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Special Issue "Edge/Fog/Cloud Computing in the Internet of Things"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 March 2019

Special Issue Editors

Guest Editor
Prof. Dr. Luis Velasco

Universitat Politècnica de Catalunya, Barcelona, Spain
Website | E-Mail
Interests: data collection and distributed data analytics; service management on the telecom cloud; software-defined networking and virtualization of network functions; application of big data analytics to networking; edge computing and IoT
Guest Editor
Dr. Marc Ruiz

Universitat Politècnica de Catalunya, Barcelona, Spain
Website | E-Mail
Interests: distributed applications; smart applications for Internet of Things; smart cities; smart agriculture
Assistant Guest Editor
Dr. Lluís Gifre

Universidad Autónoma de Madrid, Madrid, Spain
Website 1 | Website 2 | E-Mail
Interests: secure communications; secure devices; authentications; data privacy; policy-based security
Assistant Guest Editor
Dr. Josep Lluís Berral

Barcelona Supercomputing Center, Barcelona, Spain
Website 1 | Website 2 | E-Mail
Interests: big data frameworks; data-centric architectures; data-center optimization; applied learning methods; deep learning and AI with supercomputing; neural networks for data-streams

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is leading a revolution by redesigning classical devices, ranging from domestic electrical appliances to industrial robots, to integrate both programmability and network connectivity, thus, resulting in smart things. This comes with the unprecedent deployment of a variety of sensors and actuators in urban and even rural areas, as well as an almost ubiquitous connectivity.

The availability of edge and fog computing starts being supported by networking infrastructures, where computing and storage resources are located not only in the cloud, but also at the edges. The flexibility of hierarchical edge/fog/cloud computing infrastructures makes possible to decide whether artificial intelligence (AI)—including data analysis, machine learning (ML) training, and decision making—applied to data coming from sensors is carried out at the edge or uploaded to the fog and cloud infrastructures, with higher computation capabilities but far from actuators and applications running in-place. Therefore, advanced edge–fog–cloud computing architectures can provide the kind of support that IoT applications need to deploy AI methods in an efficient and scalable way. This special issue will provide special focus on applications, techniques, protocols, policies and architectures for edge and fog analytics, distributing computation on the edge/fog, and interaction between fog analytics and cloud analytics.

In addition, as a result of the distributed nature of IoT and the hierarchical edge/fog/cloud computing infrastructure, multiple challenges need to be faced to prevent or mitigate security holes, including attacks like Distributed Deny of Service (DDoS) and Man-in-the-Middle (MitM) that would lead to critical/confidential information eavesdropping, stealing algorithms/software and originating blackouts in IoT-based services, and provide acceptable level of security in their operation.

To address these challenges, this Special Issue will comprise a set of original contributions focusing on some of the key challenges in the context of edge/fog/cloud computing for IoT and will provide an overview of the current technical challenges.

Some topics of interest include, but are not limited to:

  • IoT applications taking advantage from edge/fog/cloud distributed computing
  • Distributed architectures in support of IoT applications
  • Network protocols and communication issues
  • Distributed data analytics, data processing, modeling and training
  • Edge and Fog management protocols and policies for workload communication and distribution
  • Privacy issues to prevent non-authorized data access
  • Security issues including secure communications and strategies to detect and mitigate attacks.
  • Securing the firmware, upgrade procedure and bootstrapping of devices.

Prof. Dr. Luis Velasco
Dr. Marc Ruiz
Guest Editors

Dr. Lluís Gifre
Dr. Josep Lluís Berral
Assistant 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 papers will be 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. Sensors 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

  • Edge/fog/cloud distributed computing for IoT
  • Security in IoT edge to cloud
  • IoT data privacy
  • Distributed IoT data analytics

Published Papers (1 paper)

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Research

Open AccessArticle Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting
Sensors 2018, 18(9), 3140; https://doi.org/10.3390/s18093140
Received: 25 July 2018 / Revised: 24 August 2018 / Accepted: 14 September 2018 / Published: 17 September 2018
PDF Full-text (1429 KB) | HTML Full-text | XML Full-text
Abstract
Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability
[...] Read more.
Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [ O ( 1 / V ) , O ( V ) ] . V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Edge/Fog/Cloud Computing in the Internet of Things)
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