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Edge-Based AI for the Internet of Things
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Edge-Based AI for the Internet of Things

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

Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 8989

Special Issue Editors

Dr. Kamal Singh

E-Mail Website
Guest Editor
University of Saint Etienne, France
Interests: mobile networks; Internet of Things; edge computing; NFV/SDNQoE
Special Issues, Collections and Topics in MDPI journals
Dr. Abbas Bradai

E-Mail Website
Guest Editor
University of Poitiers, France
Interests: multimedia communications over wired and wireless networks; software-defined network and virtualization.
Dr. Yassine Hadjadj-Aoul

E-Mail Website
Guest Editor
ESIR, University of Rennes 1, 35042 Rennes CEDEX, France
Interests: quality of service and quality of experience; wireless and mobile networks; future networks; performance evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The Internet of Things paradigm enables various smart objects to be connected to the Internet. This, in turn, enables smart applications that allow us to interact with our environment in an intelligent way. However, in order to achieve the full potential of IoT applications, we need AI techniques to analyze the data produced by IoT objects. AI techniques are needed to analyze the data in order to extract insights and actionable knowledge.

The use of AI approaches to analyze IoT data faces several challenges due to data heterogeneity and limited resources of most IoT objects. On the one hand, the data cannot be processed in the objects and on the other hand processing data using AI techniques in the cloud incurs a substantial amount of latency. Thus, edge-based AI techniques are required that can process the data on the edge near the applications and objects. The idea is that edge AI infrastructure will process the data in order to minimize the delay and will cooperate with the central cloud for the processing that cannot be done on the edge.

This Special Issue targets AI techniques and data processing applied to the Internet of Things. The focus will be on edge AI techniques applied to IoT data and applications. This will enable the distribution of the intelligence of IoT applications across the cloud and the edge.

Topics of interest include, but are not limited to the following:

  • Edge AI for IoT
  • Distributed data processing for IoT
  • Distributed reasoning for IoT
  • Edge AI architecture
  • Distributed AI algorithms
  • Distributed ML for IoT
  • Federated learning for IoT
  • Reinforcement learning for IoT
  • Edge machine learning for energy efficiency in IoT
  • Edge AI vs cloud AI for IoT
Dr. Kamal Singh
Dr. Abbas Bradai
Dr. Yassine Hadjadj-Aoul
Guest Editor

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.

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Published Papers (2 papers)

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Research

22 pages, 11153 KiB  
Article
Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices
by Aleks Huč, Jakob Šalej and Mira Trebar
Sensors 2021, 21(14), 4946; https://doi.org/10.3390/s21144946 - 20 Jul 2021
Cited by 14 | Viewed by 5183
Abstract
The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the [...] Read more.
The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training dataset was reduced by randomly selecting a smaller number of samples to create new datasets Di (i = 1, 2, 5, 10, 15, 20, 40, 60, 80%). Afterwards, they were used with several machine learning algorithms to identify the size at which the performance metrics show saturation and classification results stop improving with an F1 score equal to 0.95 or higher, which happened at 20% of the training dataset. Further on, two solutions for the reduction of the number of samples to provide a balanced dataset are given. In the first, datasets DRi consist of all anomalous samples in seven classes and a reduced majority class (‘NL’) with i = 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20 percent of randomly selected samples. In the second, datasets DCi are generated from the representative samples determined with clustering from the training dataset. All three dataset reduction methods showed comparable performance results. Further evaluation of training times and memory usage on Raspberry Pi 4 shows a possibility to run ML algorithms with limited sized datasets on edge devices. Full article
(This article belongs to the Special Issue Edge-Based AI for the Internet of Things)
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29 pages, 1166 KiB  
Article
Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing
by Shilin Xu and Caili Guo
Sensors 2020, 20(23), 6820; https://doi.org/10.3390/s20236820 - 29 Nov 2020
Cited by 5 | Viewed by 2734
Abstract
To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive [...] Read more.
To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives. Full article
(This article belongs to the Special Issue Edge-Based AI for the Internet of Things)
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