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Computers
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Mobile Edge Computing
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Mobile Edge Computing

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (30 May 2018) | Viewed by 38365

Special Issue Editors

Prof. Dr. Thomas Magedanz

E-Mail Website
Guest Editor
Software-based Networks (NGNI), Fraunhofer FOKUS, 10589 Berlin, Germany
Interests: software-based converging networks; service platforms; edge computing; network slicing
Dr. Alexander Willner

E-Mail Website
Guest Editor
Industrial Internet of Things (IIoT), Technische Universität Berlin, Berlin, Germany
Interests: IIoT; Edge and Fog Computing; Service Virtualization and Orchestration; Semantic Interoperability
Dr. Stefano Secci

E-Mail Website
Guest Editor
LIP6, Sorbonne Université Pierre et Marie Curie (UPMC), 75005 Paris, France
Interests: network virtualization; orchestration; optimization

Special Issue Information

Dear Colleagues,

Mobile Edge Computing/Multi-access Edge Computing (MEC) is a rapidly growing area of research, coping with the many challenges that the virtualization of mobile access networks and services is creating. In particular, the elasticity created by the virtualization of network functions can only be fully exploited if an adequate management control is in place. Relevant computing architecture, protocols, orchestration algorithms, monitoring systems, and Application Programming Interfaces (APIs) need to be specified to meet the various requirements that are imposed by different domains. This Special Issue targets related contributions that can be applied to the continuum of hierarchically distribution services between end devices, edge nodes and the cloud. In this context, interesting challenges related to edge connectivity, real-time enablement, optimization of data distribution and aggregation of data, edge intelligence and security arise. Therefore, the topics of interest for submission include, but are not limited to:

  • Mobile Edge and Fog Computing architectures and protocols
  • Connectivity enhancements for Edge Devices
  • Virtual network overlay protocols
  • Network programming (SDN) aspects in MEC provisioning
  • Network virtualization (NFV) aspects in MEC provisioning
  • Orchestration algorithms
  • Edge application design
  • Heterogeneous data aggregation and modeling
  • End device and Edge enhancements in support of MEC services
  • End device and Edge data analytics
  • Autonomous Cyber-Physical Systems at the edge
  • Security for distributed Edge clouds
  • Industrial Internet of Things (IIoT) Fog and Edge applications
Prof. Dr. Thomas Magedanz
Dr. Alexander Willner
Dr. Stefano Secci
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • MEC (Mobile Edge Computing / Multi-access Edge Computing)
  • Edge Computing
  • Edge Intelligence
  • Fog Computing
  • NFV (Network virtualization)
  • SDN (Network programming)
  • Network Slicing
  • 5G
  • IIoT (Industrial Internet of Things)
  • NGI
  • Future Networks

Published Papers (4 papers)

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Research

16 pages, 2378 KiB  
Article
Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing
by John Yoon
Computers 2018, 7(4), 63; https://doi.org/10.3390/computers7040063 - 16 Nov 2018
Cited by 3 | Viewed by 5181
Abstract
Wireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For [...] Read more.
Wireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing, or disappearing. If the abrupt appearance of sensors is a zero-day attack, and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The predefined sensor requirements or meta-data-based sensor verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only. The proposed verification on sensors can be done without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes (1) quality preservation of sensor data for mining analytics. The sensor data are trained to identify their characteristics of outliers: whether they are attack outliers, or outlier-like abrupt changes in environments; and (2) authenticity verification of dynamically moving sensors, which was possible. Previous unknown sensors are also identified by deep-learning approach. Full article
(This article belongs to the Special Issue Mobile Edge Computing)
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20 pages, 3458 KiB  
Article
Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
by Giorgio Cattaneo, Fabio Giust, Claudio Meani, Daniele Munaretto and Pietro Paglierani
Computers 2018, 7(4), 55; https://doi.org/10.3390/computers7040055 - 26 Oct 2018
Cited by 19 | Viewed by 13100
Abstract
Multi-access Edge Computing (MEC) will be a technology pillar of forthcoming 5G networks. Nonetheless, there is a great interest in also deploying MEC solutions in current 4G infrastructures. MEC enables data processing in proximity to end users. Thus, latency can be minimized, high [...] Read more.
Multi-access Edge Computing (MEC) will be a technology pillar of forthcoming 5G networks. Nonetheless, there is a great interest in also deploying MEC solutions in current 4G infrastructures. MEC enables data processing in proximity to end users. Thus, latency can be minimized, high data rates locally achieved, and real-time information about radio link status or consumer geographical position exploited to develop high-value services. To consolidate network elements and edge applications on the same virtualization infrastructure, network operators aim to combine MEC with Network Function Virtualization (NFV). However, MEC in NFV integration is not fully established yet: in fact, various architectural issues are currently open, even at standardization level. This paper describes a novel MEC in an NFV system which successfully combines, at management level, MEC functional blocks with an NFV Orchestrator, and can neutrally support any “over the top” Mobile Edge application with minimal integration effort. A specific ME app combined with an end-user app for the provision of immersive video services is presented. To provide low latency, CPU-intensive services to end users, the proposed architecture exploits High-Performance Computing resources embedded in the edge infrastructure. Experimental results showing the effectiveness of the proposed architecture are reported and discussed. Full article
(This article belongs to the Special Issue Mobile Edge Computing)
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27 pages, 1365 KiB  
Article
BlendCAC: A Smart Contract Enabled Decentralized Capability-Based Access Control Mechanism for the IoT
by Ronghua Xu, Yu Chen, Erik Blasch and Genshe Chen
Computers 2018, 7(3), 39; https://doi.org/10.3390/computers7030039 - 13 Jul 2018
Cited by 121 | Viewed by 12049
Abstract
While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and [...] Read more.
While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems. Full article
(This article belongs to the Special Issue Mobile Edge Computing)
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17 pages, 510 KiB  
Article
Improving Efficiency of Edge Computing Infrastructures through Orchestration Models †
by Raffaele Bolla, Alessandro Carrega, Matteo Repetto and Giorgio Robino
Computers 2018, 7(2), 36; https://doi.org/10.3390/computers7020036 - 20 Jun 2018
Cited by 4 | Viewed by 7140
Abstract
Edge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need [...] Read more.
Edge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need to leave some network functions idle for most of the time, which eventually results in large energy waste and poor efficiency. Existing consolidation algorithms are largely ineffective in these conditions because they lack context, i.e., the knowledge of which resources are effectively used and which ones are just provisioned for other purposes (i.e., redundancy, resilience, scaling, migration). Though the concept is rather straightforward, its feasibility in real environments must be demonstrated. Motivated by the lack of energy-efficiency mechanisms in cloud management software, we have developed a set of extensions to OpenStack for power management and Quality of Service, explicitly targeting the introduction of more context for applications. In this paper, we briefly describe the overall architecture and evaluate its efficiency and effectiveness. We analyze performance metrics and their relationship with power consumption, hence extending the analysis to specific aspects that cannot be investigated by software simulations. We also show how the usage of context information can greatly improve the effectiveness of workload consolidation in terms of energy saving. Full article
(This article belongs to the Special Issue Mobile Edge Computing)
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