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Wireless Networks Interference and Security Protection by Means of Vegetation Barriers


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The success of wireless technologies could paradoxically leads to a collapse in their performance: the interference between adjacent networks and the attacks done by users from outside the expected coverage limits are two important enemies to the well function of the networks. The proposal of this paper is simple but e±cient: the use of vegetation barriers to create shadowing areas with excess attenuations in the edge of the service area, in order to reduce the coverage distance of each wireless node, reducing the possible interference to other networks as well as improving security aspects by minimizing the signal strength outside the service area.
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Progress In Electromagnetics Research M, Vol. 21, 223–236, 2011
J. Acu˜na1, I. Cui˜nas2, *, and P. G´omez2
1Inst. de Ingenier´ıa El´ectrica, Universidad de la Rep´ublica,
Montevideo, Uruguay
2Dept. Teor´ıa do Sinal e Comunicaci´ons, Universidade de Vigo, Vigo,
Abstract—The success of wireless technologies could paradoxically
leads to a collapse in their performance: the interference between
adjacent networks and the attacks done by users from outside the
expected coverage limits are two important enemies to the well function
of the networks. The proposal of this paper is simple but efficient:
the use of vegetation barriers to create shadowing areas with excess
attenuations in the edge of the service area, in order to reduce
the coverage distance of each wireless node, reducing the possible
interference to other networks as well as improving security aspects
by minimizing the signal strength outside the service area.
The wireless paradigm has become one of the technological successes of
last years. The different standards [1, 2] allow high speed connectivity,
which in the past was the main disadvantage of wireless networks
compared to wired ones. At this point, the corporative and domestic
computing networks, which were traditionally projected following a
wired scheme, are rapidly migrating to wireless. This fact full fits the
people requirements in terms of mobility and connectivity, but it also
suffers some important problems as interference between adjacent or
neighbor networks and undesired access to the network facilities by
unknown users from outside the service area.
Received 30 September 2011, Accepted 27 October 2011, Scheduled 4 November 2011
* Corresponding author: nigo Cui˜nas (
224 Acu˜na, Cui˜nas, and G´omez
These problems could appear in domestic networks, but it is in
trade buildings where the situation becomes worse: various neighbor
networks (corresponding to different companies or departments within
the same company) could interfere one among the others, overloading
the network facilities with retransmission events, and then degrading
its performance. Besides, the number of unauthorized accesses could
grow: both for using services (people surfing the Internet “for free”,
occupying resources paid by the company) and for damaging purposes
(done by hackers).
There is several propagation research done in the area of wireless
networks, from general works to more specific ones. Typically,
deterministic methods have been proposed to model static elements,
both constructive and natural obstacles [3]. However, there are other
kinds of obstacles in the radio links that must be modeled by stochastic
procedures [4]. Such obstacles may be persons [5], furniture [6],
vegetation [7], or in general non-polygonal structures [8]. All these
obstructions can mitigate the received power in a radio link, or even
they could break the connection.
The thesis of this paper is to propose the use of vegetation
barriers to mitigate such problems. The induction of attenuation in
the radio waves propagating across vegetation media is a well-known
effect, but its consequences, mechanics and applications have not been
completely explored. This paper presents a possible application of
that attenuation effect. The vegetation obstructing the radio channel
could provide attenuation enough in the edge of the service area to:
a) reduce the distance at which elements of different networks can be
installed without generating and receiving interference; and b) shorten
the distance at which an external user could access the network servers
or facilities. Thus, a correct decision in the location of indoor or
outdoor plants could benefit the performance of the wireless network
to be protected against interference and/or external attacks.
A large measurement campaign involving seven different species
have been performed to support the proposal. Both indoor and outdoor
shrubs have been used to construct different barriers, as indicated in
Section 2. The measured attenuations, shown in Section 3, could be
used to compute the improvement in terms of interference and security
provided by the vegetation barrier, as commented in Section 4. Finally,
Section 5 summarizes the conclusions.
