An optimized internet of vehicles network for safety-based messages transmission using particle swarm optimization

This thesis addresses the problem of traffic congestion, which arises from increased vehicular queuing when the packet transmission load in the network exceeds the available buffer capacity. This condition leads to slower travel speed and also delays in travel time. Many urban cities worldwide have experienced severe congestion challenges. However, with the aid of the Internet of Things (IoT), the traffic congestion problem in the event can be managed effectively as vehicles are now equipped with sensors, actuators, data acquisition and communication systems that enable inter-vehicle communication. The Internet of Vehicles (IoV) has emerged as a key mechanism in in managing traffic congestion within modern Intelligent Transportation Systems (ITS), providing extensive opportunities for safety and mobility applications. This networking technology, provides vehicles with endless possibilities of applications including safety-related applications. Due to the high speed, dynamic mobility and unstable connectivity of vehicular nodes, maintaining stable IoV network performance remains a challenge. Previous research has explored various congestion control optimization techniques such Genetic Algorithm, Ant Colony and Ant Bee Colony for vehicular networks. While these methods improved performance to some extent, they often suffer from high computational complexity or slow convergence, limiting their suitability for real-time IoV environments. The recent proliferation of IoV network has emphasized the need for high Quality of Service (QoS) requirements in all networking scenarios. This study provides a comprehensive evaluation of the impact of vehicular mobility on IoV network performance. The analysis focuses on key QoS metrics, namely throughput, packet delivery ratio (PDR) and delay, while examining the relationship between mobility factors and medium access behaviour under varying network conditions. In achieving better performance in the QoS of the IoV network, a bio-inspired optimization approach based on the Particle Swarm Optimization (PSO) algorithm is proposed to mitigate network performance degradation caused by high node mobility. Vehicle mobility data is obtained from traffic flow simulation in SUMO and the network simulation phase is conducted in OMNeT++. Safety messages are disseminated among vehicles within the network. The experiment for the IoV network is concluded by optimizing the transmission rate to minimize the delay as the objective function in PSO algorithm. To ensure stable performance evaluation under dynamic vehicular conditions, simulations were conducted using varying simulation times and numbers of vehicles to capture both temporal network behaviour and traffic density variations. Under these settings, the throughput of the IoV network recorded average improvements of up to 23% and 14% after optimization. Packet delivery ratio (PDR) was also enhanced, with average gains of up to 37% and 15%, while network delay was reduced by up to 12% and 15% across different simulation times and vehicle densities. These results highlight the effectiveness of the optimization in improving IoV network performance under diverse conditions. The proposed PSO-based approach introduces a dynamic transmission rate control mechanism that adapts to vehicular density, effectively reducing congestion on the control channel. Overall, the findings highlight the potential for future research and practical applications in vehicular communication systems.