# Numerical and Experimental Characterization of LoRa-based Helmet-to-UAV
links on Flat Lands

G. M. Bianco, A. Mejia-Aguilar, G. Marrocco

###### Abstract

The use of the LoRa communication protocol in a new generation of transceivers
is attractive for search and rescue (SaR) procedures because they can operate
in harsh environments covering vast areas while maintaining a low power
consumption. The possibility of wearing helmets equipped with LoRa-radios and
installing LoRa transceivers in unmanned aerial vehicles (UAVs) will
accelerate the localization of the targets, probably unconscious. In this
paper, the achievable communication ranges of such links are theoretically and
experimentally evaluated by considering the possible positions of the helmet
wearer (standing or lying) on a flat field, representing a simple SaR
scenario. Simulations and experimental tests demonstrated that, for the
standing position, the ground-bounce multi-path produces strong fluctuations
of the received power versus the Tx-Rx distances. Such fluctuations can be
kept confined within 100 m from the target by lowering the UAV altitude.
Instead, for a more critical lying position, the received power profile is
monotonic and nearly insensitive to the posture. For all the considered cases,
the signal emitted by the body-worn transceiver can be exploited to localize
the helmet wearer based on its strength, and it is theoretically detectable by
the UAV radio up to 5 km on flat terrain.

###### Index Terms:

Body-area IoT, LoRa, LPWAN, on-body radio, search and rescue, radio
propagation, wearable antenna.

##  I Introduction

The††footnotetext: Work funded by the European Regional Development Fund under
the Cooperation Programme Interreg V-A Italia Austria 2014-2020, ITAT3023,
Smart Test for Alpine Rescue Technology START and by the European Regional
Development Fund, Operational Program Investment for growth and jobs ERDF
2014-2020 under Project number ERDF1094, Data Platform and Sensing Technology
for Environmental Sensing LAB-DPS4ESLAB.††footnotetext: Giulio Maria Bianco is
with the Pervasive Electromagnetics Lab, University of Rome Tor Vergata, Rome,
Italy, and also with the Center for Sensing Solution, EURAC research, Bolzano,
Italy. Email<EMAIL_ADDRESS>(contact author)††footnotetext:
Abraham Mejia-Aguilar is with the Center for Sensing Solutions, EURAC
Research, Bolzano, Italy.††footnotetext: Gaetano Marrocco is with the
Pervasive Electromagnetics Lab, University of Rome Tor Vergata, Rome, Italy.
aim of search and rescue (SaR) operations is to localize a target person and
then provide assistance. Such procedures are very common for both military and
civilian purposes, particularly regarding the identification of first
responders during fires, earthquakes and floods [1, 2] as well as in case of
avalanches and to locate lost hikers [3]. Currently, ad-hoc civilian SaR
systems are used in mountain environments and rely on radiofrequency devices:
the avalanche beacons (also known as ARVAs [4]) and the RECCO system [5].
However, the effectiveness of those technologies is hampered by the low
ranges, which spans from about $60$ m (ARVA) to about $120$ m at most (RECCO).
Moreover, both devices cannot transmit any critical data about the user’s
health status. Improvements could come from the internet of things (IoT)
systems exploiting the recently developed low-power wide-area networks
(LPWANs), such as LoRa, Sigfox and NB-IoT [6]. Thanks to their limited data
rate, these networks can cover extended communication distances while
maintaining very low power consumption [6]. They can thus enable connectivity
even in the harshest environments.

Table I: Examples of relevant use cases of LoRa. Ref. |  Topic |  Ref. |  Topic
---|---|---|---
This work |  Helmet-UAV off-body links for SaR |  [13], [14] |  Terrestrial links for mountain SaR
[8] |  Localization and tracking |  [15] |  Emergency communications
[9] |  Remote health monitoring |  [16] |  Extend the SMS coverage to unconnected areas
[10] |  Human activity recognition |  [17] |  Industrial monitoring
[11] |  UAV communications for swarm configurations |  [18] |  Wireless underground sensor networks
[12] |  Recovery of incapacitated UAVs |  [19], [20] |  Smart cities

LoRa is one of the most investigated LPWAN technologies, as it was proven to
reach a $30$ km communication range by transmitting $25$ mW [7]. Wearable LoRa
devices are currently under study for several applications ranging from
tracking [8] to remote health monitoring [9]. Relevant use cases of LoRa are
reported in Table I. LoRa was also proposed for SaR applications where the
communication range and the power consumption are pivotal features. The
authors recently proposed a LoRa-based IoT system for mountain SaR operations
[14]. The system comprises body-worn LoRa radios and a range-based
localization algorithm [21] exploiting received signal strength (RSS)
measurements to localize the target when the GPS signal is absent. Thanks to
the low energy consumption of the LoRa protocol, the radios can support the
SaR operations for more than $5$ hours and a half when they are fed with a
typical battery of $1100$ mAh capacity [14]. Meanwhile, the target can be
localized even with a small number of measurements collected quickly by
employing classical range-based localization algorithms that exploit the
monotonic proportionality between the attenuation and the transmitter-receiver
distance, as [21, 22, 23, 24]. However, only terrestrial LoRa links have been
considered so far, whereas, in SaR scenarios, the terrestrial operations are
complex and slow. Therefore, LoRa-based SaR could greatly benefit from
unmanned aerial vehicles (UAVs) searching the target from the sky. Indeed, by
equipping a UAV with a LoRa receiver, many RSS measurements could be easily
collected in a short time over a wide area.

