Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

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Integration of Cognitive Radio Technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges

Journal of Network and Computer Applications ◽

10.1016/j.jnca.2014.12.002 ◽

2015 ◽

Vol 50 ◽

pp. 15-31 ◽

Cited By ~ 102

Author(s):

Yasir Saleem ◽

Mubashir Husain Rehmani ◽

Sherali Zeadally

Keyword(s):

Cognitive Radio ◽

Unmanned Aerial Vehicles ◽

Future Research ◽

Aerial Vehicles ◽

Research Challenges ◽

Radio Technology ◽

Cognitive Radio Technology

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Unmanned Aerial Vehicles for Smart Cities

Methods and Applications of Geospatial Technology in Sustainable Urbanism - Advances in Geospatial Technologies ◽

10.4018/978-1-7998-2249-3.ch015 ◽

2021 ◽

pp. 444-477

Author(s):

Tetiana Shmelova ◽

Vitalii Lazorenko ◽

Oleksandr Burlaka

Keyword(s):

Unmanned Aerial Vehicles ◽

Aerial Photography ◽

Smart Cities ◽

First Aid ◽

Mathematical Methods ◽

Aerial Vehicles ◽

Area Monitoring ◽

The City ◽

Navigation Information ◽

Estimation Of Risk

In this chapter, the authors are presenting opportunities for the use of unmanned aerial vehicles (UAV) in town. Methods for the optimization of flight routes of UAVs in the dependence of target tasks in the city are presented, for example, area monitoring; search and rescue operations; retransmission of communication (in places, where the antenna coverage cannot be set due to terrain specifications); organization of logistics as the safe, cheap, and fast transportation method of goods; for aerial photography, for controlling traffic; for the provision of the first aid to people in emergencies; unmanned taxi. It is done using air navigation information and mathematical methods. Authors suggest dynamic programming methods, GRID analyses, expert judgment method, and fuzzy-logic methods for estimation of risk/safety of flights in the city. Optimization of flows and flexible redistribution of UAV routes in multilevel airspace is provided according to air navigation requirements and standards.

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Eyes in the Sky: Assessing the Feasibility of Low-Cost, Ready-to-Use Unmanned Aerial Vehicles to Monitor Primate Populations Directly

Folia Primatologica ◽

10.1159/000496971 ◽

2019 ◽

Vol 91 (1) ◽

pp. 69-82

Author(s):

Brandon P. Semel ◽

Sarah M. Karpanty ◽

Faramalala Francette Vololonirina ◽

Ando Nantenaina Rakotonanahary

Keyword(s):

Unmanned Aerial Vehicles ◽

Low Cost ◽

Aerial Vehicles

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Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Sensors ◽

10.3390/s19112467 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2467 ◽

Cited By ~ 2

Author(s):

Hery Mwenegoha ◽

Terry Moore ◽

James Pinchin ◽

Mark Jabbal

Keyword(s):

Unmanned Aerial Vehicles ◽

Navigation System ◽

Process Model ◽

Unscented Kalman Filter ◽

Low Cost ◽

Moment Of Inertia ◽

Model Parameters ◽

Main Process ◽

Model Based ◽

Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

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An urban expressway forwarding scheme for cognitive Internet of vehicles

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720912947 ◽

2020 ◽

Vol 16 (3) ◽

pp. 155014772091294

Author(s):

Jing Wang ◽

Huyin Zhang ◽

Sheng Hao ◽

Chuhao Fu

Keyword(s):

