Wireless Sensor Network, 2010, 2, 879-890
doi:10.4236/wsn.2010.212106 Published Online December 2010 (http://www.scirp.org/journal/wsn)
Copyright © 2010 SciRes. WSN
Predicting Ground Effects of Omnidire ctional
Antennas in Wireless Sensor Networks
John F. Janek, Jeffrey J. Evans*
Department of Electrical and Computer Engineering Technology, Purdue University, West Lafayette, USA
E-mail: jjanek@purdue.edu, jje@purdue.edu,
Received September 9, 2010; revised September 15, 2010; accepted September 25, 2010
Omnidirectional antennas are often used for radio frequency (RF) communication in wireless sensor net-
works (WSNs). Outside noise, electromagnetic interference (EMI), overloaded network traffic, large ob-
stacles (vegetation and buildings), terrain and atmospheric composition, along with climate patterns can de-
grade signal quality in the form of data packet loss or reduced RF communication range. This paper explores
the RF range reduction properties of a particular WSN designed to operate in agricultural crop fields to col-
lect aggregate data composed of subsurface soil moisture and soil temperature. Our study, using simulation,
anechoic and field measurements shows that the effect of antenna placement close to the ground (within 10
cm) signicantly changes the omnidirectional transmission pattern. We then develop and propose a predic-
tion method that is more precise than current practices of using the Friis and Fresnel equations. Our predic-
tion method takes into account environmental properties for RF communication range based on the height of
nodes and gateways.
Keywords: Omnidirectional Antenna, Ground Effects, Wireless Densor Networks
1. Introduction
Wireless sensor networks (WSN) provide a means to
monitor an environment with a high spatial and temporal
granularity. They generally consist of a collection of
sensors used to monitor one or more specific variables
over a region of interest [1]. WSNs are used for a variety
of applications such as security monitoring, traffic con-
trol, wildlife tracking, or factory monitoring. They can be
useful in dangerous environments unfit for human pres-
ence, such as an enemy base camp or a toxic field of ra-
dioactive material. Advancements in sensor design, low-
power processors, and robust wireless communication
protocols have allowed WSNs to emerge as a technology
that promises an unprecedented ability to monitor, in-
strument, and eventually control the physical world [1].
Without the cumbersome placement and maintenance
of wires and cables, WSNs are proving to be more effi-
cient and less expensive. RF technology allows for non-
line of sight (NLOS) communication around obstacles or
through walls. Some low-power RF designs are capable
of communicating a few hundred meters. RF implemen-
tation also offers the potential of hundreds to thousands
of sensor nodes to be implemented in the same network.
RF communication, however, is susceptible to interfe-
rence from noise or other wireless signals, or may over-
burden its own frequency channels with excessive wire-
less data traffic, particularly in larger networks. Physical
objects may obstruct the RF communications link. Any
object depending on position, size, and material compo-
sition, contributes to decreasing the power density of an
RF signal. To obtain and maximize a direct line-of-sight
(LOS) communication link, RF transmitters are often
placed at higher elevations or mounted to tall transmis-
sion towers, such as a cellular telephone tower. Shorter
RF communication distances such as those commonly
used in WSNs allow for significantly lower power RF
A WSN was developed to monitor and collect soil
temperature and moisture data in an agricultural crop
field as part of a project to evaluate WSN technology as
a viable means to complement other forms of remote
data collection for hydrological studies of watersheds.
The network was exposed to weather conditions that in-
cluded extreme variation in temperature, moisture, and
humidity. The testing location was a research farm lo-
cated in the mid-western United States. So as not to dis-
turb other research, the sensor nodes were required to
Copyright © 2010 SciRes. WSN
have minimal influence on the environment that might
interfere with other research and normal outside field-
work, such as irrigation and fertilizing. Therefore the
nodes were completely encapsulated (including the an-
tenna) within a Polyvinyl Chloride (PVC) enclosure.
Here the sensing elements were approximately 10 cm be-
low the soil surface while the power supply (2-AA batte-
ries), electronics, and radio were approximately 8-10 cm
above the soil surface. Another consideration was that
altering the environment by incorporating these sensor
nodes might corrupt the same data that the network was
designed to collect.
The WSN deployment for this work included modest
number of sensor nodes (6) and a base station for data
collection, storage, and forwarding to a web sever that
provided postprocessing and database services [2]. The
sensor nodes were randomly distributed around the base
station. The base station was located adjacent to a con-
stant power source, and as such was located approxi-
mately 1 meter above the soil surface. With the sensor
nodes deployed and undergoing field testing, it was ob-
served that the maximum communication range between
any sensor node and base station was approximately 10 m,
which was much shorter than expected. Lab tests that
preceded field deployment yielded a consistent commu-
nication range of 45 to 50 meters indoors without the
benefit of LOS (i.e. communication was through walls
and floors). Technical support documents from the man-
ufacturer of the RF electronics indicated that LOS com-
munication between two units was achievable up to 150
meters, three times greater than our lab tests and 15 times
greater than observed at our field test site. The technical
support document antenna height for their test condition
was 1 meter, which is acceptable for our base station but
unacceptable for our sensor nodes.