Most first responders and hikers wear a safety helmet during operations and
outdoor activities (like skiing or canyoneering), so a possible placement of a
LoRa wearable beacon is the helmet itself. Wearable helmet-mounted antennas
were proposed for disaster prevention [25] and military forces [26]. The more
common helmet antennas are dipoles [27, 28], patches [26], and loops [29].
Noticeably, as stated in Table I, a LoRa UAV-wearable device link [30] has
never been neither characterized nor modeled. Moreover, all the helmet
antennas proposed so far are designed to work when the user is vigilant and
standing so that effects of the ground when the wearer is unconscious or
injured have not been addressed. Instead, if an accident happens, the user is
likely lying on the ground, eventually in the presence of snow, and the
helmet-UAV link is expected to be greatly affected by the helmet wearer
position.

This paper aims at evaluating the performance of radio-helmet-to-UAV links
when both standing and lying users are involved. Such off-body links [31, 32]
are analyzed for the purpose of collecting through a UAV the LoRa RSS coming
from a radio-helmet wearer and activate a SaR procedure (the latter is outside
of the scope of this paper).

The modeling and experimentation are focused on line-of-sight (LoS) scenarios
in flat lands. However, despite the simple approximation, the model can
describe several SaR events, for example, _i_) mountaineers in open
environments hit by an avalanche, _ii_) lost hikers in some natural parks
after fires, _iii_) injured soldiers in desert areas, _iv_) missing citizens
and first responders after cataclysms (as earthquakes, tornadoes, tsunami)
that destroy obstacles and create mostly flat and homogeneous zones. Within
these conditions, the maximum LoRa communication ranges and the RSS stability
versus the flight height and the UAV distance are here investigated. The
propagation model accounts for several positions of the helmet wearer (both
standing and lying), the ground-bounce multi-path and the LoRa transmission
parameters (namely the variable sensitivity of the receiver). Furthermore, the
basic model here presented can be extended to more complex environments by
characterizing the radio propagation of the considered site, either through
measurement campaigns [14, 33, 34] or by ray-tracing simulations [35, 36].

The paper is organized as follows. After the introduction of the most relevant
symbols (section II), section III details the problem formulation exploiting
the link budget and simplified modeling of the helmet transmitting antenna for
some possible position of a user with respect to the ground and the UAV
receiver. A reliable gain for the helmet antenna, suitable to any condition,
is derived for application to the link analysis in section IV through
numerical simulations. An experimental campaign with a sensorized UAV and a
helmet embedding a LoRa radio is described in section V, and the measured data
are compared, for corroboration, with the numerical results. Finally, in
section VI, findings are discussed, and conclusions are drawn.

##  II Symbols

The most relevant symbols used throughout the paper are here listed for the
reader’s convenience in order of appearance.

$G_{T}$ |  radiation gain of the transmitting antenna
---|---

$G_{R}$ |  radiation gain of the receiving antenna
---|---

$\varepsilon$ |  electric permittivity: $\varepsilon_{0}\left[\varepsilon_{r}+j\cdot\textnormal{Im$\left(\nicefrac{\mathrm{\varepsilon}}{\mathrm{\varepsilon_{0}}}\right)$}\right]$
---|---

$\varepsilon_{0}$ |  permittivity of vacuum
---|---

$\sigma$ |  electrical conductivity
---|---

$P_{R}$ |  power collected by the receiving antenna
---|---

$R$ |  UAV-radio-helmet ground distance
---|---

$H$ |  flying altitude of the UAV
---|---

$h$ |  transmitter’s height from ground
---|---

$t$ |  terrain condition
---|---

$P_{T}$ |  transmitting power
---|---

$\tau_{T}$ |  power transfer coefficient of the transmitting antenna
---|---

$\chi$ |  polarization loss factor
---|---

$PL$ |  path loss
---|---

$F$ |  path-gain factor
---|---

$r$ |  ray-path
---|---

$d_{0}$ |  reference distance of the log-distance path loss model
---|---

$n$ |  path loss exponent
---|---

$\lambda$ |  wavelength
---|---

$\Gamma_{T}$ |  the module of the reflection coefficient of the transmitting antenna
---|---

$SF$ |  spreading factor
---|---

$\rho e^{j\psi}$ |  Fresnel reflection coefficient
---|---

$k_{0}$ |  propagation constant in the vacuum: $2\pi\left(\lambda\right)^{-1}$
---|---

