Xie, H., Liu, H., Chen, H., Feng, S., Wei, Z., & Zeng, Y. (2025). Efficient Multi-User Resource Allocation for Urban Vehicular Edge Computing: A Hybrid Architecture Matching Approach. IEEE Transactions on Vehicular Technology, 74(1), 1811–1816. https://doi.org/10.1109/tvt.2024.3454771
Abstract:
Advanced in the proliferation of the Internet of Things (IoT), a plethora of functions have been integrated
in vehicular networks and thereby transfered it into a smart network. However, the contradiction between the limited on vehicle computing resource and the massive data collected by these IoT devices hinders the broader adoption of vehicular network as a vast variety of on-vehicle applications are latency sensitive.
To address this issue, vehicular edge computing has become a promising technology as it can offload a large number of tasks from its proximal vehicles. However, the offloading methods recently utilized are inefficient while dealing with multiuser vehicular networks under dynamic scenarios. To design a superior offloading method that can effectively and efficiently offload tasks from vehicles to servers, multiple objectives and
constraints with various topologies should be considered. In this paper, instead of constructing a typical multi-user and multiserver vehicular edge computing scenario, a complex scenario with more uncertainties, i.e. urban scenario, is modeled. We propose a Hybrid Architecture Matching Algorithm (HAMA) to minimize the average time latency subject to the constraint on energy consumption and evaluate the proposed algorithm in the above two scenarios. Moreover, HAMA is constructed based on hybrid centralized-distributed architecture, which can process the centralized collected information on a distributed manner.
Experimental results demonstrate that the matching algorithm can significantly reduce average time latency, achieving up to a 68% improvement compared to local execution.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme. - Future Communications Research and Development Programme (FCP): 5G C-V2X Joint Radar and Communication for Connected Intelligence
Grant Reference no. : FCP-ASTAR-TG-2022-003