The measurement campaign was performed in an open area, with
separate transmitter and receiver. The transmitter was based on a
Progress In Electromagnetics Research M, Vol. 21, 2011 225
Rohde&Schwarz radio signal generator SMR-40, whereas the receiver
was constructed around a Rohde&Schwarz spectrum analyzer FSP-
40. The narrow-band measurements were performed at 2.4 and
5.8 GHz, which are frequencies in bands used by wireless standards.
Although the actual wireless world is dominated by omnidirectional
antennas, which pick up all scattering energy around the receiver,
the measurements were performed with directional antennas. This
decision was adopted as the objective was to isolate the effect of
the vegetation barrier from the environment scatterers. The use of
omnidirectional antennas would probably lead to lower attenuations
but the measurement results would also include many environment
effects which would be difficult to extract to define the attenuation
induced by the vegetation. Then, both ends of the measurement setup
were installed with log-periodic antennas Electrometrics EM6952,
which gain is 4.72 dBi at 2.4 GHz and 4.62 dBi at 5.8 GHz, placed at
1.25 meters height.
The measurement setup was completed by a linear positioner that
supports the receiving antenna and allows its movement parallel to a
vegetation barrier. The positioning platform is driven by a stepper
motor, connected to an indexer. The scheme can be observed at
Figure 1.
At the transmission end, the signal generator feds the antenna
with a 10 dBm amplitude tone. The receiver was moving along this
2.5 meter long linear table, stopping at 126 and 150 locations for 2.4
and 5.8 GHz measurements, respectively, and getting 8000 received
power samples at each stop. The data were caught following a sequence
move-stop-measure-move-. This measurement procedure was deeply
explained in [9], where a campaign at mobile phone frequencies is
presented. That paper was focused on reducing the electromagnetic
pollution at cellular systems bands, whereas the present paper is
Figure 1. Vegetation barrier configuration C0.
226 Acu˜na, Cui˜nas, and G´omez
Figure 2. Vegetation barrier
configuration C1.
Figure 3. Vegetation barrier
configuration C2.
Figure 4. Vegetation barrier
configuration C3.
Figure 5. Vegetation barrier
configuration C4.
centered in different frequency bands (those for wireless LANs), and
also oriented to different applications. So, the procedure for getting
the data is the same, at different frequencies of operation, but the
presented results and the application are completely different.
The distance between transmitter and receiver antennas was
6 meter and the vegetation barrier were installed just in the middle,
following the six configurations defined in Figures 1 to 6, and denoted
as C0 (configuration 0) to C5 (configuration 5), respectively. C0
represents the setup for a reference measurement, in line of sight
conditions between transmitting and receiving antennas. The distances
from transmitting antenna to barrier and from barrier to receiving
antenna are enough to consider that both the obstacle and the receiver
are at far field distance from the radiating element. Seven different
species were considered separately to build the vegetation barrier,
which was constructed with up to ten individuals of the same species.
The characteristics of the seven species are summarized in Table 1.
Progress In Electromagnetics Research M, Vol. 21, 2011 227
Figure 6. Vegetation barrier configuration C5.
Table 1. Dimensions of the shrubs, in cm.
Specie shrub leaf
height diameter length width
areca 150 70 25 1
schefflera 160 60 10 4.5
ficus 170 55 7 3
callistemon 150 80 7 4
camellia 165 90 8 6
Irish juniper 205 55 2 0.5
thuja 165 45 0.5 0.2
The outcomes of the measurement campaign represent almost
160 million received power samples, which obviously need a processing
to analyze the performance of the proposal. The most interesting
parameter to be extracted from the measurements is the attenuation
induced by the different barriers at each receiving point. These
attenuation values were computed by comparing the median measured
power at each measuring point (the median among the 8000 power
samples at this point) with the median power measured in line
of sight (LoS) conditions (i.e., at configuration 0). Thus, the
computation of the attenuation includes a normalization of the effects
of the antenna frequency response. Each measurement contains the
effect of transmitting and receiving antennas, the propagation path
between them, and, depending on the barrier configuration, the effect
of the vegetation (when this is within the radio channel). The
228 Acu˜na, Cui˜nas, and G´omez
measurements were done taking the LoS reference (C0) at exactly the
same environment where the obstructed LoS (OLoS) data (C1–C5)
was gotten. So, the path loss exponent would be assumed to be the
same in both scenarios and the only difference between them would
be the vegetation barrier induced attenuation between transmitter
and receiver. When comparing the results related to the reference
configuration, C0, with those related to any other configuration, the
antennas and the propagation path contributions are canceled and only
the influence of the vegetation barrier is then considered.
