www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 755

A new technology for environmental monitor-

ing is the wireless sensor network (Cardell-Oliver et al.,

2005; Bogena et al., 2007). Wireless sensor network technology

allows real-time soil water content monitoring with a high spatial

and temporal resolution for observing hydrological processes in

small watersheds (0.1–80 km2). Although wireless sensor networks

can still be considered as an emerging research fi eld, the support-

ing communication technology for low-cost, low-power wireless

networks has matured greatly in the past decade (Kuorilehto et

al., 2007; Robinson et al., 2008). Wireless environmental sensor

networks will play an important role in the emerging terrestrial

environmental observatories (Bogena et al., 2006) since they

are able to bridge the gap between local- (e.g., lysimeter) and

regional-scale measurements (e.g., remote sensing).

Recently, we developed a novel wireless soil water content

sensor network (SoilNet) that uses the low-cost ZigBee radio

network for communication (Bogena et al., 2008). Th ere are

two possible network topologies for wireless subsurface sensing:

a hybrid or underground topology. Th e choice of the network

topology is important for network reliability as well as for power

conservation. In the case of an underground topology, signals

propagate long distances through the soil, which is associated

with high losses in signal strength (Akyildiz and Stuntebeck,

2006; Li et al., 2007). Li et al. (2007) showed that the maximum

achievable separation to allow communication between under-

ground sensors can be as low as 5 m. Such a small separation is

prohibitive for many of the envisioned applications of wireless

soil water content sensor networks. Th erefore, SoilNet is designed

as a hybrid wireless underground sensor network (WUSN). Th e

hybrid topology is composed of a mixture of underground end

devices each wired to several soil sensors and aboveground router

devices. Th is allows data to be routed out of the underground

quickly, thus reducing the amount of power-intensive under-

ground steps to save power. Nevertheless, concerns remain with

respect to the feasibility of data communication through soil

material, even for a hybrid topology. One of the reasons for this

concern is that in most countries, the only frequency for unregu-

lated use of wireless networks is 2.4 GHz, which is even more

prone to soil attenuation than the 900 and 922 MHz frequencies

allowed in some parts of the world (e.g., the Americas).

Th e aim of this study is to determine the impact of soil depth,

soil water content, and soil electrical conductivity on the signal

transmission strength of hybrid wireless sensor networks. In a fi rst

step, we developed a semi-empirical model that simulates signal

attenuation for a hybrid network topology. Th is newly formulated

Hybrid Wireless Underground Sensor Networks: Quan fi ca on of Signal A enua on in SoilH. R. Bogena,* J. A. Huisman, H. Meier, U. Rosenbaum, and A. Weuthen

H.R. Bogena, J.A. Huisman, U. Rosenbaum, and A. Weuthen, Agrosphere Ins tute, ICG-4, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; H. Meier, Central Ins tute for Electronics, ZEL, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany. Received 2 Oct. 2008. *Corresponding author (h.bogena@fz-juelich.de).

Vadose Zone J. 8:755–761doi:10.2136/vzj2008.0138

© Soil Science Society of America677 S. Segoe Rd. Madison, WI 53711 USA.All rights reserved. No part of this periodical may be reproduced or transmi ed in any form or by any means, electronic or mechanical, including photocopying, recording, or any informa on storage and retrieval system, without permission in wri ng from the publisher.

A : CRIM, complex refractive index model; WUSN, wireless underground sensor network.

S S

: A I

Wireless sensor network technology allows real- me soil water content monitoring with a high spa al and temporal resolu on for observing hydrological processes in small watersheds. The novel wireless soil water content network SoilNet uses the low-cost ZigBee radio network for communica on and a hybrid topology with a mixture of under-ground end devices each wired to several soil sensors and aboveground router devices. Data communica on between the end and router devices occurs par ally through the soil, and this causes concerns with respect to the feasibility of data communica on due to signal a enua on by the soil. In this study, we determined the impact of soil depth, soil water content, and soil electrical conduc vity on the signal transmission strength of SoilNet. In a fi rst step, we devel-oped a laboratory experimental setup to measure the impact of soil water content and bulk electrical conduc vity on signal transmission strength. The laboratory data were then used to validate a semi-empirical model that simulates sig-nal a enua on due to soil adsorp on and refl ec on and transmission at the soil boundaries. With the validated model, it was possible to show that in the case of a soil layer of 5 cm, suffi cient power will remain to ensure data communica- on over longer distances for most soil condi ons. These calcula ons are fairly simplifi ed and should be considered as

