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Electronics
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Cloud and Edge Computing for Smart Systems
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Cloud and Edge Computing for Smart Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 3044

Special Issue Editors

Dr. Palden Lama

E-Mail Website
Guest Editor
Department of Computer Science, the University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: cloud computing and data centers; edge computing; big data; cyber security
Special Issues, Collections and Topics in MDPI journals
Dr. Rajendra V. Boppana

E-Mail Website
Guest Editor
Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: computer network security; mobile and ad hoc network security; secure routing protocols; anonymous communication protocols; denial of service attacks; software-defined networking; computer network performance modeling and analysis

Special Issue Information

Dear Colleagues,

The next generation of smart systems, including augmented/virtual reality, connected autonomous vehicles, smart cities, and smart healthcare monitoring systems demand high data rates, low latency, and high throughput. At the same time, robust mechanisms are required to meet their security and privacy requirements. The vision of computing for emerging smart systems encompasses a continuum of converging computing paradigms, from cloud computing to edge computing and the Internet of Things (IoT). This Special Issue invites novel contributions related to the design, implementation, and evaluation of cloud- and edge-computing-enabled smart systems and innovative solutions to improve their quality of service, reliability, privacy, and security. 

Dr. Palden Lama
Dr. Rajendra V. Boppana
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • cloud computing
  • IoT
  • smart systems
  • edge computing

Published Papers (2 papers)

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Research

15 pages, 1557 KiB  
Article
Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing
by Zhuo Li, Zecheng Li and Wei Zhang
Electronics 2023, 12(4), 960; https://doi.org/10.3390/electronics12040960 - 15 Feb 2023
Cited by 1 | Viewed by 1553
Abstract
In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the [...] Read more.
In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the constraint of limited computing resources. The method utilizes deep reinforcement learning for the selection of optimal edge servers for task deployment, followed by a greedy self-adaptive stochastic algorithm for the recruitment of sensing participants. In simulations, the proposed method demonstrated a 20% improvement in spatial coverage compared with the existing RBR algorithm and outperformed the LCBPA, SMA, and MOTA algorithms in 41, 42, and 48 tasks, respectively. This research contributes to the optimization of task allocation in MCS and advances the integration of edge computing in MCS systems. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for Smart Systems)
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18 pages, 3668 KiB  
Article
Collaborative Road Damage Classification and Recognition Based on Edge Computing
by Xiaochao Dang, Xu Shang, Zhanjun Hao and Lin Su
Electronics 2022, 11(20), 3304; https://doi.org/10.3390/electronics11203304 - 14 Oct 2022
Cited by 2 | Viewed by 1060
Abstract
Road damage brings serious threats and inconvenience to traffic safety travel. Road damage detection and recognition can assist in eliminating the potential safety hazards in time and reduce traffic accidents. The majority of the existing road damage detection methods require significant computing resources [...] Read more.
Road damage brings serious threats and inconvenience to traffic safety travel. Road damage detection and recognition can assist in eliminating the potential safety hazards in time and reduce traffic accidents. The majority of the existing road damage detection methods require significant computing resources and are difficult to deploy on resource-constrained edge devices. Therefore, the road surface data collected during the driving process of the vehicle are usually transmitted to the cloud service for analysis. However, during the driving process of the vehicle, due to problems, such as network coverage, connection, and response, it is difficult to meet the needs of real-time detection and identification of road damage. Therefore, this paper proposes a road damage classification and identification method based on edge computing. This method adds edge services. First, deep learning models are deployed on edge and cloud servers; then, a standardized entropy is set by information entropy to find the appropriate threshold as well as the best point of edge and cloud that work together to ensure high accuracy and fast response of road damage identification; finally, the cloud uses the data uploaded by the edge to assist the edge in updating the edge model. In comparison with the two cases of uploading data to the cloud server for analysis and uploading data to the edge server for analysis, the results show that the accuracy of the method is 16.21% higher than the method only executed at the edge end, and the average recognition time is 38.82% lower than the method only executed at the cloud end. While ensuring a certain accuracy, it also improves the efficiency of classification and recognition, and can meet the needs of fast and accurate road damage classification and recognition. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for Smart Systems)
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