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Unmanned Aerial Vehicle Sensor Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 7786

Special Issue Editors


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Guest Editor
Signals, Systems and Radiocommunications Department, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: UAV traffic management; drone applications; air traffic management; data fusion
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Information Processing and Telecommunications Center, Polytechnic University of Madrid, 28040 Madrid, Spain
Interests: drone applications; data fusion; interaction; augmented reality; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, drone-embarked real-time sensing applications have bloomed, ranging from aerial surveillance to remote infrastructure state monitoring, terrain surveying, etc. Those applications demand robust and high-bandwidth communication coverage, even at remote areas, also imposing high computation demands, quite often in a distributed environment involving cloud and edged computing. Further, drone command and control, especially for beyond line of sight (BVLOS) applications, demands low-latency robust communication of telemetry, command orders, and quite often video for the remote pilot. Moreover, drones are gaining autonomy and capabilities to coordinate their operations, either to avoid conflicts or to perform missions cooperatively (i.e., drone swarms). Finally, new safety requirements are being imposed on drones’ operations, to be able to use them in higher-population areas. One of the most relevant requirements is the need to integrate themselves with UAV traffic management (UTM) systems, formed of a collection of distributed preflight and along-flight traffic coordination services demanding a bidirectional datalink to the drones and/or the pilots.

Summarizing, drones can be seen as aerial (mobile) IoT sensing platforms with different communication/coordination needs both for operating safely and for performing their very diverse intended missions.

The aim of this Special Issue is to solicit papers from academia and industry researchers with original and innovative works on all aspects of UAV sensor networks, ranging from communication architectures to sensing/information topologies and mission coordination, which review and report on the start-of-the-art, highlight challenges, and point to future directions.

Topics of interest include but are not limited to:

  • 5G for UAV sensor networks
  • Ad hoc UAV sensor networks
  • Real-time Information processing in UAV sensor networks
  • Cooperative sensing for UAV sensor networks
  • UTM architectures, communication needs
  • Interaction with UAV sensor networks
  • Testbed, benchmark, and simulation studies in UAV sensor networks
  • Security and privacy issues in UAV sensor networks

Dr. Ana Bernardos
Dr. Juan Besada
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV sensor networks
  • drone applications
  • cooperative sensing

Published Papers (2 papers)

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17 pages, 10176 KiB  
Article
Quadrotor-Based Lighthouse Localization with Time-Synchronized Wireless Sensor Nodes and Bearing-Only Measurements
by Brian G. Kilberg, Felipe M. R. Campos, Craig B. Schindler and Kristofer S. J. Pister
Sensors 2020, 20(14), 3888; https://doi.org/10.3390/s20143888 - 13 Jul 2020
Cited by 5 | Viewed by 3335
Abstract
Some robotic localization methods, such as ultra wideband localization and lighthouse localization, require external localization infrastructure in order to operate. However, there are situations where this localization infrastructure does not exist in the field, such as robotic exploration tasks. Deploying low power wireless [...] Read more.
Some robotic localization methods, such as ultra wideband localization and lighthouse localization, require external localization infrastructure in order to operate. However, there are situations where this localization infrastructure does not exist in the field, such as robotic exploration tasks. Deploying low power wireless sensor networks (WSNs) as localization infrastructure can potentially solve this problem. In this work, we demonstrate the use of an OpenWSN network of miniaturized low power sensor nodes as localization infrastructure. We demonstrate a quadrotor performing laser-based relative bearing measurements of stationary wireless sensor nodes with known locations and using these measurements to localize itself. These laser-based measurements require little computation on the WSN nodes, and are compatible with state-of-the-art 2 mm × 3 mm monolithic wireless system-on-chips (SoCs). These capabilities were demonstrated on a Crazyflie quadcopter using an Extended Kalman Filter and a network of motes running the OpenWSN wireless sensor network stack. The RMS error for X positioning was 0.57 m and the error for Y positioning was 0.39 m. This is the first use of an OpenWSN sensor network to support robotic localization. Furthermore, simulations show that these same measurements could be used for localizing sensor motes with unknown locations in the future. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Sensor Networks)
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29 pages, 7487 KiB  
Article
Sensors and Communication Simulation for Unmanned Traffic Management
by David Carramiñana, Iván Campaña, Luca Bergesio, Ana M. Bernardos and Juan A. Besada
Sensors 2021, 21(3), 927; https://doi.org/10.3390/s21030927 - 30 Jan 2021
Cited by 7 | Viewed by 3617
Abstract
Unmanned traffic management (UTM) systems will become a key enabler to the future drone market ecosystem, enabling the safe concurrent operation of both manned and unmanned aircrafts. Currently, these systems are usually tested by performing real scenarios that are costly, limited, hardly scalable, [...] Read more.
Unmanned traffic management (UTM) systems will become a key enabler to the future drone market ecosystem, enabling the safe concurrent operation of both manned and unmanned aircrafts. Currently, these systems are usually tested by performing real scenarios that are costly, limited, hardly scalable, and poorly repeatable. As a solution, in this paper we propose an agent-based simulation platform, implemented through a micro service architecture, which may simulate UTM information sources, such as flight plans, telemetry messages, or tracks from a surveillance network. The final objective of this simulator is to use these information streams to perform a system-level evaluation of UTM systems both in the pre-flight and in-flight stages. The proposed platform, with a focus on simulation of communications and sensors, allows to model UTM actors’ behaviors and their interactions. In addition, it also considers the manual definition of events to simulate unexpected behaviors/events (contingencies), such as communications failures or pilots’ actions. In order to validate our architecture, we implemented a simulator that considers the following actors: drones, pilots, ground control stations, surveillance networks, and communications networks. This platform enables the simulation of the drone trajectory and control, the C2 (command and control) link, drone detection by surveillance sensors, and the communication of all agents by means of a mobile communications network. Our results show that it is possible to truthfully recreate complex scenarios using this simulator, mitigating the disadvantages of real testbeds. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Sensor Networks)
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