An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization
Abstract
:1. Introduction
1.1. Mobile Networks, Unmanned Aerial Vehicles and Their Synergies
1.2. Contributions of the Paper
- The usage of a UAV as a tool to take measurements from wireless networks in mobile communications. The usage of such a solution offers several advantages over the existing procedures: (a) it is faster to deploy a UAV or even a swarm of them than having teams of technicians and engineers performing measurements; (b) it is a far cheaper solution than having the aforementioned teams perform a similar functionality; (c) the mobility, positioning and speed that a UAV equipped with the correct sensors would have greatly surpasses what humans could perform, even with their set of measurement equipment. It must also be considered how UAVs can be used in locations that would be impossible for humans to access, or in a hazardous environment that could put human lives at risk. This tool not only performs extremely accurate measurements of 4G mobile networks, but also provides a way to visualize them and effectively offers a front-end Graphical User Interface (GUI).
- Usage of Galileo as a high accuracy Global Navigation Satellite System (GNSS) for the UAV that has been built. Galileo presents several features that, as will be explained in Section 3, make it a desirable option as a GNSS system to both provide accurate positioning of the UAV and exact readings from mobile networks. Therefore, hardware that is capable of establishing communications based on Galileo services has been put to use.
- A UAV built from scratch for the purpose of high-accuracy mobile network signal measurements: the UAV that has been built for the purposes of this paper has been done so from scratch. This was necessary due to several reasons. While a commercial solution equipped with the required sensors and a Galileo-enabled microcontroller would also be viable, it was chosen to use the presented UAV because the authors had tighter control over what was added to the UAV by mounting up the components themselves. This became especially important with the controller (Navio2) used with the UAV, as the authors were able to set what kind of hardware could use Galileo as the GNSS of choice and make use of its features.
1.3. Paper Structure
2. Related Works
2.1. Study of the State-of-the-Art
2.2. Open Issues
- It is uncommon to use UAVs for the purposes and aims shown in this paper; many of the examples included deal with other aspects that, even when they show remarkable developments, do not intend to cover the purpose of measuring mobile network signals and parameters with a UAV. This creates a situation where there are multiple valid solutions that, unfortunately, cannot be applied or ported to the application domain shown in this paper.
- Additionally, some of the papers studied in the reviewed literature rely on simulations to validate the hypotheses put forward. Although, depending on the context, this may be a valid methodology (it might even be that it is the only reasonable methodology to be used), it is usually more accurate and closer to reality using an actual prototype that will count on design, implementation and testing works to realize the contributions that are made to the existing state-of-the-art.
- There is a degree of solution customization that is missing. This is due to the fact that, in several cases, instead of offering a system tailored for the purposes of the paper that is describing the research carried out, an already built UAV is used. This might come in useful in some cases, but it limits the flexibility and usability of the UAV when a testing prototype is deployed in the real world.
- Finally, most of the solutions do not provide an end-user-friendly way of visualizing the information obtained by the UAVs used in the research works performed. Something, such as a GUI, is missing in many documents, which, from our point of view, would be an interesting option in order to offer retrieved data or conclusions in a more accessible manner.
3. System Description
- Our built solution deals with an application domain that, judging from the reviewed literature, has not been given any relevant research so far. Therefore, we are providing research with a built prototype in an area of knowledge that has been almost previously untested, except for research studies that fall within this area but are focused on other objectives.
- Use of an actual UAV enabled with Galileo as the Global Navigation Satellite System (GNSS). Rather than using an already existing UAV solution that has been built with purposes different from the ones put forward in this paper, a custom-made UAV has been developed for accurate sensing of mobile network signals. This customization has been used for two different purposes. On the one hand, a controller board (Navio2 [22]) has been used as a GNSS receptor for UAV positioning. Compared to other systems (GPS, GLONASS, Beidou, etc.), and as it will be described, Galileo offers a higher degree of accuracy and a more robust signal that offers a more exact positioning for any device making use of it. This comes in as extremely useful for this application domain, as high accuracy is required to perform mobile network measurements that are likely to change in terms of output in a relatively small location.