At that point, we have a collection of vectors composed by
attenuation values at different locations within the shadow area
behind the barrier. The median of each of these vectors is a good
representation of the median attenuation provided by each barrier in
Table 2. Median attenuation (dB) at 2.4 GHz, with horizontal
Specie barrier configuration
areca 0.1 0.2 0.4 2.9 0.1
schefflera 0.4 0.8 1.6 1.9 0.7
ficus 2.2 2.8 4.3 4.7 2.7
callistemon 2.1 2.5 3.5 3.3 1.5
camellia 3.1 3.2 3.9 5.9 2.9
Irish juniper 5.2 5.2 8.9 6.2 4.5
thuja 1.6 1.5 1.8 2.1 1.4
Table 3. Median attenuation (dB) at 2.4 GHz, with vertical
Specie barrier configuration
areca 3.2 3.5 2.5 2.1 3.2
schefflera 0.1 0.9 1.5 1.0 0.4
ficus 2.1 4.1 5.1 5.9 2.0
callistemon 3.3 3.0 6.9 5.4 3.0
camellia 5.4 5.5 6.9 8.2 5.2
Irish juniper 9.6 9.8 10.7 10.1 8.0
thuja 3.5 3.8 5.6 6.2 3.5
Progress In Electromagnetics Research M, Vol. 21, 2011 229
its shadow area. These results, obtained at 2.4 GHz, are presented in
Tables 2 and 3, for horizontal and vertical polarization respectively,
whereas Tables 4 and 5 are related to 5.8 GHz experiences. It
must be observed that the attenuation appears to be larger for
vertical polarization than for horizontal, as it occurs in most of the
measurement results related in the literature. The explanation must be
the own geometry of the vegetation, which is vertically organized: the
trunks are clearly vertical; the disposition of leaves is also dominantly
vertical, whereas the orientation of branches appears to be more
These results could be compared to those provided at [9],
obtained at lower frequencies (900, 1800 and 2100 MHz), when lower
attenuations were detected. The attenuation induced by vegetation
Table 4. Median attenuation (dB) at 5.8 GHz, with horizontal
Specie barrier configuration
areca 0.1 0.1 0.9 4.1 0.1
schefflera 0.1 0.2 5.6 6.7 1.0
ficus 6.2 5.4 9.3 11.3 5.3
callistemon 1.1 4.0 6.5 7.7 2.4
camellia 10.1 12.4 12.1 13.2 10.7
Irish juniper 6.8 5.9 13.2 10.6 8.1
thuja 3.9 5.1 6.3 6.8 4.7
Table 5. Median attenuation (dB) at 5.8 GHz, with vertical
Specie barrier configuration
areca 2.0 2.4 5.1 3.8 2.6
schefflera 2.5 2.9 6.1 6.4 2.4
ficus 7.1 8.4 10.7 9.9 7.1
callistemon 10.5 13.0 14.5 14.6 11.1
camellia 10.4 11.3 14.2 13.5 10.5
Irish juniper 15.7 13.7 21.2 19.8 15.4
thuja 5.2 7.2 12.0 8.8 4.4
230 Acu˜na, Cui˜nas, and G´omez
appears to grow with the frequency, following approximately the same
trend observed at that paper: this indicates that the proposal of
electromagnetically shielding locations by using vegetation barriers
seems to be more efficient at higher frequencies. Besides, there are
some species with wooden trunks and very dense canopies (camellia
trees, Irish junipers and white cedars) that appear to be the most
suitable to perform vegetation barriers.
An analysis of the coverage reduction provided by the vegetation
hurdles is also presented. The attenuation induced by the vegetation
barriers would be the input data for the different formulation, which
will give the coverage distances in various scenarios. These results will
be used to obtain the reduction in terms of coverage distance provided
by the shrubs.
We will assume that a location is within the coverage area when
there the received power is larger than the Sensitivity, S, of the receiver
for a given BER. The maximum distance from a transmitter with
coverage depends on many factors and it can be calculated using the
Equation (1).
Prx =Ptx +Gtx +Grx L(d)S(1)
where Prx is the reception power, Ptx is the transmission power, Gtx
is the transmission antenna gain, Grx is the reception antenna gain,
and L(d) represents the losses as a function of the distance d. So, the
maximum distance with coverage, dmax could be calculated from the
Equation (2).