a fi rst approxima on of the impact of a enua on. In actual fi eld situa ons, signal transmission may be more complex. Therefore, a fi eld evalua on of signal a enua on is a crucial next step.

www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 756

model nicely complements the work by Akyildiz and Stuntebeck

(2006) and Li et al. (2007), who focused on underground net-

work topologies. In a next step, the model was validated using

laboratory experiments in which the impact of soil water content

and bulk electrical conductivity on signal transmission strength

was measured. Th e validated model was then used to show how

the SoilNet transmitter range is aff ected by signal attenuation for

diff erent soil conditions.

Materials and MethodsModel for Radio Transmission and A enua on

To determine the maximum allowable separation between

radio transmitter and receiver for proper data communication,

a link budget equation can be used as a fi rst approximation

(Akyildiz and Stuntebeck, 2006; Li et al., 2007). Assuming that

the transmitter radiates equally in all directions, the link budget

equation can be written as

r t t r sd mP P G G L L= + + − − [1]

where Pr is the signal power at the receiver (dB m), Gt and Gr

are the gains (dB) of the transmitter and receiver antenna, respec-

tively, and Pt is the transmitter power (dB m); Lsd is the path

loss due to spherical divergence of the wavefront, which can be

approximated by (Hall et al., 1996)

sd

420log

dL

⎛ ⎞π ⎟⎜= ⎟⎜ ⎟⎜⎝ ⎠λ [2]

where λ is the free space wavelength and d is the distance between

sender and receiver. Finally, Lm represents miscellaneous losses,

which are also neglected for the moment. By rearranging Eq. [1]

and [2], the achievable range R of a sender–receiver confi guration

can be approximated by (Pratt et al., 2003)

t t r r

20104

P G G P

R

+ + −λ

=π

[3]

In this equation, the power at the receiver can now be set to the

receiver sensitivity to obtain the maximum achievable range.

For SoilNet, the signal strength of the transmitter Pt is 19

dB m, the antenna gains Gt and Gr are 2 dB, and the receiver

sensitivity Pr is −89.2 dB m. Th e frequency used in the ZigBee

technology is 2.44 GHz, resulting in a free space wavelength of

0.125 m. Th erefore, the achievable range of a SoilNet radio link is

4 km when additional path losses of the signal are not considered.

At least fi ve mechanisms may cause additional path losses

(Lm) and therefore actual ranges lower than the maximum value

provided by Eq. [3]. Th ese mechanisms are sender and receiver

losses, signal attenuation, refl ection, refraction and diff raction.

In the case of WUSN, where parts of the communication units

(including antennas) are buried in the soil, signal attenuation in

the soil will be an important reason for a lower range. Th erefore,

we will defi ne a simple model in the following that allows a fi rst assess-

ment of how severely soil attenuation aff ects signal transmission.

Soil A enua on Model

In recent years, electromagnetic measurement techniques,

such as time domain reflectometry and ground-penetrating

radar, have been increasingly used to characterize soil properties

(Huisman et al., 2003; Robinson et al., 2003). Knowledge of

electromagnetic soil properties obtained using these methods can

be used to derive estimates of radio wave attenuation in soils.

