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Applied Sciences
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Machine Learning-Based Internet of Vehicles and Internet of Things Systems
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Machine Learning-Based Internet of Vehicles and Internet of Things Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 960

Special Issue Editors

Dr. Congduan Li

E-Mail Website
Guest Editor
School of Electrical Communication Engineering, Sun Yat-sen University, Shenzhen 518000, China
Interests: information theory and coding; security; ISAC; AI; vehicle–road collaboration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Min Li

E-Mail Website
Guest Editor
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: network information theory; integrated sensing and communication; millimeter-wave communications; AI-driven wireless design; information security
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangping Zhai

E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: Internet of Flying Things; mobile networks; edge computing; resource optimization; distributed learning

Special Issue Information

Dear Colleagues, 

The rapid evolution of the Internet of Vehicles (IoV) and the Internet of Things (IoT) is transforming modern intelligent transportation and smart environments. Machine learning (ML) plays a crucial role in enhancing the efficiency, security, and adaptability of these interconnected systems. By leveraging ML techniques, IoV and IoT can achieve real-time data processing, intelligent decision-making, anomaly detection, and predictive analytics, thereby improving traffic management, vehicle automation, and smart city applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Machine learning models for IoV and IoT: Deep learning, reinforcement learning, and federated learning applications.
  2. Intelligent traffic management: ML-based congestion control, route optimization, and real-time navigation.
  3. Autonomous and connected vehicles: AI-driven vehicular communication, decision-making, and cybersecurity.
  4. Predictive analytics and anomaly detection: ML-based fault detection, predictive maintenance, and risk assessment in IoT and IoV.
  5. Security and privacy in IoV and IoT: AI-driven threat detection, blockchain integration, and intrusion prevention.
  6. Edge and cloud computing in IoT/IoV: Distributed ML models for efficient data processing.
  7. Smart city applications: AI-enhanced IoT systems for urban planning, energy management, and environmental monitoring.
  8. 5G and beyond in IoV and IoT: ML optimization in high-speed and ultra-low latency networks.

Dr. Congduan Li
Prof. Dr. Min Li
Dr. Xiangping Zhai
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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

  • machine learning
  • Internet of Vehicles (IoV)
  • Internet of Things (IoT)
  • deep learning
  • intelligent transportation
  • cybersecurity
  • smart cities
  • predictive analytics
  • autonomous vehicles
  • edge computing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 23172 KB  
Article
UGV Formation Path Planning Based on DRL-DWA in Off-Road Environments
by Congduan Li, Yiqi Zhang, Dan Song, Nanfeng Zhang, Lei Chen, Jingfeng Yang, Li Wang and Xiangping Bryce Zhai
Appl. Sci. 2025, 15(22), 12212; https://doi.org/10.3390/app152212212 - 18 Nov 2025
Viewed by 239
Abstract
Uneven terrains and complex obstacles in off-road environments present significant challenges to the stability and safety of vehicle path planning. This paper presents a hierarchical DRL-DWA path planning framework for unmanned ground vehicles (UGVs). At the global level, an energy-aware D* Lite algorithm [...] Read more.
Uneven terrains and complex obstacles in off-road environments present significant challenges to the stability and safety of vehicle path planning. This paper presents a hierarchical DRL-DWA path planning framework for unmanned ground vehicles (UGVs). At the global level, an energy-aware D* Lite algorithm generates cost-efficient waypoints considering both distance and energy consumption. At the local level, a deep reinforcement learning-enhanced DWA controller adaptively adjusts the weighting factors of evaluation functions in real time to ensure dynamic feasibility on rough terrain. The parameter selection is formulated as a Markov decision process (MDP), where a novel reward function based on elevation maps, vehicle pose, goal, and obstacle information guides the optimization for off-road navigation. Furthermore, the single UGV framework is extended to a multi-UGV system, where formation control is achieved through the leader–follower strategy. To evaluate the performance of our algorithm, we conduct experiments in 3D simulation environments featuring various terrains and obstacles. The results indicate that the proposed approach outperforms existing path planning techniques, showing a higher success rate and a lower average elevation gradient in uneven terrains. Full article
(This article belongs to the Special Issue Machine Learning-Based Internet of Vehicles and Internet of Things Systems)
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17 pages, 2347 KB  
Article
A Convolutional Neural Network-Based Vehicle Security Enhancement Model: A South African Case Study
by Thapelo Samuel Matlala, Michael Moeti, Khuliso Sigama and Relebogile Langa
Appl. Sci. 2025, 15(19), 10584; https://doi.org/10.3390/app151910584 - 30 Sep 2025
Viewed by 442
Abstract
This paper applies a Convolutional Neural Network (CNN)-based vehicle security enhancement model, with a specific focus on the South African context. While conventional security systems, including immobilizers, alarms, steering locks, and GPS trackers, provide a baseline level of protection, they are increasingly being [...] Read more.
This paper applies a Convolutional Neural Network (CNN)-based vehicle security enhancement model, with a specific focus on the South African context. While conventional security systems, including immobilizers, alarms, steering locks, and GPS trackers, provide a baseline level of protection, they are increasingly being circumvented by technologically adept adversaries. These limitations have spurred the development of advanced security solutions leveraging artificial intelligence (AI), with a particular emphasis on computer vision and deep learning techniques. This paper presents a CNN-based Vehicle Security Enhancement Model (CNN-based VSEM) that integrates facial recognition with GSM and GPS technologies to provide a robust, real-time security solution in South Africa. This study contributes a novel integration of CNN-based authentication with GSM and GPS tracking in the South African context, validated on a functional prototype.The prototype, developed on a Raspberry Pi 4 platform, was validated through practical demonstrations and user evaluations. The system achieved an average recognition accuracy of 85.9%, with some identities reaching 100% classification accuracy. While misclassifications led to an estimated False Acceptance Rate (FAR) of ~5% and False Rejection Rate (FRR) of ~12%, the model consistently enabled secure authentication. Preliminary latency tests indicated a decision time of approximately 1.8 s from image capture to ignition authorization. These results, together with positive user feedback, confirm the model’s feasibility and reliability. This integrated approach presents a promising advancement in intelligent vehicle security for regions with high rates of vehicle theft. Future enhancements will explore the incorporation of 3D sensing, infrared imaging, and facial recognition capable of handling variations in facial appearance. Additionally, the model is designed to detect authorized users, identify suspicious behaviour in the vicinity of the vehicle, and provide an added layer of protection against unauthorized access. Full article
(This article belongs to the Special Issue Machine Learning-Based Internet of Vehicles and Internet of Things Systems)
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