Alam, Amjad, Somasiri, Nalinda ORCID: https://orcid.org/0000-0001-6311-2251, Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090 and Ali, kamran (2025) Meta-Heuristic Fusion for 5G VANETs: A GWO–PSO–ACO Framework Balancing. Latency, Energy and Spectrum. Lex localis - Journal of Local Self-Government, 23 (56). pp. 6918-6947.

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Abstract

Next-generation vehicular applications such as augmented-reality navigation and cooperative collision avoidance demand sub-second response times, low on-board energy use, and judicious utilisation of the scarce 5G/DSRC uplink spectrum. We address these conflicting requirements by formulating task-offloading in 5G-enabled vehicular ad-hoc networks (VANETs) as a multi-objective optimisation that minimises end-to-end latency and vehicular energy consumption while maximising deadline reliability and spectral efficiency. A detailed system model captures variable-size tasks generated by mobile vehicles, bandwidth-constrained LTE/5G and Wi-Fi channels, finite-capacity edge servers at roadside units (RSUs), and a remote cloud. Soft-deadline penalties are imposed on tasks whose latency exceeds 1s, and channel-congestion costs discourage excessive simultaneous off-loads. To solve the resulting NP-hard problem we propose an integrated GWO–PSO–ACO swarm optimiser: Grey-Wolf encircling provides global exploration, Particle-Swarm velocity updates accelerate exploitation, and Ant-Colony pheromone learning refines discrete task channel assignments. All three sub-swarms share the best candidate each iteration, yielding rapid yet robust convergence. Extensive simulations with random vehicle velocities (20–100 km/h) and varying numbers of vehicles (1–100, each generating one task) demonstrate that the proposed hybrid GWO–PSO–ACO algorithm consistently outperforms standalone PSO, ACO, and GWO baselines. Averaged over realistic workloads (1-40 MB tasks), the Hybrid achieves total latency reductions of approximately 21.9% and 29.3% compared to PSO and GWO, respectively, while lowering vehicular energy consumption by 12-18% and maintaining high reliability levels of ≈80-85% up to critical load points. Spectral efficiency is improved by up to 0.5% at low-to-moderate loads, and composite objective values are reduced by as much as 30–40% under heavy-load conditions. Convergence analysis confirms that the Hybrid reaches near-optimal solutions in fewer iterations than the baselines, making it suitable for real-time vehicular scenarios. Parameter variation tests further validate its scalability under heavier loads and stricter spectrum budgets. These results indicate that the proposed Hybrid optimiser is a robust and effective edge–cloudorchestration mechanism for future QoS-and spectrum-aware V2X services.

Item Type:	Article
Status:	Published
DOI:	10.52152/sfa8jj59
Subjects:	T Technology > T Technology (General) > T201 Patents. Trademarks
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/13900

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