Optimal data dissemination by using meta-heuristic strategy over 802.11p in VANETs to improve efficiency and effectiveness

The advancement of Information and Communication Technology (ICT) has accelerated the development of Intelligent Transportation Systems (ITS), driving advancements in Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV). VANETs facilitate real-time data exchange in self-organizing wireless networks, enhancing road safety, traffic efficiency, and passenger experience. However, ensuring efficient real-time data dissemination in VANETs, particularly in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios, remains challenging. Multi-hop broadcasting, while mitigating latency, introduces scalability and reliability issues in dense traffic, leading to data redundancy, congestion, and the Broadcast Storm Problem (BSP). In a nutshell, data dissemination in real-world scenarios is a multi-objective optimization problem, or even NP-hard problem. Existing approaches, despite addressing Quality of Service (QoS) standards, often suffer from scalability issues and incur additional overhead for traffic density estimation. To address these challenges, we propose the COOT optimization algorithm, a nature-inspired meta-heuristic approach aimed at enhancing scalability and reliability while reducing communication overhead in VANETs. The COOT algorithm effectively balances exploration and exploitation, dynamically adjusting its search space to optimize performance under varying conditions. In LOS conditions, COOT achieves improvements of 8.36 % in data delivery ratio, 8.64 % in neighborhood awareness, 5.95 % in channel utilization, 17.39 % in latency, 24.95 % in End-to-End (E2E) delay, and 17.06 % in Mean Square Error (MSE) over contemporary algorithms. In NLOS conditions, enhancements are 11.54 %, 14.62 %, 11.67 %, 10.82 %, 15.04 %, and 18.11 %, respectively. These results, supported by statistical and graphical analysis, demonstrate the COOT algorithm's efficacy in unpredictable vehicular environments, making it a strong solution for real-time data dissemination.