non-invasive method for detecting changes in soil … · muhammad iqbal dalel (n6459544) page ii...

NON-INVASIVE METHOD FOR DETECTING

CHANGES IN SOIL MOISTURE USING

WIRELESS SENSOR NETWORKS

MASTER’S TITLE

Muhammad Iqbal Darma Dalel, B.Eng

Submitted in fulfilment of the requirements for the degree of

Master of Engineering (Research)

Science and Engineering Faculty

Queensland University of Technology

2012

Master’s Thesis

Muhammad Iqbal Dalel (n6459544) Page i

Master’s Thesis

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Abstract

There are several popular soil moisture measurement methods today such as

time domain reflectometry, electromagnetic (EM) wave, electrical and acoustic

methods. Significant studies have been dedicated in developing method of

measurements using those concepts, especially to achieve the characteristics of non-

invasiveness. EM wave method provides an advantage because it is non-invasive to

the soil and does not need to utilise probes to penetrate or bury in the soil. But some

EM methods are also too complex, expensive, and not portable for the application of

Wireless Sensor Networks; for example satellites or UAV (Unmanned Aerial

Vehicle) based sensors.

This research proposes a method in detecting changes in soil moisture using

soil-reflected electromagnetic (SREM) wave from Wireless Sensor Networks

(WSNs). Studies have shown that different levels of soil moisture will affects soil’s

dielectric properties, such as relative permittivity and conductivity, and in turns

change its reflection coefficients. The SREM wave method uses a transmitter

adjacent to a WSNs node with purpose exclusively to transmit wireless signals that

will be reflected by the soil. The strength from the reflected signal that is determined

by the soil’s reflection coefficients is used to differentiate the level of soil moisture.

The novel nature of this method comes from using WSNs communication signals to

perform soil moisture estimation without the need of external sensors or invasive

equipment. This innovative method is non-invasive, low cost and simple to set up.

There are three locations at Brisbane, Australia chosen as the experiment’s

location. The soil type in these locations contains 10–20% clay according to the

Australian Soil Resource Information System. Six approximate levels of soil

moisture (8, 10, 13, 15, 18 and 20%) are measured at each location; with each

measurement consisting of 200 data. In total 3600 measurements are completed in

this research, which is sufficient to achieve the research objective, assessing and

proving the concept of SREM wave method. These results are compared with

reference data from similar soil type to prove the concept. A fourth degree

Master’s Thesis

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polynomial analysis is used to generate an equation to estimate soil moisture from

received signal strength as recorded by using the SREM wave method.

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Table of Contents

Abstract ........................................................................................................................ ii

Table of Contents ........................................................................................................ iv

List of Figures ............................................................................................................. vi

List of Tables............................................................................................................. viii

List of Abbreviations................................................................................................... ix

Statement of Original Authorship ................................................................................ x

CHAPTER 1: INTRODUCTION ............................................................................. 1

1.1 Introduction......................................................................................................... 1

1.2 Significance and Contribution of Research ........................................................ 3

1.3 Research Questions and Objectives .................................................................... 4

1.4 Limitation and Scope of Research ...................................................................... 4

1.5 Summary ............................................................................................................. 5

CHAPTER 2: LITERATURE REVIEW ................................................................. 6

2.1 Introduction......................................................................................................... 6

2.2 Radio Wave Propagation .................................................................................... 6

2.2.1 Free Space ................................................................................................. 6

2.2.2 Ground Reflection Model.......................................................................... 7

2.2.3 Electrical Properties of Materials and the Fresnel Reflection Coefficient 9

2.2.4 Incorporating Reflection Coefficient into Free Space Equation ............. 12

2.3 Relationship between Dielectric Properties of Soil and Its Reflection

Coefficient .................................................................................................................. 12

2.4 Recent Studies on Methods of Measuring Soil Moisture ................................. 15

2.5 Summary ........................................................................................................... 20

CHAPTER 3: RESEARCH METHODOLOGY .................................................. 21

3.1. Introduction....................................................................................................... 21

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3.2. Equipment ......................................................................................................... 21

3.3 Experimental Set Up ......................................................................................... 23

3.4 Experiments ...................................................................................................... 27

