Abstract
In Vehicular Ad-Hoc Networks (VANETs) play a crucial role in modern Intelligent Transportation Systems (ITS) by enabling real-time Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Long-Term Evolution for Vehicle-to-Everything (LTE-V2X) communication, particularly using the LTE-V2X side-link mode known as PC5 Mode, provides improved coverage, lower latency, and enhanced reliability compared to traditional Dedicated Short-Range Communication (DSRC)-based systems. However, existing LTE-V2X implementations face major challenges such as inefficient resource allocation, network congestion, high transmission delays, and security vulnerabilities. This research presents the Optimized Long-Term Evolution for Vehicle-to-Everything Resource Sharing (OLRS) framework, which integrates adaptive resource allocation, dynamic power control, and multi-hop emergency message forwarding with security-enhanced communication mechanisms. The proposed framework utilizes Roadside Unit (RSU)-assisted congestion management to prioritize safety-critical messages, such as emergency alerts, and ensures secure, efficient, and scalable data exchange between vehicles. By optimizing power control, spectrum allocation, and emergency message dissemination, OLRS significantly improves key network performance metrics, achieving a 40% reduction in latency, a 10% increase in Packet Delivery Ratio (PDR), and enhanced Signal-to-Interference-plus-Noise Ratio (SINR). The framework was implemented and tested using Cisco Packet Tracer for network simulation and Wireshark for real-time packet analysis. The results demonstrate improved data throughput, enhanced communication reliability, and stronger security against cyber threats. The proposed model provides a practical and scalable solution for high-density vehicular networks, with direct applications in collision avoidance, autonomous driving coordination, and intelligent traffic management.
Keywords: Cisco packet tracer, Cyber security, Long-term evolution for vehicle to everything, Resource allocation, Spectrum management, Vehicle to Vehicle communication, Vehicular ad hoc networks, Wire shark.
View PDF