Multi-RAT-Enabled Data Offloading on Wheels
Edge computing architectures have greatly benefited from high-performance end systems and new-generation cellular networks. Smart cities now have the potential to synthesize all the aspects of a heterogeneous communication and computing infrastructure, leveraging high-definition sensors to propose multiple applications to a variety of road users. However, with the advent of connected vehicles and due to the short lifetime of OBU-to-RSU links, the issue of mobility threatens the reliability of task and data offloading. We thus propose a multi-RAT data offloading scheme exploiting device-to-device communications. In particular, considering the large amounts of data generated by an on-board lidar, we establish a bilateral offloading scheme where the RSU hosts a management algorithm. Based on this algorithm, different packet sizes may be requested based on the networking performance of each vehicle. Through extensive field trials and simulations, performance evaluation shows clear performance discrepancies between C-V2X and DSRC, with C-V2X being more appropriate for dynamically-managed offloading. 4192-byte packets sent at a 10 ms interval achieve the highest throughput with 96 % delivery rates. The proposed algorithm then achieves 30-to-45% increases in throughput when compared to an OBUcentered scheme due to improved resource allocation.
Authors
- André‐Luc Beylot (ORCID: https://orcid.org/0000-0002-9049-4723)
- Lucas Bréhon–Grataloup (ORCID: https://orcid.org/0000-0002-0661-8480)
- Rahim Kacimi (ORCID: https://orcid.org/0000-0002-2784-4305)
- Igor Dias Da Silva
Institutions
- Centre National de la Recherche Scientifique (FR)
- Université Toulouse III - Paul Sabatier (FR)
- Université Toulouse-I-Capitole (FR)
- Institut de Recherche en Informatique de Toulouse (FR)
- Université Toulouse - Jean Jaurès (FR)
- Institut Polytechnique de Bordeaux (FR)
Publication Details
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1109/icc52391.2025.11161210
- Primary Topic
- Modular Robots and Swarm Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00