3.4.1 Performance Characterisation of Waspmote and Antennas .................... 27

3.4.2 Proving Concept and Field Measurements.............................................. 29

3.5 Summary ........................................................................................................... 29

CHAPTER 4: RESULTS ......................................................................................... 31

4.1 Introduction....................................................................................................... 31

4.2 Test Results ....................................................................................................... 31

4.2.1 Transmission Distance and Attenuation by Vegetation .......................... 31

4.2.2 Attenuation by Angle ............................................................................. 35

4.2.3 Field Experiments ................................................................................... 36

4.2.4 Polynomial Equation ............................................................................... 46

4.3 Summary ........................................................................................................... 47

CHAPTER 5: CONCLUSIONS.............................................................................. 49

REFERENCES ......................................................................................................... 52

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List of Figures

Figure 1 Signal attenuation decay rate in free space from equation (1)....................... 7

Figure 2 5 dBi (bottom) and 2 dBi antennae for 2.4 GHz frequency ......................... 7

Figure 3 Ground reflection two-ray model [9] ............................................................. 8

Figure 4 Geometry for calculating reflection coefficient between two dielectrics [11]10

Figure 5 Fresnel reflection coefficients for = 4 and = 12 as a function of

incidence angle [9] .............................................................................................. 11

Figure 6 Relationship between soil moisture and its dielectric properties [10] ......... 13

Figure 7 Soil relative permittivity at different soil moistures and frequencies [23] .. 14

Figure 8 Waspmote wireless sensor node with omni-directional antenna. ................ 21

Figure 9 WaspMote’s node with the yagi (right) and omni (left) antennas attached . 22

Figure 10 Lutron PMS714 soil moisture sensor ........................................................ 22

Figure 11 Three test sites at Brisbane, Queensland, Australia (Courtesy of Google

Map) .................................................................................................................... 23

Figure 12 ASRIS data on Australian surface soil texture [49]................................... 24

Figure 13 Experimental set up ................................................................................... 25

Figure 14 SREM wave method configuration. .......................................................... 26

Figure 15 WaspMote Gateway connected to a laptop ............................................... 26

Figure 16 Three scenarios from left right and bottom, grass, bush, and forest .......... 28

Figure 17 Transmission distance test results in dBm ................................................. 31

Figure 18 Comparison between horizontal and vertical polarisation......................... 32

Figure 19 Path Loss Exponent Calculation from Vertical and Horizontal Polarization33

Figure 20 Theoretical relationship between RSS and reflection coefficient.............. 36

Figure 21 RSS values based on reflection coefficients from Table 4 (εr = 4 – 14) ... 38

Figure 22 Measurement data from three test sites ..................................................... 40

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Figure 23 Comparison between a fourth degree polynomial curve for measured data

and theoretical values with RSS measured as dBm ............................................ 43

Figure 24 Seventh and ninth degree polynomial curve of measured data and

theoretical values ................................................................................................ 45

Figure 25 Curve fitting to generate an empirical polynomial function ...................... 46

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List of Tables

TABLE 1 DIFFERENT MATERIAL’S ELECTRICAL PROPERTIES ............................................... 9

TABLE 2 COMPARISON BETWEEN SEVERAL COMMERCIALLY AVAILABLE SOIL

MOISTURE SENSORS ....................................................................................................................... 19

TABLE 3 WASPMOTE’S SIGNAL STRENGTH ATTENUATION BECAUSE OF FOLIAGE ..... 34

TABLE 4 HARDWARE PERFORMANCE CHARACTERISATION RESULTS ............................ 35

TABLE 5 RECEIVED SIGNAL STRENGTH AT DIFFERENT SOIL CONDITIONS .................... 37

TABLE 6 THEORETICAL SIGNAL STRENGTH VALUES FOR 10–20% CLAY SOIL ............... 38

TABLE 7 MEAN SIGNAL STRENGTH VALUES FROM SOIL MOISTURE MEASUREMENTS41

TABLE 8 GOODNESS OF FIT OF THE DEGREES OF POLYNOMIAL ........................................ 44

TABLE 9 GOODNESS OF FIT OF THE FOURTH DEGREE POLYNOMIAL CURVE ................. 47

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List of Abbreviations

SREM Soil-Reflected Electromagnetic

RSS Received Signal Strength

EM Electromagnetic

WSNs/WSNs Wireless Sensor Network/Networks

TDR Time Domain Reflectometry

FDR Frequency Domain Reflectometry

LOS Line of Sight

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Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best of

my knowledge and belief, the thesis contains no material previously published or written

by another person except where due reference is made.