Several tests were developed and performed in this
work to study the relationship between antenna location
above the soil and RF transmission range. Tests included
simulation, anechoic chamber measurements, and field
tests. Simulations were performed to establish baseline
performance of an ideal monopole antenna in benign
operating conditions using simulated perfect electrical
conductor (PEC) and earth ground planes. While some
RF simulation packages offer the ability to simulate a va-
riety of media which can be configured with atmospheric
conditions and terrain compositions similar to ours, we
were more interested in observing any unusual characte-
ristics in ideal theoretical performance. Anechoic cham-
ber testing was used to control the operating environment
using real world communication and measurement equi-
pment to establish a “mid-point” between simulation ex-
periments and eld tests. Field tests were then conducted
to provide a realistic representation of RF communica-
tion performance outdoors. Field testing, while more ex-
pensive and time-consuming, was performed with the
objective of comparing and contrasting performance re-
sults observed in the simulations and anechoic chamber
The purpose of this work was to develop a better un-
derstanding of the relationships between RF communica-
tion range and the deployment conditions the WSN was
exposed to; namely the node to base station antenna hei-
ght relationship. Environmental conditions such as wea-
ther, and the physical obstructions that normally change
over a growing season were also studied. By better un-
derstanding the variables and their relationships to each
other, improved prediction methods can be developed so
both node to base station and node to node deployments
can be more easily designed and reliably implemented.
In Section 2 we discuss work related to modeling RF
communication links. Our experiments and results are
presented in Section 3 and in Section 4 we conclude and
make suggestions for future work toward developing an
improved prediction model.
2. Related Work
During initial tests in an open (cut) grass field, it was
observed that communication distance had degraded be-
tween the base station and the nodes by a factor of five
relative to preliminary communication range tests con-
ducted in a laboratory. Regardless of the distance speci-
fied in the hardware documentation, line-of-sight be-
tween two antennas did not guarantee successful commu-
nication in our wireless link. Propagation patterns of
wireless systems may be affected by outside influences
such as vegetation, solar radiation, climate conditions,
and interference from other RF sources. Several inde-
pendent, but inter-related relationships have been devel-
oped to characterize and quantify RF propagation loss
The free space propagation loss accounts for a trans-
mitted signal weakening as it travels over a given dis-
tance at a constant frequency. Friis developed an analyt-
ical relationship of power received to the power trans-
mitted in free space [3] as shown in Equation (1)
where the subscripts ‘t’ and ‘r’ represent the transmitting
and receiving antennas, respectively, P is power in watts,
G is a unit-less antenna gain, d is the distance between
the antennas in meters, and λ (wavelength) is measured
in meters.
Another loss phenomenon which can be partially over-
come by antenna design is the “Fresnel effect”. As an
Copyright © 2010 SciRes. WSN
electromagnetic wave radiates from an antenna, it effec-
tively “spreads out” as its distance increases. The Fresnel
effect is analogous to the difference in light dispersion
between a laser and a flashlight. If both devices are poin-
ted at a flat surface, the laser beam’s area of light is very
small and condensed due to the parallel rays. As the laser
is moved closer and farther from the flat surface, the area
of light remains unchanged. A flashlight however illustr-
ates the Fresnel effects. As a flashlight is moved closer to
the flat surface the area of light becomes smaller, as the
flashlight is moved farther away the area of light on the
flat surface becomes larger. Just like the light from the
flashlight, RF energy from an antenna has the same ef-
fect. This results in the area of radiated energy increa-
sing (spreading out) over distance, resulting in lost power
that never reaches the receiver.
The Fresnel zone is the area which includes radiated
energy. Objects within this zone can cause signal degra-
dation and power loss from the transmitter to the receiver.
The more obstacles within the Fresnel zone, the more
wave reflections and phase shifts will be created, which
can lead to received power losses. Likewise, the closer
an obstacle is to the center of the LOS path, the greater
impact that obstacle will have on the propagated wave.
Moreover, as antenna height gets close to the earth’s sur-
face (as with the case of our WSN), the earth’s surface
enters the Fresnel zone, creating an obstacle for commu-
The Fresnel zone equation is
17.32 4
where r is the radius of the Fresnel zone in meters, d is
distance between the antennas in kilometers, and f is
frequency in GHz. The value of 17.32 is a normalization
constant derived from the use of meters, kilometers and
GHz as units of measure. It is generally accepted that
obstacles will not have a significant impact on the RF
propagation as long as the object is only obstructing the
outer 40% of the Fresnel zone radius, r. As a result, equ-
ation (2) is modified: (in terms of meters and kilome-
10.39 4
where 17.32 × 0.6 = 10.39.
