Abstract
Unmanned aerial systems (UAS) consist of a swarm of unmanned aerial vehicles (UAVs) with edge resources and collaboration with ground-control-servers (GCS) are useful for heavy computation use cases e.g., traffic management, public safety, and disaster response management. Inefficient setups and collaboration decisions, often stemming from edge/cloud network misconfigurations, can lead to suboptimal resource utilization and delayed response times. In this paper, we present a novel scheme for (soft) real-time learning-based UAS-Edge-Server collaboration and orchestration strategies to achieve pertinent allocations of both computation resources and communication strategies. Our approach includes i) policy-based pre-application collaboration and benchmark analysis as well as ii) learning-based multi-agent deep Q-network (DQN) algorithm that optimizes UAV swarm trajectories during application. Evaluation results demonstrate that our policy-based approach Pareto-optimally trade-off performance (e.g., accuracy, streaming) and disaster response time. In addition, our DQN approach significantly enhances edge-cloud resource cooperation, improving network performance metrics like throughput and round-trip time by a minimum of 12% compared to state-of-the-art edge-internet-of-things (EIoT) collaboration algorithms. Furthermore, through real-world emulations, we illustrate how our orchestration attains 87% of the Oracle baseline network throughput performance while maintaining a comparable disaster response time for various video analytics-based disaster scenarios.
