Hybrid-Vehfog: A Robust Approach for Reliable Dissemination of Critical Messages in Connected Vehicles

TL;DR

Hybrid-Vehfog is a novel hybrid fog computing and multi-hop dissemination approach designed to reliably transmit critical messages in dense and obstacle-shadowed vehicular environments, enhancing road safety.

Contribution

It introduces a dynamic hybrid technique combining fog computing and multi-hop methods for reliable message dissemination in challenging urban vehicular scenarios.

Findings

Outperforms existing schemes like CMDS, CLBP, PrEPARE, and Fog-NDN across various vehicle densities.

Demonstrates robustness and efficiency in obstacle-shadowing urban environments.

Effective deployment of drones enhances message delivery reliability.

Abstract

Vehicular Ad-hoc Networks (VANET) enable efficient communication between vehicles with the aim of improving road safety. However, the growing number of vehicles in dense regions and obstacle shadowing regions like Manhattan and other downtown areas leads to frequent disconnection problems resulting in disrupted radio wave propagation between vehicles. To address this issue and to transmit critical messages between vehicles and drones deployed from service vehicles to overcome road incidents and obstacles, we proposed a hybrid technique based on fog computing called Hybrid-Vehfog to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. Our proposed algorithm dynamically adapts to changes in an environment and benefits in efficiency with robust drone deployment capability as needed. Performance of Hybrid-Vehfog is carried out in Network Simulator (NS-2) and Simulation of Urban Mobility (SUMO) simulators. The results showed that Hybrid-Vehfog outperformed Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP), PEer-to-Peer protocol for Allocated REsource (PrEPARE), Fog-Named Data Networking (NDN) with mobility, and flooding schemes at all vehicle densities and simulation times.