Abstract
Unmanned aerial vehicles (also known as drones) equipped with high-resolution video cameras have become increasingly popular for applications such as public safety and smart farming. However, inefficient configurations in drone video analytics due to misconfigured edge networks can lead to degraded video quality and inefficient resource utilization. In this paper, we propose a novel scheme for network edge orchestration that utilizes both offline and online learning-based approaches to achieve pertinent selections of network protocols and video properties in multi-drone-based video analytics. Our approach utilizes both supervised and unsupervised machine learning algorithms to make decisions regarding network protocols and video properties during the pre-takeoff stage of the drones (i.e., offline stage). Additionally, our approach incorporates a reinforcement learning-based multi-agent deep Q-network algorithm for drone trajectory optimization during flights (i.e., online stage) and a memory-to-memory multi-hop data forwarding strategy for drone swarm video transmission. Our evaluation results demonstrate that our offline orchestration approach can suitably choose network protocols (i.e., among TCP/HTTP, UDP/RTP, QUIC), while our unsupervised learning approach outperforms existing methods and achieves efficient offloading while improving network performance (i.e., throughput and round-trip time) by at least 25%, with satisfactory video quality. Furthermore, we demonstrate through trace-based and real-field experiment testbeds how our online orchestration in terms of decision-making and data forwarding strategies achieves 91% of the oracle baseline network throughput performance with comparable video quality. Overall, our approach offers a promising solution for optimizing drone video analytics and enhancing the overall performance of drone-swarm-based applications.
