ProBcNet: A Privacy-Preserving Adaptive Route Optimization Protocol for Internet of Consumer Vehicular Networks

Ashi Gupta, Rakesh Ranjan, Bharat Gupta, Debasis Das, Chinmay Chakraborty

Abstract: Conventional routing protocols utilized in Internet of Consumer Vehicles suffer from privacy invasion, vulnerability to attacks and network performance issues such as high latency, large overhead and low throughput. It is due to the highly dynamic nature of these vehicular networks. Hence, this paper proposes a hybrid communication protocol, ProBcNet, to enhance the security, privacy, and routing efficiency in Internet of Consumer Vehicles. The proposed protocol incorporates Physical Unclonable Functions to ensure secure vehicle authentication, utilizes Federated Learning and Reinforcement Learning for decentralized route optimization; and leverages Blockchain Technology to guarantee the immutability of logged data. Extensive simulations demonstrate that ProBcNet significantly outperforms state-of-the-art routing protocols. Specifically, ProBcNet achieves a higher throughput of 33.2 kbps, ensuring a stable network performance while reducing latency to 0.72 sec in comparison to the conventional approach. It also minimizes overhead to 0.41. Additionally, it optimizes packet delivery ratio to 0.723 demonstrating its suitability for real-time consumer vehicular applications.

External IDs:dblp:journals/tce/GuptaRGDC25