ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Networks and Communications
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1564270
Regional Computing for VBD Offloading in Next-Generation Vehicular Networks
Provisionally accepted
- 1University of Sargodha, Sargodha, Pakistan
- 2Jeddah University, Jeddah, Makkah, Saudi Arabia
- 3King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
- 4Rabdan Academy, Abu Dhabi, United Arab Emirates
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The rapid growth of autonomous and Connected Vehicles (CVs) has led to a massive increase in Vehicular Big Data (VBD). While this data is transforming the Intelligent Transportation System (ITS), it also poses significant challenges in processing, communication, and resource scalability. Existing cloud solutions offer scalable resources; however, incur long delays and costs due to distant data communication. Conversely, edge computing reduces latency by processing data closer to the source; however, struggles to scale with the high volume and velocity of VBD. This paper introduces a novel Regional Computing (RC) paradigm for VBD offloading, with a key focus on adapting to traffic variations during peak and off-peak hours. Situated between edge and cloud layers, the RC layer enables near-source processing while maintaining higher capacity than edge or fog nodes. We propose a dynamic offloading algorithm that continuously monitors workload intensity, network utilization, and temporal traffic patterns to smartly offload tasks to the optimal tier (vehicle, regional, or cloud). This strategy ensures responsiveness across fluctuating conditions while minimizing delay, congestion, and energy consumption. To validate the proposed architecture, we develop a custom Python-based simulator, RegionalEdgeSimPy, specifically designed for VBD scenarios. Simulation results demonstrate that the proposed framework significantly reduces processing latency, energy usage, and operational costs compared to traditional models, offering a scalable and effective alternative for next-generation vehicular networks.
Keywords: Vehicular Big Data, regional computing, Network Optimization, IntelligentTransportation Systems, Edge computing
Received: 21 Jan 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Badshah, Alsahfi, Alesawi, Alfakeeh, Bukhari and Daud. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Afzal Badshah, University of Sargodha, Sargodha, Pakistan
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