Analysis of API-Based Communication Performance for drone's operation in U-Space

Abstract

The increasing number of drone's flights is posing several challenges such as airspace safety and particularly in urban and restricted areas. Safety could be enabled by considering several types of geo-zones allowing or preventing drone's operation. In addition, ensuring reliable and efficient communication between drone operators and the U-space service providers represents an important challenge to address and more specifically in situations of unauthorized intrusion into restricted airspace. This paper presents some preliminary results of a SESAR funded Project AI4HyDrop project (An AI-based Holistic Dynamic Framework for safe Drone Operations in restricted and urban areas) which aims to integrate drones safely into controlled airspace. The research study aims to analyze the latency and throughput in API-based communication to send intrusion alerts, using a developed algorithm to simulate the sending and receiving of these notifications. The results show that shorter time intervals (10 ms) significantly impact latency and throughput, suggesting that the system begins to deteriorate near the limit. However, the effect of message payload size and multiple systems broadcasting warnings was minimal. The overall finding suggests that API-based communication system can transmit drone detection warnings with sufficiently low latency as required in the current requirements of drone operations.