Optimization of Roadside Unit Deployment in VANET Network in Makassar City using Aquila Optimizer Algorithm

Abstract

Effective deployment of Roadside Units (RSUs) is a critical challenge in Vehicular Ad-hoc Networks (VANETs), particularly in dense urban environments where network coverage, infrastructure cost, and communication reliability must be carefully balanced. The RSU placement problem is inherently complex due to its large combinatorial search space, spatial constraints, and dynamic traffic distribution. This study proposes the application of the Aquila Optimizer (AO), a modern metaheuristic algorithm inspired by the hunting strategies of Aquila birds, to address the RSU deployment optimization problem in Makassar City. The objective is to minimize the number of active RSUs while maintaining high network performance in terms of Packet Delivery Ratio (PDR). The proposed approach integrates SUMO for realistic vehicular mobility modeling and NS-2 for network communication simulation. Compared with Particle Swarm Optimization (PSO), Virus Optimization Algorithm (VOA), and a conventional intersection-based deployment strategy, AO demonstrates superior optimization capability. The algorithm exhibits faster and more stable convergence behavior, effectively balancing exploration and exploitation during the search process, enabling it to identify more efficient RSU configurations in complex urban topologies. Simulation results show that AO achieves optimal service coverage using only 10 RSUs while maintaining a PDR of 89%. In contrast, PSO requires 19 RSUs and VOA requires 21 RSUs, both achieving approximately 85% PDR. These results indicate that AO provides a more cost-efficient and scalable solution for RSU deployment planning, making it a promising approach for intelligent transportation infrastructure development in smart city environments.

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