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Edge Intelligence for Internet of Things: Architecture, Privacy, and Applications
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Edge Intelligence for Internet of Things: Architecture, Privacy, and Applications

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

Deadline for manuscript submissions: closed (25 January 2024) | Viewed by 2219

Special Issue Editors

Dr. Youyang Qu

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: privacy; IoT; trusted execution environments; data security; personalized privacy protection; federated learning; cybersecurity; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Yanchen Qiao

E-Mail Website
Guest Editor
Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China
Interests: network security; malware analysis
Dr. Keshav Sood

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: modern network security (Software-defined networking, Internet of Things, and 5G)
Special Issues, Collections and Topics in MDPI journals
Dr. Bruce Gu

E-Mail Website
Guest Editor
College of Engineering and Science, Victoria University, Footscray, VIC 3011, Australia
Interests: artificial intelligence; cybersecurity; privacy; IoT

Special Issue Information

Dear Colleagues,

In recent years, the integration of advanced smart sensors, edge computing and artificial intelligence has jointly resulted in an emerging research direction known as “Edge Intelligence (EI)” which also boosts the development of the Internet of Things (IoT). It has attracted increasing attention from both the industry and academia. Consequently, numerous tech giants, e.g., CISCO1, Microsoft2, and AWS3, have developed polyfunctional EI solutions. Such benefit further unleashes the potential of AI frontiers by pushing them to the network edge. However, in the Edge Intelligent environments, data are collected by sensors and other edge devices with limited resources to deploy protection mechanisms, which make them prone to diverse security and privacy risks, e.g., sensitive information leakage and poisoning attacks. To boost the performance of EI in IoT scenarios, various advanced techniques are deployed, including Natural Language Processing (NLP), Computer Vision (CV), etc. This brings further privacy concerns such as image privacy and semantic privacy. Federated Learning (FL), as a distributed machine learning paradigm, has been proved to provide both privacy protection and performance improvement for edge computing and edge intelligence. An increasing number of variants of FL has been proposed to further improve its feasibility in real-world applications, such as asynchronous FL, decentralized FL, hierarchical FL, etc. With this Special Issue, the researchers are encouraged to further explore the potential of FL that can be applied to enhance and popularize Edge Intelligence.

The interested topics include but not limited to

  • Novel paradigms for performance improvement of Edge Intelligence.
  • Exclusive privacy concerns in existing FL paradigms, especially considering limited resources in IoT scenarios.
  • Attacks or countermeasures on Edge Intelligence, such as backdoor attacks, member inference attacks, etc.
  • Federated techniques like Federated NLP, Federated CV, etc.
  • Large-scale optimization towards various perspectives.
  • Edge intelligence enhanced federated learning.
  • Sensor-driven Edge Intelligence applications, like sports management, choreography arts, education, supply chain, etc.

Dr. Youyang Qu
Dr. Yanchen Qiao
Dr. Keshav Sood
Dr. Bruce Gu
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

  • edge intelligence
  • privacy
  • federated learning
  • NLP
  • CV
  • applications

Published Papers (2 papers)

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Research

25 pages, 14363 KiB  
Article
Object-Oriented and Visual-Based Localization in Urban Environments
by Bo-Lung Tsai and Kwei-Jay Lin
Sensors 2024, 24(6), 2014; https://doi.org/10.3390/s24062014 - 21 Mar 2024
Viewed by 467
Abstract
In visual-based localization, prior research falls short in addressing challenges for the Internet of Things with limited computational resources. The dominant state-of-the-art models are based on separate feature extractors and descriptors without consideration of the constraints of small hardware, the issue of inconsistent [...] Read more.
In visual-based localization, prior research falls short in addressing challenges for the Internet of Things with limited computational resources. The dominant state-of-the-art models are based on separate feature extractors and descriptors without consideration of the constraints of small hardware, the issue of inconsistent image scale, or the presence of multi-objects. We introduce “OOPose”, a real-time object-oriented pose estimation framework that leverages dense features from off-the-shelf object detection neural networks. It balances between pixel-matching accuracy and processing speed, enhancing overall performance. When input images share a comparable set of features, their matching accuracy is substantially heightened, while the reduction in image size facilitates faster processing but may compromise accuracy. OOPose resizes both the original library and cropped query object images to a width of 416 pixels. This adjustment results in a 2.4-fold improvement in pose accuracy and an 8.6-fold increase in processing speed. Moreover, OOPose eliminates the need for traditional sparse point extraction and description processes by capitalizing on dense network backbone features and selecting the detected query objects and sources of object library images, ensuring not only 1.3 times more accurate results but also three times greater stability compared to real-time sparse ORB matching algorithms. Beyond enhancements, we demonstrated the feasibility of OOPose in an autonomous mobile robot, enabling self-localization with a single camera at 10 FPS on a single CPU. It proves the cost-effectiveness and real-world applicability of OOPose for small embedded devices, setting the stage for potential markets and providing end-users with distinct advantages. Full article
(This article belongs to the Special Issue Edge Intelligence for Internet of Things: Architecture, Privacy, and Applications)
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20 pages, 2133 KiB  
Article
Mobility-Aware Federated Learning Considering Multiple Networks
by Daniel Macedo, Danilo Santos, Angelo Perkusich and Dalton C. G. Valadares
Sensors 2023, 23(14), 6286; https://doi.org/10.3390/s23146286 - 10 Jul 2023
Viewed by 948
Abstract
Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility [...] Read more.
Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility can hinder training by causing a client dropout when a device loses connection with other devices on the network. To address this issue, we propose a FL coordination algorithm, MoFeL, to ensure efficient training even in scenarios with mobility. Furthermore, MoFeL evaluates multiple networks with different central servers. To evaluate its effectiveness, we conducted simulation experiments using an image classification application that utilizes machine models trained by a convolutional neural network. The simulation results demonstrate that MoFeL outperforms traditional training coordination algorithms in FL, with 156.5% more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects. Full article
(This article belongs to the Special Issue Edge Intelligence for Internet of Things: Architecture, Privacy, and Applications)
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