Article: ACIRO: adaptive clustering and intelligent routing optimisation in software defined vehicular networks Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2025 Vol.50 No.2 pp.103 - 122 Abstract: To improve communication efficiency and reliability in software-defined vehicular networks (SDVN) utilising density-based clustering and reinforcement learning approaches. The proposed approach, adaptive clustering and intelligent routing optimisation (ACIRO), employs a central controller to optimise cluster structure and routing decisions. The optimisation is based on density and entropy-based advantage actor-critic (EBA-AC) models. A novel density-based fuzzy C-means (DB-FCM) algorithm is introduced to optimise cluster formation, while cluster head selection criteria include distance, energy, and density parameters. Furthermore, EBA-AC is utilised to determine the optimal path considering link availability, distance, and density parameters. The proposed ACIRO approach is implemented and evaluated using the Mininet-WiFi emulator. Comparative analysis with existing methods demonstrates improvements in cluster and network performance metrics, including cluster stability, throughput, packet delivery ratio, and end-to-end delay. The ACIRO approach offers an effective solution for optimising cluster-based routing in dynamic vehicular environments. Inderscience Publishers - linking academia, business and industry through research

Title: ACIRO: adaptive clustering and intelligent routing optimisation in software defined vehicular networks

Authors: A. Sajithabegam; T. Menakadevi

Addresses: Department of Information Technology, Adhiyamaan College of Engineering, Dr. M.G.R. Nagar, Hosur, Tamil Nadu – 635109, India ' Department of Electronic and Communication Engineering, Adhiyamaan College of Engineering, Dr. M.G.R. Nagar, Hosur, Tamil Nadu – 635109, India

Abstract: To improve communication efficiency and reliability in software-defined vehicular networks (SDVN) utilising density-based clustering and reinforcement learning approaches. The proposed approach, adaptive clustering and intelligent routing optimisation (ACIRO), employs a central controller to optimise cluster structure and routing decisions. The optimisation is based on density and entropy-based advantage actor-critic (EBA-AC) models. A novel density-based fuzzy C-means (DB-FCM) algorithm is introduced to optimise cluster formation, while cluster head selection criteria include distance, energy, and density parameters. Furthermore, EBA-AC is utilised to determine the optimal path considering link availability, distance, and density parameters. The proposed ACIRO approach is implemented and evaluated using the Mininet-WiFi emulator. Comparative analysis with existing methods demonstrates improvements in cluster and network performance metrics, including cluster stability, throughput, packet delivery ratio, and end-to-end delay. The ACIRO approach offers an effective solution for optimising cluster-based routing in dynamic vehicular environments.

Keywords: density; energy; distance; clustering; routing; optimisation; vehicular ad hoc networks; VANET; software-defined vehicular networks; SDVN.

DOI: 10.1504/IJAHUC.2025.149458

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.50 No.2, pp.103 - 122

Received: 30 Apr 2024
Accepted: 31 Mar 2025
Published online: 01 Nov 2025 *

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Title: ACIRO: adaptive clustering and intelligent routing optimisation in software defined vehicular networks

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