- End-user-friendly capabilities have been provided as well. One of the three subsystems of the prototype is devoted to the development of a Graphical User Interface that will be able to display, in an accurate manner, what kind of data are being collected, where they were taken from and the meaning of them. This tool makes it easier to infer knowledge from the data acquired in any location the UAV flies.
- Open-source Unmanned Aerial Vehicle: this is the UAV that the authors of this paper have used to install the device used for measurements and to move it in a three-dimensional space.
- Mobile Data Acquisition System (MDAS): this is used to collect data regarding the signal power levels in the areas where it is transported. It composed by a mobile phone and two applications: one to collect data and another to format it. It will be integrated as part of the hardware used as the UAV base station.
- Graphical User Interface (GUI): this is a software program required to visualize the information that is shown to the end user.
- Galileo GNSS: this is used as a pivotal part for this proposal, as it offers location features that are more accurate than the most widely used equivalent GNSS systems. This subsystem is taken for granted, as Galileo is already built and precedes the inception of the proposed system described in this paper. It was shown in [23] how the received Galileo signal (the one that will be used for the measurements) can be described in its Intermediate Frequency (IF) as:
- Base station monitoring: credentials used to access the base station could be leaked, or there could be other privacy failures that enable a spurious third party to monitor what kind of flight the UAV is carrying out.
- UAV command spoofing: this attack is related to the previous one in the sense that it will require accessing the base station. Once the spurious party has managed to do so, it can alter the commands sent to the UAV to follow a different pattern or perform actions that could potentially lead to damaging the UAV to a greater or lesser extent.
- Data tampering: in this case, information collected from the UAV flight can be tampered with in two locations: (a) either in the base station or (b) in the MDAS. It could lead to misinterpretations regarding network coverage or receiving/sending signals in mobile networks.
- UAV hijacking: this cyberattack involves taking the UAV away from the location used for experimentation to somewhere where a spurious third party can take advantage of it. It will usually involve tampering with the Wi-Fi communications sent and received from the base station or with the GNSS signal; the latter is far less likely due to the extra security that it will make use of.
- ArduPilot as the base station software: as described in the following section, ArduPilot has been used as the software for managing flights and UAV missions. Specifically, it makes use of a separate Mission Planner module used to conceive flights for specific missions that involve specific movements. As it was said in [24]: “ArduPilot and Mission Planner have the ability to add security to over-the-air MAVLink transmissions by adding packet signing using an encrypted key”, so this program can enable additional security features. The fact that ArduPilot is an open-source software development also aids in auditing command and information transfers ([25]) in case it is required. Thus, using ArduPilot and its Mission Planner will play a significant role in discouraging tampering with the UAV missions or the data collected.
- Galileo as the GNSS: not only does it offer a higher degree of signal accuracy for device positioning (in our case, the UAV), but it also has additional security enabled by means of the Galileo Open Service Navigation Message Authentication (NMA), which provides “an authentication mechanism that allows a GNSS receiver to verify the authenticity of the GNSS information and of the entity transmitting it, to ensure that it comes from a trusted source” [26]. In this way, security in the communications between the GNSS and the UAV is upgraded and alterations in UAV missions or data collection become more difficult.
- Wi-Fi as the wireless protocol used to establish communications between the UAV and the base station: the Wi-Fi protocol utilized for wireless communications is the 802.11ac iteration, which has security enabled in the signal sent throughout the used 5GHz frequency band. In this way, communications can be secured in the system at the physical level and will make it more difficult to tamper with the UAV’s behaviour or the received data.
- Credentials for base station access: security capabilities can be added by providing an authentication mechanism that will filter the access to the hardware used, such as the base station. In this way, a first layer of security can be provided that will make it harder for spurious parties to access it, so that base station monitoring and data tampering can be prevented.
