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 keeping 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 keeping 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 Hopping 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.
3.2.2. Software Components for the MDAS
3.3. Graphical User Interface
- Data load: the data are generated from the measurements collected by the radio frequency sensor (that is to say, the MDAS used to collect information) and can be stored either on an external device or on a server. Files use a Comma Separated Value (CSV) extension. Once located, the file containing the data will be uploaded to the GUI and used for further processing. Note that the operating system that is used belongs to the Windows suite and does not require any further sophistication: if a MATLAB image can be installed and run on a computer, it is capable of running the GUI. MATLAB images in other operating systems will result in using the GUI on them as well.
- Data pre-processing: due to the noisy environment (in terms of radio frequency) where the measurements are taken, it is necessary to perform a prior analysis of the recorded data to detect anomalies in the recordings. Therefore, it is mandatory to identify outliers, missing values and/or perform a normalization to create a standard data structure that can be interpreted by the GUI. Processing and analysis of measurements involves numerical analysis of the measured values and interpretation of these results. From this process the main features of the measures are extracted, defined as: (a) power levels; (b) power-to-noise ratio levels; (c) frequencies of detected carriers; (d) measurement time intervals; (e) radiofrequency sensor position (latitude, longitude, elevation).
- Signal measurement representation over a map: this is one of the most prominent functionalities of the GUI; it consists of displaying the numerical values extracted from the measurements in a friendly environment. This allows for a better understanding of the results with the aim of performing an analysis and interpretation in greater detail, and with the possibility of showing radiation levels on a real positioning map. There are several settings included with that option, such as downloading and showing maps, along with showing power levels over the map. This latter option makes it possible to distinguish coverage areas according to their power levels.
4. System Testing and Performance
4.1. System Testing Considerations
- Flight duration: the UAV flights were scheduled to last at least five minutes to obtain significant information about the drone’s flying capabilities, bearing in mind that some height and distance should be manageable as well. They were measured in ranges of hundreds of meters, so they guaranteed that the drone could fly significant distances to collect information.
- Manoeuvring: the open-source drone built by the team members performed all possible movements in three dimensions (yaw, pitch, roll) to ensure that it had full mobility when in the air.
- Data collection: the data that was collected was required to be significant for the purposes of the presented system. Therefore, it was required that it contained the parameters deemed as mandatory according to the non-functional requirements set in the previous section.
- Unfavourable weather conditions: the measurements to be taken relied on the built UAV being able to freely fly in a space as open as possible. Unfortunately, when the equipment associated with the MDAS was finally ready to be installed in the drone, there were severely unfavourable weather conditions. Consequently, UAV flights had to be planned and performed with extreme care not to damage the equipment that was being used. Despite these inconveniences, the authors of the paper were able to obtain accurate information about power levels from third and fourth generation mobile networks and map them with the coordinates that reflected where the UAV was when the measures were taken.
- Information formatting data: as previously explained, the information collected is directly obtained from the Network Cell Info application for Android terminals. The data are obtained in Comma-Separated Values (CSV) format, and come with a plethora of parameters (the coordinates, altitude and power level of the strongest signal that is being measured) of different usability for the purpose of the proposal. The most useful data had to be picked from all the parameters to have it represented in a suitable manner.
- Program coding: the GUI was programmed using MATLAB (Matrix Laboratory) as the programming language, due to its facilities to operate with matrix-formatted data. MATLAB imposed several syntax rules that must be followed, which, in the end, posed no issue at all.
4.2. System Deployment in the Real Scenario
- Open-air flight under normal conditions: the first experiment was aimed at testing what the UAV performance looked like under the regular conditions expected to be used. Expectations were that it would perform with next to no issues. The results showed no significant trouble during the UAV flight: both the hardware and software used (Figure 9 shows the information obtained from the AutoPilot program used for flight missions before the UAV took off) regularly performed and no reprogramming or hardware mounting were required.
- Landing under normal conditions: in this case, the experiment dealt with landing the drone without any damage or issue resulting from a normal landing, so that it could be used without any restriction in its usefulness. Expectations were that this task could be carried out without any problems; the results confirmed these expectations.
- Open-air flight during unfavourable weather conditions: during the experiments that were carried out, it became evident that, in order to be truly useful, the UAV would have to operate under weather conditions likely to be unfavorable. While it is not expected that the UAVs will have to work in a hostile environment that would damage the electronics (heavy rain, electrical storms), the UAV should be usable enough to guarantee that an occasional, light rainfall or wind drifts will not result in serious damage to the UAV that will incapacitate it to perform its duties. As described before, during the tests carried out with the UAV, it was proven that it could still work under such usage conditions, which had not been forecasted by the authors of this paper with enough importance during flight missions. Expectations were that the UAV would still be capable of normal flight under this kind of weather. While the results confirmed this, in terms of light rain and wind drifts, further testing was stopped in order to not damage the UAV.
- Landing under unfavourable weather conditions: the UAV had to land in unfavourable weather conditions to prove that it could be recovered from flight missions without any problems. Expectations (the UAV could be recovered without having taken damage after the flight) were met again in this experiment.
- 5.
- Information collection from mobile network signals: this is the main purpose of the system built and the reason why all the measurements are taken. In this case, the experiments were carried out to collect information related to 4G Long-Term Evolution (LTE) coverage in the area of testing and experimentation. The experiment consisted of, once the MDAS had been mounted to the UAV, setting the latter to fly and cover a large three-dimensional area with a speed and manoeuvrability that could not effectively be matched by a team of human operators. Expectations before performing this experiment contemplated the possibility of obtaining accurate information regarding mobile network signals.
