Research – MNC Lab

VCC is an emerging research field that leverages the resources of vehicles, such as their computing, storage, and communication capabilities, to create a cloud-like infrastructure.
This dynamic and mobile cloud can provide various services to vehicles and their passengers, including traffic management, infotainment, and safety-critical applications.
Key research areas in VCC include dynamic resource allocation, mobility management, cloud recontruction, and efficient communication protocols.
Researchers are exploring how to effectively manage the dynamic and unpredictable nature of VCC environments, ensuring reliable and secure services.

CCVN shifts the communication paradigm from location-based addressing to content-based retrieval, which is inherently suited for the high mobility of vehicles.
V2V precaching is a proactive strategy that stores popular data in the local caches of neighboring vehicles before an explicit request is made, effectively turning vehicles into mobile data hubs.
Current research focuses on developing sophisticated mobility prediction and content popularity models to determine the optimal timing and location for data placement.
This approach significantly minimizes content delivery latency and reduces the signaling overhead on the core network by utilizing the short-range communication of moving nodes.
Modern studies increasingly leverage Machine Learning (ML) and cooperative caching schemes to handle the unpredictable topology and intermittent connectivity of the vehicular environment

CCVNs proactively stores popular content at Roadside Units (RSUs) or edge servers based on content names to facilitate faster retrieval.
This strategy aims to significantly reduce content delivery latency and backhaul pressure by predicting vehicle trajectories and pre-positioning data before a vehicle enters a specific RSU’s coverage area.
Research focuses on optimizing cache replacement policies and developing high-accuracy mobility prediction models to effectively manage the intermittent connectivity of high-speed vehicles.
Recent studies leverage Deep Reinforcement Learning (DRL) to dynamically allocate cache resources, balancing the trade-off between storage costs and user Quality of Service (QoS).

UAV-based Vehicular Networks utilize Unmanned Aerial Vehicles as flexible, high-altitude relays or edge servers to provide the high-bandwidth connectivity required for bandwidth-intensive multimedia traffic.
Research primarily focuses on joint trajectory optimization and resource allocation to maintain a stable Quality of Experience (QoE) for high-definition video streaming amidst high mobility.
Sophisticated interference management and beamforming techniques are being developed to mitigate signal fluctuations caused by the dynamic movement of both aerial and ground nodes.
Furthermore, current studies integrate AI-driven predictive modeling to proactively adjust to channel conditions, ensuring seamless and low-latency data delivery in complex urban environments.