3.1. Unmanned Aerial Vehicle
3.1.1. Hardware Components for the UAV
- Raspberry Pi 3B+: this is the component used to run the operating system used by the open-source drone to govern the other components [27]. This provides an entry point to modify every setting possible for drone flights, which is maximized by using the ArduPilot program, as explained in the software section of this paper. As far as this proposal is concerned, its pinout is used combined with the Navio2 Autopilot for flight guidance and coordination. The Raspberry 3B+ makes use of a processor with a clock frequency of 1.4 GHz and 1 GB of SDRAM memory, which offers enough computational capabilities for the purpose described in this paper, and, since it can provide IEEE 802.11.b/g/n/ac and Bluetooth connectivity, it has the required wireless interfaces for connectivity and data transfer.
- Navio2: this is a controller board used, as in every UAV, to manage the drone flight on a real-time basis, making the UAV more stable and kee** it afloat in a safer way. Depending on the requirements, it makes use of either Linux-based Application Performance Monitoring (APM) or a tailored middleware to work with the Robot Operating System (ROS). This controller has a high resolution MS5611 barometer and 14 Pulse Width Modulation (PWM) output ports for control. One of its most prominent features is that the GNSS module (uBlox M8N) can use Galileo as the GNSS of choice. Considering its high accuracy level, and the fact that there are fewer applications that make use of Galileo (as opposed to, for example, GNSS), Galileo was used for UAV positioning.
- Other components required for UAV assembly (batteries, drone frame kit, motors and propellers) were also required. They are described as follows:
- Frame F450 [28]: For the frame or “skeleton” of the UAV, a Frame F450 with landing gear was chosen. This frame makes it possible to assemble all the parts on it and ensure stability to the drone. Among its main characteristics are resistance, lightness and a comparatively small size, which enable mounting several components while kee** low battery consumption benefits due to weight or stability.
- MaxPRO 2650 Batteries [29]: 11.1V and 2650mAh batteries were used to power the UAV. They belong to the LiPo battery (Lithium polymer) family, which are the most used for drones since they allow fast discharges and can provide significant amounts of energy in a short time, in addition to being light and small compared to others.
- Motors [30]: Set of four Emax 2213–935KV motors. These brushless UAV motors have 7.1 A as maximum current and are specially designed for 11.1V (3s) LiPo batteries, so they are a suitable choice for the drone battery. They include 10X4.5 propellers and have a thrust of 860g for each motor and 935KV (revolutions per minute/volt), which enable them to lift and manoeuvre the UAV without any issues.
- FS-T6 programmable digital transmitter/receiver with six channels in 2.4 GHz [31]. Programming is easy and intuitive, which enables emergency or landing scenarios where a fast response is required. It has low power consumption and ultra-fast signal reaction with interference-free Automatic Frequency Hop** Digital System (AFHDS) technology. It works under a 500 Hz bandwidth, 1024 sensitivity, Liquid-Crystal Display (LCD), Pulse Position Modulation (PPM)/Pulse Code Modulation (PCM) security coding and supports up to 20 UAV models with this kind of receiver.
3.1.2. Software Components for the UAV
- Raspbian: this is the operating system run by the Raspberry Pi 3B+ mounted on the drone [32]. It is used for typical operating system duties: organizing memory accesses, providing a mechanism to manage the underlying hardware or, more importantly in the case of this proposal, providing a software ground from where to execute other programs that are more user oriented. Raspbian can make use of both a Graphical User Interface of its own or just a Command Line Interface, but its capability for running the AutoPilot planner is the most important functionality that it offers to the whole system presented in this paper.
- ArduPilot: this is the flight planner used to manage all aspects of the taking off, in-air and landing navigation of the drone. It displays essential information on arming (turning on) or disarming the UAV and provides dashboards to recalibrate essential flight parameters of the drone, such as propeller regimes or commands to be carried out during flight. Although it has been conceived to be used in drones based on Arduino (hence the name ArduPilot), it is compatible with drones that make use of Raspbian and Raspberry Pi devices as the hardware backbone of the vehicle. ArduPilot works in the following manner: once it has been installed as a program, it will run as any other common piece of software on top of the operating system (in this case, Raspbian). Once it is executed, the vehicle operator will be given the option to configure the hardware, taking into account the kind of UAV (copter, rover, plane or even submarine) depending on what has been mounted and the controller set of the hardware (in our case, Navio2). Afterwards, as shown in Figure 5, further configuration details are completed.
