# Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks

arXiv: https://arxiv.org/abs/2312.02471 · HTML: https://mlsanity.com/paper/2312.02471/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [The Internet of Battle Things](https://mlsanity.com/paper/1712.08980/) (2017)
- [Distributed Exact Weighted All-Pairs Shortest Paths in Near-Linear Time](https://mlsanity.com/paper/1811.03337/) (2018)
- [Delay-aware Backpressure Routing Using Graph Neural Networks](https://mlsanity.com/paper/2211.10748/) (2022)
- [Mobile Edge Computing Network Control: Tradeoff Between Delay and Cost](https://mlsanity.com/paper/2005.07854/) (2020)
- [A Novel Graph-based Computation Offloading Strategy for Workflow Applications in Mobile Edge Computing](https://mlsanity.com/paper/2102.12236/) (2021)

## Built on by (most cited first, 3 of 3)

- [Fully Distributed Online Training of Graph Neural Networks in Networked Systems](https://mlsanity.com/paper/2412.06105/) (2024)
- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking](https://mlsanity.com/paper/2512.10874/) (2025)

## Abstract (arXiv)

Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network congestion caused by tasks from multiple mobile devices, especially in wireless multi-hop networks. To fill this gap, we propose a low-overhead, congestion-aware distributed task offloading scheme by augmenting a distributed greedy framework with graph-based machine learning. In simulated wireless multi-hop networks with 20-110 nodes and a resource allocation scheme based on shortest path routing and contention-based link scheduling, our approach is demonstrated to be effective in reducing congestion or unstable queues under the context-agnostic baseline, while improving the execution latency over local computing.