- Sim: refers to the Subscriber Identity Module (SIM) card number used by the smartphone mounted as part of the MDAS subsystem running in combination with the UAV. There are two possible slots for SIM cards (1 and 2), depending on the local characteristics of the network mobile system for each country. For European countries, it is number 1.
- Radio type: references the kind of mobile network used when collecting information. In this case, it is LTE, as previously explained. Other options would be the Global System for Mobile (GSM) communication or Code-Division Multiple Access (CDMA).
- Radio: reference to the radio signal measured. Since there is no other possible option than LTE (GSM might use General Packet Radio Service (GPRS), for example) it has been labelled as LTE.
- Carrier: defines which network operator the used network belongs to. As can be seen in Figure 10, in this case it is Orange (France Telecom).
- MCC: an acronym that means Mobile Country Code (MCC), typical of mobile network systems. Since the experiments were run in Spain, it shows MCC 214.
- MNC: referred to as Mobile Network Code (MNC), which is the code used to identify the mobile network operator performing operations in a country. Since Orange/France Telecom is the network operator used for this experiment, and the data collection takes place in Spain (with a MCC of 214), it is identified as 3.
- Area: references the Tracking Area Code (TAC) used in LTE communications for area identification, which can range from 0 to 65,535. In the case of the measurements taken, the area is identified as 1250.
- cellid: a figure identifying the 4G cell used to collect information. The ones that appear in Figure 10 are 60,683 and 2,081,280, due to the fact that the UAV flew from one cell to another when collecting information.
- enbrnc: a node identifier that refers to evolved node base stations that are expected to behave according to the commands received from Radio Network Controllers (RNC) that are linked to the different cells used in the LTE system. Therefore, they have been identified as 237 and 8130 in Figure 10.
- lcid: an acronym that refers to the Logical Channel Identifier used in the corresponding Media Access Control Service Data Unit (MAC SDU) in communications in LTE. It changes several times (11 to 2, 1 and 0) in Figure 10.
- xarfcn: refers to the Absolute Radio-Frequency Channel Number used in the communications; 0 (as shown in Figure 10) is linked to band 1 in LTE.
- band: references the band used for mobile communications. While it is shown as zero in Figure 10, it actually means that the very first band in an array of available bands (starting at 0) is the one used here. Therefore, it refers to band 1, which is the first one available on LTE networks.
- sigl: refers to the signal level of power. The data collected and displayed in Figure 10 shows how levels go from 2 (the strongest one shown in the figure) to 4 (the weakest), so they summarize the signal power shown afterwards.
- ASU: an acronym for Arbitrary Strength Unit, and it is a parameter used for measurement mapping in LTE communications when signal power is taken into account. Thus, the highest ASU figures are the ones where signal power is stronger, and the weakest ones show lower ASU figures in comparison.
- signal: this value has the greatest importance for the purpose of the research in the paper, as it shows the strength of the measured power signal in decibels. In Figure 10, they are shown to cover a range between −75 dB (stronger, better signal quality) and −114 dB (weaker, worse signal quality).
- lat: refers to the latitude of the device used to measure the information from the 4G LTE signal, with the precision expected to be obtained from Galileo.
- lon: in a similar way, this feature is used to reflect the longitude of the device used for data measurements. Note that by using latitude and longitude combined, the location of the experiments performed and how the UAV moved around that area can be known.
- acc: this field refers to the expected accuracy of the measured positions or, from a different point of view, the likely positioning error to be measured. The values displayed in Figure 10 are shown in centimetres.
- time: a timestamp used to show the moment when the measurement was taken. The format used is Epoch, so it can be utilized to trace the moment when flying tests were carried out.
- speed: refers to the speed of the device used to calculate the measurements. The values for each piece of data collected appear in Figure 10; it must be considered that the information obtained is measured as scalar data rather than vectorial data, so only one field is provided. Measurements are taken in meters per second.
- bearing: refers to the azimuth angle (defined in this context as the angle between the object and the magnetic north of the Earth) taken during the MDAS measurements (which is directly linked to those taken by the UAV on which the MDAS is installed).
- alt: refers to the altitude that the MDAS was at when the measurements were taken. Figure 10 shows altitude values all well above 700 m; this is because the altitude measured takes 0 as sea level, and the testing location was placed in a land area already above more than 600 m over sea level.
- device: provides a description of the starting characters of the mobile phone that is used to collect information. Considering the smartphone that has been used as part of the MDAS (Xiaomi mi 10 Lite), it should come as no surprise that it is identified as Xiaomi_M2002J9G, which is the code used for such smartphones in mobile communications.
- 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
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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 |
. |
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© 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/).
Share and Cite
Buggiani, V.; Ortega, J.C.Ú.; Silva, G.; Rodríguez-Molina, J.; Vilca, D. An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization. Sensors 2023, 23, 1285. https://doi.org/10.3390/s23031285
Buggiani V, Ortega JCÚ, Silva G, Rodríguez-Molina J, Vilca D. An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization. Sensors. 2023; 23(3):1285. https://doi.org/10.3390/s23031285
Chicago/Turabian StyleBuggiani, Vittorio, Julio César Úbeda Ortega, Guillermo Silva, Jesús Rodríguez-Molina, and Diego Vilca. 2023. "An Inexpensive Unmanned Aerial Vehicle-Based Tool for Mobile Network Output Analysis and Visualization" Sensors 23, no. 3: 1285. https://doi.org/10.3390/s23031285