$\varphi$ |  incidence angle
---|---

$\hat{\rho}_{T}$ |  polarization versor of the transmitting antenna
---|---

$\hat{\rho}_{R}$ |  polarization versor of the receiving antenna
---|---

##  III Helmet-to-UAV link

With reference to Fig. 1, the helmet-to-UAV link involves a LoRa transmitter
(working in the $863-873$ MHz LoRa band [37]) connected to a helmet antenna
having radiation gain $G_{T}$ and a UAV equipped with a receiving antenna of
gain $G_{R}$. The user is assumed in four possible positions: standing, lying
on the front, side-lying, lying on the back (Fig. 2). Two reference boundary
ground conditions are considered: a perfect electric conductor (PEC) ground
(approximating a land covered by snow) and extremely dry terrain [i.e., rocks;
permittivity $\varepsilon=\varepsilon_{0}\left(4.8-0.4j\right)$, electrical
conductivity $\sigma=10^{-4}$ S/m [38]]. The behavior of any real terrain is
expected to be comprised between the aforementioned two extreme boundary
conditions.

The helmet-to-UAV link is hence described by the power $P_{R}$ collected by
the receiver on the UAV according to the following link budget (all terms in
dB scale)

Figure 1: Sketch of the radio-helmet-to-UAV link and relevant parameters.

$\begin{array}[]{c}P_{R}\left(R,H,h,t\right)=P_{T}+G_{T}\left(R,H,h,t\right)+G_{R}\left(R,H,h\right)\\\
+\tau_{T}(h,t)+\chi\left(R,H,h,t\right)-PL\left(R,H,h\right)+F(R,H,h,t)\end{array},$
(1)

where $P_{T}$ is the transmitting power, $\chi$ is the polarization loss
factor, $h$ is the transmitter height from the ground, _H_ is the flying
altitude of the UAV, _R_ is the UAV’s ground distance from the target, and $t$
is the terrain condition (dry or wet). The log-distance _PL_ (path loss)
expression is $PL\left(r\right)=PL\left(d_{0}\right)+10\cdot
n\cdot\log_{10}\left(\nicefrac{{r}}{{d_{0}}}\right)$, being $d_{0}$__ a
reference distance, _n_ the path loss exponent and $r$ the ray-path. Since LoS
ground-to-air channels of low-altitude UAVs can be approximated by the free-
space link [39], the conditions
$d_{0}=\nicefrac{\mathrm{\lambda}}{\mathrm{4\pi}}$ and $n=2$ are assumed.
Being $\Gamma_{T}$ the module of the reflection coefficient of the
transmitting antenna, $\tau_{T}=(1-\Gamma_{T}^{2})$ is the corresponding power
transfer coefficient. The receiving antenna is instead considered perfectly
matched to the receiver. The theoretical maximum communication distance is
evaluated by imposing $P_{R}$ equal to the receiver’s sensitivity, which
depends on the bandwidth and the spreading factor (_SF_) of the LoRa signal as
in Table II. Based on [14] and [41], a signal bandwidth of $125$ kHz and a
variable $SF$ value are considered throughout the paper.

Table II: Sensitivity of the LoRa SX1276 transceiver for different _SF_ values at bandwidth $125$ kHz. [40] Spreading Factor | Sensitivity [dBm]
---|---
12 | -136
11 | -133
10 | -132
9 | -129
8 | -126
7 | -123

Finally, the term _F_ is the path-gain factor that accounts for the multi-path
in the case of a standing user through the flat-earth two-ray propagation
model [39, 42]. With reference to the geometrical parameters in Fig. 1

$F=20\log_{10}\left[\left|1+\rho
e^{j\psi}\sqrt{\frac{G_{T}\left(\theta_{1}^{{}^{\prime}}\right)G_{R}\left(\theta_{1}\right)}{G_{T}\left(\theta_{2}^{{}^{\prime}}\right)G_{R}\left(\theta_{2}\right)}}e^{-jk_{0}\left(r-r_{1}-r_{2}\right)}\right|\right],$
(2)

where $k_{0}$ is the free-space propagation constant, $\rho e^{j\psi}$ is the
Fresnel reflection coefficient, $\theta_{2}$ identifies the direction of the
direct path, and the antennas’ gains are in linear scale. By assuming a smooth
ground, then $\varphi_{1}=\varphi{}_{2}=\varphi$. The Fresnel reflection
coefficient for an electromagnetic wave polarized parallelly to the ground is
evaluated from the permittivity of the land itself as [42]

$\rho
e^{j\psi}=\frac{\sin\varphi-\sqrt{\nicefrac{{\varepsilon}}{{\varepsilon_{0}}}-\cos^{2}\varphi}}{\sin\varphi+\sqrt{\nicefrac{{\varepsilon}}{{\varepsilon_{0}}}-\cos^{2}\varphi}}.$
(3)

For PEC reflecting surfaces, then $\rho e^{j\psi}=-1$. If the user is lying,
the antenna is on the ground, the multi-path disappears, _F_ is dropped out
from (1), and the ground effect is accounted for in the gain of the helmet
antenna as described in the next section.

