Title: Data sharing based on conflict graph and clustering in VANET

Authors: Xu Ding; Xiang Zheng; Bixun Zhang; Pengfei Xu; Lei Shi

Addresses: Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment, Hefei University of Technology, Hefei, 230009, China; Institute of Industry and Equipment Technology, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

Abstract: Efficient data sharing in autonomous driving is expected to improve road safety. However, time-varying network topologies, limited roadside unit (RSU) coverage, and communication limitations make efficient data sharing challenging. Therefore, we propose an RSU-assisted hybrid scheduling (RAHC) scheme, including centralised scheduling within the RSU and self-organised scheduling in the RSU blind zone. For centralised scheduling, we model data transmission and transmission conflicts as conflict graph based on real-time network topology, and solve for optimal transmission paths while satisfying communication constraints, and increase the chance for vehicles in RSU blind areas to obtain data by carrying and forwarding data. For self-organised scheduling, we use degree of link similarity (DLS) to cluster vehicles and select the vehicle with the largest service capacity (SC) in the cluster to serve the opposite vehicle. This reduces duplication and redundancy of data transmission and improves sharing efficiency. Simulation results demonstrate the superiority of the solution.

Keywords: VANETs; RSU assisted; cooperative data sharing; topology construction; graph theory; cluster-based data sharing.

DOI: 10.1504/IJSNET.2023.134302

International Journal of Sensor Networks, 2023 Vol.43 No.2, pp.63 - 77

Received: 29 May 2023
Accepted: 18 Jul 2023
Published online: 17 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article