The paper proposes an extension of a previous algorithm for the geolocation of missing people, which is aimed at a reduction in search times. The proposed technique involves the use of femtocells on board the drone, and therefore ofers the possibility for identifying a mobile terminal based on the estimate of the power levels. In particular, a multi-drone system is proposed that allows for better performance in terms of reduction in localization times, which are halved in the case of simultaneous use of 4 drones.
ger as the only means for covering a disaster area but
as a localization system leads to novel studies
conThe occurrence of a natural disaster in urban or sub- ducted in [
The development in technological innovation is of- UAV systems. Through the femtocell cover, placed on
ten able to provide support to the problems that must the drone, it is possible to create a connection with the
be faced in the event of a post-natural disaster. For terminals and locate them using the received power
example, on a social level it is of crucial importance values. In particular in [
Another research field is people identification and time optimization and processing algorithm using a localization [11, 12, 13], in particular the techniques multi-drone-femtocell system. employed searching for missing persons in post-earthquakeThe use of multiple drones with femtocells on board scenarios. allows scanning the monitoring area more rapidly; the
Several methods have been proposed to date includ- algorithm is responsible for making the two or four ing the localization of mobile terminals by radiofre- drones cooperate, in order to follow their respective quency (RF) signals, in scenarios where rubble is a sour- paths with the minimum overlap. This mechanism leads ce of significant attenuation to the propagation of the to a considerable reduction in the flight and processing electromagnetic signal [14, 15, 16]. times of each drone and therefore avoids considerable The idea of using the drone-femtocell system no lon- waste of flight energy [20].
The paper is structured as follows: section II deIInCfYoRrmIMaEtic2s0,2M0:aItnhteemrnaattiicosn,aanldCoEnnfgeirneneecreinfogr, OYonulinnge,RJeuselyar0c9he2r0s2i0n scribes the proposed method, i.e. the method of op" roberta.avanzato@phd.unict.it (R. Avanzato); timizing flight and processing times through multiple francesco.beritelli@dieei.unict.it (F. Beritelli) drone-femtocell systems; section III shows the performances obtained using this method as the number of drones used and the size of the monitoring area vary; © 2020 Copyright for this paper by its authors. Use permitted under Creative CPWrEooUrckReshdoinpgs IhStpN:/c1e6u1r3-w-0s.o7r3g CCoEmUmoRns WLiceonrsekAsthtriobuptioPnr4o.0cIneteerdnaitniognasl ((CCC EBYU4R.0)-.WS.org) the final section is dedicated to conclusions.
In this paper, the "Cluster-based Fast Proximity
Algorithm" proposed in [
Therefore, by using multiple drone-femtocell systems, the need arises to remodel the algorithm for optimizing the drone flight time, in order to intelligently cover each sub-area of the monitoring area. To this end, once the optimization algorithm is applied, the graphs relating to processing times, flight times and energy expenditure are obtained as the number of drones used and the size of the matrix that defines the monitoring area vary. To apply the algorithm, the following constraints were introduced: • coverage radius of the femtocell on board the drone equal to half the diagonal of the starting grid; detect; • the terminals hook onto the first femtocell they • the drones depart from the edges of the grid with a time lag of one minute, to prevent them from passing through the same point at the same time; • uneven distribution of terminals.
The drone-femtocell system and the details of the
classification and localization algorithms are defined
(1)
(2)
(3)
(4)
(5)
(6)
(7)
in [
= = 2 ( − 1)
The equations that determine the processing time, lfight time and energy, respectively, in the case of two and four drones are the following:
− = ∗ (5 ∗ + 1) − = ∗ 20 + 2 [ ∑
3 ( =2 2 −2 )] = ∗ ( − + − )
The processing, flight and energy expenditure times in the case of two drones are defined, respectively, by (2), (3), and (4). However, in the case of 4 drones, the processing, flight and energy expenditure times are defined by (5), (6), and (7), respectively.
