Multi-Hop Task Routing in Vehicle-Assisted Collaborative Edge Computing

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)2444-2455
Number of pages12
Journal / PublicationIEEE Transactions on Vehicular Technology
Issue number2
Online published5 Sept 2023
Publication statusPublished - Feb 2024



Collaborative edge computing has emerged as a novel paradigm that allows edge servers (ESs) to share data and computing resources, effectively mitigating network congestion in traditional multi-access edge computing (MEC) scenarios. However, existing research in collaborative edge computing often limits offloading to only one hop, which may lead to suboptimal computing resource sharing due to challenges such as poor channel conditions or high computing workload at ESs located just one hop away. To address this limitation and enable more efficient computing resource utilization, we propose a multi-hop MEC approach that leverages omnipresent vehicles in urban areas to create a data transportation network for task delivery. Here, we propose a general multi-hop task offloading framework for vehicle-assisted collaborative edge computing where tasks from users can be offloaded to powerful ESs via potentially multi-hop transmissions. Under the proposed framework, we formulate an aggregated service throughput maximization problem by designing the task routing path subject to end-to-end latency requirements, spectrum, and computing resources. To efficiently address the curse of dimensionality problem due to vehicular mobility and channel variability, we develop a deep reinforcement learning, i.e., multi-agent deep deterministic policy gradient, based multi-hop task routing approach. Numerical results demonstrate that the proposed algorithm outperforms existing benchmark schemes. © 2023 IEEE.

Research Area(s)

  • Collaborative edge computing, Computation offloading, Deep reinforcement learning (DRL), Multi-hop routing, Optimization, Relays, Resource management, Routing, Servers, Spread spectrum communication, Task analysis, Vehicular networks

Citation Format(s)

Multi-Hop Task Routing in Vehicle-Assisted Collaborative Edge Computing. / Deng, Yiqin; Zhang, Haixia; Chen, Xianhao et al.
In: IEEE Transactions on Vehicular Technology, Vol. 73, No. 2, 02.2024, p. 2444-2455.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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