Multi-hop Computational Offloading with Reinforcement Learning for Industrial IoT Networks

To serve advanced use-cases in industrial internet of things (IIoT) setups, communication and computation over wireless networks have faced overlapping resource management challenges. Two crucial resources in this context are radio re-sources and computational resources. The problem to achieve the ultra-low latency for mission critical applications is motivating enterprises to invest in offloading capability of computation heavy tasks while retaining the bandwidth efficiency of edge nodes. This work proposes a novel multi-hop offloading framework powered by deep reinforcement learning to aid the edge nodes in making intelligent decisions on task offloading. The proposed method is benchmarked against existing state of the art techniques to measure task completion delay and algorithmic runtime.