- Secure Shell (SSH) is used as the protocol and program used to communicate with the Raspberry Pi installed at the UAV from a terminal. For configuration and parameter changes, it is required to set the UAV to its desired parameters, so this protocol and its Command-Line-Interface-based tool are most useful.
3.2. Mobile Data Acquisition System (MDAS)
3.2.1. Hardware Components for the MDAS
- It is of critical importance that the smartphone used is dual-band-enabled, so that the accuracy of the data obtained can be as great as possible.
- The mobile phone should be compatible with Galileo as the GNSS. With this feature, the possible choices to use smartphones with those characteristics are significantly narrower. As already described, the use of Galileo as the GNSS enables a greater level of accuracy (Precise Point Positioning via High Accuracy Service, or PPP via HAS) and security (Navigation Message Authentication or NMA) that, to the best of our knowledge, cannot be matched with other global satellite systems, so there is an incentive to use it.
- 6.
- Combined UAV and MDAS landing. As previously determined, an experiment regarding how landing could be carried out with the whole measuring equipment mounted was performed. While the addition of hardware to the UAV presented some challenges in terms of how the latter would move (which included landing), in the end, we had the expectation that the drone would be usable with the MDAS. The obtained results corroborated this.
- UAV trajectory: while there were several tests carried out with the UAV flying different paths, there are several concrete aspects that should be mentioned about the flight missions that were carried out: the first flight took place from coordinates 40.4307993 degrees latitude and −3.656102 degrees longitude, hereinafter represented as (40.4307993, −3.656102)-, to (40.4640321, −3.4387723). Another was carried out from (40.4307993, −3.656102) to (40.4629463, −3.4404305) and a third took place from (40.4298989, −3.6573602) to (40.4522076, −3.4264832). Two more flights (with their information available in [37]) were carried out with similar positioning, which, in the end, provided information comparable to the previous three. Rather than using fully autonomous flight near a populated area, the latter flights were carried out by having a qualified drone pilot executing all of the manoeuvres, so the UAV trajectory was influenced by the pilot-controlled movements and there were no uniform shapes during the flight that were executed. To avoid any kind of legal issue, permission was requested to perform those flights.
- UAV distance, height and speed while flying: there are some other pieces of information that can be inferred with regards to the UAV flight performance. Considering the coordinates previously provided, during the first flight, the UAV moved from two points separated by 18.78 kilometres, whereas, in the second and third flights, the starting and finishing points were separated by 18.61 and 19.71 kilometres, respectively. Height is visible in the field marked as “alt” in Figure 10, and it fluctuates between 640.7 and 718.7 m. As mentioned before, the existing ground elevation must be considered. For example, for terrestrial coordinates (40.4307993, −3.656102), it can be claimed that, due to the topographical characteristics of the Earth at that specific point, the altitude is 674 m. Since the flight altitude was registered as 718.7 m, the UAV was 718.7 − 674 = 44.7 m above ground. The finishing point for the first flight was 588 m. As for the second flight, the starting point was 665 m high and the last had an altitude of 558.19 m. The starting point of the third flight was also 665 m high and the last was located at an altitude of 569 m.
- 3.
- Energy consumption of the UAV: as described before, a MaxPRO 2650 battery has been used to power the drone; it provides 11.1 Volts and a current of 2650 mAh. Considering the features related to the rotors (the most energy consuming element of the drone) and the other systems used in the UAV, a calculator was used to be aware of how long a flight could be and what energy and electricity consumption figures could be expected [38]. As shown in Figure 11, a maximum flight length of 26 min and 9 s can be obtained, with a more conservative figure of near 21 min for a safe flight that will deplete only 80% of the battery. Although these figures are largely theoretical, they were useful to get an idea of how long a flight could be. Additionally, information about energy consumption for the most important components of the drone could be calculated. Again, Figure 11 shows how 30.4 Amperes is the maximum current drawn from the battery at full flying load, whereas the Maximum power consumption expected from the UAV would be 337.44 Watts. Other compelling information (current drawn from the battery at selected flying load, charger specifications, etc.) has been obtained as well.