(a)(b)(c)

Figure 2: The three possible positions of a lying user (a) on the front, (b)
on the side, and (c) on the back.

The link budget in (1) is here parametrized regarding the user’s position
(through the transmitter gain) and the features of the receiving antenna.

###  III-A Model of the Rx antenna

The UAV is hereafter assumed to be equipped with a circularly polarized (CP)
patch as in [43]. The gain $G_{R}$ (in linear scale) can be roughly
approximated with an ellipsoid having rotational symmetry [44] whose elliptic
section can be expressed in polar coordinates (w.r.t. Fig. 1)

$G_{R}\left(\theta\right)=\frac{2a_{z}^{2}a_{\xi}\sin\theta}{a_{z}^{2}\left(\cos\theta\right)^{2}+a_{\xi}^{2}\left(\sin\theta\right)^{2}}.$
(4)

The patch is assumed to be the pole and lie on the $\left(x,y\right)$ plane.
The ellipse axes hence are

$a_{z}=\frac{G_{R,max}}{2}$ (5)

$a_{\xi}=a_{z}\cot\left(\frac{BW_{z\xi}}{2}\right),$ (6)

where {$G_{R,max}$, $BW_{z\xi}$} are the maximum gain of the antenna and its
half-power beamwidth, respectively.

###  III-B Model of the Tx antenna

The choice of the reference helmet antenna could, in principle, consider both
a linearly-polarized (LP) or a CP device as for the receiver onboard the UAV.
A CP antenna would permit to minimize the interference due to the ground since
the reflection would invert the polarization verse so that the reflected field
is filtered out by the receiver antenna having opposite polarization. However,
using a CP antenna on the helmet too would introduce some drawbacks since the
polarization of the helmet antenna could be seen as reversed by the receiver
because of the unpredictable Tx-Rx mutual positions during the UAV flight and
the actual posture of the user. Sharp polarization mismatches could occur
hence preventing the establishment of the communication. For the sake of
generality, the transmitting antenna is assumed to be a flat dipole (copper
trace $35$ μm thick) wrapped onto the top of the helmet (Fig. 3) according to
two possible arrangements (w.r.t. the anatomical planes): along the median
sagittal plane (hereafter _sagittal dipole_), and the coronal plane (_coronal
dipole_). As shown next in section III-C, this arrangement guarantees a
reliable polarization matching in most conditions.

The gain $G_{T}$ of the transmitting antenna is dependent on the working
conditions. In particular, when the user is lying, the interaction with the
ground is expected to disturb the radiation pattern strongly. To simplify the
evaluation of the link budget in (1), the transmitting antenna is taken into
account through an equivalent uniform gain pattern whose value is derived from
statistical analyses involving the user’s possible positions. For this
purpose, the radiation performances of all the combinations of antennas and
positions are numerically evaluated utilizing the software CST Microwave
Studio Suite 2018. The numerical model††Model available at
https://grabcad.com/library/helmet-184. employed for the simulations includes:

  * •

a homogeneous numerical phantom of a human head with average relative
permittivity and conductivity $\varepsilon_{r}=42.7,\ \sigma=0.99$ S/m [45]
respectively;

  * •

a lossless foamed plastic ($\varepsilon_{r}=1.5$ [46]) shell with the
dimensions of a typical helmet for mountaineering (derived from the Vayu 2.0
model by Salewa, having perimeter length of $63$ cm);

  * •

ground modeled as a conducting plate.

(a)

(b)$\qquad\qquad\qquad\qquad\qquad($c)

Figure 3: (a) Helmet and head numerical phantoms. The flat dipole (size in mm)
along (b) the medial sagittal and (c) the coronal planes.

The dipoles were preliminarily tuned at $868$ MHz in the standing user case so
that the reflection coefficient is $\varGamma_{T}\leq-10$ dB in the whole LoRa
band. The proximity with the PEC ground in the case of a lying user produces a
moderate impedance mismatch (Fig. 4), nevertheless preserving the same maximum
value as before. If the terrain is dry, the reflection coefficients are less
affected by the helmet position and are more similar to the standing user
case.

Figure 4: Simulated reflection coefficients of the helmet antennas in Fig. 3
in four user positions over PEC terrain. The LoRa 863-873 MHz band is
highlighted in grey.

By referring to the radiation gain patterns at the zenith (Fig. 5), it is
clear that the two antenna layouts behave similarly for a standing user. In
contrast, relevant differences are visible in the case of a lying user, with
the maximum radiation occurring either along the zenith or the horizon
depending on the position of the helmet.

