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Intelligent Provisioning and Management Technologies for IoT-Based Edge Networks
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Intelligent Provisioning and Management Technologies for IoT-Based Edge Networks

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

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 6293

Special Issue Editors

Prof. Dr. Choonhwa Lee

E-Mail Website
Guest Editor
Department of Computer Science, Hanyang University, Seoul, Korea
Interests: blockchain; distributed/cloud computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eun-Sung Jung

E-Mail Website
Guest Editor
1. Department of Software and Communications Engineering, Hongik University, Sejong, Korea
2. Division of Computer Science and Engineering, Hanyang University, Seoul, Korea
Interests: cloud computing for big data; high-performance computing for big facilities; IoT data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Zohaib Latif

E-Mail Website
Guest Editor
Department of Computer Science, Hanyang University, Seoul, Korea
Interests: software defined networking; Internet of Things; edge computing; blockchain

Special Issue Information

Dear Colleagues,

In recent years, the Internet of Things (IoT) has become very popular due to its ability to control real-world infrastructure. However, these devices generate a massive amount of data that can hardly be accommodated by resource-constrained IoT devices. Although cloud computing has proven successful in resolving the issue, it introduces other difficulties, such as delays due to the long distance between the cloud and IoT devices, for which we are witnessing edge computing gaining traction as a promising solution. For some years, the edge computing vision has kept the research community exploring the possibilities of key enabling technologies to deliver on its promise.

There have been some drastic developments in network virtualization, software defined networking, artificial intelligence, and cloud/distributed computing technologies. SDN/NFV can dynamically control the underlying devices for resource allocation with the help of virtualization techniques. ML/DL can be helpful in intelligent and efficient decision-making for edge resource management and offloading decisions. Additionally, IoT data can be stored and processed across the network and blockchain technology is expected to provide viable solutions to the provenance problem of the data. This Special Issue focuses on the recent advancement in distributed/cloud computing for wireless and IoT networks. Theoretical and practical solutions for distributed computing systems related to the design, analysis, and implementation of edge computing and networking are highly desirable.

Topics discussed in this SI include, but are not limited to, the following:

  • Resrouce allocation and task scheduling for edge devices/servers;
  • Dynamic configuration of IoT edge networks using SDN/NFV;
  • Blockchain-based management and orchestration for IoT networks;
  • ML/DL support for ultra-low latency applications at the edge of the network;
  • Resource management of edge devices using ML/DL;
  • Blockchain technology in edge and cloud computing.

Prof. Dr. Choonhwa Lee
Prof. Dr. Eun-Sung Jung
Dr. Zohaib Latif
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. Sensors 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 2600 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

  • software defined networking (SDN)
  • network function virtualization (NFV)
  • machine learning (ML)
  • deep learning (DL)
  • blockchain
  • Internet of Things (IoT)
  • cloud computing
  • edge computing
  • resource management and orchestration

Published Papers (3 papers)

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Research

26 pages, 9214 KiB  
Article
Comparative Study on Distributed Lightweight Deep Learning Models for Road Pothole Detection
by Hassam Tahir and Eun-Sung Jung
Sensors 2023, 23(9), 4347; https://doi.org/10.3390/s23094347 - 27 Apr 2023
Cited by 2 | Viewed by 1755
Abstract
This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading [...] Read more.
This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading to the misjudgment of targets. To address this challenge, TensorFlow and PyTorch were used to initialize various distributed model parallel and data parallel techniques. Despite the success of these techniques, communication constraints were observed along with certain speed issues. As a result, a hybrid pipeline was proposed, combining both dataset and model distribution through an all-reduced algorithm and NVlinks to prevent miscommunication among gradients. The proposed approach was tested on both an edge cluster and Google cluster environment, demonstrating superior performance compared to other test settings, with the quality of the bounding box detection system meeting expectations with increased reliability. Performance metrics, including total training time, images/second, cross-entropy loss, and total loss against the number of the epoch, were evaluated, revealing a robust competition between TensorFlow and PyTorch. The PyTorch environment’s hybrid pipeline outperformed other test settings. Full article
(This article belongs to the Special Issue Intelligent Provisioning and Management Technologies for IoT-Based Edge Networks)
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17 pages, 732 KiB  
Article
A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks
by Zohaib Latif, Qasim Umer, Choonhwa Lee, Kashif Sharif, Fan Li and Sujit Biswas
Sensors 2022, 22(21), 8434; https://doi.org/10.3390/s22218434 - 02 Nov 2022
Viewed by 1896
Abstract
Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A [...] Read more.
Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems’ impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively. Full article
(This article belongs to the Special Issue Intelligent Provisioning and Management Technologies for IoT-Based Edge Networks)
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15 pages, 975 KiB  
Article
Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis
by Abdullah Alghamdi, Jiang Zhu, Guocai Yin, Mohammad Shorfuzzaman, Nawal Alsufyani, Sultan Alyami and Sujit Biswas
Sensors 2022, 22(18), 6786; https://doi.org/10.3390/s22186786 - 08 Sep 2022
Cited by 5 | Viewed by 1894
Abstract
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer [...] Read more.
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers’ raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users’ shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework. Full article
(This article belongs to the Special Issue Intelligent Provisioning and Management Technologies for IoT-Based Edge Networks)
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