A Biologically Inspired Intelligent and Energy Efficient Route Optimization Clustering Algorithm for Internet of Vehicles (IoV)

The swift evolution from vehicular ad hoc networks (VANETs) to the Internet of Vehicles (IoV) landscape has posed substantial routing optimization challenges regarding high mobility, dynamic topologies and intermittent connectivity. Conventional routing protocols such as AODV, DSR and GPSR are often unable to cater to the requirements of the IoV environment as they can result in latency, control overhead and overall scalability. To help tackle these limitations, this work proposes COANET (crayfish optimization algorithm‐based route optimization for IoV networks), which is an innovative bio‐inspired framework based on the crayfish optimization algorithm (COA). COANET's core utilized the crayfish behaviours of foraging, competition and summer resort to allow the dynamic balancing of exploration and exploitation during routing decisions. We implement these behaviours as explicit algorithmic operators and provide reproducible specifications to support replication. The framework is supported by energy‐aware clustering, hybrid exploration‐exploitation and multi‐metric optimization to optimize latency, energy efficiency and packet delivery. To validate COANET, simulation performance results show that COANET, as compared to traditional protocols, improves the packet delivery ratio by 15–20% while reducing end‐to‐end delay by 30% and energy efficiency by 41.5%. Additionally, COANET reduced control overhead by 52.7% in both urban and highway scenarios, thus affirming its robustness and ability to scale for next‐generation IoV Systems.