Intelligent Cluster Head Selection and Aquila Optimizer Based Multi-Objective Routing (AOMOR) Protocol with Digital Twin Simulation for VANET

Abstract

The next-generation Intelligent Transportation Systems (ITS) use Vehicular Ad Hoc Networks. This facilitates real-time vehicle-roadside infrastructure communication. However, maintaining stable cluster topologies and guaranteeing consistent Quality of Service is problematic because of VANETs intrinsic high mobility and adaptable topology. In this paper, proposed a Self-Adaptive Marine Predators Algorithm (SAMPA) with an Aquila Optimizer-based Multi-Objective Routing (AOMOR) strategy for Cluster Head Selection (CHS). Instead of demanding all nodes to maintaining links over the entire network, the local interactions among clusters facilitates in lowering the routing complexity. SAMPA, a nature-inspired metaheuristic algorithm, models the foraging strategies of marine predators in their search for prey. It is employed here to select the most suitable CHs by solving a Multi-Objective Optimization (MOO) problem that balances key metrics, including Node Density (ND), Residual Energy (RE), mobility, Link Quality (LQ), and Connectivity Degree (CD). For the routing process, AOMOR employs the hunting strategies of Aquila (eagle) species, which ivolnve searching, swooping, and attacking behaviors, to determine the optimal communication path. Mean Routing Load (MRL), Packet Delivery Ratio (PDR), throughput, End-to-End (E2E) delay, and Control Packet Overhead (CPO) represent several of the objectives, are taken into account by QoS-aware routing. To further enhance adaptability, the Digital Twin technology is integrated in the suggested method, which facilitates real-time analysis of network activities, traffic prediction, and informed decision-making for routing strategies. Proposed structure in improving the efficiency of CHS and improving QoS provisioning in VANETs are demonstrated by evaluating its performance using important metrics such as PDR, Packet Loss Ratio (PLR), E2E delay, throughput, and Average Residual Energy (ARE).