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Special Issue "Internet of Things and Artificial Intelligence in Transportation Revolution"

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

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Prof. Miltiadis D. Lytras
E-Mail Website
Guest Editor
1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi, Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues and Collections in MDPI journals
Dr. Kwok Tai Chui
E-Mail Website
Guest Editor
Department of Technology, School of Science and Technology, The Open University of Hong Kong, Hong Kong SAR
Interests: big data; bioinformatics; computational intelligence; data science; energy monitoring and management; intelligent transportation; optimization; semantic web
Special Issues and Collections in MDPI journals
Dr. Ryan Wen Liu
E-Mail Website
Guest Editor
Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: big data; computational transportation science; computer vision; data science; image processing; machine learning

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) has become the leading infrastructure for our modern and smart transport. Connected sensing devices provide crucial information for further analytics via artificial intelligence (AI) which aims at optimizing the performance of transportation systems and applications. One of the best examples is the autonomous vehicle which is supported by numerous sensors like GPS, sonar, Lidar, radar, camera, inertial and odometry modules.

There are many use cases of IoT applications in intelligent transportation system (ITS) named geo-fencing, asset utilization, inventory management, public transport management, traffic monitoring, situational awareness, urban planning, fleet management, predictive maintenance, and traffic safety enhancement. However, similar to other applications, the adoption of IoT in transportation also experiences technical challenges, for instance, security, privacy, standard, regulation, connectivity, network infrastructure and investment cost.

This special issue is intended to report high-quality research on recent advances towards IoT and AI in transportation revolution, more specifically to the state-of-the-art theories, methodologies and systems for the design, development, deployment and innovative use of those convergence technologies for providing insights into the theoretical and technological revolution in transportation science and engineering. The topics of interest include, but are not limited to the following:

  • Sensor-based traffic data acquisition in transportation revolution
  • IoT big data storage and mining techniques
  • Large scale traffic data mining and visualization
  • Multi-sensor data fusion for IoT applications
  • AI techniques for IoT big data in ITS systems
  • Signal, image and video processing technologies in ITS systems
  • Autonomous, semi-autonomous and IoT control
  • New theories and applications of AI techniques in transport IoT
  • Unmanned aerial vehicles-assisted communications in transport IoT
  • IoT-based situational awareness framework for ITS systems
  • 5G-enabled IoT sensors and techniques in transportation revolution

Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Dr. Ryan Wen Liu
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 1800 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 (3 papers)

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Research

Open AccessArticle
Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units
Sensors 2019, 19(23), 5283; https://doi.org/10.3390/s19235283 - 30 Nov 2019
Abstract
The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the [...] Read more.
The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments. Full article
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Open AccessArticle
Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications
Sensors 2019, 19(20), 4518; https://doi.org/10.3390/s19204518 - 17 Oct 2019
Abstract
As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely [...] Read more.
As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely applied in indoor localization in recent years. However, the crowdsourced data can hardly be fused easily to enable usable applications for the reason that the data are collected by different users, in different locations, at different times, with different noises and distortions. Although different data fusing methods have been implemented in different crowdsourcing services, we find that they may not fully leverage the data collected from multiple dimensions that can potentially lead to a better fusion results. In order to address this problem, we propose a more general solution, which can fuse the multi-dimensional crowdsourced data together and align them with the consistent time and location stamps, by using the features of the sensory data only, and thus build high quality crowdsourcing services from the raw data samplings collected from the environment. Finally, we conduct extensive evaluations and experiments using different commercial devices to validate the effectiveness of the method we proposed. Full article
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Open AccessArticle
Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning
Sensors 2019, 19(18), 4055; https://doi.org/10.3390/s19184055 - 19 Sep 2019
Cited by 1
Abstract
This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is [...] Read more.
This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protégé language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance. Full article
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