems, self-learning architecture, self-learning-enabled 6G edge intelligence. |
[18] | 2021 | Edge intelligence, 6G, Ultra-reliable low-latency, COVID-19, Internet of drones, Holographic communication. |
[22] | 2020 | 6G, architecture, B5G, cellular communication, convergence, orchestration, sub-networks, wireless networks. |
[30] | 2021 | Aggregation, differentiated services (Diffserv), edge intelligence, network traffic, preference logic, quality of experience (QoE), quality of service (QoS). |
[32] | 2020 | Massive MIMO, holographic beamforming, Internet of everything (IoE), Machine learning, Distributed security. |
[33] | 2021 | MEC, EGT, Temporal multiplex network, Social network, 6G. |
1.5. Organization of the Paper
- In Section 2 we briefly introduce background and methods.
- In Section 3 we detail the novel evaluation scheme, the analytical methodology and the performance evaluation in a complex networked scenario.
- In Section 4 we conclude by discussing the potential implications of the proposal and the future works.
1.6. Contributions of the Paper
- We propose a novel evaluation scheme, which has been called CoKnowEMe (context knowledge evaluation model), for IoMT microservices in accordance with the complex approach suitable for the forthcoming 6G generation, taking into account what this paradigm will introduce in terms of enabling technologies, in particular driving towards more edge intelligent capabilities, such as computing, machine learning and evaluation of quality of services close to the users.
- We shed light on both architectural and analytical procedure, following a complex approach, consisting of three different interoperable levels, underlining how the interoperability of these chained different levels changes in accordance with the context of use.
- We follow the complex approach in line with the future 6G network and with the moving of the intelligence from the central cloud to edge computing resources since the computing of each evaluation module can represent a resource at edge level. A completely dynamic and heterogeneous topology requires an intelligence at edge level also for the evaluation of the services provided in order to introduce the same degree of adaptability. To this aim, we have also performed simulations to display properties of a networked set of heterogeneous attributes.
- We conduct a deep investigation concerning suitable attributes for each category considered in the evaluation scheme, summarizing this procedure and the findings of the selection and classification in the supplementary information document.
2. Materials and Methods
2.1. IoMT, Microservices and Edge Intelligence
2.2. A Complex Perspective towards 6G
2.3. Acceptability
2.4. Usability
2.5. User Experience
2.6. Weighted Sum Model (WSM)
3. Results
3.1. Scenario
3.2. Glossary
- Attribute: A measurable physical or abstract property of a service entity that can be measured using a quality metric.
- Metric: A measurement scale (i.e., nominal, ordinal, interval, ratio or absolute) combined with a measurement approach (i.e., measurement method or measurement function) that describes how the measurement is to be conducted. Each metric can also have multiple ways in which it can be calculated. There are three types of metrics:
- -
- Basic metric: A metric that does not depend on any other metric and uses a measurement method as a measurement approach.
- -
- Derived metric: A metric derived from other basic or derived metrics, using a measurement function as a measurement approach.
- -
- Indicator: A high-level quantitative metric derived from other metrics and using an analysis model as a measurement approach.
- Measurement method: A logical sequence of operations that are used to quantify a quality attribute using a basic metric.
- Unit of measurement: Quantity taken as a sample and term of comparison for the measurement of all quantities of the same species.
3.3. CoKnowEMe: Architectural and Analytical Model
- Supplier: Person, organization or body, belonging to a specific sector that provides a specific type of service.
- Broker: Intermediary who negotiates the relationship between consumer and service provider.
- Consumer: Person or organization that maintains a commercial relationship and uses the services made available by service providers.
- End user: The people or organizations that are the customers of the service.
- Developer: Intended as a service partner, it can be a developer, integrator, tester, etc.