A third loss phenomenon involves reflections which
contribute to phasing artifacts that complicate reception.
The 2-ray ground reflection model has been observed as
an accurate model for predicting a wireless system per-
formance above an earth surface [4]. As the RF waves
propagate, they spread and reflect off nearby objects
and surfaces, with some of the reflected waves reaching
the receiver antenna. The ground reflection model ac-
counts for direct distance and the distance of a ground
reflected wave.
In Figure 1, the height of the transmitting and receiv-
ing antennas is expressed as ht and hr respectively. RLOS
is the direct distance in meters for an RF wave traveling
from transmitter to receiver, R1 is the distance in meters
from transmitter to the ground, R2 is the reflected equiv-
alent of R1 to the receiver in meters, d is the distance
between the antennas, in meters. θ1 and θ2 (in degrees)
are equal and represent the angle of incidence from the
transmitter and to the receiver. At angles of 10˚ to 90˚ for
θ, some RF waves would reflect off the earth’s surface
while some would be absorbed by the earth’s sur- face.
The amount of RF reflection and RF absorption depends
on the earth’s dielectric, εr, and conductive, σ, properties.
These properties are used to calculate the Fresnel reflec-
tion coefficient of the surface. The equations for calcu-
lating the Fresnel reflection coefficient, Γ, are shown in
Equations (4-6) from [3];
 (4)
where θ = θ1 = θ2 from Figure 1, εr is the relative di-
electric of the earth’s surface, and σ is the measure of
conductivity in the earth’s surface. Variations in earth
conductivity, σ throughout the continental United States
have been mapped in [5]. The WSN field in this study
has a conductivity of 8 mmhos/m, which is considered a
medium conductivity. Typically, regions of varied eleva-
tion have lower conductivities (1 to 5) while flat ter-
rained regions have higher conductivities (10 to 30) [5].
As two identical RF waves travel over the same dis-
tance, the phase between them remains unchanged. How-
ever, if those same two identical RF waves travel differ-
ent distances, they will no longer be in phase and one will
Figure 1. 2-Ray ground reection model.
Copyright © 2010 SciRes. WSN
either lead or lag the other. The difference in phase is
related to the difference in distance traveled. For instance,
the distance of RLOS is shorter than the combined dis-
tances of R1 + R2 (Figure 1). The phase difference (Δ)
can be calculated as shown in Equation (7):
  (7)
where R1, R2, and RLOS are distances measured in meters
and λ is wavelength measured in meters.
This model is used mainly for transmission distances
over several kilometers with antennas mounted at least
50 meters in the air as defined in [4] and [5]. At these
distances, the angle of incidence (θ1,2 from Figure 1)
would be approximately equal to 0, creating a “grazing
angle” in which the reflected RF wave R2 reaches the
receiving antenna by slightly grazing the surface and ref-
lecting to the receiver. Also, it is generally accepted that
the RF energy at the grazing angle is not absorbed by the
earth’s surface. This makes the earth’s surface appear as
a perfect electric conductor ground plane (PEC). Thus
the relative dielectric and ground conductivities are es-
sentially irrelevant at the grazing angle [4]. At distances
so large that θ1,2 are approximately 0, the distances RLOS
and R1 + R2 become approximately equal, meaning that
the phase difference (from Equation (7)) between the
direct and reflected RF propagated waves is approxi-
mately zero.
Additional loss components in wireless communica-
tion links include divergence, terrain roughness coeffi-
cient and the shadowing function. Divergence accounts
for RF waves spreading apart due to the earth’s curvature.
Terrain roughness is the standard deviation of the terrain
heights between the transmitting and receiving antenna.
The shadowing function at low grazing angles accounts
for geometric shadowing, where the propagated wave
cannot cover all locations in a region. It can be compared
to a mountain casting a shadow over a valley because the
sun is either rising or setting. However, the sensor net-
work in this study was implemented on a small, flat and
smooth field relative to these loss components. Therefore,
these three terms have little significance for characteriz-
ing RF propagation in this study [3].
