HeteroSAS: A Heterogeneous Resource Management Framework for All-in-the-Air Social Airborne Sensing in Disaster Response

Social airborne sensing (SAS) is emerging as a new sensing paradigm that leverages the complementary aspects of social sensing and airborne sensing (i.e., UAVs) for reliable information collection. In this paper, we present HeteroSAS, a heterogeneous resource management framework for all-in-the-air SAS in disaster response applications. Current SAS approaches use UAVs to only capture data, but carry out computation on ground-based processing nodes that may be unavailable in disaster scenarios and thus consider a single model of UAV along with only one type of task (i.e., data capture). In this paper, we explore the opportunity to exploit the complementary strengths of different UAV models to accomplish all stages of sensing tasks (i.e., data capturing, maneuvering, and computation) exclusively in-the-air. However, several challenges exist in developing such a resource management framework: i) handling the uncertain social signals in presence of the heterogeneity of UAVs and tasks; and ii) adapting to constantly changing cyber-physical-social environments. The HeteroSAS framework addresses these challenges by building a novel resource management framework that observes the environment and learns the optimal strategy for each UAV using techniques from multi-agent reinforcement learning, game theory, and ensemble learning. The evaluation with a real-world case study shows that HeteroSAS outperforms the state-of-the-art in terms of detection effectiveness, deadline hit rate, and robustness on heterogeneity.