Advances in Edge and Cloud Computing

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 8686

Special Issue Editors

Department of Computer Science, Montclair State University, Montclair, NJ, USA
Interests: edge computing; cloud computing; data center networking; green computing

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Guest Editor
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Interests: mobile computing; crowdsensing; data trading and management; blockchain and privacy protection

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Guest Editor
Department of Computer Engineering and Information Technology, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Interests: fog computing; cloud computing; Internet of Things (IoT); big data processing
Department of Computer Science, Saint Joseph's University, Philadelphia, PA, USA
Interests: social information-assisted system; cybersecurity; privacy

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Guest Editor
College of Computer Science and Technology, China Three Gorges University, Yichang 443002, China
Interests: mobile crowdsensing; mobile edge computing; VANETs
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Special Issue Information

Dear Colleagues,

Currently, we are witnessing a proliferation of services and applications that are provided at the edge of the Internet. The Internet edge includes traditional access networks, such as wireless local area networks (WiFi), wireless metropolitan area networks (3G, 4G, and 5G), as well as various emerging systems, such as the Internet of Things (IoT), smart homes, smart cities, and connected vehicle systems. Although the requirements from edge services and applications are becoming higher and higher, edge devices (such as mobile phones, tablets, sensors, autonomous agents, and other IoT devices) are still limited in terms of storage size, computation capacity, communication bandwidth, battery life, etc. Recently, the edge computing paradigm, which extends cloud computing to the edge of the Internet, has been proposed to support various services and applications at the edge of the Internet.

In this Special Issue, we invite submissions of original research in various fields closely related to edge and cloud computing, such as fog computing, dew computing, cloudlets, mobile edge computing, multi-access edge computing, edge-computing-assisted intelligence systems, emerging applications with edge computing, IoT systems, smart homes, smart cities, connected vehicles, etc.

Dr. Dawei Li
Prof. Dr. Minjun Xiao
Dr. Sadoon Azizi
Dr. Wei Chang
Dr. Ning Wang
Prof. Dr. Huan Zhou
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. Network 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 1000 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

  • mobile edge computing
  • multi-access edge computing
  • edge computing
  • cloud computing
  • cloudlet
  • IoT systems
  • fog computing
  • dew computing
  • edge computing and smart homes, smart buildings, smart cities
  • edge computing assisted autonomous driving
  • edge computing and connected vehicles
  • edge computing and emerging applications

Published Papers (3 papers)

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Research

22 pages, 459 KiB  
Article
Edge Data Center Organization and Optimization by Using Cage Graphs
by Pedro Juan Roig, Salvador Alcaraz, Katja Gilly, Cristina Bernad and Carlos Juiz
Network 2023, 3(1), 93-114; https://doi.org/10.3390/network3010005 - 18 Jan 2023
Cited by 2 | Viewed by 1679
Abstract
Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend [...] Read more.
Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers. Full article
(This article belongs to the Special Issue Advances in Edge and Cloud Computing)
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13 pages, 610 KiB  
Article
EchoIA: A Cloud-Based Implicit Authentication Leveraging User Feedback
by Yingyuan Yang, Jiangnan Li, Sunshin Lee, Xueli Huang and Jinyuan Sun
Network 2022, 2(1), 190-202; https://doi.org/10.3390/network2010013 - 21 Mar 2022
Cited by 1 | Viewed by 1903
Abstract
Implicit authentication (IA) transparently authenticates users by utilizing their behavioral data sampled from various sensors. Identifying the illegitimate user through constantly analyzing current users’ behavior, IA adds another layer of protection to the smart device. Due to the diversity of human behavior, existing [...] Read more.
Implicit authentication (IA) transparently authenticates users by utilizing their behavioral data sampled from various sensors. Identifying the illegitimate user through constantly analyzing current users’ behavior, IA adds another layer of protection to the smart device. Due to the diversity of human behavior, existing research tends to utilize multiple features to identify users, which is less efficient. Irrelevant features may increase the system delay and reduce the authentication accuracy. However, dynamically choosing the best suitable features for each user (personal features) requires a massive calculation, making it infeasible in the real environment. In this paper, we propose EchoIA to find personal features with a small amount of calculation by leveraging user feedback derived from the correct rate of inputted passwords. By analyzing the feedback, EchoIA can deduce the true identities of current users and achieve a human-centered implicit authentication. In the authentication phase, our approach maintains transparency, which is the major advantage of IA. In the past two years, we conducted a comprehensive experiment to evaluate EchoIA. We compared it with four state-of-the-art IA schemes in the aspect of authentication accuracy and efficiency. The experiment results show that EchoIA has better authentication accuracy (93%) and less energy consumption (23-h battery lifetimes) than other IA schemes. Full article
(This article belongs to the Special Issue Advances in Edge and Cloud Computing)
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17 pages, 1461 KiB  
Article
A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
by Shuaibing Lu, Jie Wu, Jiamei Shi, Pengfan Lu, Juan Fang and Haiming Liu
Network 2022, 2(1), 106-122; https://doi.org/10.3390/network2010008 - 24 Feb 2022
Cited by 7 | Viewed by 3444
Abstract
Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the [...] Read more.
Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency. Full article
(This article belongs to the Special Issue Advances in Edge and Cloud Computing)
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