Signature: _________________________

Date: _________________________

Chapter 1: Introduction Page 1

Muhammad Iqbal Dalel (n6459544) Page 1

Chapter 1: Introduction

1.1 INTRODUCTION

This study investigates how electromagnetic (EM) waves, used by

wireless sensor networks (WSNs) to communicate with other applications, can be

utilised for environmental parameter determination. The main objective is to utilise

the attenuation of the surrounding environment that the wireless signals perceive for

measurement or estimation, without the use of invasive instruments. Malajner et al.

(2009) studied the accuracy of using received signal strength indicators (RSSIs) to

measure distances between WSNs nodes [1]. Giacomin, Vasconcelos and Silva

(2007) pursued a study on estimating vegetation moisture from received WSNs

signals [2]. With those concepts in mind, this research developed an alternative

method for detecting changes in soil moisture using WSNs signals.

WSNs provide new methods of remote monitoring as they are designed

to work with virtually no need for human intervention for long periods [3, 4]. They

can provide continuous data, logged at a selected interval of time or be only activated

by certain events detected by their sensors. Based on these aspects, WSNs present

useful characteristics that may help satisfy particular application purposes.

WSNs may need to be able to operate for long periods without human

intervention and therefore may rely on high capacity batteries and/or alternative

energy systems such as solar panels to optimise the battery use. Low power

consumption, small and compact, practical installation, weather and tamper proof in

some cases, and with a total height from the ground rarely exceeding 1.5 m are some

of the main characteristics and constraints to take into account when implementing

WSNs. Designing an effective method for detecting changes in soil moisture using a

WSNs application needs to consider all these aspects.

One of the most accurate methods of measuring soil moisture is weighing

the soil, then drying it in the oven until all water has evaporated [5, 6]. The

difference in weight before and after drying is the weight of the moisture in that soil

sample. This method, although it provides a relatively accurate measurement, is

destructive, labour intensive and non-economical [5, 6]. Regardless, this method is

usually employed to calibrate other methods of measuring soil moisture.



Other methods do not directly measure the moisture in the soil; rather

they measure other variables that change when moisture levels change. For example,

some methods measure dielectric properties, emissivity, or speed of sound waves in

the soil. Measuring such variables provides a less invasive and overall more practical

and economical alternative than the direct measurement method; however, it may

introduces error and therefore reduce accuracy [5, 6].

These indirect methods of measuring soil moisture may be categorised

according to their means of detecting the variables. These are EM wave, electrical,

radiological, acoustic and physical based methods. EM wave methods, such as time

domain reflectometry (TDR) and frequency domain reflectometry (FDR), measure

the EM wave travel time in a waveguide buried in the soil. EM wave methods that

are more elaborate include capturing soil-reflected EM waves transmitted from a

satellite or an airplane. Electrical methods measure either the resistance or

capacitance of the soil. Acoustic method measure the travel time of a sound wave

between a transmitter and a receiver buried in the soil. Physical methods measure the

gravitational soil water potential.

Except for measuring the emissivity of the soil, which are expensive

processes, all methods described here required some sort of sensor (usually a steel

rod) to be inserted or buried in the soil. The major disadvantages of this approach are

that the inserted sensor disturbs the soil (an invasive method), it needs to be

connected through potentially impractical cables and it might corrode or be damaged

over time. Dry and hard soil conditions present further difficulties for probe

insertion. Additionally, since the sensors are highly precise and sensitive, they are

expensive to install and replace.

Recent research efforts have focused on developing methods to measure

soil moisture that have the characteristics of being non-invasive, practical, compact,

economical and accurate, although there is no one method that has satisfied all these

criteria. Adamo et al. (2004) [7] pursued the concept of using acoustic waves to

detect changes in soil moisture, but that method still needed an undersoil sensor.

