Social Vehicle Swarms: A Novel Perspective on Social-aware Vehicular Communication Architecture
This addresses the need for improved vehicular communication architectures in the internet of vehicles domain, but appears incremental as it builds on existing concepts with new methods.
The paper tackles the problem of analyzing socially aware internet of vehicles by proposing a novel perspective called social vehicle swarms, using an agent-based model to reveal hidden patterns in data, with results including the introduction of supportive technologies like deep reinforcement learning for effective information detection.
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.