−
{ = ∗ 5 ∗ − = ∗ [2
+ 1 [ 2 ] (2 + 2 −2 )] + 4 3
} = ∗ ( − + − )
divided into 9 rows and 9 columns, the phases of the
algorithm and the drone path, respectively, when 1, 2
In this section we will evaluate the performance of the and 4 drones are employed. The diferences concern
lfight time optimization and processing algorithm in the number of processing points and the flight
segthe case of 1 drone, 2 drones and 4 drones. As already ments of each individual drone. In the case only one
seen in [
To test the performance of the system, a practical ing and flight times of each individual drone. However, example of a matrix of size = 9 will be considered, using 4 drones it is possible to observe that the number i.e. a 9 × 9 matrix (with a resolution of 2 meters, thus of phases each drone must complete decreases while obtaining a monitoring area of 18 × 18 meters). Using maintaining the size of the grid unchanged; this haptwo drones, positioned at opposite edges of the area, it pens because each drone is responsible for scanning is noted that the number of phases that the algorithm a smaller sub area equal to almost half of the origiruns is given by (1) and remains unchanged compared nal one. This decrease occurs every time 4 drones are to the case of using only one drone, i.e. 3 phases are used, regardless of the size of the grid. There is also a carried out. further decrease in the processing points (equal to 14) Fig. 1, Fig. 2, and Fig. 3 show the monitoring area and in the flight segments (equal to 28).
Fig. 4 shows the flight and processing time trends of see that the maximum decrease is obtained by passing a matrix M = 9, as the number of drones used varies. from 2 to 4 drones, with the flight time being halved. As for the processing time, it can be observed that it Regarding the energy, represented in Fig. 5, a net significantly decreases using 4 drones, going from 570 decrease is obtained, passing from the use of 2 drones seconds (9.5 minutes) with one drone to 420 seconds (total energy equal to 50.44Wh) to 4 drones (33.95 Wh). (7 minutes) with 4 drones. Using two drones, how- With one drone, on the other hand, there is an energy ever, the processing time drops to 480 seconds (8 min- consumption of 60.62 Wh. utes). While, the total flight time varies from 680 sec- To generalize the considerations made, additional onds (11.33 minutes) using one drone, to 560 seconds graphs were obtained as the size of the matrix on which (9.33 minutes) with 2 drones, decreasing up to 280 sec- the localization algorithm is applied varies. Fig. 6, Fig. 7 onds (4.67 minutes) using 4 drones. In this case we can and Fig. 8 represent, respectively, the trend of the curves relating to processing time, flight and energy consumption, based on the use of 1, 2 or 4 drones. These figures confirm what has been said for a 9 × 9 matrix. In fact, as regards the processing time, the greatest reduction is obtained by passing from 1 to 4 drones.
In terms of flight time and energy, there is a sharper decrease from 2 to 4 drones. Once the number of drones has been fixed, processing times, flight times and energy increase hand in hand with the increase in the size of the matrix, but with a diferent trend. The processing time increases linearly, while the flight time and energy grow according to an exponential trend.
For example, having set the use of drones equal to 2, the flight, processing and energy consumption times were obtained as the size of the matrix changed. The data is represented in Table 1.
Another interesting graph that has been obtained concerns the total duration of the journey of each drone, given by the sum of the processing and flight times. As shown in Fig. 9, once the size of the matrix is fixed the total duration of the journey is considerably reduced, almost halving going from 2 to 4 drones.
Considering that during a search and rescue operation of missing persons time is a determining factor, being able to locate terminals in the shortest possible timeframe is a major advantage.
In this paper, a study was presented concerning the combined use of drones and femtocells. In particular, the paper analyses the cases UAV-femtocell systems are used to create ad hoc emergency networks during disaster scenarios or to facilitate search and rescue operations for civilians missing in post-earthquake scenarios. Using a real simulation scenario, the following were considered: