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To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs
The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm.
Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul
The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture
IoT devices have limited energy and computing resources, thus it is required to provide an advanced solution for a system that requires the support of ABSs.
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks
UAVs have limited energy and computing power, and may not be able to perform all of the intense image classification tasks locally and within an appropriate amount of time.
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