According to Dane and Topp (2002) the soil attenuation α (dB

m−1) can be expressed as

( )''0 r b

2'

'' 'br

0

60 28.68

1 1 /2 2r

r

f

f

π π ε ε +σα =

⎧ ⎫⎪ ⎪⎡ ⎤⎪ ⎪⎛ ⎞ε σ⎪ ⎪⎟⎪ ⎪⎜⎢ ⎥⎟+ + ε + ε⎜⎨ ⎬⎟⎢ ⎥⎜ ⎟⎜⎪ ⎪π ε⎝ ⎠⎢ ⎥⎪ ⎪⎣ ⎦⎪ ⎪⎪ ⎪⎩ ⎭

[4]

where f is the frequency (Hz), ε0 is the dielectric permittivity of

free space (8.854 ×10−12 F m−1), σb is the bulk electrical conduc-

tivity (S m−1), and εr′ and εr′′ are the relative real and imaginary

dielectric permittivity at frequency f, respectively. Th e commonly

used complex refractive index model (CRIM) is a three-phase

model to calculate the soil dielectric permittivity from the permit-

tivity of the solid (εs), water (εw), and air (εa) phase at a specifi c

frequency (Roth et al., 1990):

( ) ( )2

* *s w a1

⎡ ⎤ε = −η ε + θ ε + η−θ ε⎢ ⎥⎣ ⎦ [5]

where ε* is the complex permittivity (ε′ + iε′′), η is the soil poros-

ity (m3 m−3), and θ is the soil water content (m3 m−3). To derive

Eq. [5], it was assumed that water is the only source of dielectric

losses (i.e., permittivity of air and solid phase are independent of

frequency). Th e real and imaginary dielectric permittivity of water

is calculated from the Debye model for single relaxation (Debye,

1929) and the known dielectric properties of water (Hasted,

1973). Th e bulk electrical conductivity σb is derived using the

Rhoades model (Rhoades et al., 1976):

( )2b w sa bσ = σ θ + θ +σ [6]

where σw is the electrical conductivity of the soil water solution,

a and b are fi tting parameters, and σs is the surface conductivity

of the soil matrix. Because σs is diffi cult to measure directly, it is

thus often used as a fi tting parameter (Persson and Uvo, 2003).

When an electromagnetic wave moves from one medium

into a second medium, it is partly refl ected and partly transmitted.

Th e fraction of the intensity of incident electromagnetic wave that

is refl ected from the interface is given by the refl ection coeffi cient

R. Th e remaining energy is transmitted with a coeffi cient T = 1

− R. Th e refl ection coeffi cient can be calculated according to the

Fresnel equation (e.g. Lorrain et al., 1988) with

2

2 1

2 1

1Z Z

R TZ Z

⎛ ⎞− ⎟⎜ ⎟= − = ⎜ ⎟⎜ ⎟⎜ +⎝ ⎠ [7]

where Z is the electromagnetic impedance defi ned as

0 r*

0 r

Zμ μ

=ε ε

[8]

where μ0 is the magnetic permeability of free space (μ0 = 4π

× 10−7 H m−1) and μr is the relative magnetic permeability of

the soil. Th e magnetic permeability can be neglected for many

soils (μr = 1; van Dam et al., 2002). In the case of SoilNet, the

www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 757

sender is in air in a cavity within the soil. Th erefore, Eq. [7] can

be simplifi ed to2 2*

*

1 11

11R T

⎛ ⎞ ⎛ ⎞⎟− ε − ε⎜ ⎟⎜⎟⎜ ⎟⎟= − = ≈⎜⎜ ⎟⎟ ⎜ ⎟⎜ ⎟ ⎟⎜ + ε⎟ ⎝ ⎠⎜ + ε⎝ ⎠ [9]

In the following, we will neglect the eff ect of the imaginary per-

mittivity on R, which introduces a maximum error of 0.005% for

most soils at 2.4 GHz. From Eq. [9], it is clear that part of the

signal will be refl ected at the lower and upper boundary of the

material under investigation and that multiple refl ections will also

occur. When interference can be neglected, the total attenuation

Rc due to refl ection of a material layer is

( )c 10

210log

1

RR

R

⎛ ⎞⎟⎜ ⎟= ⎜ ⎟⎜ ⎟⎟⎜ +⎝ ⎠ [10]

We assume that all refl ected energy is adsorbed in the transmitter

box within the experiments. In an actual fi eld situation, this will

not be the case, which is a potential source of error in our simple

modeling approach.

