Help from the Sky: Leveraging UAVs for Disaster Management

By Milan Erdelj, Enrico Natalizio, Kaushik R. Chowdhury, Ian F. Akyildiz. IEEE pervasive computing, 2017

888 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/2459ab79268f7b8344a8bdfb8f79ba8dde921efc

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In Aerospace Engineering

In Computer Networks and Communications

Citations in the corpus

Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey (2021, background): “These data, either in content or time, are extremely informative, and, thus, object detection and tracking have entered the era of mass UAVs [1], [2], which have played an increasingly important role in land cover mapping [4], [5]; smart agriculture [6], [7]; smart cities [8]; traffic monitoring [6]”
Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset (2020, background): “UAVs offer new features and convenience including fast deployment, high maneuverability, wider and adjustable viewpoints, and less human intervention [13, 14, 15, 16, 17].”
Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning (2018, background): “Erdelj et al., in [1], present a survey of the advances in drone technology focused on wireless sensor networks and disaster management.”
Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach (2018, background): “The remaining cellular coverage holes would then come from disaster scenarios, which significantly disrupt the search and rescue operations [4].”
Securing UAV Communications via Joint Trajectory and Power Control (2018, methodology): “Note that the location of any ground node as a potential eavesdropper can be practically detected and tracked by the UAV via using an optical camera or synthetic aperture radar (SAR) equipped on the UAV [36], [37].”
Joint Trajectory and Power Optimization for UAV Relay Networks (2018, background): “Recently, UAVs become especially helpful in the situations with widely scattered users, large obstacles such as hills or buildings that deteriorates the quality of links, and communication disabilities due to natural disasters [2].”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “With the restricted capacities of UAVs, MEC is a promising technology to handle the aforementioned challenges [279].”
Big Data in Natural Disaster Management: A Review (2018, background): “Erdeji et al. [38] reviewed the overall structure of the WSN and UAV systems for natural disasters, and proposed the Inundation Monitoring and Alarm Technology in a System of Systems to incorporate smartphone data with WSN for enhanced situational awareness.”
Space-Air-Ground Integrated Network: A Survey (2018, background): “Leveraging the immunity of satellite/UAV to infrastructure damages, the satellite/UAV-enabled WMN is able to provide wireless access services to users in the disaster areas [151], [152].”
Drone networks: Communications, coordination, and sensing (2018, background): “Autonomous unmanned aerial vehicles (UAVs), also called drones, have received increasing interest for environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, and relay communications [1, 2, 3, 4, 5, 6, 7].”
The current state and future outlook of rescue robotics (2019, background): “Their overhead perspective can be useful for surveying and situational awareness (Marconi et al., 2012; Erdelj et al., 2017), but they can also navigate through small spaces or fly over obstacles that may be obstructed for ground-based platforms (Falanga et al.”
Wireless Sensor Networks and Multi-UAV systems for natural disaster management (2017, background): “In [21], authors analyze the overall structure of the WSN and multiUAV system needed for assessing the different types of natural disasters, and outline the challenges that such a system should cope with.”