4.3. Result Discussion
5. Conclusions and Future Works
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sousa, J.J.; Toscano, P.; Matese, A.; Di Gennaro, S.F.; Berton, A.; Gatti, M.; Poni, S.; Pádua, L.; Hruška, J.; Morais, R.; et al. UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications. Sensors 2022, 22, 6574. [Google Scholar] [CrossRef] [PubMed]
- Apaza, J.; Scipión, D.; Lume, D.; Saito, C. Development of two UAVs for volcano studies in southern Peru. In Proceedings of the 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru, 15–18 August 2017; pp. 1–4. [Google Scholar] [CrossRef]
- Koganti, T.; Ghane, E.; Martinez, L.R.; Iversen, B.V.; Allred, B.J. Map** of Agricultural Subsurface Drainage Systems Using Unmanned Aerial Vehicle Imagery and Ground Penetrating Radar. Sensors 2021, 21, 2800. [Google Scholar] [CrossRef] [PubMed]
- Rodríguez-Molina, J.B.; Corpas, B.; Hirsch, C.; Castillejo, P. SEDIBLOFRA: A Blockchain-Based, Secure Framework for Remote Data Transfer in Unmanned Aerial Vehicles. IEEE Access 2021, 9, 121385–121404. [Google Scholar] [CrossRef]
- Díaz, V.H.M.; Martínez, J.-F.; Cuerva, A.; Rodríguez-Molina, J.; Rubio, G.; Jara, A. Semantic as an Interoperability Enabler in Internet of Things; Chapter Nine of Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems; River Publishers: Gistrup, Denmark, 2013; Volume 1, pp. 315–342. [Google Scholar]
- Casalbore, D.; Di Traglia, F.; Romagnoli, C.; Favalli, M.; Gracchi, T.; Tacconi Stefanelli, C.; Nolesini, T.; Rossi, G.; Del Soldato, M.; Manzella, I.; et al. Integration of Remote Sensing and Offshore Geophysical Data for Monitoring the Short-Term Morphological Evolution of an Active Volcanic Flank: A Case Study from Stromboli Island. Remote Sens. 2022, 14, 4605. [Google Scholar] [CrossRef]
- Li, C.; Chen, H.; **ong, Y.; Chen, Y.; Zhao, S.; Duan, J.; Li, K. Analysis of Chinese Typical Lane Change Behavior in Car–Truck Heterogeneous Traffic Flow from UAV View. Electronics 2022, 11, 1398. [Google Scholar] [CrossRef]
- Addabbo, P.; Angrisano, A.; Bernardi, M.L.; Gagliarde, G.; Mennella, A.; Nisi, M.; Liberata Ullo, S. UAV system for photovoltaic plant inspection. IEEE Aerosp. Electron. Syst. Mag. 2018, 33, 58–67. [Google Scholar] [CrossRef]
- Luo, W.; Zhang, Z.; Fu, P.; Wei, G.; Wang, D.; Li, X.; Shao, Q.; He, Y.; Wang, H.; Zhao, Z.; et al. Intelligent Grazing UAV Based on Airborne Depth Reasoning. Remote Sens. 2022, 14, 4188. [Google Scholar] [CrossRef]
- Umeyama, A.Y.; Salazar-Cerreno, J.L.; Fulton, C.J. UAV-Based Far-Field Antenna Pattern Measurement Method for Polarimetric Weather Radars: Simulation and Error Analysis. IEEE Access 2020, 8, 191124–191137. [Google Scholar] [CrossRef]
