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Electronics
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Active Mobility: Innovations, Technologies, and Applications
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Active Mobility: Innovations, Technologies, and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 6237

Special Issue Editors

Dr. Yeray Mezquita

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Guest Editor
Department of Informatics and Automatics, University of Salamanca, 37007 Salamanca, Spain
Interests: blockchain and Internet of Things technologies; edge computing; systems optimization; artificial intelligence
Dr. Javier Parra Domínguez

E-Mail Website
Guest Editor
Department of Business Studies, School of Economics and Business, University of Salamanca, 37007 Salamanca, Spain
Interests: accounting; econometry; sustainability; finance; business intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Rodriguez

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; distributed computing; machine learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Active mobility, which includes a variety of human-powered means of transportation, such as walking, bicycling, and scootering, has recently attracted a lot of attention. The development of active mobility presents a possible option to address important challenges in metropolitan areas linked to congestion, pollution, and public health. In this context, we are delighted to announce a Special Issue dedicated to exploring the intersection of active mobility and technology.

The aim of the Special Issue is to compile contributions from researchers, practitioners, and enthusiasts in the area of active mobility and its integration with electronic systems, with a particular emphasis on the developments and uses of technology in this field. We invite researchers, practitioners, and enthusiasts to contribute their findings on topics related to active mobility and its integration with electronic systems. Smart mobility solutions, data analytics, the Internet of Things (IoT), wireless sensor networks, and human–machine interactions are just a few of the topics covered.

The contributions to this Special Issue will shed light on the transformative potential of the fusion of active mobility and electronic technologies, providing insights into the latest developments, challenges, and opportunities in this field. We encourage authors to explore the theoretical frameworks, practical analyses, novel use cases, and innovative applications of active mobility and electronic systems. The objective is to promote interdisciplinary cooperation and encourage upcoming research that advances and seamlessly integrate active mobility into the digital environment. Manuscripts may provide state-of-the-art reviews, empirical studies, methodological approaches, or design and developments of these systems. We are confident that this Special Issue will be a valuable platform for knowledge exchange and the dissemination of cutting-edge research on active mobility and the future of last-mile logistics.

The suggested topics of interest, although not exhaustive, include the following:

  • Smart mobility solutions for urban transportation;
  • Internet of Things (IoT) in urban mobility systems;
  • Data analytics and machine learning for urban mobility;
  • Wireless sensor networks for urban mobility monitoring;
  • Urban mobility and sustainable city logistics;
  • Urban mobility and public health: impacts and benefits;
  • Urban mobility and energy efficiency: integration and optimization.

Dr. Yeray Mezquita
Dr. Javier Parra Domínguez
Dr. Sara Rodriguez
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. Electronics 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 2400 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

  • active mobility
  • machine learning
  • data analytics
  • Internet of Things
  • energy efficiency
  • smart urban planning
  • wireless sensor networks

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Published Papers (4 papers)