The Friis free space propagation equation (Equation
(1)) has been accepted as a good representation of RF
waves propagating in free space. A “propagation factor”,
Fprop, can be inserted into the Friis free space equation to
account for losses from the earth [3]. Equation (8) in-
cludes the Fresnel reflection coefficient, Γ, the phase
difference, Δ, divergence Factor, D, terrain roughness
coefficient, ρs, and the shadowing function S(θ).
prop s
DS e
  (8)
Since the divergence factor, terrain roughness coefficient,
and shadowing function have little significance in this
work, Equation (8) can be simplified as
 (9)
Once Fprop is determined, it can be used as a multiplier in
the Friis free space equation
rt t rprop
 (10)
Despite the potential impacts of ground effects on RF
wave propagation, some wireless system designs (inclu-
ding ours) require an antenna to be installed within close
proximity to the earth surface. One way to reduce ground
losses is to make the earth more conductive with a wire
mesh on the surface or slightly buried around the antenna
[6]. In the case of our sensor network, a buried wire
mesh would be infeasible to implement as it would in-
terfere with the growth of the crops root system in the
Another technique to decrease losses that occur when
an antenna is near the ground: by using a 2 or 4 element
antenna array [6]. Strategic spacing between the antennas
in the array has been found to reduce the amount of
ground losses for the entire array. This results in an over-
all increase in antenna efficiency consequently leading to
greater propagation distance even while close to the
ground. From these examples, it can be seen that altering
the antenna design or altering the earth’s physical proper-
ties can increase antenna efficiency. Our WSN nodes
however require a physically small and very energy effi-
cient design, warranting antenna arrays infeasible.
Ground effects are not the only causes of propagation
loss in wireless communications links. Climate condi-
tions such as precipitation (rain and snow) are potential
causes for wireless link signal attenuation. Because of in-
accuracies in predicting the weather, it is difficult to an-
ticipate how a wireless link will perform. However, rain
does not cause a substantial degree of attenuation at fre-
quencies below a few GHz, and the our WSN sensor
nodes operate at 900 MHz [7,8]. Wet snow or sleet af-
fects wireless links more than rain at all frequencies be-
cause it tends to be larger (thicker) and has a higher wa-
ter content (wetter) as it falls, causing many reflections
that can lower amplitude and alter the phase of the in-
coming waves. Colder, dry snow fall, on the other hand,
is generally composed of ice and air thus causing insig-
nificant attenuation on many wireless systems [7]. All
types of snow can accumulate and stick to the antennas
and equipment, and the melting of that snow creates wa-
ter on the equipment, creating a large source of reflec-
tions and overall system degradation.
Simulations of dipole antennas above wet and dry
soils have been studied [9]. The study focused primarily
Copyright © 2010 SciRes. WSN
on creating more computer efficient time-domain simu-
lations; yet they chose to simulate a vertical dipole over
an earth-like soil layer. The simulation showed the ef-
fects of varying antenna height above the soil. Example
simulations illustrated how to configure an antenna mod-
el and view the results, which indicated earth-surface ref-
lections as the antenna height was adjusted to a proximi-
ty near the ground. Their antenna simulation results
showed examples of RF propagated waves as they were
either absorbed or reflected by the lossy earth. Other stu-
dies [10,11] tested the communication range of commer-
cial WSN nodes by measuring received signal strength
and amount of packet loss based on different climate
conditions. Our work is different in that we are explicitly
investigating RF communication performance as a func-
tion of antenna proximity to the ground.
3. Experiments
The sensor nodes in our WSN were composed of COTS
and in-house designed and fabricated components. The
node electronic hardware incorporated a microprocessor,
a RF transceiver, and on-board memory. The micropro-
cessor, an ATmega 128 L, is equipped with six analog-
to-digital converter (ADC) channels available for exter-
nal sensor use. Two of those channels were occupied by
a soil temperature sensor and a soil moisture sensor.
These sensors were purchased components from Deca-
gon, Inc. and included a model ECT soil temperature
sensor and either an EC-5 or EC-10 soil moisture sensor.
The node RF transceiver was a Chipcon CC1000, ca-
pable of transmitting between 300 MHz and 1000 MHz
from –20 dBm to +10 dBm depending on frequency.
Each node utilized a quarter-wave monopole antenna for
communication. Our team designed an enclosure to house
and protect the node electronics and batteries, and to
mount the soil temperature and moisture sensors at a spe-
cified depth (either 5 or 10 cm) below the soil surface.
The electronics, batteries, and antenna were housed inside
the weather resistant enclosure and located above ground.
The base station was mounted in a purchased weath-
er-proof enclosure about 1 meter above the soil. Its pur-
pose was to collect, store, and forward data that was rec-
orded by the sensor nodes. The forwarding function ena-
bled access to the data via the Internet, which was dis-
played in a tabular fashion [2].
The experiments developed for this study included
simulation studies, anechoic chamber characterizations,
and field measurements. Three antennas were used for
anechoic and field testing. They are denoted in this work
as “Control”, “node monopole”, and “constructed mo-
nopole”. The “control” was used in all anechoic chamber
testing and in some outdoor testing. It was a 900 MHz
“rubber-duck” whip antenna from Hyperlink Technolo-
gies, with an operating range from 860 MHz to 960 MHz
and gain of 3 dBi.