Th e total signal attenuation Atot is the sum of soil attenuation

as well as signal refl ection:

tot cA d R=α + [11]

with d being the soil depth (m). Akyildiz and Stuntebeck (2006)

and Li et al. (2007) used a more elaborate dielectric mixing model

based on Dobson et al. (1985) and Peplinski et al. (1995). We

believe, however, that the empirical dielectric mixing model of

Peplinski et al. (1995) is not supported by a suffi ciently large data-

base and feel that the combined CRIM–Fresnel model approach

outlined above is better suited to get a fi rst indication of the

attenuation of radio waves in soils.

Calcula on of Signal Travel Distance through the Soil

Equation [11] shows that the total signal attenuation

depends strongly on the length of the signal propagation path in

the soil. Figure 1 outlines the hybrid network topology we want

to evaluate in this paper. Th e SoilNet box is buried at a shallow

depth, and the receiving antenna is placed some distance away

relatively close to the soil surface. Th e Fresnel equations indi-

cate that direct waves between sender and receiver will not occur

because all waves with an incident angle fl atter than the critical

angle αc will be completely refl ected. Th erefore, the propagation

path through the soil will always be relatively short for any signal

received (see Fig. 1). If we assume that the height of the receiv-

ing antenna above the surface can be neglected compared to the

separation between sender and receiver, we can determine the

propagation path through the soil from the critical angle, which

is given by

c

1arcsin

⎛ ⎞⎟⎜α = ⎟⎜ ⎟⎟⎜⎝ ⎠ε [12]

Th e value of αc decreases from ?30° for dry soils to ?10° for

wet soils. Th e propagation path of the signal through the soil can

be derived from

( )ccos

ab=

α [13]

where a is the depth of the sender antenna. If we assume that the

center of the antenna shown in Fig. 1 is installed at 0.09 m below

the surface, the travel distance b will decrease from approximately

0.14 m in dry soil to approximately 0.09 m in wet soil.

Laboratory Experimental SetupFigure 2 shows the experimental setup used to validate the

derived soil attenuation model. We placed a SoilNet ZigBee end

device (Bogena et al., 2008) with a Jennic high-power transmitter

(2.44 GHz, ~80 mW; Jennic Ltd., 2007) in a box (height, 0.61

m; width, 0.54 m; depth, 0.34 m). On top of the transmitter box

was a second box (height, 0.1 m; width, 0.55 m; depth, 0.35 m)

that contained the material under investigation (e.g., soil). Th e

plastic material of the upper box did not cause signifi cant signal

attenuation. A receiver antenna was installed 0.25 m above the

bottom of the upper box. Both transmitter and receiver antenna

are directional with a gain of 10 dB, and therefore, the radiation

pattern was confi ned to the center of the upper box. Th e receiving

antenna was connected to a spectrum analyzer (FSH6, Rohde &

Schwarz, Munich, Germany) to analyze the signal spectrum and

strength. To avoid spurious refl ections from the surroundings, the

inside of the transmitter box was lined with absorber plates and

F . 1. Outline of the signal propaga on path of a transmi er–receiver confi gura on of the SoilNet hybrid network topology. αc = cri cal angle, a = the depth of the sender antenna, b = propaga on path of the signal through the soil.

F . 2. Lateral and top view of the laboratory experimental setup for determining signal a enua on due to soil adsorp on at diff er-ent soil water contents and bulk electrical conduc vity.

www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 758

the outside was wrapped with aluminum foil. Th ese precautions

ensured that all received signals were actually transmitted through

the material under investigation.

In total, four diff erent experiments were performed (see

Table 1). In the fi rst experiment, the upper box was fi lled with a

mixture of 2-isopropoxyethanol (iC3E1, 80 % v/v) and demin-

eralized water (20% v/v). According to Kaatze et al. (1996), the

dielectric properties of this liquid mixture are ε′ = 18.19 and ε′′ =

8.24 at 2.44 GHz. Th is permittivity corresponds with a wet soil.

