I have seen plenty of existing works on applied Reinforcement Learning (RL) policies for optimized scheduling in IoT networks including Q-learning, DQNs, and the newer ones including PPO for congestion control in networks. What are some existing gaps/limitations in this? Also, how can emerging technologies like generative AI help scheduling problems in IoT?
Ragini Gupta
You can explore the application of Generative AI to enhance IoT scheduling by predicting maintenance needs, optimizing resource allocation, and adapting schedules in real time. Furthermore, Generative AI can be applied to extract valuable insights for informed decision-making, enabling organizations to better manage the complexity and scale of IoT networks. This leads to improved overall operational efficiency, reduced downtime, and more effective operations across various IoT applications.
I don't think Generative AI can be used for scheduling.
One alternative to RL you can look at is Online Learning.
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