- Zhai, Y.; Mai, C.; Liao, J.; Wang, W.; Zeng, J.; Qin, C.; Donida Labati, R.; Piuri, V.; Scotti, F. A³PNet: Antijam and Accurate Antenna Parameters Measuring Network for Mobile Communication Base Station Using UAV. IEEE Trans. Instrum. Meas. 2022, 71, 5007515. [Google Scholar] [CrossRef]
- Duan, H.; Zhang, Q. Visual Measurement in Simulation Environment for Vision-Based UAV Autonomous Aerial Refueling. IEEE Trans. Instrum. Meas. 2015, 64, 2468–2480. [Google Scholar] [CrossRef]
- Balestrieri, E.; Daponte, P.; De Vito, L.; Picariello, F.; Tudosa, I. Guidelines for an Unmanned Aerial Vehicle-Based Measurement Instrument Design. IEEE Instrum. Meas. Mag. 2021, 24, 89–95. [Google Scholar] [CrossRef]
- Cui, Z.; Briso-Rodríguez, C.; Guan, K.; Zhong, Z.; Quitin, F. Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems. IEEE Access 2020, 8, 110565–110574. [Google Scholar] [CrossRef]
- Jiang, D.; Zeng, Z.; Zhou, S.; Guan, Y.; Lin, T. Integration of an Aeromagnetic Measurement System Based on an Unmanned Aerial Vehicle Platform and Its Application in the Exploration of the Ma’anshan Magnetite Deposit. IEEE Access 2020, 8, 189576–189586. [Google Scholar] [CrossRef]
- Li, X.; Shang, S.; Lee, Z.; Lin, G.; Zhang, Y.; Wu, J.; Kang, Z.; Liu, X.; Yin, C.; Gao, Y. Detection and Biomass Estimation of Phaeocystis globosa Blooms off Southern China From UAV-Based Hyperspectral Measurements. IEEE Trans. Geosci. Remote Sens. 2022, 60, 4200513. [Google Scholar] [CrossRef]
- Ede, B.; Kaplan, B.; Kahraman, I.; Keşir, S.; Yarkan, S.; Ekti, A.R.; Baykaş, T.; Görçin, A.; Çırpan, H.A. Measurement-Based Large Scale Statistical Modeling of Air–to–Air Wireless UAV Channels via Novel Time–Frequency Analysis. IEEE Wirel. Commun. Lett. 2022, 11, 136–140. [Google Scholar] [CrossRef]
- Hamdalla, M.Z.M.; Bissen, B.; Hunter, J.D.; Liu, Y.; Khilkevich, V.; Beetner, D.G.; Caruso, A.N.; Hassan, A.M. Characteristic Mode Analysis Prediction and Guidance of Electromagnetic Coupling Measurements to a UAV Model. IEEE Access 2022, 10, 914–925. [Google Scholar] [CrossRef]
- Eltner, A.; Baumgart, P.; Maas, H.; Faust, D. Multi-temporal UAV data for automatic measurement of rill and interrill erosion on loess soil. Earth Surf. Process. Landf. 2012, 40, 741–755. [Google Scholar] [CrossRef]
- Arnold, T.; De Biasio, M.; Fritz, A.; Leitner, R. UAV-based measurement of vegetation indices for environmental monitoring. In Proceedings of the 2013 Seventh International Conference on Sensing Technology (ICST), Wellington, New Zealand, 3–5 December 2013; pp. 704–707. [Google Scholar] [CrossRef]
- Krause, S.; Sanders, T.G.M.; Mund, J.-P.; Greve, K. UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring. Remote Sens. 2019, 11, 758. [Google Scholar] [CrossRef] [Green Version]
- Navio2: Autopilot HAT for Raspberry Pi. Powered by ArduPilot and ROS. Available online: https://navio2.emlid.com/ (accessed on 23 December 2022).