L(dmax) = Ptx +Gtx +Grx S(2)
Three propagation models have been chosen to calculate the losses
from the transmitter to the receiver, and the distance that limits
the coverage, dmax. Two of them are full indoor models and the
other has part of the path indoor and part of the path outdoor.
The considered propagation models are going to be identified as the
empirical indoor-to-outdoor [10], International Telecommunications
Union (ITU) indoor [11], and the statistical indoor [12]. This selection
of models covers the different situations mentioned in the introduction
section: the possible interference between adjacent wireless networks
within the same building (the full indoor models), and the hacker
attack from the surroundings of the building hosting the network (the
indoor to outdoor model). The following paragraphs describe the three
considered propagation models: empirical indoor-to-outdoor, ITU-R,
and statistical path loss.
Progress In Electromagnetics Research M, Vol. 21, 2011 231
4.1. Empirical Indoor-to-outdoor Model
The empirical indoor-to-outdoor model [10] is formulated as in
Equation (3). It is an outdoor-indoor model, so it considers a wall
between the transmitter and the receiver.
L(d) = Li+Lo=1.8f2+ 10.6f+ 5.8Nw5.5 + 62.3
+10 ¡3.3·104f6+ 3.2¢log(d/5) (3)
At the equation, Lis the total path loss, in dB; fis the frequency
of transmission, in GHz; dis the distance between transmitter and
receiver, in m; and Nwis the number of walls between the transmitter
and the outdoor receiver.
4.2. ITU-R Model (Indoor)
The ITU model [11] is defined by Equation (4). The model has
been proposed for a frequency range from 900 MHz to 5.2 GHz, and
it considers 1 to 3 floors.
L(d) = 20 log f+Nlog d+Pf(n)28 (4)
where the different parameters are: f, the frequency in MHz; N, the
distance power loss coefficient (N= 30.5 at 2.4 GHz); n, number of
floors between the transmitter and receiver; and Pf(n), the floor loss
penetration factor (one floor — 15 dB).
The distance power loss coefficient, N, is the quantity that
expresses the loss of signal power with distance. This coefficient
is empirical. The floor penetration loss factor is another empirical
constant which depends on the number of floors the waves need to
penetrate. Some values for both parameters are proposed in [11].
4.3. Statistical Path Loss Model (Indoor)
The statistical path loss model [12] is described by the Equation (5).
Prx =Ptx
where nis the mean path loss exponent, and its proposed values
depends on the environment: Classroom LoS n= 1.8, corridors LoS
n= 1, one wall OLoS n= 3.4 and multiple walls OLoS n= 3.46.
The effect of the vegetation barriers must be translated into excess
attenuations to the models results: the attenuation induced by the
232 Acu˜na, Cui˜nas, and G´omez
barriers has to be added to that attenuation computed by the proposed
models. These excess attenuations correspond to the values presented
in Tables 2 to 5. So, the three models have been used to analyze
the coverage distances with and without vegetation barriers, being the
propagation path losses in presence of vegetation barriers as indicated
by Equation (6), which modifies Equation (2). Lbarrier represents the
contribution of the vegetation barrier to the total attenuation.
L(dmax) = Ptx +Gtx +Grx SLbarrier (6)
Some parameters were chosen to do this calculus, which are common
to the three models: Ptx = 20 dBm, S=78 dBm, Gtx = 6 dBi,
Grx = 2 dBi.
The results presented in this section has been computed by using
two standard excess attenuation (Lbarrier) of 5 and 10 dB, representing
two of the possible attenuations due to the vegetation barrier. This has
been done in order to reduce the number of considered scenarios, which
in other case would be as many as 140: four times (two frequencies,
two polarizations) the 35 measured barriers (five configurations with
seven vegetation species). In fact, the use of measured results led to
more exact computations in terms of distances. However, we decided to
present results corresponding to 5 and 10 dB attenuation to illustrate
the performance of the proposal, because of the large amount of data
available. The results related to both attenuation levels appear to
be significant to demonstrate the validity of the proposal; whereas
provided attenuation values are useful to compute the exact coverage
distances at each situation. Thus, these results can show an illustration
of the performance of the proposal by using a reduced amount of
data. So, we computed dmax in line of sight conditions, and also when
Lbarrier equals 5 and 10 dB, by means of the three proposed propagation
models. Table 6 contains the computed values of coverage distance,
provided at 2.4 GHz, a common frequency band for wireless networks
Table 6. Coverage distances (m), with different barrier attenuations
and propagation models.
