Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
This addresses congestion issues for edge intelligence in mobile and smart devices, but it is incremental as it builds on existing distributed frameworks.
The paper tackles the problem of network congestion in wireless multi-hop networks during computational offloading by proposing a congestion-aware distributed task offloading scheme, which reduces congestion and improves execution latency over local computing in simulations with 20-110 nodes.
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.