What question did this study set out to answer?

This research aims to enhance cooperative awareness message (CAM) dissemination in congested traffic using an ACO-based clustering approach.

April 23, 2026

ACO‐Based Clustering Framework: A Metaheuristic Approach for Efficient CAM Broadcast Over Control Channel in Internet of Vehicles (IoV)

Key Points

This research aims to enhance cooperative awareness message (CAM) dissemination in congested traffic using an ACO-based clustering approach.
Developed a hybrid ACO-based clustering mechanism combined with a vehicle-ID-based MAC.
Evaluated the proposed model using MATLAB simulations under varying traffic conditions and contention window sizes.
Compared the ACO method with conventional communication paradigms like contention-based and vehicle-ID-based back-off algorithms.
ACO-based clustering significantly decreases collision probability and transmission delay, especially in high-density traffic scenarios.
Utilizing about one-fourth of the contention window size leads to noticeably lower average packet delays compared to existing methods.
Simulation results validate the efficiency of the proposed model against conventional IEEE 802.11p framework simulations.

Abstract

ABSTRACT In the Internet of vehicles (IoV), the regular dissemination of cooperative awareness messages (CAMs) is essential for facilitating safety‐critical applications. However, in congested traffic conditions, simultaneous CAM broadcasts may result in high collision rates and significant delay. To overcome this issue, the concept of ACO‐based clustering is introduced in this paper. It proposes a hybrid approach that combines an ant colony optimization (ACO)‐based clustering mechanism and a vehicle‐identity–based medium access control (MAC) mechanism for the periodic broadcast of CAMs, aiming to mitigate collisions in saturated traffic scenarios. Furthermore, this paper presents a comparative study of various communication paradigms in IoV networks, including the conventional contention‐based mechanism, a vehicle‐ID–based back‐off algorithm, and our ACO‐based clustering mechanism. A MATLAB‐based simulation framework has been developed to evaluate the performance of our proposed model under varying traffic densities and contention window (CW) sizes. The ACO‐based clustering algorithm dynamically organizes vehicles into clusters, selects optimal cluster heads to coordinate transmissions, and further assigns a unique random back‐off number to each vehicle based on its ID to reduce contention and enhance scalability. Simulation results demonstrate that the ACO‐based approach significantly reduces collision probability and transmission delay compared to the other contention‐based methods, particularly in high‐density scenarios. To further ascertain the performance of our model, ACO is applied to the conventional IEEE 802.11p framework and the simulation outcomes are compared against the proposed approach. In addition, an ablation study is conducted to analyze the individual impact of the clustering and backoff mechanisms. The analytical models are also validated by comparing their results with those obtained From Monte Carlo simulations. Furthermore, it can be observed that by utilizing nearly one‐fourth of the CW size, the proposed approach achieves lower average packet delay than existing studies. These findings underscore the potential of bio‐inspired algorithms for optimizing communication efficiency in next‐generation IoV environments.

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