Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.

Area of Science:

• Computer Science
• Electrical Engineering
• Transportation Systems

Background:

• Internet of Vehicles (IoV) technology offers significant potential for improving driving experiences and transportation systems.
• IoV systems face unique challenges compared to traditional Internet of Things (IoT) due to vehicle mobility and complex road environments.
• Developing efficient IoV systems requires addressing these mobility and environmental complexities.

Purpose of the Study:

• To review and analyze existing studies on enabling collaborative intelligence in IoV systems.
• To focus on collaborative communications, collaborative computing, and collaborative machine learning approaches within IoV.
• To identify open research issues and future research directions in IoV collaborative intelligence.

Main Methods:

• Literature review and comparative analysis of existing studies on IoV collaborative intelligence.
• Categorization of studies based on collaborative communications, computing, and machine learning.
• Discussion of the advantages and disadvantages of current research approaches.

Main Results:

• Existing studies explore various methods for collaborative intelligence in IoV.
• Collaborative communications, computing, and machine learning are key enablers for advanced IoV functionalities.
• A significant gap exists in comprehensive solutions addressing the unique challenges of IoV.

Conclusions:

• Collaborative intelligence is crucial for unlocking the full potential of IoV.
• Further research is needed to overcome the inherent complexities of mobile and dynamic IoV environments.
• Future directions include developing robust and scalable collaborative strategies for IoV systems.