Calla et al. (2008) [8] used a passive radiometer to capture satellite bound EM wave

emissions, but their elaborate equipment was impractical for many portable

applications.

This study reports a non-invasive, practical and economical method to

detect changes in soil moisture using wireless signals from WSNs. By reflecting EM



waves to the soil at a certain angle of incidence and observing the corresponding

received signal strength (RSS) a relationship between RSS and soil moisture can be

developed. This is because changes in soil moisture affect the soil’s dielectric

properties. The strength of the reflected EM wave depends on the dielectric

properties of the reflected surface [9-11].

This method, herein called the soil-reflected electromagnetic (SREM)

wave method, utilises a transmitter antenna placed in the immediate vicinity of a

WSNs node with the purpose of exclusively transmitting wireless signals for soil

moisture measurement. The transmitter uses a single directional antenna that points

at a certain angle of incidence to the soil. The neighbouring WSNs node captures the

soil-reflected signal and records its RSS, which may be translated into a measure of

soil moisture. The node can then transmit the recorded data to where it is to be used.

This SREM wave method is non-invasive and can be used to detect

changes in soil moisture. The use of wireless signals means impractical cables and

connectors are not needed. Moreover, using the method only involves measuring

distances between the WSNs nodes and the antenna’s angle of incidence. Most

commercially available WSNs nodes are already capable of recording transmission

RSS; therefore, there should be no need for major developments of additional WSNs

software and operating systems.

This research used a Waspmote 2.4 GHz sensor, a state of the art WSNs

node sensor implementing the IEEE 802.15.4 standard. Although the SREM wave

method only requires a simple transmitter adjacent to the WSNs node, in this

research two identical WSNs nodes were used: one as a dedicated transmitter and the

other a standard WSNs for the receiver.

1.2 SIGNIFICANCE AND CONTRIBUTION OF RESEARCH

The SREM wave method has advantages over other methods as it is non-

invasive, low cost and practical. Invasive methods can disturb the accuracy of soil

moisture reading. Moreover, connecting cables presents problems with impracticability,

while dry soil adds difficulty when it is too hard to insert the sensor probes.

Soil moisture is an important type of data in many fields including

agriculture, farming and environmental monitoring. Farmers can determine the right

time to irrigate their fields by looking at soil moisture data to save water, or as a part of

preliminary test to determine whether a soil is suitable for farming [12-14]. Continuous



soil moisture monitoring made possible with WSNs would allow data to be gathered on

rain distribution [13, 14]). Rhebergen (2003) [15] concluded that soil moisture content

plays a vital part in ground penetrating radar calibration to detect land mines.

Although there are many studies on using WSNs to estimate soil moisture

[3, 16], there is no research investigating the SREM wave method. Therefore, this study

contributes a method to estimate soil moisture. The nature of the contribution comes

from using WSNs communication signals to perform soil moisture estimation without

the need of external sensors or invasive equipment. This innovative method allows the

use of simple, low-cost radio transmitters to estimate soil moisture. The method also

involves low computational costs because it is only necessary to measure RSS, a feature

that is commonly available in most WSNs.

Another contribution from this research is the derivation of a function

that depicts the relationship between soil moisture and RSS. Results from this

research will provide the basis for further studies on the use of WSNs for estimating

soil moisture content.

1.3 RESEARCH QUESTIONS AND OBJECTIVES

The research question that this research was designed to address was:

‘Can we use soil-reflected electromagnetic waves to estimate soil moisture using

wireless sensor networks?’

The main research aim was to study and assess the concept of a non-invasive,

low cost and practical method for estimating soil moisture content using WSNs. To

answer the research question the following research objectives were developed:

1. Assess the concept of detecting different soil moisture levels using the

signal strength of SREM waves.

2. Propose an empirical function depicting the relationship between soil

moisture and signal strength from WSNs.

3. Analyse the proposed method results by comparing them to results from

theoretical and existing empirical methods.

