Segmentation-based Vehicle Location Detection (SVLD) Mechanism for Network Dwelling Range Extension and RSU Load Reduction in VANET V2R Communication

Vehicular Ad hoc Networks (VANETs) are specialized vehicle networks designed to provide a diverse variety of applications and services while maximizing network performance and efficiency. Nevertheless, VANETs have obstacles such as ever-changing network structure, rapid movement of vehicles in various directions, data overload, and limited duration of connectivity, all of which can result in poor efficiency in gathering and transferring data provided by vehicles. This hinders the progress of creating time-sensitive applications on VANETs. Implementing a vehicular infrastructure such as the Roadside Unit (RSU) is crucial for improving the efficiency of message distribution in VANETs. The dynamic nature of the vehicle environment in VANET leads to constraints in data delivery, such as problems with high density and connectivity duration. High-velocity vehicles can enter and exit the coverage area of RSU transmissions without obtaining all the data they requested. This puts additional strain on the RSUs and leads to greater rates of dropped requests, delays, and reduced responsiveness. Furthermore, in extensive networks characterized by frequent and rapid changes in connectivity and dynamic network structure, such as urban highways, Vehicle-to-roadside (V2R) communication experiences brief periods of connectivity when traveling at high speeds. Additionally, V2R communication does not receive any services when outside the Network Dwelling Range (NDR) of an RSU. This study introduces the Segmentation-Based Vehicle Location Detection (SVLD) mechanism that involves fragmenting the NDR of RSUs into overlapping segments and identifying vehicles within these segments. Experiments have been conducted in simulation using OMNeT++, SUMO and Veins with the goal of expanding the NDR and increase the duration of connectivity in V2R communication. Additionally, it aims to alleviate the burden on the RSU by facilitating a smooth transfer of workload to the next RSU. The results demonstrate a substantial expansion of the NDR using the RSU's NDR overlapping approach, as well as a reduction of 36.4% in the RSU's load.