Advances in Machine Learning Applications to Autonomous Vehicular Networks

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

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