## 1 Introduction
These stem primarily from the unique characteristics of vehicular networks, including their highly dynamic topology and the predictable yet fast-changing mobility patterns of vehicles [11; 13]. Additionally, VCNs inherit several limitations from traditional Mobile Ad Hoc Networks (MANETs), such as unstable wireless links and multi-hop communication issues under varying densities and conditions [14; 15]. This study focuses on addressing the dynamic nature of VANET topologies and the need for robust routing protocols that can adapt to diverse and rapidly changing environments. In real-world scenarios, vehicles frequently travel across different network zones (e.g., from urban areas to highways), encountering significant shifts in node density, mobility patterns, and signal propagation conditions [16]. These variations pose major challenges in ensuring consistent routing performance. A protocol that performs well in a dense city environment may experience significant degradation in a sparse highway scenario due to disrupted link connectivity and increased path failure rates [17; 18]. While many existing VANET routing protocols are tailored for specific scenarios--urban grids, highways, or intersections--few studies have addressed the design of routing protocols that are adaptable across multiple mobility and topological conditions [19]. To bridge this gap, we propose a systematic optimization approach, referred to as the Chameleon Method, which offers real-time adaptability and efficiency in uncertain, multi-scenario environments. Just as a chameleon dynamically changes its skin color to match its surroundings, our method dynamically tunes routing parameters in response to traffic and topology variations. This approach is coupled with a statistical Design of Experiments (DOE) technique to ensure rigorous, data-driven optimization. The proposed Chameleon Method is integrated into two widely studied VANET routing protocols: Quality of Service-enabled Ad Hoc On-Demand Distance Vector (QOS-AODV) and Greedy Perimeter Stateless Routing (GPSR). Moreover,  
Figure 1: VANET Architecture: An overview of the Vehicular Ad Hoc Network structure illustrating communication between vehicles (V2V), between vehicles and roadside infrastructure (V2I), and integration with centralized systems via the internet or cloud to support intelligent transportation services. this integration aims to enhance their adaptability, stability, and QoS performance across both city and highway environments--addressing a critical gap in current VCN protocol design. In this study, we present a robust parameter optimization framework designed to enhance the performance of QOS-AODV and GPSR routing protocols in Vehicular Ad Hoc Networks (VANETs). The framework integrates two tailored optimization profiles: the Traffic-Oriented Model (TOM), which prioritizes throughput and packet delivery, and the Delay-Efficient Model (DEM), which aims to balance delay and reliability. Distinct from prior approaches, our methodology employs a Taguchi-based statistical optimization technique for efficient tuning of protocol parameters. This data-driven approach ensures that the routing protocols are optimally configured under varying network conditions. Furthermore, the optimized protocols are validated through simulation in both urban and highway scenarios, capturing the dynamic topology and traffic fluctuations typical of real-world vehicular networks. The results demonstrate significant performance improvements in packet delivery ratio (PDR), throughput, and end-to-end delay, underscoring the novelty and practical relevance of the proposed models within the VANET research landscape. Figure 2 presents a taxonomy of VANET routing protocols, categorized based on their core design strategies and awareness mechanisms. The protocols are grouped into five primary categories: Greedy Stateless, Infrastructure-Assisted, Street-Aware, Connectivity-Aware, and baseline protocols.