@inproceedings{219bf061337242348f66ad7830c4d98d,
title = "SEAD: Towards a Social-Media-Driven Energy-Aware Drone Sensing Framework",
abstract = "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 Social-media-driven Energy-Aware Drone (SEAD) sensing framework to address the above challenges. In particular, we develop a reinforcement learning-based 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 bottom-up game-Theoretic task allocation approach to guide drones effectively to the event locations. The evaluation with a real-world disaster case study show that SEAD noticeably outperforms state-of-The-Art baselines in terms of detection effectiveness and energy efficiency.",
keywords = "Disaster response, Energy aware, Reinforcement learning, SEAD, Social sensing, UAV",
author = "Rashid, {Md Tahmid} and Zhang, {Daniel Yue} and Lanyu Shang and Dong Wang",
note = "Funding Information: ACKNOWLEDGMENT This research is supported in part by the National Science Foundation under Grant No. CNS-1845639, CNS-1831669, CBET-1637251,Army Research Office under Grant W911NF-17-1-0409. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. REFERENCES Publisher Copyright: {\textcopyright} 2019 IEEE.; 25th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2019 ; Conference date: 04-12-2019 Through 06-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICPADS47876.2019.00097",
language = "English (US)",
series = "Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS",
publisher = "IEEE Computer Society",
pages = "647--654",
booktitle = "Proceedings - 2019 IEEE 25th International Conference on Parallel and Distributed Systems, ICPADS 2019",
address = "United States",
}