1.4 LIMITATION AND SCOPE OF RESEARCH

The main research aim is to assess the concept of estimating soil moisture

with SREM waves using WSNs. Waspmote 2.4 GHz WSNs nodes with IEEE 802.15

Standard protocol are used. This study reports the hardware requirement and



configuration of the SREM wave method and analyses the results by comparing them to

theoretical studies.

The expected limitations on this research include the sensitivity of the

WSNs nodes and the radiation pattern of the antenna. The sensitivity limitation in

detecting RSS of the Waspmote’s WSNs nodes is 1 dBm. The Yagi antenna cannot

completely eliminate the direct-path signal and this limitation will cause interference

with the soil-reflected signals.

The experiments’ benchmark is a portable soil moisture sensor; thus, the

results are highly dependent on this device. This is not a major concern because the goal

is to investigate the concept; thus, it is sufficient to show that the SREM wave method

can differentiate between different levels of soil moisture. The data collected are only

from soil at Brisbane, Australia, comprising ten to twenty per cent clay, as determined

from data in the Australian Soil Resource Information System (ASRIS). To assess the

method further, additional studies of different types of soils are needed.

1.5 SUMMARY

There are five chapters in this thesis. Chapter 2 presents a literature review

and describes the basic concepts of wireless signal propagation and EM waves. It

discusses the relationship between soil moisture, soil dielectric properties and the related

behaviour of EM waves. It also describes the available methods for measuring and

detecting soil moisture, the current studies in that field and the directions current

research trends are heading. It discusses the importance of non-invasive, inexpensive and

simple to set up and operate methods of monitoring soil moisture in WSNs applications.

Chapter 3 describes the research design and what experimental set up was

needed to achieve the objectives of this research. It discusses the goals of each research

phase and their importance. That chapter describes the steps taken in determining the

performance characteristics of the hardware used and the efforts made to investigate the

concept of detecting the changes in soil moisture using soil-reflected wireless signals.

Chapter 4 presents the results of the steps taken during research design and

of the field experiments. Chapter 5 concludes the thesis and discusses the limitations and

implications of this research, and presents suggestions for future study and development.

Chapter 2: Literature Review Page 6


Chapter 2: Literature Review

2.1 INTRODUCTION

This review contains five sections starting with this introduction. The next

part explains the basics of EM wave propagation and factors present in EM waves that

are applicable to this study. The third section describes the relationship between soil

dielectric properties and soil reflection coefficients. The fourth section describes how to

estimate soil moisture by detecting the changes in its dielectric properties. The next

section describes recent methods for measuring soil moisture, how they compare to the

method outlined in this thesis as well as their advantages and characteristics. A

conclusion ends the chapter.

This chapter explains the necessary theories that are the basis for the SREM

wave method. It also discusses current methods available on measuring soil moisture and

their characteristics. The EM wave-based method is one of the simplest ways to achieve

the goal of measuring soil moisture, but it is still in need of further study and

development to overcome its limitations.

2.2 RADIO WAVE PROPAGATION

2.2.1 Free Space

How an EM wave propagates through space can be explained with the Friis

equation for free space propagation [9]. This equation describes, in terms of received

power, the relationship when a transmitter at point A makes a transmission to a receiver

at point B with distance R between them. The equation indicates that a lower frequency

and higher antenna gain will improve transmission range.

(1)

Equation 1 [9] indicates that the received power ( ) will depend on the transmit

power ( ), the wavelength (λ) and the gains of the transmitting and receiving antennas

( , , respectively) in an ideal (free space) environment. Received power will decrease



over distance (Figure 1), although in real-world situations the decay rate is also affected

by reflection, diffraction, scattering due to obstacles and various atmospheric conditions

[9, 17-19].

Figure 1 Signal attenuation decay rate in free space from equation (1)

Larger wavelengths or lower frequency signals will produce a better

received-power decay rate compared to received power from higher frequency/smaller

wavelength signals. Higher antenna gain is also beneficial to received power, although

antenna size might be a constraint (see Figure 2).

