# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 3 Experimental Architecture and Results
### Sensor Offloading--Stationary 5G-UAV  
During this experiment, the 5G-UAV platform was transmitting sensor data to the edge cloud processing unit while utilizing the 5G connection. During the stationary experiment, available sensor data were used to determine the capabilities of the current connection. Figure 3 illustrates the uplink capabilities of the 5G network and describes how much data reaches the receiving processing unit at the edge cloud. The streaming data are visualized both in the transmitter's endpoint and the receiver's endpoint, and they are illustrated as tx and rx, respectively. The vertical dashed lines differentiate between different experiments, and each bar shows the mean data rate for the transmitted and received sensor data. As expected, only a subset of the available sensor data can be transmitted uncompressed and in real-time across the 5G interface. The uplink capabilities of the 5G network define the limit, which is approximately 94 Mbps, as shown in Table 1. It is important to note that this value corresponds to the uplink throughput achieved at very high signal-to-interference-noise ratio (SINR) values. The receiving end of the architecture experienced significant degradation for the streaming sensor data that exceeded the uplink's capabilities. This is an expected behavior since the produced data significantly surpasses the uplink capacity of this 5G network. However, when the produced data remains within the uplink limits, the data are transmitted without any degradation. Similar results are present in the observations regarding the sampling rate of the sensors. Figure 4 illustrates the degradation in the sampling frequency of the offloaded sensors. From these two figures, it is evident that it is not possible to transmit all the data generated by the UAV's sensors. Therefore, it is preferable to adjust the application layer to make sure that the 5G link is not overloaded. In addition, the mobility of the UAV and the resulting channel quality variations will affect the throughput of the 5G link. To combat these variations, large data buffers can be placed in  
Figure 3: UAV streaming sensors over the 5G cellular network. Large data buffers in the 5G-UAV would be sufficient to avoid dropped packets in low channel quality conditions but at the price of transmitting delayed packets. Sensor data compression is an obvious candidate that would significantly improve the single 5G-UAV case and make similar applications more scalable when considering multiple agent scenarios. Finally, the offloading of a specific process needs to be carefully evaluated in terms of throughput and latency requirements, the cost of computational resources on the UAV, the onboard power consumption, and others. For example, in [10], an object detection example is presented, where an offloading mechanism is developed and concerns the benefit of the offloading procedure while considering the communication constraints. Table 2 showcase some characteristic offloading cases considering the available sensors on this 5G-UAV platform. As mentioned above, this sensor configuration can support most UAV autonomous missions. Additionally, the expected throughput requirements for the considered sensors are also depicted. Finally, Figure 5 shows the fraction of the uplink capacity, i.e., here the calculated 94 Mbps (see Table 1), needed for each sensor data type and the required overhead. Here the regarded platform is equipped with a lidar sensor, a depth camera sensor, and an IMU sensor, and it has access to the Vicon capture system, from where the complete state of the robot is provided as ground truth. Furthermore, the pie chart depicts the consumed throughput corresponding to the architecture's overhead, i.e., the overhead regarding the VPN and the remaining available throughput.