The node monopole antenna was a quarter-wave de-
vice, designed to operate in conjunction with a ground
plane to mimic a dipole antenna [12]. However, this mo-
nopole was not above an adequately sized ground plane
for it to be considered efficient. Antenna manufacturer
documentation suggested that when using a monopole
antenna a ground plane with a dimension of at least 0.5λ
or greater should be used. In our case this is impractical
as our node ground plane is less than 0.0625λ in size.
The “constructed monopole” was a handmade quarter-
wave monopole antenna. It was fabricated with a node
monopole antenna and an N-type female panel-mount
connector. The panel-mount’s square shape was used to
connect to a solid ground enclosure and the size of the
piece is approximately the same size as the ground plane
used for the node monopole.
3.1. Simulations
The simulation software package HFSS was used for this
work [13]. HFSS was used to simulate antenna perfor-
mance based on environment type (free-space or atmos-
phere), antenna height above a ground plane, and ground
plane composition (perfect conductor or earth-like).
A quarter-wave monopole antenna was simulated
above a ground plane with no outside sources of interfe-
rence in the simulation environment. The antenna was
impedance matched to a frequency of 900 MHz and was
first simulated in free space, which showed the characte-
ristics similar to an ideal dipole. Next, the antenna was
placed above a perfect electrical conductor (PEC) ground
plane. This ground plane was a “few wavelengths” larger
than the longest dimension of the monopole itself, repre-
senting an infinite ground plane [12,14].
Once these simulations were complete, the PEC
ground plane was replaced by an earth-like ground plane.
The ground plane was configured with similar dielectric
(15) and conductivity (0.008) characteristics of terrain in
our WSN field location (see Figure 2(d) from [5]). All
antenna simulations were executed varying the antenna
height from 0.5λ to 2.5λ in 0.5λ steps. The results of
these simulations produced 3-dimensional radiation plots
and gain measurements. The radiation plots illustrate an
overall shape of the pattern for the antenna as it approa-
ched the ground plane.
Figures 2 and 3 are HFSS 3-dimensional radiation
plots for a quarter-wave monopole above a PEC ground
plane, and a quarter-wave monopole above an earth-like
ground plane respectively. The radiation pattern for each
height was simulated and illustrate how the vertical pat-
Copyright © 2010 SciRes. WSN
Figure 2. HFSS quarter-wave monopole above PEC ground.
tern changes with the approaching ground plane. Noti-
cefor item 6 in Figure 3 a slight parabolic radiation pat-
tern (shape) emanates from the bottom, leaving space
under the parabola where no transmission occurs. This
shape suggests that antennas placed close to the ground
could suffer a loss in transmission distance (i.e. adjacent
parabolic radiation patterns may not intersect). This ef-
fect is significantly reduced as the antenna height is ele-
vated above the ground as shown in Figure 3.
3.2. Anechoic Chamber Measurements
Anechoic chamber tests were conducted to understand
the antenna’s operational characteristics in a controlled
environment. The chamber is designed to eliminate in-
ternal reflections and outside interference such as elec-
tromagnetic interference [12,15]. The goal for these tests
was to measure the vertical and horizontal radiation pat-
terns of our omnidirectional monopole antenna. The cha-
mber used in this study had a usable size of 1.4 m wide,
2.4 m long and 1.8 m high. The chamber’s walls, floor,
and ceiling are also covered in absorbent foam to absorb
RF radio waves to minimize internal reflections. It is
Figure 3. HFSS quarter-wave monopole above earth ground.
desirable to place antennas further than one wavelength
away from the foam walls, as antennas placed closer than
one wavelength away can interfere with the radiation
pattern of RF energy propagated by the antenna [16,17].
The wavelength for a 900 MHz radio wave is 0.33 m,
leaving a useable volume measuring 0.74 m width, 1.74 m
length, and 1.14 m height inside the chamber.
The transmitter and receiver antennas were placed 1m
apart from each other. This provided an adequate dis-
tance between the antennae, which also allowed each an-
tenna to be greater than 1 wavelength from the walls,
floor, and ceiling. Measurement equipment was external
to the chamber, leaving only the antennae, their stands,
and cables inside.
Vertical and horizontal radiation pattern tests were
conducted independently. The constructed monopole and
node monopole antennae were each tested as the anten-
nae under test (AUTs). The transmitter was a Fluke
6062A RF generator and it was programmed to transmit
a 900 MHz signal at +5 dBm output power. An Agilent
N1996A spectrum analyzer was used to measure the re-
ceived signal. In both tests the control antenna was used
as the receiver and a 0.91 m by 0.91 m aluminum sheet
Copyright © 2010 SciRes. WSN
was used as a ground plane. In the horizontal radiation
test, the control antenna was at a fixed location and
The AUT was attached to a device that would rotate the
AUT horizontally at fixed intervals of 15˚. This test was
repeated with the AUT at vertical heights ranging from 3λ
(1 m) down to 0.5λ (16 cm) to measure horizontal radia-
tion pattern differences due to the presence of a ground
plane. Limitations caused by the combination of test stand
mechanics and the physical size of the antenna made
measurements at 0.25λ unreliable. The control height was
fixed at 3λ (1 m). An example comparison between the
anechoic chamber and an HFSS simulation with an anten-
na height of 2λ (66 cm) is shown in Figure 4.
