Special Issue "Advances in Machine Learning Applications to Autonomous Vehicular Networks"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 2979

Special Issue Editors

Dr. Daniel Gutiérrez Reina
E-Mail Website
Guest Editor
Electronic Engineering Department, University of Seville, Calle San Fernando, 4, 41004 Sevilla, Spain
Interests: multi-hop networks; sensor networks; VANETs; FANETs; evolutionary computation; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sergio Toral Marín
E-Mail Website
Co-Guest Editor
Electronic Engineering Department, University of Seville, Calle San Fernando, 4, 41004 Sevilla, Spain
Interests: machine learning; UAVs; multi-hop networks; deep learning

Special Issue Information

Dear Colleagues,

Autonomous vehicular networks (AVNs) have experienced enormous attention from the research community and industry in the last decade. A plethora of applications can be accomplished by the cooperation and coordination of a fleet of vehicles that communicate with each other through wireless links. AVNs can be found both in aerial and aquatic scenarios for applications including monitoring and sensing, communication services, disaster relief, and goods delivery, among others. Many issues should be addressed in a distributed manner for the successful implementation of such applications. Therefore, the classical and new issues of mobile networks should be reformulated for the case of AVN scenarios.

Machine learning techniques have gained tremendous momentum in the last few years due to the availability of massive data and high computational resources at a low cost. However, classical machine learning approaches, such as supervised and unsupervised learning and evolutionary algorithms, work on centralized systems. Consequently, suffering synchronization and scalability problems in distributed systems like AVNs. This Special Issue will publish novel approaches of machine learning techniques for application in AVN scenarios. The main topics of interest include, but are not limited to the following:

  • Supervised machine learning techniques for AVNs
  • Unsupervised machine learning techniques for AVNs
  • Evolutionary computation for AVNs
  • Genetic programming for AVNs
  • Swarm intelligence for AVNs
  • Deep learning for AVNs
  • Reinforcement learning and deep reinforcement learning for AVNs
  • Bayesian optimization for AVNs
  • Game theory for wireless AVNs
  • Neural networks for AVNs
  • Soft computing approaches for AVNs
  • Blockchain approaches for AVNs

Dr. Daniel Gutiérrez Reina
Prof. Dr. Sergio Toral
Guest Editors

Manuscript Submission Information

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Keywords

  • Autonomous vehicular systems;
  • Machine learning;
  • UAVs;
  • Drones;
  • Deep learning;
  • Evolutionary computation.

Published Papers (3 papers)

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Research

Article
An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications
Electronics 2021, 10(13), 1605; https://doi.org/10.3390/electronics10131605 - 04 Jul 2021
Cited by 1 | Viewed by 794
Abstract
Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense [...] Read more.
Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques. Full article
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Article
A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study
Electronics 2021, 10(8), 963; https://doi.org/10.3390/electronics10080963 - 18 Apr 2021
Cited by 3 | Viewed by 855
Abstract
Bayesian optimization is a sequential method that can optimize a single and costly objective function based on a surrogate model. In this work, we propose a Bayesian optimization system dedicated to monitoring and estimating multiple water quality parameters simultaneously using a single autonomous [...] Read more.
Bayesian optimization is a sequential method that can optimize a single and costly objective function based on a surrogate model. In this work, we propose a Bayesian optimization system dedicated to monitoring and estimating multiple water quality parameters simultaneously using a single autonomous surface vehicle. The proposed work combines different strategies and methods for this monitoring task, evaluating two approaches for acquisition function fusion: the coupled and the decoupled techniques. We also consider dynamic parametrization of the maximum measurement distance traveled by the ASV so that the monitoring system balances the total number of measurements and the total distance, which is related to the energy required. To evaluate the proposed approach, the Ypacarai Lake (Paraguay) serves as the test scenario, where multiple maps of water quality parameters, such as pH and dissolved oxygen, need to be obtained efficiently. The proposed system is compared with the predictive entropy search for multi-objective optimization with constraints (PESMOC) algorithm and the genetic algorithm (GA) path planning for the Ypacarai Lake scenario. The obtained results show that the proposed approach is 10.82% better than other optimization methods in terms of R2 score with noiseless measurements and up to 17.23% better when the data are noisy. Additionally, the proposed approach achieves a good average computational time for the whole mission when compared with other methods, 3% better than the GA technique and 46.5% better than the PESMOC approach. Full article
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Article
Time Efficient Unmanned Aircraft Systems Deployment in Disaster Scenarios Using Clustering Methods and a Set Cover Approach
Electronics 2021, 10(4), 422; https://doi.org/10.3390/electronics10040422 - 09 Feb 2021
Cited by 1 | Viewed by 768
Abstract
Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network [...] Read more.
Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network of elements that can communicate, the system is said to form an Unmanned Aircraft System (UAS). This system is particularly effective when the aircraft within are organized into swarms with sets of objectives to accomplish. The extensive use of swarms into UASs is more and more exploited nowadays due to the decreasing cost of those aircraft. In the present work we are interested in a particular application of UASs, namely their deployment in disaster scenarios for communications services provision to targets on the ground. These ground targets, however, are not part of the UASs and should not be confused with ground-based controllers. The present work does not only focus on coverage for ground targets but also on a guaranteed minimum number of covers for each target, which is called the redundancy requirement. The research work also ensures that the deployed UAS forms a unique connected component so that a steady stream of communication is kept with the targets to cover. Research work similar to the present perform the initial deployment of their aircraft in a different manner, either randomly, based on a predetermined grid formation, or using other elaborated methods. This work proposes a new solution based on the use of clustering algorithms, combined to a design of the problem formulated as a set cover optimization model. The clustering phase is used to discretize the search space and ease the optimization phase by locating regions of interest, and then a further procedure is applied, only when needed, to reconnect scattered connected components and guarantee connectivity in the networks. This way of doing it has achieved a deployment of UASs with maximum coverage for all targets, a guaranteed minimum number of covers for each of them, and results in a competitive computation time. The latter also allowed for more scalability by extending the tests to very large input instances. Full article
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