Cognitive Radio ◽

Traffic Safety ◽

Smart Cities ◽

Radio Spectrum ◽

Base Stations ◽

Radio Link ◽

Integer Optimization ◽

Internet Of Vehicles ◽

Urban Expressway ◽

Channel Availability

The Internet of vehicles is an essential component for building smart cities that can improve traffic safety and provide multimedia entertainment services. The cognitive radio–enabled Internet of vehicles was proposed to resolve the conflict between the increasing demand of Internet of vehicles applications and the limited spectrum resources. The multi-hop transmission is one of the most important issues in cognitive radio–enabled Internet of vehicles networks. Nevertheless, most existing forwarding solutions designed for the cognitive radio–enabled Internet of vehicles did not consider the urban expressway scenario, where primary base stations are densely installed with small coverage areas. In this case, it is difficult to ensure that the sender and the receiver of the same cognitive radio link have similar channel availability statistics, which makes cognitive radio links more likely to be interrupted. To address this challenge, we develop a multi-hop forwarding scheme to minimize the end-to-end delay for such networks. We first formulate the delay minimization problem as a non-linear integer optimization problem. Then, we propose an approach to select the relay candidates by jointly considering the high mobility of vehicles and the unique cognitive radio spectrum usage distributions in urban expressway scenarios. Finally, we propose the low-latency forwarding strategies by considering the channel availability and the delay cost of different situations of relay candidates. Simulations show the advantages of our proposed scheme, compared with state-of-art methods.

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Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Electronics ◽

10.3390/electronics8121532 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1532 ◽

Cited By ~ 6

Author(s):

Jamie Wubben ◽

Francisco Fabra ◽

Carlos T. Calafate ◽

Tomasz Krzeszowski ◽

Johann M. Marquez-Barja ◽

...

Keyword(s):

Pattern Recognition ◽

Unmanned Aerial Vehicles ◽

High Precision ◽

Low Cost ◽

Target Position ◽

Flight Behavior ◽

Ground Pattern ◽

Aerial Vehicles ◽

Exact Position ◽

Precision Landing

Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56 × 56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.

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Operational Framework for Rapid, Very-high Resolution Mapping of Glacial Geomorphology Using Low-cost Unmanned Aerial Vehicles and Structure-from-Motion Approach

Remote Sensing ◽

10.3390/rs11010065 ◽

2019 ◽

Vol 11 (1) ◽

pp. 65 ◽

Cited By ~ 17

Author(s):

Marek W. Ewertowski ◽

Aleksandra M. Tomczyk ◽

David J. A. Evans ◽

David H. Roberts ◽

Wojciech Ewertowski

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicles ◽

Structure From Motion ◽

Low Cost ◽

Control Point ◽

Glacial Geomorphology ◽

High Resolution Mapping ◽

Aerial Vehicles ◽

Resolution Mapping ◽

Very High

This study presents the operational framework for rapid, very-high resolution mapping of glacial geomorphology, with the use of budget Unmanned Aerial Vehicles and a structure-from-motion approach. The proposed workflow comprises seven stages: (1) Preparation and selection of the appropriate platform; (2) transport; (3) preliminary on-site activities (including optional ground-control-point collection); (4) pre-flight setup and checks; (5) conducting the mission; (6) data processing; and (7) mapping and change detection. The application of the proposed framework has been illustrated by a mapping case study on the glacial foreland of Hørbyebreen, Svalbard, Norway. A consumer-grade quadcopter (DJI Phantom) was used to collect the data, while images were processed using the structure-from-motion approach. The resultant orthomosaic (1.9 cm ground sampling distance—GSD) and digital elevation model (7.9 cm GSD) were used to map the glacial-related landforms in detail. It demonstrated the applicability of the proposed framework to map and potentially monitor detailed changes in a rapidly evolving proglacial environment, using a low-cost approach. Its coverage of multiple aspects ensures that the proposed framework is universal and can be applied in a broader range of settings.

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Method for Estimating Solar Energy Potential Based on Photogrammetry from Unmanned Aerial Vehicles

Electronics ◽

10.3390/electronics9122144 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2144

Author(s):

Jose Eduardo Fuentes ◽

Francisco David Moya ◽

Oscar Danilo Montoya

Keyword(s):

Solar Energy ◽

Unmanned Aerial Vehicles ◽

3D Model ◽

Low Cost ◽

Atmospheric Conditions ◽

Solar Panels ◽

Energy Potential ◽

Aerial Vehicles ◽

3D Data ◽

Surface Models

This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels.

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