Vertical radiation pattern tests required that the AUT
be held in a fixed angular position and height. The con-
trol antenna was repositioned vertically in 15 degree in-
crements about the AUT and kept exactly 1m from the
AUT. These tests were also performed with the AUT at
vertical heights ranging from 3λ (1 m) down to 0.25λ (8
cm) to measure any affects on the vertical radiation pat-
tern due to the presence of a ground plane.
Vertical height limitations of the chamber restricted
vertical pattern measurements to less than the full 180˚
desired range. However this does not translate into a
problem with the sensor nodes because they were de-
signed to be positioned in a horizontal plane, not a ver-
tical plane. A larger chamber however would have better
enabled more comprehensive data in the vertical plane. It
was assumed that maximum power would be received
at0 horizontal. Received power should decrease as the
control antenna’s height was adjusted. The results in
Figure 5 appear to be less uniform when compared to
the simulated radiation patterns from Figures 2 and 3.
Figure 5 does illustrate however that at higher AUT
Figure 4. Horizontal radiation pattern (Anechoic chamber
vs. HFSS).
Figure 5. Anechoic chamber vertical pattern results.
elevations (82.5 cm and 66 cm) the vertical radiation
pattern is significantly more consistent than the lower
3.3. Field Measurements
The outdoor range test was designed to measure the RF
communication distance of two sensor nodes in a real-
world environment. The tests were conducted at a uni-
versity agriculture research facility located in the mid-
western United States.
Three different test fields were studied: bare (not
planted or tilled) soil, soybeans, and corn. The tests were
conducted in mid-July, so the crops were approaching
their full mature size. The same nodes were used for all
tests to eliminate variations from one node to another.
One node was programmed with a “transmit blink” pro-
gram, where it flashed its on-board LED at 1 Hz, and
transmitted a communication signal for any receivers to
flash their LEDs. The second node was programmed
with a “receive blink” program such that when it is in
range of the transmitter, the receiver node received a
message from the transmitter and responded by flashing
its LED. When the receiver was out of range, the signal
was lost and the LED stopped flashing.
Tests were performed where the transmitting node
height was varied from 2.5λ (82.5 cm) to 0.25λ (8.25 cm)
above the ground. Marker flags were setup at increments
of 4.1 m, 6.1 m or 3.1 m depending on the crop, up to a
total distance of approximately 150 meters. The receiver
node was moved to a distance and its minimum height
above the ground was measured. In other words, at a set
distance the receiver was lowered until it would lose re-
ception of the transmitted signal. As the distance away
Copyright © 2010 SciRes. WSN
from the transmitter increased, the receiver would nec-
essarily need to be higher above the ground in order to
receive correctly from the transmitter. This was repeated
until the receiver node was at a distance sufficient to
require its antenna to be located at least 2.4 m above the
ground in order to receive the transmitted signal. This
test was repeated for each of the transmitter heights and
for the 3 different crop types.
The results of these tests show communication ranges
at different antenna heights, whether both antennae are
high (2λ lambda and above), both antennas are low (sim-
ilar to node-to-node communication), or one is high and
one is low (base station-to-node communication).
Figures 6-8 illustrate the communication response in
bare soil, soybean, and corn environments respectively.
There are several observations that can be made. First
there is a clear parabolic transmission effect, meaning
that communication occurs in the upper left portion of
each graph. For example, examining Figure 6 we see
that a transmitter at a height of 0.25λ (8.5 cm and de-
noted as 0.25 in the legend) needs a receiver to be
mounted at least 100 cm in the air in order to be heard at
a distance of 40 m. A receiver that is also at a height of
8.5 cm must be within 15 m to be heard.
Second, the communication performance in a mature
corn field was better than expected, meaning the area to
the upper left portion of the graph (where communica-
tion occurs) is larger than the lower right (where com-
munication does not).
4. Communication Range Prediction
The Friis free space equation is useful for calculating
the expected free space loss in a system. However, it
does not account for earthly and atmospheric losses.
Both Golio and Goldsmith presented similar analytical
treatments which modify the Friis free space equation
with the inclusion of a propagation factor for the pres-
ence of the earth (not the atmosphere) by incorporating
Equation (9) into Equation (10).
2R+R tr
  (11)
Using a Taylor series approximation, Goldsmith mod-
ified the phase delay equation:
2R+R tr
  (12)
This equation is more useful since it incorporates the
antenna heights (t
h and r
h) into the propagation loss
factor. Recall that the results from section III on propa-
gation patterns and losses are also based on antenna
height and distance. In section II, the propagation beha-
Figure 6. Required receiver height vs. receiver distance
(Bare soil).