Standing user

Lying user

Front

Side

Back

Figure 5: Simulated radiation gain of the helmet antennas (sagittal and
coronal dipoles) over planes passing through the zenith. In the case of a
lying user, a PEC ground is assumed. The dry terrain case returns the same
patterns with an offset of $-2$ dB.

The most appropriate antenna arrangement is the one maximizing the uniformity
of the radiation pattern in the upper half-space in all the considered user
configurations, especially in the lying ones. The complementary cumulative
distribution function (CCDF) for $G_{T}>G_{0}$ in the whole half-
space††$CCDF(G_{0})$ is the percentage of the half-space where the gain of the
antenna in all the three lying configurations is more than $G_{0}$. is used as
a metric [Fig. 6(a)]. Accordingly, the coronal dipole outperforms the sagittal
one. By considering, for instance, the threshold value of probability $75\%$,
the corresponding lower bound gain $G_{0}\left(CCDF=75\%\right)$ of the
coronal dipole is about $2$ dB higher than the corresponding value of the
sagittal dipole.

In conclusion, the coronal dipole is hereafter considered the transmitting
antenna whose equivalent gain is $G_{T}=G_{0}\left(CCDF=75\%\right)$, keeping
the different values for standing and lying cases and kind of terrain, as
summarized in Table III.

|
---|---
(a) | (b)
Figure 6: Complementary cumulative distribution functions (a) $CCDF(G_{0})$ for the sagittal and coronal dipoles by taking into account the data of all the three lying positions of the user on a PEC ground (Fig. 2), and (b) $CCDF(\chi_{0})$ for the coronal dipole when considering both the standing and the lying postures.  Table III: Selected equivalent Tx gain $G_{0}\left(CCDF=75\%\right)$ for different user and terrain conditions. Standing user |  $-4.1$ dBi
---|---
Lying user | Wet terrain | Dry terrain
$-4.5$ dBi |  $-6.5$ dBi

###  III-C Polarization Loss Factor

Like the antennas’ gains, the polarization loss factor heavily varies with the
helmet wearer’s position and the terrain condition. The loss factor is
evaluated from the polarization versors of the transmitter
$\left(\hat{\rho}_{T}\right)$ and the receiver $\left(\hat{\rho}_{R}\right)$
as

$\chi=\left|\hat{\rho}_{T}\bullet\hat{\rho}_{R}^{*}\right|^{2}$ (7)

$\left|\cdot\right|$, ∗ and $\bullet$ being the module, the complex conjugate,
and the inner product operators, respectively.

To model the polarization effects, the polarization of the receiving CP patch
is assumed constant and equal to
$\hat{\rho}_{R}=\sqrt{2^{-1}}\left(1+j\right)$, whereas the versor of the
transmitting antenna is numerically evaluated when considering all the
position-terrain combinations and all the possible observation angles [Fig.
6(b)]. From the simulations, $\chi\geq-3$ dB in $75\%$ of the cases and,
accordingly, the assumption $\chi=-3$ dB is considered for the next evaluation
of the received power $P_{R}$.

##  IV Link evaluation

Following the above framework, the radio-helmet-to-UAV link is numerically
evaluated to predict the maximum achievable communication range and analyze
the two-ray interference through coverage maps. The communication range is
expressed as the radio-helmet-UAV ground distance since it is a crucial
parameter for most SaR operations. The user-UAV link must be as monotonic as
possible to apply range-based location algorithms exploiting the RSS
effectively.

The RSS is evaluated according to (1). The parameters of the receiving antenna
to derive $G_{R}(\theta)$ are referred to the Keonn Advantenna-p11
($BW_{z\xi}=100^{\textnormal{o}}$, $G_{R,max}=3.2$ dBi), which is then used
for the experimentation described in the next section. The received power at
the UAV side is evaluated for a variable flight altitude $5$ m $\leq H\leq
120$ m of the UAV.†† _H_ = 120 m is the highest altitude of a UAV that is
compliant with EU regulations on civilian applications (open category). [47]
The corresponding maximum radio range $R_{max}$, wherein the received power
$P_{R}$ equals the sensitivity of the receiver, is derived. The transmitter
must moreover comply with the ERP (effective radiated power) regulation for
LoRa systems. By enforcing the EU constraint [48] over the maximum LoRa
irradiation (i.e., ERP $=14$ dBm at $868$ MHz), the maximum transmitter power
to insert in (1) is $P_{T}=14$ dBm. All computations refer to an $h=1.7$ m
tall user. Finally, from Fig. 4, $\tau_{T}(h,t)\simeq\tau_{T}=-0.01$ dB. The
values of the parameters employed in the numerical simulations are resumed in
Table III and Table IV.

