Optimizing Real-Time Vehicular Communications With a Cross-Layer Model for Energy-Efficient Clustering and Blockchain-Based Congestion Control

Abstract

Vehicular ad hoc networks (VANETs) are essential components of intelligent transportation systems that facilitate real-time communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Despite their importance, VANETs face challenges, such as high node mobility, energy limitations, security risks, and ever-changing network topologies. Existing clustering and routing algorithms often struggle to manage the instability caused by mobility, energy disparities, and secure congestion-free communication simultaneously. To address these challenges, this work introduced an integrated cross-layer framework featuring three innovative algorithms: mobility-aware black hole clustering (M-BHC), energy-aware piranha optimization algorithm (EPOA), and cross-layer multi-attribute blockchain routing with congestion control (CL-MABRC). The M-BHC algorithm enhances the stability of clusters and counters black-hole attacks by forming clusters dynamically based on real-time vehicle mobility patterns. EPOA optimizes the selection of cluster heads (CHs) by reducing energy consumption through a bio-inspired resource allocation strategy modeled on piranha predation behavior. CL-MABRC addresses network congestion and security using blockchain-based verification and cross-layer routing decisions informed by multi-attribute metrics. Extensive simulations were conducted with a setting of 100 veh/km². The proposed framework showed significant performance improvements over benchmark protocols, such as optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR), enhanced location-aided ant colony routing (ELAACR), trust-based multi-objective honey badger algorithm (TMOHBA), and robust cryptographic scheme for reliable data communication (RCSRC). It achieved a throughput of 99.89 Kbps, end-to-end delay of 3.9 ms, collision rate of 21.8%, energy consumption of 41.98%, and jitter of 0.05 ms. Together, the M-BHC, EPOA, and CL-MABRC algorithms create a robust, energy-efficient, and secure communication framework for VANETs, enhancing scalability, reliability, and real-time performance in transportation systems.