SEAD: Towards a Social-Media-Driven Energy-Aware Drone Sensing Framework

Md Tahmid Rashid, Daniel Yue Zhang, Lanyu Shang, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 25th International Conference on Parallel and Distributed Systems, ICPADS 2019
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781728125831
StatePublished - Dec 2019
Externally publishedYes
Event25th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2019 - Tianjin, China
Duration: Dec 4 2019Dec 6 2019

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
ISSN (Print)1521-9097


Conference25th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2019


  • Disaster response
  • Energy aware
  • Reinforcement learning
  • SEAD
  • Social sensing
  • UAV

ASJC Scopus subject areas

  • Hardware and Architecture


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