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IoT
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Advanced Quality of Service Approaches in Edge Computing
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Advanced Quality of Service Approaches in Edge Computing

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 12814

Special Issue Editors

Dr. Giovanni Nardini

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, PI, Italy
Interests: 5G/B5G networks; multi-access edge computing; resource allocation algorithms; modeling and simulation; performance evaluation of computer networks
Dr. Carlo Puliafito

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, PI, Italy
Interests: mobile edge/fog computing; distributed computing; Internet of Things; serverless computing; service continuity

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has undoubtedly become part of everyday life, affecting several sectors such as, for instance, industry, urban mobility and healthcare. This trend is going to be exacerbated by the continuous evolution of mobile networks, from 5G to beyond-5G and 6G, which will allow IoT devices to enjoy extremely fast and ubiquitous connectivity to the Internet. In this context, Edge Computing is key to supporting the diffusion of low-latency IoT applications, by bringing a cloud-like computational environment to the network edge, hence closeness to the end devices.

Nevertheless, the stringent Quality of Service (QoS) requirements of applications in several scenarios, such as safety- and mission-critical ones, require careful design of the Edge Computing system, as well as of the underlying communication infrastructure and the edge-based applications themselves. Moreover, the high heterogeneity of applications and devices may lead to different, possibly conflicting, QoS requirements. As a result, tradeoffs may need to be considered in the deployment of the Edge Computing system.

The scope of this Special Issue is to investigate advanced architectures, algorithms and protocols in the field of Edge Computing and next-generation mobile networks addressing QoS properties such as data rate, latency and error rate, but also additional aspects such as scalability, mobility, reliability, and energy efficiency.

The topics of interest of this Special Issue include, but are not limited to:

  • QoS-aware architectures for Edge Computing systems.
  • QoS-driven design of edge-based applications and services.
  • QoS requirements for verticals based on Edge Computing.
  • QoS-oriented resource allocation for Edge Computing systems and mobile networks.
  • QoS-driven orchestration and migration of services in Edge Computing systems.
  • Artificial Intelligence for QoS in Edge Computing systems.
  • QoS-aware Network Function Virtualization and Software-Defined Networking for Edge Computing.
  • Network slicing approaches for QoS in Edge Computing systems.
  • Modeling and simulation of QoS in Edge Computing systems.
  • Performance evaluation of Edge Computing-based applications and services.
  • Performance evaluation of Edge Computing-enabled networks.
  • Real experiments and testbeds of Edge Computing systems.

Dr. Giovanni Nardini
Dr. Carlo Puliafito
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. IoT is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Edge Computing
  • Fog Computing
  • Quality of Service
  • Internet of Things
  • Mobile networks
  • 5G/B5G
  • Performance evaluation

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Published Papers (3 papers)

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Research

14 pages, 1320 KiB  
Article
Defining and Assessing Quality in IoT Environments: A Survey
by Aggeliki Sgora and Periklis Chatzimisios
IoT 2022, 3(4), 493-506; https://doi.org/10.3390/iot3040026 - 7 Dec 2022
Cited by 4 | Viewed by 2892
Abstract
With the proliferation of multimedia services, Quality of Experience (QoE) has gained a lot of attention. QoE ties together the users’ needs and expectations to multimedia application and network performance. However, in various Internet of Things (IoT) applications such as healthcare, surveillance systems, [...] Read more.
With the proliferation of multimedia services, Quality of Experience (QoE) has gained a lot of attention. QoE ties together the users’ needs and expectations to multimedia application and network performance. However, in various Internet of Things (IoT) applications such as healthcare, surveillance systems, traffic monitoring, etc., human feedback can be limited or infeasible. Moreover, for immersive augmented and virtual reality, as well as other mulsemedia applications, the evaluation in terms of quality cannot only focus on the sight and hearing senses. Therefore, the traditional QoE definition and approaches for evaluating multimedia services might not be suitable for the IoT paradigm, and more quality metrics are required in order to evaluate the quality in IoT. In this paper, we review existing quality definitions, quality influence factors (IFs) and assessment approaches for IoT. This paper also introduces challenges in the area of quality assessment for the IoT paradigm. Full article
(This article belongs to the Special Issue Advanced Quality of Service Approaches in Edge Computing)
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17 pages, 782 KiB  
Article
Performance Evaluation of Federated Learning for Residential Energy Forecasting
by Eugenia Petrangeli, Nicola Tonellotto and Carlo Vallati
IoT 2022, 3(3), 381-397; https://doi.org/10.3390/iot3030021 - 19 Sep 2022
Cited by 7 | Viewed by 3284
Abstract
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy [...] Read more.
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy and ensure timely feedback to perform reconfiguration of the distribution network. In this context, edge computing is expected to be an enabling technology to ensure decentralized data collection, management, processing and delivery. At the same time, federated learning is an emerging paradigm that fits naturally in such an edge-computing environment, providing an AI-powered and privacy-preserving solution for time-series forecasting. In this paper, we present a performance evaluation of different federated-learning configurations resulting in different privacy levels to the forecast residential energy consumption with data collected by real smart meters. To this aim, different experiments are run using Flower (a popular federated learning framework) and real energy consumption data. Our results allow us to demonstrate the feasibility of such an approach and to study the trade-off between data privacy and the accuracy of the prediction, which characterizes the quality of service of the system for the final users. Full article
(This article belongs to the Special Issue Advanced Quality of Service Approaches in Edge Computing)
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22 pages, 4753 KiB  
Article
An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
by Martina Pappalardo, Antonio Virdis and Enzo Mingozzi
IoT 2022, 3(1), 169-190; https://doi.org/10.3390/iot3010011 - 26 Feb 2022
Cited by 6 | Viewed by 5068
Abstract
The Internet of Things (IoT) brings Internet connectivity to devices and everyday objects. This huge volume of connected devices has to be managed taking into account the severe energy, memory, processing, and communication constraints of IoT devices and networks. In this context, the [...] Read more.
The Internet of Things (IoT) brings Internet connectivity to devices and everyday objects. This huge volume of connected devices has to be managed taking into account the severe energy, memory, processing, and communication constraints of IoT devices and networks. In this context, the OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices, and related service enablement, through a management server usually deployed in a distant cloud data center. Following the Edge Computing paradigm, we propose in this work the introduction of a LWM2M Proxy that is deployed at the network edge, in between IoT devices and management servers. On one hand, the LWM2M Proxy improves various LWM2M management procedures whereas, on the other hand, it enables the support of QoS-aware services provided by IoT devices by allowing the implementation of advanced policies to efficiently use network, computing, and storage (i.e., cache) resources at the edge, thus providing benefits in terms of reduced and more predictable end-to-end latency. We evaluate the proposed solution both by simulation and experimentally, showing that it can strongly improve the LWM2M performance and the QoS of the system. Full article
(This article belongs to the Special Issue Advanced Quality of Service Approaches in Edge Computing)
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