Coverage distance (m)
ITU-R Statistical
No barrier 0 37 81 87
Standard 1 5 26 55 62
Standard 2 10 18 37 44
Progress In Electromagnetics Research M, Vol. 21, 2011 233
The first row at Table 6 (no barrier) gives the maximum distances
calculated using the three models previously mentioned without
vegetation barrier. This represents a reference for the other results.
The second and third rows show the new distances with 5 and 10dB
of excess attenuation due to the standard vegetation barriers, defined
as a good representative of the actual performance.
The difference between these rows and the first one in indoor
models (ITU-R [11] and statistical [12]) columns indicates how
close nodes from two adjacent networks could be installed, avoiding
interference events, when vegetation barriers providing attenuations of
5 or 10 dB are installed. The selection of the indoor model could lead
to different results, but both provide values in similar magnitude order:
maximum coverage distances of 81 and 87 meter in open conditions, for
ITU-R and statistical respectively; and coverage from 55 to 62 meter
when the barriers induce attenuation of 5 dB and from 37 to 44 meter
when the induced attenuation is 10 dB. So, in general terms, in such
indoor environments the distance appears to be reduced to the 70%
with the 5 dB standard barrier and to the 50% with that inducing
attenuation of 10 dB. This indicates that the prevention of interference
between wireless networks is possible by installing vegetation barriers.
The analysis made by the empirical indoor-to-outdoor model [10]
is related to the hacker capability to illegally connect to the network
from a place out of the company domains. It can be seen that
with a 5 dB barrier this distance is reduced to 70% percent and with
10 dB to the 50% approximately. In many cases these reduction made
impossible to be connected from the street or from a car, as the network
coverage could be limited to the company building and gardens: thus,
the task of the possible hacker would be more difficult than in non
vegetation surrounded networks.
Another scenario could be defined when many access points have
to be deployed in adjacent areas. In such situations, it is very
important to install all access points as close as possible. The relation
between the distance among nodes, Dand the coverage radius R,
represents a measure on how close they can be installed. This relation
is enunciated in Equation (7).
R= 1 + µNinterf
At this equation, Ninterf is the number of adjacent nodes, atbarr is the
attenuation of the barrier, cis the power of the carrier signal, and i
is the power transmitted by the adjacent nodes that could produce
interference. Considering c/i to be approximately 100 and n= 3.4,
Table 7 shows D/R without barrier, and with both previously defined
standard barriers (inducing attenuations of 5 and 10 dB).
234 Acu˜na, Cui˜nas, and G´omez
Table 7. Relation between distance among nodes and coverage radius.
Vegetation barrier Excess attenuation (dB) D/R
No barrier 0 4.9
Standard 1 5 3.8
Standard 2 10 3.0
The first row at the Table 7 (No barrier) is included as a reference
to the other two rows, to compare the computed ratios. A reduction
can be observed in the parameter D/R: from near 5 without barrier to
3.8 with standard 1 barrier or to 3.0 with standard 2. This reduction
in D/R leads to a more efficient application of WiFi technology in
intensive use environments, as office buildings are. This reduction is
directly related to the decrease in frequency re-usage distance, which
indicates an improvement in the capacity of the network. For example,
if the access points cover areas with radius of around 20meters, the
neighbors could be located at 98, 76 or 60 meters (interference free
distances) depending on the attenuation induced by the vegetation
barrier: 0, 5 or 10 dB respectively.
A large measurement campaign has been developed in order to
analyze the attenuation induced by vegetation barriers, with different
configurations. The measurements were done taking a LoS reference
at exactly the same environment where we get the OLoS data. So, the
path loss exponent would be assumed to be the same in both scenarios
and the only difference between them would be the vegetation barrier
induced attenuation between transmitter and receiver. Thus, the LoS
path loss would be canceled by comparing the LoS reference and each
measurement series, and only attenuation due to the barrier is the
result of the comparison. Attenuations up to 21 dB at 5.8 GHz and up
to 10 dB at 2.4 GHz have been detected. These shadowing capabilities
of the vegetation lines are then translating into coverage distance
reduction, which is proposed to be used in the ambit of wireless
networks, in two directions: the reduction of the free interference
distance between nodes from adjacent networks, and the protection
against hacker attacks that wirelessly connects to the network from
streets or parking areas.