- Gallardo, F.; Yuste, A.P. SCER Spoofing Attacks on the Galileo Open Service and Machine Learning Techniques for End-User Protection. IEEE Access 2020, 8, 85515–85532. [Google Scholar] [CrossRef]
- MAVLink2 Signing Website. Available online: https://ardupilot.org/planner/docs/common-MAVLink2-signing.html (accessed on 23 December 2022).
- ArduPilot-Versatile, Trusted, Open. Available online: https://ardupilot.org/ (accessed on 23 December 2022).
- Tests of Galileo OSNMA Underway. Available online: https://www.gsc-europa.eu/news/tests-of-galileo-osnma-underway#:~:text=The%20Galileo%20OSNMA%20is%20an,been%20modified%20in%20any%20way (accessed on 23 December 2022).
- Raspberry PI 3B Datasheet. Available online: https://www.alliedelec.com/m/d/4252b1ecd92888dbb9d8a39b536e7bf2.pdf (accessed on 23 December 2022).
- F450 Integrated 4 Axis Quadcopter Frame PCB for Flamewheel F450, Multicopter. 2022. Available online: https://www.multicoptero.com/es/tienda-on-line/drones-dji/dji-f450-f550/ (accessed on 23 December 2022).
- LiPo MaxPro Battery 3S 11,1V 2650mAh 30C (Size 3S-2200)-Azor. Available online: http://azormodelismo.com/tienda/product_info.php?products_id=1428 (accessed on 11 December 2022).
- EMAX MT2213 935KV CCW. Available online: https://rc-innovations.es/shop/Emax-MT2213-935kv-CCW-motor-brushless-drones-economicos#attr= (accessed on 11 December 2022).
- FST6 6CH PROGRAMABLE 2.4GHZ FLYSKY-iHobbies, Jetcat Spain. Available online: https://www.ihobbies.es/emisora-fst6-6ch-programable-24ghz-flysky-p-1-50-17214/ (accessed on 11 December 2022).
- Operating System Images. Available online: https://www.raspberrypi.com/software/operating-systems/ (accessed on 23 December 2022).
- **aomi Mi 10 Lite 5G Smartphone 6GB 128GB 6.57′. AMOLED 48MP Quad-Cámara 4160mAh (Typical) NFC, Gris: **aomi: Amazon.es: Electrónica. Available online: https://www.amazon.es/**aomi-Mi-10-Lite-Quad-c%C3%A1mara/dp/B089Y35SKG (accessed on 11 December 2022).
- Network Cell Info & Wifi—Google Play Applications. Available online: https://play.google.com/store/apps/details?id=com.wilysis.cellinfo (accessed on 11 December 2022).
- Geo++ RINEX Logger. Available online: https://play.google.com/store/apps/details?id=de.geopp.rinexlogger&hl=en&gl=US (accessed on 26 December 2022).
- Mathworks—The GUI Options Dialog Box. Available online: https://www.mathworks.com/help/matlab/creating_guis/gui-options.html (accessed on 26 December 2022).
- GitHub User Jrodrimo Web Site. Available online: https://github.com/jrodrimo/GALENCODER (accessed on 26 December 2022).
- Drone LiPo Battery Calculator. Available online: https://www.translatorscafe.com/unit-converter/en-EN/calculator/multicopter-lipo-battery (accessed on 10 January 2023).
