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Special Issue "Machine Learning for IoT Applications and Digital Twins"

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

Deadline for manuscript submissions: 15 September 2020.

Special Issue Editors

Dr. Javad Rezazadeh
Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney (UTS), Australia
Interests: Internet of Things (IoT); localization; Machine Learning
Dr. Omid Ameri Sianaki
Website
Guest Editor
Victoria University Business School, Australia
Interests: Internet of Things (IoT); Machine Learning; data analytics
Dr. Reza Farahbakhsh
Website
Guest Editor
Institut Polytechnique de Paris, Telecom SudParis, CNRS Lab, Evry, France
Interests: Internet of Things (IoT); data science; social networks

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT), one of the emergent technologies that has improved the living environment of human beings, is the source of Big Data generation. In IoT networks, there are many ubiquitous interconnected sensors from different machines or devices. There is a necessity of having novel tools and techniques for processing the huge volume of data and transform them to knowledge. In addition, machine learning techniques have been used comprehensively for a variety of IoT applications. Analysis of IoT sensor data with machine learning algorithms is key for achieving useful information for prediction, classification, data association. and data conceptualization.

On the other hand, Digital Twin integrates IoT, Artificial Intelligence, and Machine Learning with Software Analytics to create digital living.

Thus, this Special Issue welcomes original contributions and review papers on Machine Learning for IoT applications and Digital Twin, in the following potential areas:

  • Machine Learning for Smart City/Smart Home/Smart Transportation;
  • Machine Learning for Smart Health/Smart Wearable Devices;
  • Machine Learning for Smart Industry/Smart Grid/Smart Agriculture;
  • Digital Twins integrated with IoT;
  • Smart Applications of Digital Twin;
  • Data-driven scenarios based on Digital Twin leveraging AI;
  • Blockchain and Security for Digital Twin.

Dr. Javad Rezazadeh
Dr. Omid Ameri Sianaki
Dr. Reza Farahbakhsh
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 papers will be 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 2000 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.

Published Papers (4 papers)

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Research

Open AccessArticle
A Generalized Threat Model for Visual Sensor Networks
Sensors 2020, 20(13), 3629; https://doi.org/10.3390/s20133629 - 28 Jun 2020
Abstract
Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to [...] Read more.
Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to traditional networks, sensor networks typically face numerous additional vulnerabilities due to the dynamic and distributed network topology, the resource constrained nodes, the potentially large network scale and the lack of global network knowledge. These vulnerabilities allow attackers to launch more severe and complicated attacks. Since the state-of-the-art is lacking studies on vulnerabilities in VSNs, a thorough investigation of attacks that can be launched against VSNs is required. This paper presents a general threat model for the attack surfaces of visual sensor network applications and their components. The outlined threats are classified by the STRIDE taxonomy and their weaknesses are classified using CWE, a common taxonomy for security weaknesses. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins)
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Open AccessArticle
Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications
Sensors 2020, 20(8), 2417; https://doi.org/10.3390/s20082417 - 24 Apr 2020
Abstract
Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the [...] Read more.
Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins)
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Open AccessArticle
Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
Sensors 2020, 20(8), 2276; https://doi.org/10.3390/s20082276 - 16 Apr 2020
Cited by 1
Abstract
Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, [...] Read more.
Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins)
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Open AccessArticle
Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices
Sensors 2020, 20(5), 1359; https://doi.org/10.3390/s20051359 - 02 Mar 2020
Cited by 1
Abstract
Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control [...] Read more.
Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well. Therefore, we may need to apply independent reinforcement learning to each IoT device individually, which requires a costly or time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent IoT device shares their learning experience (i.e., the gradient of loss function) with each other, and transfers a mature policy model parameters into other agents. They accelerate its learning process by using mature parameters. We incorporate the actor–critic proximal policy optimization (Actor–Critic PPO) algorithm into each agent in the proposed collaborative architecture and propose an efficient procedure for the gradient sharing and the model transfer. Using multiple rotary inverted pendulum devices interconnected via a network switch, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices and that the learning speed can be faster if more agents are involved. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins)
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