The results are encouraging because the barriers seem to produce
attenuation enough to reach interesting reductions in the distance to
the adjacent nodes, avoiding interference, and also in the distance at
Progress In Electromagnetics Research M, Vol. 21, 2011 235
which a hacker must be installed to access a network.
The coverage distances were computed by using three different
models, both indoor and indoor-to-outdoor, and these results indicate
that this reduction is more important the larger the attenuation is.
As an example, for 5 dB and 10 dB excess attenuations due to the
vegetation barriers, reductions of distance from 30 to 50% could be
achieved, compared to scenarios with no barriers.
The relation between the distance among nodes Dand the
coverage radius Rhas been also analyzed as a measure on how close
nodes from two adjacent networks could be installed when a line of
shrubs is used to separate the coverage areas. The improvement of
network efficiency in presence of vegetation barriers, in terms of the
reduction in frequency re-usage distance has been then computed.
As these barriers are not expensive, environment friendly and well
accepted by the people, the success of the proposal is expected.
This work was supported by the Autonomic Government of Galicia
(Xunta de Galicia), Spain, through project InCiTe 08MRU045322PR.
This project has been partially financed with EU funds (FEDER
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... Employing multiple channels is considered in [380] in order to overcome interference and obtain a higher performance. [382]. They also propose a coexistence algorithm that increases channel utilization up to 20% providing fairness among both technologies. ...
... Thus, on-ground deployments would not be suitable for the characteristics of this type of crop. Asparagus, like some types of shrubs [382], present a medium height, the foliage density is uniform as well, and the separation between plants can be greater between rows. In this case, on-ground deployments would be possible if the nodes are deployed between rows. ...
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... The results showed that the obtained attenuation depended on the frequency with results varying from 10.99 dB at 15 GHz to 24.23 dB at 9 GHz. J. Acuña et al. presented in [23] a study on the interferences caused by vegetation barriers to wireless networks with the aim to reduce the signal strength at the areas where coverage is not wanted. Several species of shrubs with different dimensions were studied. ...
... Thus, on-ground deployments would not be suitable for the characteristics of this type of crop. Asparagus, like some types of shrubs [23], present a medium height, the foliage density is uniform as well, and the separation between plants can be greater between rows. In this case, on-ground deployments would be possible if the nodes are deployed between rows. ...
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Deploying wireless sensor networks (WSN) in rural environments such as agricultural fields may present some challenges that affect the communication between the nodes due to the vegetation. These challenges must be addressed when implementing precision agriculture (PA) systems that monitor the fields and estimate irrigation requirements with the gathered data. In this paper, different WSN deployment configurations for a soil monitoring PA system are studied to identify the effects of the rural environment on the signal and to identify the key aspects to consider when designing a PA wireless network. The PA system is described, providing the architecture, the node design, and the algorithm that determines the irrigation requirements. The testbed includes different types of vegetation and on-ground, near-ground, and above-ground ESP32 Wi-Fi node placements. The results of the testbed show high variability in densely vegetated areas. These results are analyzed to determine the theoretical maximum coverage for acceptable signal quality for each of the studied configurations. The best coverage was obtained for the near-ground deployment. Lastly, the aspects of the rural environment and the deployment that affect the signal such as node height, crop type, foliage density, or the form of irrigation are discussed.
... In addition, the model approach introduced in this paper allows the discrimination of the position of the receiver, and thus, provides information about possible shadow areas along the barrier. This feature is very useful when predicting radio coverage, so that gaps in the coverage can be easily mitigated, more so in the design of electromagnetic site shielding either to protect sensitive areas [23], or to increase the security of wireless networks [24,25]. ...
