Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment.

Real-time control solutions for unmanned aerial vehicles (UAVs) have attracted great interest in recent years. Most existing control methods use Wi-Fi technology. While Wi-Fi is inexpensive and easy to use, it has only a limited transmission range. Thus, 4G/5G cellular networks have been proposed as an alternative enabling technology. This study focuses on the problem of improving the appropriateness of the control commands sent by the ground control station (GCS) to the UAV over the control and nonpayload communication (CNPC) link of the UAV through the cellular network. To satisfy the low-latency requirement of the CNPC link, multiaccess edge computing (MEC) technology is leveraged to collocate the GCS and base station. The effectiveness of the proposed edge-based approach is demonstrated by conducting experiments on two LTE platforms with different MEC deployment methods. An edge-enabled UAV control solution is proposed in which each end-to-end control delay in the UAV-GCS system is estimated based on the preceding delay such that the location of the UAV at the moment it receives the control command from the GCS can be predicted in advance and taken into consideration by the GCS when formulating an appropriate control decision. To this end, an analytical modeling method is proposed for estimating the expected error range of each control delay based on a bimodal distribution approximation of the empirical control delays observed at the UAV. Finally, an event-driven simulator is developed to confirm the accuracy of the analytical predictions of the control delay based on the expected error between consecutive delays.