Analysis and Design of an Edge Computing Enabled Real-Time Object Detection Platform for Drone-as-a-Service using Network Calculus

Numerous Drone-as-a-Service (DaaS) applications, such as surveillance, search and rescue, and infrastructure inspection, may employ real-time object detection to achieve computer vision-based autonomous functions. However, running object detection algorithms, e.g., YOLO, locally on a drone requires extensive computational power, which is expensive in terms of cost and energy consumption. Conversely, edge computing facilitates the implementation of an affordable and efficient platform where drones compress and transmit images to an edge server for real-time object detection. Nevertheless, DaaS designers applying Edge Computing Enabled Real-Time Object Detection (ECOD) must be cognizant of the network design and performance of the ECOD platform to ensure object detection in real-time. In our research, we propose an approach to analyzing the delay performance of an ECOD platform utilizing network calculus. A testbed was implemented to evaluate the effectiveness of this approach. The analysis result provides principled guidance for the ECOD platform design lacking in previous studies. Examples are provided in this paper to illustrate how to apply the guidance to the ECOD platform design in terms of traffic profile, network capacity, and delay requirements in DaaS.