Vehicular Intelligence: Towards Vehicular Network Digital-Twin

Vehicular network (VN) is considered as one of the enabling technologies that requires stable connectivity among the vehicles. The significant dynamics in vehicular structure on the road necessitate the rapid information flow in managing crucial events. Dynamically, each network has a given lifetime from the initial formulation of a networked group to the final diminishment of the group. Throughout this lifecycle, a group of vehicles form a network and open the door for further vehicles to join, or existing vehicles to leave. Vehicles departing the group may rupture the data network and cause fragmentation or isolation of part of the network structure that raises the need for smart self-healing. The response to these events may cause latency in handling the flow of data, and hence efficient operation with minimal latency is required. Such network demands a flexible and autonomous self-structure approach with real time computational capabilities. Realizing this vision requires a network that supports low latency for interaction among the members as with the edge, fog and/or cloud. It is anticipated that driving towards the novel concept of vehicular network digital twin will offer the platform with backup intelligence and support for designing the scenarios that can have better interaction with road dynamic events with minimal latency. The objective of the paper is modelling the latency associated with the dynamics of the vehicular grouping approach to investigate the performance of a given dynamic group running on the road. This is aligned with road model boundary and limitations. Herein, the applicability of the grouping approach is tested based on the road capacity supported by the road boundary and number of lanes. Testing using Contiki-Cooja simulator has been implemented focusing on the latency and packet loss performance measures for the self-healing and self-formation phases.