Figure 7. Required receiver height vs. receiver distance
Figure 8. Required receiver height vs. receiver distance
vior as the grazing angle
approaches 0(Figure 1) was
introduced. A propagation wave that appears to graze the
earth’s surface is not absorbed by the earth. As a result,
is approximately equal to –1. In addition, when
approximately equal to 0, RLOS is then approximately
equal to 12
During the range testing, the antenna heights (t
h and
h) were relatively low (< 1 m) and the distance between
antennas was much greater ,.
DhhSo, 0
 ,12
, and
converted in Equa-
tion (12). Equation (11) is now rewritten as (with the
Taylor approximation from Equation (12)):
Copyright © 2010 SciRes. WSN
tr trtr
rt t
 
 
 
 
where r
P and t
P are in units of dΒm. Converting
this in terms of logarithms we have:
 
10 log20 log40 log
rt trtr
PP GGhhd (14)
An environmental propagation factor is still missing
from Equation (14). If one were to solve for distance, d,
with all other parameters known, there would be no way
to distinguish if the distance, d, was determined for a
particular environment (such as bare soil, corn, or soy-
bean). Therefore an “environmental” propagation factor
is required to distinguish the communication environ-
ment. To determine such a propagation factor, theoretical
results from Equation (14) were compared to measured
results from our field tests.
First, ,
P the receiver sensitivity, is specified at
–98dBm according to the transceiver specifications. It
was assumed that during testing the receiver node re-
ceived a signal from between –90 to –98 dBm (If the
receiver height was lowered, it would lose the signal,
indicating a signal magnitude of lower than –98dBm).
Because the actual received signal was not measured, the
expected receiver sensitivity,
P, was approximated
to –95 dBm.
Second, Equation (14) was used to determine a value
for r
P based on the experimental results. For example,
using the soil environment with a height t
h of 82.5 cm,
G andt
G were approximated to be 1.52 (from HFSS
simulation results for an ideal monopole), and transmit
power was fixed at +5 dBm. Receiver antenna height,
h, and distance, dmeasured, data were recorded during
the outdoor range tests from section III-C.
510log 1.521.52
The power difference can now be determined based on
expected and measured power
 
PPP (16)
This process was repeated for all values of soil with
ht = 82.5 cm which gave an array of values for
r diff
The interquartile range was found for the array of values
and outliers were eliminated. The average of the remain-
ing values in the array was then calculated and the result
was placed in a table. This example process resulted in a
value representing the propagation factor for soil with
the one antenna at 82.5 cm. This method was repeated
for the 3 environments (soil, soybeans, corn) and for the
6 different heights of t
h, (82.5, 66, 50, 33, 16,8), for a
total of 18 values. Table 1 shows the 18 propagation
factors, under the assumption of
P= 95dBm;t
and r
G= 1:52.
Equation (14) was modified to include the new “envi-
ronmental” propagation factor, penv. One value is selected
from Table 1 based on a corn, soybean, or soil environ-
ment and is inserted into the env
Pvalue of the modified
Equation (17).
10 log20 log
rt trtr
 
 (17)
Table 1 and Equation (17) were used to create a series
of profiles for the vertical radiation pattern of the trans-
mitter antenna. Figure 9 to 11 are illustrations of the data
produced by the mathematical model. As the antenna
was lowered towards the ground, the parabolic effect na-
rrowed and it appeared as if more RF energy was ra-
diated in an upward, vertical direction.
There were no obstacles in the soil environment above
the ground surface (i.e. no crops) to impede the RF sig-
nal strength. As a result, of the 3 crop environments, the
soil test promoted the greatest distance with the lowest
Figure 9. Radiation distance vs. antenna height (Soil).
Figure 10. Radiation distance vs. antenna height (Soybean).
Table 1. Environmental propagation values based on theo-
retical and measured values.
Soil 20.1619.62 17.67 20.40 17.2716.33
Soybean 24.7226.1630.96 27.57 28.6728.92
Corn 24.2022.4123.31 21.54 24.3922.35
Copyright © 2010 SciRes. WSN
Figure 11. Radiation distance vs. antenna he ight (Cor n).
receiver heights. With ht at 82.5 cm, 66 cm, and 50 cm,
there was virtually no difference in receiver height hr.
This indicated that there would be little value in distance
gained by increasing the antenna from 50cm to 82.5 cm.
The soybean crop canopy was 45 cm to 55 cm above
the surface. As a result, there is little difference between
the measured values at antenna heights of 33 cm and 50 cm.
When ht was at 82.5 cm and 66 cm, the antenna was fully
above the soybean canopy leaving no obstacles. With the
antenna at a height of 8 cm, the thick soybean stems and
antenna proximity to the ground caused considerable sig-
nal attenuation.