Authors | Advantages | Disadvantages |
---|---|---|
[10] | Usage of UAVs for wireless network measurements. | Simulated environment for some tailored use cases rather than actual prototype. |
[11] | Antijam and accurate antenna parameters’ measurement framework is created. | No scope with open-source tool to perform mobile network measurements. |
[12] | Autonomous Aerial Refuelling system designed. | Outside of the scope of this paper. Large UAV required for the application domain. |
[13] | UAV can be used as a Mobile Measurement Platform (MMP). | No information or instructions regarding the application domain of this paper. |
[14] | Proves that UAVs can be used for mobile network-related applications. | Focus oriented on the usability of UAVs as mobile base stations. |
[15] | High precision aeromagnetic measurement equipment. | Requires large, oil-powered UAV. |
[16] | UAV used for monitoring and parameter measurement in a natural environment. | Application domain far from the scope of this paper. |
[17] | System created for Large-Scale Statistical Modelling of Air–to–Air Wireless UAV Channels. | Communications follow an Air-to-Air pattern; no GUI is provided. |
[18] | Thorough study on how UAVs operate in congested wireless environments. | Application domain far from the scope of this paper. |
[19] | Usage of UAV for measuring even rill and inter-rill erosion on the European loess belt. | Application domain far from the scope of this paper. |
[20] | Usage of UAV for Normalized Difference Vegetation Index measurement | Application domain far from the scope of this paper. |
[21] | UAV-based methodology to measure tree height for intensive forest monitoring | Application domain far from the scope of this paper. |
Open Issue | Proposed Solution | Means Used for the Solution |
---|---|---|
No application domain research | Research using UAVs to collect information from mobile networks | Tailored UAV Galileo as GNSS |
Lack of prototype deployment | Using an actual UAV whenever it is suitable for development and testing capabilities. | Tailored UAV Galileo GNSS |
Lack of UAV tailoring for the application domain | Using an actual UAV adapted to the collection of mobile network data. | Tailored UAV Galileo GNSS End user capabilities (GUI) |
Poor data visualization resources | Development of a GUI where significant information can be collected | End user capabilities (GUI) |
Security Threat | Countermeasure |
---|---|
Base station monitoring | Credentials for base station access. |
UAV command spoofing | Wi-Fi as the wireless protocol. Galileo as the GNSS. ArduPilot as the base station software. |
Data tampering | Credentials for base station access. Wi-Fi as the wireless protocol. Galileo as the GNSS. ArduPilot as the base station software. |
UAV hijacking | Wi-Fi as the wireless protocol. Galileo as the GNSS. ArduPilot as the base station software. |
Requirement | Description |
---|---|
Functional Requirement 1 | The UAV must be able to perform yaw, pitch and roll manoeuvres |
Non-functional Requirement 1 | The UAV must be able to have a mobile phone as the payload |
Non-functional Requirement 2 | The UAV must be able to stand off the ground for at least one minute |
Non-functional Requirement 3 | The UAV must use Galileo as the GNSS for positioning |
Non-functional Requirement 4 | The UAV must be able to fly away tens of meters |
Requirement | Description |
---|---|
Functional Requirement 1 | The MDAS must be portable so it can be installed in a UAV. |
Non-functional Requirement 1 | The MDAS must be fully operational with a regular smartphone. |
Non-functional Requirement 2 | The MDAS must be able to collect information about coordinates. |
Non-functional Requirement 3 | The MDAS must be able to collect information about altitude. |
Non-functional Requirement 4 | The MDAS must be able to obtain information about signal power levels. |
Non-functional Requirement 5 | The MDAS must be able to run the program used for signal logging. |
Requirement | Description |
---|---|
Functional Requirement 1 | The GUI must have a dashboard where signal information can be visualized. |
Functional Requirement 2 | The GUI must be able to visualize data on a map. |
Non-functional Requirement 1 | The GUI must run on any regular laptop without having any performance issues. |
Non-functional Requirement 2 | The GUI must show accurate data about coordinates. |
Non-functional Requirement 3 | The GUI must show accurate data about altitude. |
Non-functional Requirement 4 | The GUI must show accurate data about mobile network signals in terms of power. |
Experiment Performed | Expected Result | Obtained Result. Deviations |
---|---|---|
Open-air flight under normal conditions | Regular flight of the UAV | Regular flight of the UAV |
Landing under normal conditions | Regular landing of the UAV | Regular landing of the UAV |
Open-air flight during unfavourable weather conditions | Acceptable flight of the UAV | Acceptable flight of the UAV |
Landing under unfavourable weather conditions | Acceptable landing of the UAV | Acceptable landing of the UAV |
Data collection from UAV | Information collected from the mobile network | Files with information from the mobile network |
Combined UAV and MDAS landing | Regular landing of the UAV | Regular landing of the UAV |
. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).