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Research

15 pages, 2460 KiB  
Article
A Deep Learning-Based Intrusion Detection Model Integrating Convolutional Neural Network and Vision Transformer for Network Traffic Attack in the Internet of Things
by Chunlai Du, Yanhui Guo and Yuhang Zhang
Electronics 2024, 13(14), 2685; https://doi.org/10.3390/electronics13142685 - 9 Jul 2024
Viewed by 1013
Abstract
With the rapid expansion and ubiquitous presence of the Internet of Things (IoT), the proliferation of IoT devices has reached unprecedented levels, heightening concerns about IoT security. Intrusion detection based on deep learning has become a crucial approach for safeguarding IoT ecosystems. However, [...] Read more.
With the rapid expansion and ubiquitous presence of the Internet of Things (IoT), the proliferation of IoT devices has reached unprecedented levels, heightening concerns about IoT security. Intrusion detection based on deep learning has become a crucial approach for safeguarding IoT ecosystems. However, challenges remain in IoT intrusion detection research, including inadequate feature representation at the classifier level and poor correlation among extracted traffic features, leading to diminished classification accuracy. To address these issues, we propose a novel transformer-based IoT intrusion detection model, MBConv-ViT (MobileNet Convolution and Vision Transformer), which enhances the correlation of extracted features by fusing local and global features. By leveraging the high correlation of traffic flow, our model can identify subtle differences in IoT traffic flow, thereby achieving precise classification of attack traffic. Experiments based on the open datasets TON-IoT and Bot-IoT demonstrate that the accuracy of the MBConv-ViT model, respectively, 97.14% and 99.99%, is more effective than several existing typical models. Full article
(This article belongs to the Special Issue Active Mobility: Innovations, Technologies, and Applications)
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22 pages, 4693 KiB  
Article
Urban Traffic Simulation Using Mobility Patterns Synthesized from Real Sensors
by Fábio Gonçalves, Gonçalo O. Silva, Alexandre Santos, Ana Maria A. C. Rocha, Hugo Peixoto, Dalila Durães and José Machado
Electronics 2023, 12(24), 4971; https://doi.org/10.3390/electronics12244971 - 12 Dec 2023
Cited by 4 | Viewed by 1552
Abstract
Smart cities are an ongoing research topic with multiple sub-research areas, from traffic control to optimization and even safety. However, testing the new methodologies or technologies directly in the real world is an almost impossible feat that, inclusively, can result in disaster. Thus, [...] Read more.
Smart cities are an ongoing research topic with multiple sub-research areas, from traffic control to optimization and even safety. However, testing the new methodologies or technologies directly in the real world is an almost impossible feat that, inclusively, can result in disaster. Thus, there is the importance of simulation. Simulation enables testing new and complex methodologies and gauging their impact in a realistic context without adding any safety issues. Additionally, these can accurately map real-world conditions depending on the simulation configuration. One key aspect of the simulation is the traffic flows in the simulated region. These may be hard to find and, if ill-set, may introduce bias in the results. This work is on the characterization of the traffic in the city center of Guimarães, Portugal. An urban simulation scenario was established, using SUMO as the mobility traffic simulator, with traffic patterns derived from real-world data provided by Guimarães City Hall and using Eclipse MOSAIC for extended vehicular simulation. Apart from mobility patterns analysis, this work also provides publicly accessible datasets, simulations, and applications made available to future research works. Full article
(This article belongs to the Special Issue Active Mobility: Innovations, Technologies, and Applications)
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30 pages, 9262 KiB  
Article
Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds
by Evan Lowe and Levent Guvenc
Electronics 2023, 12(23), 4765; https://doi.org/10.3390/electronics12234765 - 24 Nov 2023
Cited by 3 | Viewed by 1840
Abstract
An autonomous vehicle (AV) uses high-level decision making and lower-level actuator controls, such as throttle (acceleration), braking (deceleration), and steering (change in lateral direction) to navigate through various types of road networks. Path planning and path following for highway driving are currently available [...] Read more.
An autonomous vehicle (AV) uses high-level decision making and lower-level actuator controls, such as throttle (acceleration), braking (deceleration), and steering (change in lateral direction) to navigate through various types of road networks. Path planning and path following for highway driving are currently available in series-produced highly automated vehicles. In addition to these, emergency collision avoidance decision making and maneuvering are another key and essential feature that is needed in a series production AV at highway driving speeds. For reliability, low cost, and fast computation, such an emergency obstacle avoidance maneuvering system should use well-established conventional methods as opposed to data-driven neural networks or reinforcement learning methods, which are currently not suitable for use in highway AV driving. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for AVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM framework offers usage of the AV’s sensing, perception, control, and actuation system abilities as one cohesive system to avoid an on-road obstacle, based first on performance feasibility and second on passenger comfort, and it is designed to be well integrated within an AV’s high-level control and decision-making system. To demonstrate the efficacy of the proposed method, co-simulation including the AV’s EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and interpreted in the context of an entire AV system, with implications for future work. Full article
(This article belongs to the Special Issue Active Mobility: Innovations, Technologies, and Applications)
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24 pages, 3596 KiB  
Article
Autonomous Vehicle Decision-Making with Policy Prediction for Handling a Round Intersection
by Xinchen Li, Levent Guvenc and Bilin Aksun-Guvenc
Electronics 2023, 12(22), 4670; https://doi.org/10.3390/electronics12224670 - 16 Nov 2023
Cited by 2 | Viewed by 1030
Abstract
Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round [...] Read more.
Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging because it is more difficult to perceive the other vehicles in and near the intersection. Thus, this paper focuses on the decision-making of autonomous vehicles for handling round intersections. The round intersection is introduced first, followed by introductions of the Markov Decision Process (MDP), the Partially Observable Markov Decision Process (POMDP) and the Object-Oriented Partially Observable Markov Decision Process (OOPOMDP), which are used for decision-making with uncertain knowledge of the motion of the other vehicles. The Partially Observable Monte-Carlo Planning (POMCP) algorithm is used as the solution method and OOPOMDP is applied to the decision-making of autonomous vehicles in round intersections. Decision-making is formulated first as a POMDP problem, and the penalty function is formulated and set accordingly. This is followed by an improvement in decision-making with policy prediction. Augmented objective state and policy-based state transition are introduced, and simulations are used to demonstrate the effectiveness of the proposed method for collision-free handling of round intersections by the ego vehicle. Full article
(This article belongs to the Special Issue Active Mobility: Innovations, Technologies, and Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Urban traffic simulation using mobility patterns synthesized from real sensors
Author: Santos
Highlights: - Traffic Simulation; - Simulation of Urban MObility; - Urban Mobility Datasets; - Eclipse MOSAIC

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