Th e upper box was fi lled with 0.065 m of this liquid mixture and

slowly drained to measure the signal attenuation as a function

of depth of the dielectric medium. Th e remaining three experi-

ments were performed with sand (see Table 1). Th e upper box was

fi lled with sand in layers of 0.01 m and carefully compacted to

mean dry bulk densities between 1.51 and 1.62 g cm−3. During

the experiments, the soil was wetted in several steps with a spray

fl acon (see Table 1). Th e total water content of the soil was deter-

mined using a high-precision scale that measured the weight of

the entire system including the transmitter box. Two soil water

content sensors (Echo TE, Decagon Devices, Inc., Pullman, WA)

were installed at 0.02 and 0.03 m depth to determine

the soil water content profi le. Measurements of soil

attenuation were conducted when the soil water pro-

fi le was near equilibrium. Experiment 2 and 3 were

duplicate experiments where demineralized water

with no electrical conductivity was used. In Exp. 4,

tap water with an electrical conductivity of 0.0479 S

m−1 was used instead of demineralized water. In the

fourth experiment, the bulk soil electrical conductivity

σb was determined using the ECH2O TE probes. Th is

sensor measures σb by applying an alternating electrical

current to two outer electrodes, and measuring the volt-

age between two inner electrodes (Decagon, 2006).

Results

Measurements of Signal A enua onExp. 1: Eff ect of Soil Depth on Signal A enua on

Th e measured and modeled attenuation of Exp. 1

are shown in Fig. 3, in which the depth of the iC3E1–

water mixture is normalized by the wavelength. In

general, the measurements are well described by the

model (R2 = 0.91). We assume that the oscillations

in the measurements are due to interference because

the minima and maxima correspond with half and full

wavelengths, respectively. Th e simple CRIM–Fresnel

model cannot of course reproduce these interfer-

ences and this could lead to a maximum error of 4 dB

depending on soil depth and soil water content.

Exp. 2 and 3: Eff ect of Soil Water Content on Signal A enua on

Th e measured signal attenuation for Exp. 2 and 3 are shown

in Table 2 and Fig. 4. Th e measured signal attenuation increases

approximately linearly with soil water content. Clearly, there is

considerable scatter in the data with deviations of more than 2

dB between the experiments. Although care was taken to produce

identical conditions during both experiments, diff erent soil water

content profi les may have been present for the same bulk soil

T 1. The materials used for the laboratory attenuation experiments.

Experimental runs

Materials Soil bulk density

g cm−3

Exp. 1 2-isopropoxyethanol + dis lled water –Exp. 2 Sieve sand + dis lled water 1.62Exp. 3 Sieve sand + dis lled water 1.51Exp. 4 Sieve sand + tap water 1.52

F . 3. Measured signal a enua on during Exp. 1 versus depth per wavelength and modeled signal a enua on. CRIM, complex refrac- ve index model.

T 2. Measured soil water contents measured signal strength and signal a enua on during three soil we ng experiments.

Experimental runs

Soil water contentBulk

electrical conduc vity

Measured signal

Signal a enua on

Calculated from

weight

ECH2O TE probe at

2-cm depth

ECH2O TE probe at

3-cm depth

——— m3 m−3 ———— S m−1 ————— dB —————Exp. 2 0 0.006 0.015 −27.39 −0.89

0.048 0.027 0.039 −28.73 −2.230.092 0.066 0.071 −30.56 −4.060.134 0.090 0.109 −33.26 −6.760.176 0.126 0.161 −33.86 −7.36

0.219 0.198 0.222 −32.89 −6.39

0.260 0.240 0.257 −36.10 −9.600.302 0.288 0.280 −36.30 −9.800.346 0.301 0.313 −35.72 −9.22

Exp. 3 0 0.010 0.015 −27.80 −1.300.045 0.022 0.013 −29.27 −2.770.087 0.111 0.040 −29.05 −2.550.131 0.125 0.099 −31.20 −4.700.172 0.142 0.134 −31.20 −4.700.217 0.172 0.167 −33.90 −7.400.260 0.206 0.226 −36.40 −9.900.304 0.242 0.263 −34.80 −8.30