Table IV: Considered values for the numerical RSS evaluation. Parameter | Value | Parameter | Value
---|---|---|---
$P_{T}$ |  $14$ dBm | $R$ |  $\left[0;10000\right]$ m
$G_{R,max}$ |  $3.2$ dBi | $H$ |  $\left[5;120\right]$ m
$BW_{z\xi}$ | $100^{o}$ |  $h$ (standing) |  $1.7$ m
$\chi$ |  $-3$ dB |  $h$ (lying) |  $0$ m
$f$ |  $868$ MHz | $\tau_{T}$ | -0.01 dB

Although both the lying-user and the standing-user links are in LoS
conditions, the evaluated RSS is rather different. Firstly, by considering the
standing-user case, Fig. 7(a) shows the maximum estimated communication
distances versus _H_ for dry or wet terrains. The link range is mostly
unaffected by the terrain’s wetness, which instead impacts the interference
fringes, as shown next. The achievable $R_{max}$ spans from about $3.8$ km to
$6.5$ km, depending on the _SF_. For instance, by increasing $SF$ from $7$ to
$12$, the predicted $R_{max}$ for $H=50$ m lengthens from $2$ km to $3$ km.
Focused coverage maps for _R_ $\leq 500$ m, wherein the interference pattern
is highly variable, are depicted in Fig. 7(b,c). The RSS is not monotonically
related to the Tx-Rx distance, even in the ideal free-space case. The nulls in
the RSS patterns are sharp due to the strong interference, especially for
short horizontal distances. Regarding the ground’s condition, the wetter the
terrain is, the stronger the reflected field is and, consequently, the more
relevant the fluctuations are. An example of RSS profiles due to different
terrain’s wetness is shown in Fig. 8. When comparing Fig. 8(a) with Fig. 8(b),
it is evident that the fluctuations can be restricted to shorter distances $R$
by lowering $H$. In particular, for $H=15$ m, the interference fringes are
restricted to $R<100$ m.

(a)

(b)(c)

Figure 7: Theoretical coverage maps of the LoRa transmitting radio-helmet and
the UAV receiver in the standing user case for different flying heights _H_
and radial distances _R_. (a) Maximum communication distances versus the UAV
height when varying the _SF_ value in case of dry and wet terrain. RSS for
$R\leq 500$ m in case of (b) dry terrain and (c) wet terrain.

(a)

(b)

Figure 8: Numerically evaluated RSS for $1\ \text{m}\leq R\leq 2500\
\textnormal{m}$ and different terrain’s wetness. The assumed intermediate
permittivities are $\varepsilon_{0}\left(15-0.4j\right)$ for a slightly wet
terrain and $\varepsilon_{0}\left(30-0.4j\right)$ for a moderately wet terrain
[38]. Two different flying altitudes are considered (a) $H=50$ m and (b)
$H=15$ m.

In the case of a lying user, the multi-path is absent so that the RSS
decreases monotonically with the Tx-Rx distance (Fig. 9). Unlike the previous
case, the wetness of the terrain has a significant impact on the maximum
range, especially for high _SF_ values. Indeed, if the wearer is lying on the
ground, the on-helmet antenna’s gain improves with the terrain wetness of $2$
dB (as in Tab. III), at most, leading to a $21$% higher radio range. The
maximum communication range is up to $9.5$ km if the terrain is wet, and hence
it is more extended than the corresponding distance in the case of a standing
user.

(a)(b)

Figure 9: Theoretical coverage maps of the LoRa transmitting radio-helmet and
the UAV receiver in the lying user case. (a) Maximum communication distances
versus the UAV height when varying the _SF_ value and (b) RSS for $R\leq 500$
m (wet terrain).

##  V Experimentation

The above numerical achievements are corroborated by preliminary
experimentation with a UAV equipped with a LoRa receiver and a typical
mountain helmet embedding a transmitter to reproduce the simulated scenarios.
Both vertical and horizontal flights are performed to verify the model’s
prediction.

The LoRa transmitting and receiving modules are Pycom LoPy-4 programmable
boards (embedding the LoRa SX1276 transceiver; $P_{T}=14$ dBm, bandwidth of
$125$ kHz, carrier frequency $868$ MHz, coding rate $4/5$ [14]). The spreading
factor is set to $SF=12$ to maximize the receiver sensitivity and characterize
the link as far as possible.

The transmitting antenna is a commercial dipole (by Pycom company [49]) fixed
on a Vayu 2.0 helmet for mountaineering along the coronal plane (Fig. 10). The
helmet is worn by a 1.7 m tall volunteer as in the simulations.

(a)(b)

Figure 10: Radio-helmet prototype hosting a coronal dipole, (a) top view and
(b) side view with the indication of the LoPy-4 board installed in the
interior of the helmet.

The input parameter of the helmet-mounted dipole is measured by a portable
Vector Network Analyzer (MS2024A by Anritsu) when the volunteer is standing
and lying over dry and wet terrains. Overall, despite some frequency shift
between standing and fallen positions w.r.t. the nominal LoRa band, as
predicted by the simulations (Fig. 4), the obtained reflection coefficient
(Fig. 11) is always $\Gamma_{T}<11$ dB at the useful frequencies, and it is
well comparable with the simulated $\tau_{T}$ value in Table IV. The maximum
observed downward frequency shift is about $55$ MHz over all the conditions,
so the helmet antenna must thus ensure a frequency bandwidth of at least $65$
MHz to cover the whole UHF LoRa band regardless of the wearer posture.

