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Quality attributes in edge computing for the Internet of Things: A systematic mapping study
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0998-6585
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0326-0556
2021 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 13, article id 100346Article in journal (Refereed) Published
Abstract [en]

Many Internet of Things (IoT) systems generate a massive amount of data needing to be processed and stored efficiently. Cloud computing solutions are often used to handle these tasks. However, the increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage. Edge computing may help to improve IoT systems regarding important quality attributes like latency, energy consumption, privacy, and bandwidth utilization. However, deciding where to deploy the various application components is not a straightforward task. This is largely due to the trade-offs between the quality attributes relevant for the application. We have performed a systematic mapping study of 98 articles to investigate which quality attributes have been used in the literature for assessing IoT systems using edge computing. The analysis shows that time behavior and resource utilization are the most frequently used quality attributes; further, response time, turnaround time, and energy consumption are the most used metrics for quantifying these quality attributes. Moreover, simulation is the main tool used for the assessments, and the studied trade-offs are mainly between only two qualities. Finally, we identified a number of research gaps that need further study.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 13, article id 100346
Keywords [en]
Internet of Things, Edge computing, Quality attributes, Metrics, Systematic mapping study
National Category
Computer Systems Communication Systems Embedded Systems
Identifiers
URN: urn:nbn:se:mau:diva-39120DOI: 10.1016/j.iot.2020.100346ISI: 000695695700015Scopus ID: 2-s2.0-85106740791OAI: oai:DiVA.org:mau-39120DiVA, id: diva2:1517234
Available from: 2021-01-13 Created: 2021-01-13 Last updated: 2024-02-05Bibliographically approved
In thesis
1. Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
Open this publication in new window or tab >>Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision.

To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. p. 141
Series
Studies in Computer Science ; 13
Keywords
Internet of Things, Edge computing, Decision Support, Quality Attrib-utes, Metrics, Simulation
National Category
Communication Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
urn:nbn:se:mau:diva-37068 (URN)10.24834/isbn.9789178771592 (DOI)978-91-7877-158-5 (ISBN)978-91-7877-159-2 (ISBN)
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Note

Note: The papers are not included in the fulltext online

Available from: 2020-12-03 Created: 2020-12-02 Last updated: 2024-03-07Bibliographically approved

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Ashouri, MajidDavidsson, PaulSpalazzese, Romina

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