SEAD: Towards A SocialMediaDriven EnergyAware Drone Sensing Framework Autonomous unmanned aerial vehicles (UAVs) have become an important tool for efficient disaster response. Despite the virtues of UAVs in disaster response applications, various limitations (e.g., requiring manual input, finite battery life) hinder their mass adoption. In contrast, social sensing is emerging as a new sensing paradigm that utilizes signals provided by human sensors to gather awareness of the events occurring in the physical world. Despite being inherently broader in scope, a shortcoming of social sensing is the reliability of the sensing data that are contributed by humans. In this paper, we introduce the concept of jointly exploiting the reliability of drones and the scope of social sensing to efficiently uncover the truthful events during disasters. However, such a tight integration of social and physical sensing introduces several technical challenges. The first challenge is satisfying the conflicting objectives of event coverage of the application and energy conservation of drones. The second challenge is adapting to the dynamics of the physical world and social media. In this paper, we present a Socialmediadriven EnergyAware Drone (SEAD) sensing framework to address the above challenges. In particular, we develop a reinforcement learningbased drone dispatching scheme that adapts to the physical and social environments and launches an appropriate proportion of drones for event exploration. We further utilize a bottomup gametheoretic task allocation approach to guide drones effectively to the event locations. The evaluation with a realworld disaster case study show that SEAD noticeably outperforms stateoftheart baselines in terms of detection effectiveness and energy efficiency.

Optimizing InterCluster Flights of PostDisaster Communication Support UAVs In the aftermath of largescale disasters, critical communication infrastructure is often destroyed. Ad hoc networks can restore wireless communication with basic functionalities, especially for civilians in the affected areas. However, as humans form groups and tend to stay around important locations like shelters in such situations, the network is highly intermittent. Autonomous Unmanned Aerial Vehicles can act as controllable and highly mobile data carriers between separated network clusters to enable delaytolerant intercluster communication. A possible heterogeneous set of usable aerial vehicles and the necessity to adapt the system to various environments requires the system to be highly flexible. Combined with severe constraints in energy usage or number of available vehicles, there is a need to increase the efficiency of intercluster flights. In this paper, we present approaches to optimize intercluster flights based on communication performance and energy efficiency. The resulting optimization model is a powerful tool for operators to adjust system settings to match required demands.