Measured $\Gamma_{\textnormal{T}}$ \- Commercial coronal dipole

Figure 11: The measured reflection coefficient of the coronal dipole installed
over the helmet when the wearer is standing and then lying on dry or wet
terrain in the three postures.

The UAV is a DJI Phantom 3 Pro hosting a $137$ mm $\times 137$ mm CP patch
(Advantenna-p11 Keonn as before). The battery pack and the LoRa receiver are
fixed at the landing gear of the UAV (Fig. 12).

Figure 12: DJI Phantom 3 Pro UAV equipped with a LoRa receiver. The LoRa
receiver is powered by a power bank and is composed of a LoPy-4 board and a
Keonn Advantenna-p11 CP patch.

(a)

(b)

(c)(d)

Figure 13: (a) Satellite view of the location for the experimental helmet-UAV
LoRa link measurement, (b) elevation profile of the field, (c) sketch of the
vertical flights, and (d) of the horizontal flights.

The RSS measurements are performed during a period of consecutive sunny days
over an uncultivated field near Colle Romito††GPS coordinates (41.551526,
12.567001). [Ardea, Lazio, Italy; Fig. 13(a)]. The field is flat [Fig. 13(b)]
and covered by bushes. Thus, the test field can be considered dry ground. In
the experiments, the UAV is driven at $1$ m/s approximate speed along both
horizontal trajectories at fixed altitudes and vertical trajectories at a
fixed radial distance from the user. The RSS in the dBm scale is estimated
from the RSSI (received signal strength indicator) and from the SNR (signal-
to-noise ratio) that are returned by the receiver, as [14]

$P_{R}=RSSI-10\log_{10}\left(1+\frac{1}{SNR}\right)+c_{0},$ (8)

where $P_{R}$ is in dBm scale, the RSSI is in dB scale, the SNR is in linear
scale, and $c_{0}$ (dBm scale) is a constant parameter obtained from a single-
point calibration [40] through the Anritsu MS2711A spectrum analyzer.

In the first test, the UAV takes off at a distance $R=500$ m from the
volunteer and then flies up vertically up to $H=120$ m [Fig. 13(c)]. In the
second test, the UAV flies horizontally at two fixed altitudes $H=\\{$$50$ m,
$15$ m} for distances $1$ m $\leq R\leq 2700$ m [Fig. 13(d)]. During the
flights, $1921$ data packets are received overall.

The measured RSS profiles are reported in Fig. 14 and Fig. 15 and compared
with the simulated ones for vertical and horizontal flights, respectively.
Interestingly, the RSS is rather insensitive to the lying user’s particular
position, and the modeling through a numerically evaluated equivalent gain is
proven to be effective.

Overall, in spite of some differences, the measurements corroborate the
phenomena derived by the numerical model in section IV (Fig. 7 and Fig. 9),
even in the presence of the significant multi-path in the standing
configuration. In detail, simulations overestimate the RSS of just $2$ dB on
the vertical and horizontal paths shown in Fig. 14 and Fig. 15(a,b,d).
Regarding Fig. 15(c), there are some differences with the simulations at the
end of the profile, where the model underestimates the RSS. The measured
profile follows the flat-earth two-ray model for $R\leq 700$ m but,
afterwards, it shows an intermediate behavior between the two-ray and the
single-ray propagation. In this condition, the incident rays intercept the
ground at grazing angles ($\varphi\leq 1.4^{\textnormal{o}}$) so that slight
surface irregularities are no longer negligible [42]. Moreover, the combined
effect of the absorption by the low vegetation, scattering, and the earth
elevation [Fig. 13(b)] can attenuate further the reflected field. The result
is a weaker interference and, consequently, a lengthening of the link range,
benefiting the target identification from long distances.

Vertical flights

(a)(b)

Figure 14: RSS measured by the on-UAV LoRa ($SF=12$) receiver during the
vertical flights at fixed ground distance _R_ $=500$ m from the transmitter.
Measurements for a (a) standing user and (b) lying user.

Horizontal flights

(a)(b)

(c)(d)

Figure 15: RSS measured by the on-UAV LoRa ($SF=12$) receiver during the
horizontal flights at different fixed flying heights _H_ and for different
user positions: (a) _H_ $=50$ m, standing user, (b) _H_ $=50$ m, lying user,
(c) _H_ $=15$ m, standing user, and (d) _H_ $=15$ m, lying user.