Conversely, corn stalks at their base was thinner than
the soybeans. In fact, a direct line of sight for was
viewed down the corn row for about 30 meters. Across
the corn row, the stems were randomly distributed and
line of sight was short (about 2 m). Higher up the corn
stalks (0.5 m to 2.5 m), the leaves dominate and become
thicker, which contributed to more attenuation. Above
the leaves, (greater than 2.5 m) the corn stalks were at
their thinnest, with few leaves or obstacles. It is assumed
that above 2.5 m an RF signal be affected by a minimal
amount of attenuation from the corn stalks (measure-
ments were not taken above 2.5 m).
To verify the accuracy of the prediction method, a
comparison was made between the simulated curves and
the measured values. Of the many different plots, the
measured values appeared to closely follow the predicted
propagation curve. An example of closely related data is
shown in Figure 12. It should be pointed out that the
presence of the crops did affect the overall curves. For
instance, Figure 13 shows the predicted values of corn
with a transmitter height, ht, at 16 cm. This places the
transmitter basically in air, but as the receiver increases
in height, its trajectory is affected by the presence of the
One noticeable difference between the predicted val-
ues and the measured data was that the predicted values
created a parabola and thus were only at a value of 0 m
Figure 12. Predicted vs. measured (Soybean, ht = 50 cm)
Figure 13. Predicted vs. measured (Corn, ht = 16 cm).
receiver height for a distance of 0m. For all crop envi-
ronments, the receiver antenna was at a height of 0 m for
at least a few meters, before the receiver antenna had to
be raised. This can be seen in Figure 12 for the distance
values of –10 m and +10 m; the receiver antenna was
still on the ground at minimum height up to 10 meters
away from the transmitter, while the predicted value for
this distance is approximately 0.11 m for height. Figure
12 Predicted vs. measured (Soybean,t
h= 50 cm) Figure
13 Predicted vs. measured (Corn,t
h= 16 cm)
5. Conclusions and Future Work
This work sought to develop a better understanding of-
sensor node communication performance degradation as
a function of antenna proximity to the earth’s surface
combined with the effects of obstacles such as typical
Midwestern U.S. agricultural crops. Experimental results
agreed with the hypothesis that antenna proximity to the
ground plays a significant role in limiting the RF range.
Test results from simulations, anechoic chamber mea-
surements, and field experiments all indicated that as the
Copyright © 2010 SciRes. WSN
height of an omnidirectional monopole antenna approa-
ched a ground plane, its radiation pattern changed. More
specifically, its horizontal propagation shortened, and a
parabola-like image of the vertical radiation pattern
compressed, suggesting the existence of areas of limited
to no horizontal radiation close to the ground. While at-
tempts to measure an extended vertical radiation pattern
were problematic in the anechoic chamber, our simula-
tion results suggest that the vertical radiation pattern in-
creases only if the ground plane is a perfect electric con-
ductor (which reflects) and not an earth-like ground sur-
face (which absorbs).
Field testing confirmed simulation and anechoic
chamber test results and motivated the development of
an additional loss component, an “environmental” factor,
to be incorporated into an existing analytical model of
received power r
P as a function of transmit power t
antenna gains t
G; r
G, heights ht; hr, and distance d.
The environmental factor was developed empirically, by
measuring the loss of communication between nodes of
variable height in various obstacle conditions including
unobstructed (bare soil) conditions, mature soybean, and
mature corn fields. Measurement results were then ap-
plied as an average of measurements to establish a re-
ceived power difference relative to theoretical (expected)
values env
P. Comparisons made between using the new
model and actual field measurements demonstrate that
more accurate prediction is possible.
Tests to study the effects of humidity produced more-
questions regarding the isolation of variables and so were
not reported here. These tests were conducted in the mor-
ning and the afternoon, and as such the humidity, tem-
perature, and dew point all changed over time as the data
were being recorded. Outdoor range testing was accom-
plished with the use of a simple software program. The
transmitter sent out a signal with instructions that any
receiver should “blink” its LED. When the LED stopped
blinking, the receiver was deemed out of range. Given
that these tests were ultimately for the benefit of a wire-
less sensor network, more detailed results may have been
possible with a more comprehensive test setup.
Even with these considerations, our model developed
to account for the effects of environmental obstacles
(crops) on receive power (and therefore propagation dis-
tance) demonstrates promise as a useful tool in the dep-
loyment of wireless sensor networks whose nodes must
be in close proximity to the earth’s surface and where the
sensor field contains obstacles in the form of agricultural
crops or other plant life. This model can be used by oth-
ers to aid in the deployment of similar sensor networks.
In the future we plan on developing more granularity in
our model, accounting for humidity and the degree of
packet loss.
6. Acknowledgements
The authors would like to acknowledge the many helpful
suggestions of the anonymous reviewers for improving
the quality of this paper.
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