Exp. 4 0 0.006 0.010 0 −28.20 −1.700.045 0.026 0.016 0.0005 −28.96 −2.460.089 0.072 0.072 0.003 −28.00 −1.500.133 0.106 0.111 0.004 −31.76 −5.260.177 0.132 0.139 0.0035 −32.06 −5.560.223 0.160 0.180 0.005 −33.30 −6.800.268 0.200 0.212 0.007 −35.50 −9.000.312 0.290 0.272 0.028 −37.00 −10.50

www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 759

water content, which is supported for some bulk water contents

by the soil water content measurements at two depths (see Table

2). Analogous to the results of Exp. 1, the multiple refl ections and

interferences caused by such an unequal water content profi le are

a likely source for the relatively strong variation in the measure-

ments presented in Fig. 4.

Figure 4 also presents the modeled signal attenuation for

Exp. 2 and 3. Th e required model parameters were either mea-

sured or taken from the literature. Th us, no model calibration

was performed. Th e porosity of the soil was determined from the

measured bulk density presented in Table 1 and a solid particle

density of 2.65 g cm−3. Th e permittivity of the solid phase was

set to 4.7 and the soil water electrical conductivity was neglected

for Exp. 2 and 3. Th e model was able to reproduce the general

trend of the measured data very well, as indicated by the high

coeffi cient of determination (R2 = 0.90). It can also be seen that

the infl uence of bulk density on the modeled attenuation is not

very strong because the diff erence between the model results for

Exp. 2 and 3 is small.

Figure 5 shows the individual contributions of soil attenua-

tion and signal refl ection on the total signal attenuation for Exp.

2 and 3. For complete dry soil, the soil attenuation is insignifi cant.

However, since we assume that the solid phase of the soil has a

permittivity of 4.7, the Fresnel equation predicts a signifi cant

contribution of the signal refl ection (mean value: −0.572 dB)

to the total signal attenuation. Th e validity of this assumption

is supported by the signal attenuation measurements (see Fig.

4). Th e slope of the signal refl ection curve shows a hyperbolic

form, whereas soil attenuation increases linearly. At soil satura-

tion, soil attenuation contributes more than 60% of the total

signal attenuation.

Exp. 4: Eff ect of Bulk Electrical Conduc vity

Figure 6 shows the measured σb during Exp. 4. Th e predicted

σb using the Rhoades model Eq. [6] with a = 1.85, b = 0.0385

and σs = 0.000589 S m−1 is also shown. Th ese parameters are

well within the range observed by others (e.g., Persson and Uvo,

2003). Th e measured σb near soil saturation (θ = 0.312 m3 m−3)

deviates strongly from the calculated one, which we attribute to

the presence of free water directly connecting the electrodes of

the probe. Th e other measurements are well represented by the

Rhoades model (R2 = 0.868).

Th e attenuation measurements of Exp. 4 are also presented

in Fig. 4. It is clear that the higher electrical conductivity of

the tap water did not signifi cantly increase signal attenuation

compared with the experiments using demineralized water. Th e

modeled signal attenuation using the Rhoades model confi rms

the modest increase in attenuation when using tap water instead

of demineralized water (Fig. 4). Th e reason for the low impact

of bulk electrical conductivity is the high operating frequency

of our system. It can be seen in Eq. [4] that the impact of bulk

electrical conductivity decreases when the operating frequency

increases. For bulk electrical conductivities larger than 0.1 S

m−1 (e.g., clay and saline soils) and for systems with a lower

F . 4. Measured signal a enua on during Exp. 2, 3, and 4 versus soil water content and modeled signal a enua on. CRIM, complex refrac ve index model.

F . 5. Individual contribu ons of signal refl ec on and soil a enua- on on total signal a enua on for Exp. 2 and 3.

F . 6. Measured (ECH2O TE probe) versus simulated (Rhoades model) bulk soil electrical conduc vity sensor for diff erent soil water contents.

www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 760

operating frequency, the eff ect of electrical conductivity on

signal attenuation will be stronger.

