Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things
This addresses task offloading efficiency for industrial IoT systems, but it is incremental as it applies existing MARL and emergent communication methods to a specific domain.
The paper tackles the problem of computation offloading and multichannel access in Industrial Internet of Things by using a multi-agent reinforcement learning framework with emergent communication, resulting in improved channel access success rate and number of successfully computed tasks compared to baselines.
In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.