The Tx-Rx distance slightly affects also the time delay between the
transmission and the reception of the packets. In the performed flights, the
measured delay ranges between 820 ms (at the shortest helmet-UAV mutual
distance) up to 1022 ms (upper bound distance) and is comparable with the
values reported in [50]. By considering the typical packet transmission rate
(roughly 1 packet every 3 s), the above delays will not cause any loss of the
received packet sequence. Accordingly, the observed packet delivery ratio
(PDR) in the experiments is of the order of 95% and nearly constant in all the
cases. In more complex environments with non-flat grounds, the soft
degradation of the LoRa signals will lead to reduced radio ranges with the
corresponding decrease of the PDR [7, 14]. Concerning the implications on
localization, the time delay could slightly raise the localization errors,
especially if the target is moving. In SaR operations, however, the
transmitter is mostly stationary since the wearer could be fallen or at most
slowly moving within a confined area. The corresponding error in estimating
the position of the target is therefore expected to be modest. Furthermore,
the UAV will generally get closer and closer to the target during SaR
procedures so that the time delay reduces, the PDR increases, and the
estimation of the target position is progressively refined.

It is worth specifying that, due to hardware constraints and law regulations,
the above experimental tests did not allow us to appreciate the complete
communication range corresponding to the last received packet. For this
purpose, the UAV should have flown up to a much longer horizontal distance
($R>2700$ m), thus exiting from the test area and overflying various obstacles
(houses, roads, woods). Anyway, in past experiments [13], we placed a LoRa
radio over an $85$-m-high overpass while the other radio was kept inside a car
that was driven away from the overpass. This arrangement, comparable with the
helmet-UAV system, achieved communications up to $8.4$ km, in reasonable
agreement with the numerical estimations.

##  VI Discussion and conclusion

The helmet-to-UAV communication over flat lands based on the LoRa protocol has
been numerically characterized and corroborated by a preliminary experimental
campaign. The UAV is theoretically capable of collecting the helmet signal up
to $5$ km in a flat scenario even in the worst case (standing user, perfectly
flat and wet terrain), and up to $9.5$ km in case of wet terrain and lying
user when the UAV flies at an altitude of $120$ m. The RSS in the case of a
lying user is robust concerning the position of the helmet on the ground, and
it monotonically decreases with the Tx-Rx distance. Instead, the RSS with a
standing user (e.g., a lost hiker wandering on a snowy land) experiences
fluctuations that are sharper the wetter the terrain, due to the ground-bounce
multi-path, especially near to the helmet’s wearer.

The basic model extension to a more complex radio propagation environment,
including non-LoS links, can be quickly drawn if an equivalent log-distance PL
model is available. For example, the authors recently modeled the LoRa
propagation in snowy mountain fields, even when the transmitter is buried
[14]. Burying the transmitter under $1$ m of snow adds an entry loss of about
$60$ dB, which can be accounted for in the transmitter’s equivalent gain.
Instead, the retrieved log-distance parameters for a transmitter placed on the
snow were $n=3.17$ and $PL(d_{0}=1\ \textnormal{m})=56.7$ dB, and these values
are still approximately valid in the case of a lying user in a snowy plain.
Hence, the path loss model and the eventual path-gain factor must be inserted
appropriately in the proposed link budget.

It is worth considering that the communication could be more challenging or
even forbidden in harsher conditions that excessively reduce the equivalent
antenna gain, as, for instance, when the helmet is buried under more than 1 m
of wet snow or deeply submerged by water (e.g., fallen into a river). In other
cases, particular configurations of obstacles may highly raise the multi-path
and the shadowing, as we observed when the target was on the bottom of a
canyon while the UAV was flying outside it [51].

Finally, considering the model’s application to SaR operations, the lying user
case is the most interesting because the user could be unconscious and even
partially covered and, consequently, not detectable by a camera. The helmet
wearer can be rescued through classic range-based localization algorithms like
those mentioned in section I. Instead, in the case of a standing user, the
interference fringes make applying the above localization methods more
challenging [52]. Spatial fluctuations can be mitigated by lowering the UAV’s
flying altitude. For instance, the RSS oscillations are confined between $100$
m far from the target for an altitude $H=15$ m. The fluctuations can also be
reduced by placing the wearable antenna near the ground (e.g., on a boot). In
any case, thanks to the local confinement of RSS fluctuations, the UAV can
roughly estimate the target position from longer distances. Then, the UAV can
approach the estimated location and, when the signal begins fluctuating, it
could use the support of optical or thermal cameras to reach the user, who is
standing and, likely, conscious. Hence, while the high-RSS fringes are
produced by the reflection from the flat ground when the user is standing, the
eventual low-RSS fringes are generated by the multi-path from non-flat
terrains and will be observed for longer distances. Furthermore, the dynamic
refinement of the position estimation while approaching the target allows also
to contrast the packet’s loss caused by the harsh environment. Regardless of
the target’s posture, the UAV could move along non-linear trajectories to
speed up the search, as proposed in [53, 54], by dynamically accounting for
the signal variations and the topology of the area. A swarm of multiple
coordinated UAVs [55, 56] could also be deployed to provide data from
different angles and distances to mitigate the multi-path effect.

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