Th e CRIM–Fresnel model is only a fi rst approximation of

the processes causing signal attenuation. Nevertheless, we believe

that the validation results presented here are a good indication that

it is possible to use this model for the analysis of signal attenuation

in real world situations.

Sensi vity Analysis of Signal A enua onTh e validated model can be used to gain insights into signal

attenuation and the reduction of the transmission range as a func-

tion of soil depth, soil water content, and soil water electrical

conductivity. Th e calculations presented here are all based on the

considerations shown in Fig. 1 for an antenna depth of 0.09 m.

Figure 7a presents the modeled signal attenuation in the soil as

a function of soil water content and frequency for a soil with a

porosity of 0.40 m3 m−3, a solid phase permittivity of 4.7, and a

soil water electrical conductivity of 0.001 S m−1. It is evident that

attenuation depends strongly on both the operating frequency

and soil water content. Figure 7b shows the reduction of the total

range with increasing frequency and increasing soil water content.

For the SoilNet operating frequency of

2.44 GHz, the transmission range is

reduced by about 80% at soil satura-

tion. For other operating frequencies,

the reduction of the transmitter range

varies from 40% at 0.1 GHz to 85%

at 3.1 GHz. If an underground topol-

ogy is preferred over a hybrid topology,

a low operating frequency should be

preferred to optimize the transmit-

ter range, as was already shown by

Akyildiz and Stuntebeck (2006) and

Li et al. (2007).

Figures 7c and 7d present the

signal attenuation in the soil and the

reduction of the transmitter range, as

a function of soil water content and

soil porosity, for the SoilNet operat-

ing frequency of 2.44 GHz (same soil

properties as above). Th e attenuation

is very strong for wet soils (9.5–24

dB for θ = 0.40 m3 m−3), even for the

relatively low bulk electrical conduc-

tivity of this example. Fully saturated

soils with a high porosity will lead to

extreme reductions of the transmitter

range (>90%).

Figures 7e and 7f present the signal

attenuation in the soil, as a function

of soil water content and soil water

electrical conductivity, for the SoilNet

operating frequency of 2.44 GHz (other

soil properties as above). Th e attenu-

ation is even stronger when the soil

water electrical conductivity increases

(17–30 dB for θ = 0.40 m3 m−3 and

a soil water conductivity ranging from

0.01 to 1 S m−1). For a bulk electrical

conductivity of 0.5 S m−1, which can occur in saline soil, the

achievable transmission range is reduced to 11% of the free air

transmission range.

Th e sensitivity analysis shows that for very unfavorable soil

conditions (high electrical conductivity and high porosity), the

transmission range of the SoilNet radio link will be reduced by

more then 90%. Nevertheless, a range of 400 m can potentially

be achieved when further aboveground losses due to scattering

can be neglected.

ConclusionsTh e theoretical considerations, as well as the laboratory evalu-

ation of the signal attenuation of underground communication

platforms, have shown that in the case of a hybrid network topol-

ogy, suffi cient power will remain to ensure data communication

over longer distances for most soil conditions, using a transmitter

depth of 0.09 m. Th is is in contrast to underground network

topologies for which the large attenuation requires too many

underground devices to secure data transmissions during wet

periods. Th e assumptions made in the radio transmission model

should be considered as a fi rst approximation of the impact of

F . 7. Modeled soil a enua ons for diff erent soil water contents, frequencies, soil porosi es, and soil water electrical conduc vi es as well as reduc ons of the aboveground transmi er range using the link budget equa on.

www.vadosezonejournal.org · Vol. 8, No. 3, August 2009 761

attenuation. In actual fi eld situations, signal transmission may be

more complex. Th erefore, a fi eld evaluation of signal attenuation

is a crucial next step.

AWe gratefully acknowledge fi nancial support by the SFB/TR 32

‘‘Pattern in Soil–Vegetation–Atmosphere Systems: Monitoring, Model-ling, and Data Assimilation’’ funded by the Deutsche Forschungsge-meinschaft (DFG).

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