Social Vehicle Swarms: A Novel Perspective on Social-aware Vehicular Communication Architecture

TL;DR

This paper introduces social vehicle swarms as a new approach to socially aware vehicular communication, utilizing agent-based models and advanced technologies like deep reinforcement learning to improve data relevance and privacy.

Contribution

It proposes a novel social vehicle swarm framework with an agent-based model and integrates deep learning, data mining, and sub-cloud computing for enhanced vehicular communication.

Findings

Identifies hidden patterns in vehicular data

Demonstrates effective detection of significant information

Discusses key challenges and future research directions

Abstract

Internet of vehicles is a promising area related to D2D communication and internet of things. We present a novel perspective for vehicular communications, social vehicle swarms, to study and analyze socially aware internet of vehicles with the assistance of an agent-based model intended to reveal hidden patterns behind superficial data. After discussing its components, namely its agents, environments, and rules, we introduce supportive technology and methods, deep reinforcement learning, privacy preserving data mining and sub-cloud computing, in order to detect the most significant and interesting information for each individual effectively, which is the key desire. Finally, several relevant research topics and challenges are discussed.