3.3.1. Approach to Literature Review, Entry Selection and Classification
3.3.2. First Level: Acceptability
3.3.3. Second Level: Usability
3.3.4. Third Level: User Experience
3.3.5. Use Context
3.3.6. Analytical Model
- 1
- considering the level of acceptability, the first, starting from the bottom, calculated as expressed in Equation (1); this value will constitute the output of the considered level and quantifies the acceptability degree of the service;
- 2
- going up to the usability level, we have to consider what has been obtained at the level of acceptability, as expressed in Equation (2):
- 3
- proceeding towards the last level, we iterate the procedure, obtaining:
3.3.7. Pseudocode of the Model
Algorithm 1 CoKnowEMe Evaluation Algorithm for IoMT Microservices |
|
3.3.8. Performance Evaluation in a Complex Networked Scenario
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Short Biography of Authors
| Grazia Veronica Aiosa received her B.S. degree in Computer Engineering (2016) and M.S. degree in Telecommunication Engineering (2019) from the Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI) at University of Catania, Italy. She is currently an Early Stage Researcher at DIEEI, University of Catania. Her research interests include multi-layer networks, social contagion and collective attention. |
| Barbara Attanasio received her B.S. degree in Computer engineering (2016) and M.S. degree in Telecommunications engineering (2018) from the Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI) at University of Catania, Italy. She is currently a Ph.D. student in Systems, Energy, Computer and Telecommunications Engineering at DIEEI, University of Catania. Her research interests Multi Access Edge Computing, Evolutionary Game Theory, Multi-layer networks, Data Science and IoT. |
| Aurelio La Corte is Associate Professor in Telecommunication Engineering at the University of Catania, Italy. He received the degree in electrical engineering in 1988 and the Ph.D. in Electronic Engineering and Computer Science in 1994. He has more than 25 years of experience in scientific and teaching activity and specific experience in designing telecommunications networks and systems and in managing complex projects. Responsible for various ICT-related activities at the University of Catania, he has been involved in coordinating, designing and developing networks and ICT services.His scientific interests include telecommunication systems and integrated services, innovative ICT services and technological innovation. |
| Marialisa Scatá received her B.S. and M.S. degrees in Telecommunication Engineering, from (DIEEI), University of Catania, Italy. She received the Ph.D. in Computer Science and Telecommunication Engineering from University of Catania under the supervision of Prof. Aurelio La Corte, with whom she has been collaborating since 2009. During the Ph.D. she attended several schools and conferences. Currently, she works as Postdoctoral Researcher at DIEEI, University of Catania. She serves as reviewer for several top-tier journals. She has an interdisciplinary approach to research and her interests include bio-inspired models, ICT, telecommunications, social networks, complex networks, multi-layer networks, social contagion and epidemic spreading, evolutionary game theory, data mining, machine learning, healthcare applications and cognitive networks. |
Region | Operator (Number of Covered Cities) | Launch | Penetration Rate Forecast |
---|---|---|---|
Australia | Optus (14), Telstra (46), Vodafone (8) [5] | 05/22/2019 [5] | - |
Austria | A1 Telekom (129), Drei (Three) Austria (4), Magenta Telekom (T-Mobile Austria) (28) [5] | 03/26/2019 [5] | - |
Belgium | Proximus (79) [5] | 04/02/2020 [5] | - |
Canada | Bell (5), Rogers (4), Telus (5) [5] | 01/15/2020 [5] | - |
Czech Republic | O2 (2) [5] | 06/19/2020 [5] | - |
Finland | DNA (21), Elisa (30), Telia (8) [5] | 07/01/2019 [5] | - |
European Union | - | - | 29% (2025) [6] |
Germany | Telecom Deutschland (20), Vodafone (96) [5] | 07/16/2019 [5] | 98% (2022) [2] |
Gulf Cooperation Council | - | - | 73% (2026) [7] |
Hungary | Maygar Telekom (2), Vodafone (2) [5] | 10/17/2019 [5] | - |
India | - | - | 26% (2026) [7] |
Ireland | Eir (19), Vodafone (5) [5] | 08/13/2019 [5] | - |
Italy | TIM (8), Vodafone (5) [5] | 06/06/2019 [5] | - |
Japan | KDDI (15), NTT Docomo (35), Softbank (12) [5] | 03/25/2020 [5] | - |
Korea | KT (85), LGU+ (85), SKT (85) [5] | 04/03/2019 [5] | 90% (2026) [2] |
Latin America | - | - | 34% (2026) [7] |
Latvia | Tele2 (2) [5] | 01/22/2020 [5] | - |
Middle East and North Africa | - | - | 18% (2026) [7] |
Netherlands | Vodafone Ziggo (50% of the Netherlands) [5] | 04/28/2020 [5] | - |
New Zeland | Vodafone (4) [5] | 12/10/2019 [5] | - |
Norway | Telenor (4), Telia (2) [5] | 03/13/2020 [5] | - |
North America | - | - | 84% (2026) [7] |
North-East Asia | - | - | 65% (2026) [7] |
Poland | Plus (7), T-Mobile (11) [5] | 05/12/2020 [5] | - |
South East Asia and Oceania | - | - | 33% (2026) [7] |
Spain | Vodafone (22) [5] | 15/06/2019 [5] | - |
Sub-Saharan Africa | - | - | 7% (2026) [7] |
Sweden | 3-Sweden (5), Tele2 (3), Telia (12) [5] | 24/05/2020 [5] | - |
Switzerland | Sunrise (384), Swisscom (90% population) [5] | 01/04/2019 [5] | - |
United Kingdom | EE (80), O2 (60), Three (66), Vodafone (44) [5] | 30/05/2019 [5] | - |
United States | AT&T (335), Sprint (9), T-Mobile (6000), Verizon Wireless (35) [5] | 03/04/2019 [5] | - |
World | - | - | 12% (2024), 50% (2034) [2] |
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Aiosa, G.V.; Attanasio, B.; La Corte, A.; Scatá, M. CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario. Future Internet 2021, 13, 177. https://doi.org/10.3390/fi13070177
Aiosa GV, Attanasio B, La Corte A, Scatá M. CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario. Future Internet. 2021; 13(7):177. https://doi.org/10.3390/fi13070177
Chicago/Turabian StyleAiosa, Grazia Veronica, Barbara Attanasio, Aurelio La Corte, and Marialisa Scatá. 2021. "CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario" Future Internet 13, no. 7: 177. https://doi.org/10.3390/fi13070177