Modeling vegetation is a recurrent problem for wireless communications industry. The raising number of available frequency bands increases this issue, since most of the existing methods nowadays rely on measurement campaigns. The presence of vegetation in urban areas (such as parks or gardens) is bothersome for radio planners, which have to deal with an in-excess attenuation difficult to predict due to the large number of different cases (i.e. vegetation species, topologies of vegetation volumes, frequencies…). Usually, these vegetation formations appear in the form of forests or barriers, emphasizing the problem, since their impact in the transmitted power is not negligible. This paper proposes the use of Artificial Neural Networks as powerful tools to model and infer the excess attenuation induced by vegetation formations. The study is held at cellular frequency bands (2G/3G/4G) for different vegetation species and barrier configurations, where a multilayer perceptron has been trained over existing experimental data at 2G/3G frequencies. We demonstrate the efficiency of the model to predict accurately the attenuation in the frequencies for which it has been trained for, and to infer and extend the model obtained to new frequencies, e.g. 4G, while maintaining an overall low median error. The proposed framework, which is sought to be a powerful tool for radio planners to predict attenuation due to a vegetation formation, has been validated against measurements conducted in controlled environments at several mobile radio frequencies, but it could be easily extended to other radio frequencies, such as WiFi, WiMax or 5G frequency bands, as long as a proper training is performed, to include different propagation effects at such bands.
... Vegetation lines induce attenuation in all cases at higher frequencies (S-and C-bands). However, depending on the species and the configuration of the barrier, a light gain can occur in the received power, at lower frequencies: the interpretation of this fact leads to multiple interaction between leaves and the multipath components these leaves create, which can interfere constructively in the receiver [20,29]. ...
Several studies indicate that vegetation attenuates and scatters radio waves. Although researchers considered vegetation as a soft obstacle in terms of total attenuation, actual measurements show that trees or shrubs could induce important attenuations. This opens a possible use when looking for radar invisibility shields to protect selected targets. Is it possible to use vegetation as an invisibility cover? If so, under which conditions this procedure would work? These are the questions this paper tries to answer, analysing the degradation in the probability of detection that a vegetation cover induces in the radar system. We present the results from large measurement campaigns on attenuation due to vegetation, performed at radar UHF and microwave frequencies; then we use them to evaluate the reduction in the radar performance when trying to detect a target protected by a vegetation cover. We demonstrate that vegetation covers lead to reductions in probability of detection to values below 0.1 considering single pulse radars, which means that that the target would be undetectable. We conclude that the proposal would be reliable, even taking into account other additional effects, as pulse integration, the natural time variability of the induced attenuation, and the noise increase due to the vegetation scattering.
Conference Paper
The use of different kind of soft barriers has been proposed to take advantage of the attenuation they induce with different objectives (i.e. mitigating the interference between networks, or reducing the electromagnetic pollution). The studies on such barriers are focused on the attenuation they induce, but no care on the stability along time or distance of such attenuation has been taken. Thus, large median attenuation with high variability along time (or distance) on the attenuation values would not be very useful for some applications. A method to analyse the stability of the attenuation induced by obstacles of any kind is presented along this letter, supported by the analysis of vegetation barriers data.
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Abstract—Ray tracing algorithms rely on two dimensional or three dimensional database. They use ray optical techniques referred to as the uniform theory of diffraction (UTD) using building database given as polygons. Building geometries can also be modelled as having non-planar geometries, and this would be important in modeling of shadowing loss due to curved structures in urban radio propagation. To demonstrate modelling of buildings as non-polygonal geometries, a particular building composition involving 3D cruved geometries is chosen, and shadowing loss for this building composition is studied via UTD ray tracing. Building structure considered in this study involves main canonical shapes of non-planar geometries including cone, cylinder and sphere. Single and multiple interaction of surface diffractions, effect of creeping waves are taken into consideration in the analysis. Also with TUBITAK, UEKAE/G222 Ataturk Bulvari, No:211, Kat:7/20, Kavaklidere,
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Measurement results for human body shadowing and local environmental effects in short-range indoor radio channels are presented. A narrowband measurement system, comprising a signal generator, two identical triangular monopoles and a spectrum analyser, was used in the measurements. When the radio link was periodically blocked by a human body with various objects in and around the link, fading depths of up to 15 dB and even more were observed at spot frequencies of 3–11 GHz band. Standard deviation and its range for human body blockage are estimated for different radio link scenarios simulating real environments. The distribution of human body shadowing was analysed and compared with known distribution functions.
