The College of Education for Pure Sciences, Department of Computer Science, discussed a master's thesis on
(Efficient Optimization-Based Routing Protocol for Ad Hoc Vehicular Networks)
The thesis presented by the researcher (Hanadi Hussein Karim) included
Abstract
The study of the vehicular network technology (Ad-Hoc VANET) is a vital element for building a mobile communication network that uses moving vehicles as nodes. The effectiveness of this technology depends largely on maintaining good quality of service (QoS) to improve communication between vehicles. Data packets are routed in urban areas using various protocols, including the Road Vehicle Traffic Routing Protocol (RBVT). Two types of protocols are used, namely the reactive RBTV-R and the proactive RBTV-P. The RBTV-R protocol forms source paths from successive intersections, is signal-independent and uses an improved broadcast mechanism. The RBTV-P protocol is proactive, where the source node creates routing tables for reachable intersections using multi-hop discovery control message (CPs) packets, which are then broadcast to other nodes via route update packets. This allows other nodes to create their own routing tables, ensuring real-time road connectivity information.

This study addresses improving the performance of Ad-Hoc vehicular networks by fine-tuning routing protocols to achieve reliable connectivity. VANETs face significant obstacles due to vehicles moving at high speeds, network topology changing regularly, and extremely stringent safety standards. The main challenge in VANETs is to send, receive, and relay messages between vehicles in a reliable, secure, and timely manner to ensure the safety of traffic and road crews.

This study compares the performance of the Road Vehicular Traffic (RBVT) routing protocol, RBVT-P and RBVT-R. It focuses on improving the performance of the two protocols through key metrics such as average end-to-end latency, packet delivery overhead, average path length, and average delivery ratio. To solve the above problems, the Snake Optimization (SOA) algorithm is used. The Snake Optimization algorithm allows routing protocol settings to be adjusted based on network factors, enhancing the performance of the protocol while reducing packet delays, overhead, and system load.
Simulations conducted using the Objective Network Benchmark Testbed (OMNeT++ version 6.0) in C++ and the Urban Mobility Simulator (SUMO version 1.19.0) showed that combining RBVT with the Snake Optimization Algorithm (SOA) significantly improves network performance. This algorithm reduces path lengths, end-to-end latency, and increases delivery rates, especially in dense and complex networks. For example, the end-to-end latency when using the SOA-RBVT-P optimization was significantly reduced compared to the original RBVT-P protocol. For example, the latency was reduced from 1.2 ms to 0.223 ms at a packet rate of 2 packets/s, and from 5.2 ms to 0.874 ms at a packet rate of 10 packets/s. The simulations also showed a similar reduction in end-to-end latency at higher packet rates. In addition, combining RBVT with the Snake Optimization Algorithm (SOA) consistently reduced path lengths and significantly improved packet delivery ratio compared to using RBVT-P alone. These results highlight the effectiveness of SOA-RBVT in improving communication efficiency and safety in complex vehicular network environments.