Ain Shams Engineering Journal (Jul 2023)
OPBRP - obstacle prediction based routing protocol in VANETs
Abstract
Vehicular Ad-hoc Networks (VANETs) are recently getting high attention from different researchers due to increasing traffic problems, especially in densely populated countries. Increasing rates of accidents call for an Intelligent Transportation System (ITS) with efficient performance to reduce and mitigate this trend. The required enhancement of the ITS can be more focused on traffic performance, integrity, and reduction of the vehicles’ CO2 emissions. Existing routing protocols for VANETs consider different situations and methods to establish reliable communication between the vehicles and infrastructures. However other situations have not been addressed carefully like the link stability between vehicles during packet exchange. This paper develops an Obstacle Prediction Based Routing Protocol (OPBRP) for vehicle detection, packets transmission to Roadside Units (RSU) and choosing a better route in terms of reliability via using vehicle's Kinematics and Mobility prediction in VANET. Two fundamental contributions are included in this paper: (1) Upgrading prediction routing protocol to transfer packets using a reliable path, and (2) Adding new logic in choosing the intermediate nodes to the destination to achieve a higher Packet delivery ratio (PDR). The OPBRP uses the predictive greedy as forwarding algorithm and the predictive perimeter forwarding as recovery algorithm after introducing enhancements to both algorithms to meet the requirements of VANETs environment. To materialize the value achieved from the mentioned contributions we tested the newly developed OPBRP against the existing routing protocols using Vehicle in Network Simulation (Veins) which shows that our proposed protocol outperformed current existing routing protocols in terms of PDR by achieving 18.46% improvement, end-to-end delay (E2E-Delay) by achieving 10.51% improvement, and total power consumption used in transmission by achieving 23.80% improvement.