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A complete study for the deployment of a wireless sensor network in a forest based on ZigBee is presented in this paper. First, due to the lack of propagation models for peer to peer networks in forests, propagation experiments were carried out to determine the propagation model. This model was then used for planning and deploying an actual wireless sensor network. The performance of the network was compared with the expected theoretical behavior to extract some conclusions that are presented in the paper. Finally, some general conclusions, as an estimation of the minimum number of routers necessary to cover a given area, are extracted from the experiments and presented in the paper.
Conference Paper
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Path-loss in indoor environments is investigated at 1.8 GHz, using a power-law model where the exponent of the distance is the descriptive random variable. The model is extremely simple and easily applicable in practice by working engieers who need only make a few measurements to get a knowledge of path loss sufficient for many applications. The model fits well experimental data in various environments under different propagation conditions, including cross polarization, and the observed behavior of the exponent suggests the possibility of using only one distribution function to statistically characterize path loss.
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Public fear of exposure to electromagnetic fields has led to many countries introducing legislation on the topic. Some have even introduced `sensitive areas` where field levels should be significantly reduced. This study presents an efficient and ecological method to reduce the exposure to electromagnetic fields for the mobile global system for mobile communication (GSM) and universal mobile telecommunications system (UMTS) frequency bands. We propose the use of tree (or bush) barriers as a shielding method around places that may need to be protected. A major measurement campaign has provided a significant amount of data to characterise the performance of such soft walls. The campaign involves seven vegetation species and five different barrier configurations. Furthermore, a parametrical characterisation of the attenuation levels achieved with this method is presented and validated. This aids in choosing the configuration barriers and the vegetation species that provide the desired attenuation levels in exclusion zones.
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This letter introduces analytical expressions for the modeling of path loss and shadow fading in residential indoor-to-outdoor scenarios. The formulas have been calibrated using channel power measurements at the radio frequencies of common cellular systems and are thus suitable for channel modeling in femtocell networks. The expressions presented here can be used as a simple propagation model in system-level simulators (SLS), as well as for comparison to other models. Furthermore, its compact formulation simplifies its use for theoretical studies of two-tier networks, while its empirical nature strengthens its validity.
Automatic traffic monitoring and surveillance have become essential for effective road usage and management. Various sensors have been used to estimate traffic parameters, but their installation and maintenance is often difficult and costly. Among the technologies being investigated, computer vision promises the most flexible and reliable solutions to estimate traffic parameters. This paper proposes a wireless sensor network (WSN) architecture for autonomous traffic monitoring, based on computer vision techniques for automatic scene analysis and interpretation. The paper first discusses the motivation for the work and the relevant design issues. Then, the proposed architecture and the relevant modules are described in detail. Finally, experimental results are shown, which prove the accuracy of the proposed approach.
Conference Paper
Propagation of radio signals from a base above clutter, such as buildings and trees to a mobile immersed in clutter, is treated theoretically by accounting for random diffuse scattering at the mobile. Closed form expressions are derived for path gain as well as for angular spectrum at the base. The resulting predictions are in close agreement with widely accepted models and empirical results. The angular spectrum at the base is found to be Lorentzian of width close to that reported for urban measurements in Aarhus. Small-scale fading and distance-depedent loss are treated in a unified way, as opposed to the heuristic methodologies, which formulate them as separate factors.
External nonstatic effects on the indoor radio channel can be classified according to their natures; they can be quasistatic, as are those due to the furnishing and decoration of rooms, or clearly dynamic, as are those forced by the movement of people and pets within the propagation environment. So, it can be stated that people acting and walking nearby the antennas of radio-communication systems introduce nondeterministic effects in the radio channel, whereas walls, ceiling, floors, and even doors and windows are structural elements whose influence can be modeled by ray-tracing tools, the activity of people (both movement and variations in the distribution of elements such as furniture or decorative objects) introduces stochastic components in the channel characteristics prediction. Deterministic tools cannot model these effects, but their experimental knowledge will allow radio-network designers to keep guard values for the different design parameters at the same time that they plan the radio network. In this manner, the quality of service provided by these communication systems could be preserved. This paper shows the results and analysis of measurement campaigns performed in static and dynamic conditions, in order to compare the behavior of both narrow and wide-band parameters of the radio channel, including people activity within the environment.