Figure 2 5 dBi (bottom) and 2 dBi antennae for 2.4 GHz frequency

2.2.2 Ground Reflection Model

In application, a single direct path between the transceiver and the

receiver, as in the case of propagation in a free space, is unlikely. A ground reflection

two-ray model offers a more accurate representation than a free space propagation

model as the former takes into account both the direct path and reflection from the

ground (Figure 3). Within a line of sight (LOS) with minimum vegetation and a

relatively flat environment, such as a farm, signal reflection will most likely come

from the ground itself.



Figure 3 Ground reflection two-ray model [9]

Figure 3 [9] indicates that two propagating waves will arrive at the

receiver. The reflected wave will differ in its propagation path from the direct-path

wave. The LOS wave ( ) travels along the shortest possible path from the

transmitter to the receiver (Equation [2]). The ground wave ( travels at an angle of

incidence ) is then reflected off the ground before arriving at the receiver.

The angle of incidence ) depends on the height of transmitting and receiving

antennas. The reflected energy will depend on the Fresnel reflection coefficient ( Γ )

as shown in equation [3].

( )

( (

)) (2)

Direct-path wave E-field.

( ) Γ

( (

)) (3)

Reflected wave E-field.

The two waves differ in their paths, the LOS path (d ) and reflected path

(d). The equation in Equation 3 shows that Γ substantially influences the reflected

wave energy. As the reflected wave arrives at the receiver, it will cause constructive



and destructive interference to the LOS wave, depending on their phase and time

difference.

In other words direct path and ground reflection waves influence each

other. Equation 4 [20] is the propagation model for outdoor applications of WSNs

that combine the free space and ground reflection. The round reflection coefficient is

taken into account to calculate the ground reflection influence to the direct path/free

space waves.

(

)

(4)

The next section describes in more detail the effects of the reflection

coefficient.

2.2.3 Electrical Properties of Materials and the Fresnel Reflection Coefficient

Different materials have different electrical properties [9, 11, 21, 22] provide

examples of materials with different relative permittivity ( ) and conductivity (σ) at

several frequencies (Table 1) [9]. With some materials the electrical properties varies

with frequencies.

Table 1 Different Material’s Electrical Properties



EM waves propagating from one medium into another that has a different

electrical property will have part of their energy reflected and another part

transmitted, depending on the dielectric properties of those mediums (Figure 4 [11]).

A perfect conductor will reflect all of the energy from the EM wave without any loss

[9].

Figure 4 Geometry for calculating reflection coefficient between two dielectrics [11]

Dielectrics behave differently from a perfect conductor. The amount of

energy reflected and transmitted is related to Γ, which is a function of the material’s

dielectric properties, the frequency and polarisation of the wave and the angle of

incidence [9, 11]. Figure 4 shows the geometry for calculating Γ between two

dielectrics at parallel polarisations (E-field in the plane of incidence). At a

perpendicular polarisation, the E-field will be in the opposite direction. The formulae

for Γ values for different antenna polarisations, parallel and perpendicular, are

presented in Equation 5 [9].

(5)

In equation 6, η is the intrinsic impedance of the medium, as shown in Equation 5.

η = √(μ/εi) (6)



Figure 5 [9] shows that changes in a medium’s relative permittivity ( )

will change the Γ value. The angle of incidence ) is also considered when

determining the Γ value (Figure 5). Antenna polarisation, parallel (vertical) and

perpendicular (horizontal), is important when deriving the value of Γ. Moreover,

there is a correlation between frequency and conductivity, although permittivity is

generally constant [23]

Figure 5 Fresnel reflection coefficients for = 4 and = 12 as a function of incidence angle

[9]

At two different relative permittivity values, two mediums will have two

different reflection coefficient values [9]. Figure 5 shows that to get a large

difference in Γ, an angle of incidence ) of more than approximately 40° is needed.

Thus, if we want to differentiate different values of Γ, the angle of incidence needs to

be as high as possible.



2.2.4 Incorporating Reflection Coefficient into Free Space Equation

In this research application, the energy from the direct-path wave is not to be

included. This can be achieved by using a directional antenna that is pointed towards the

ground at an appropriate angle of incidence. As a result, what would be received would

be the signal from the ground-reflected wave. In theory, the direct-path signal could be

completely eliminated; however, the Yagi directional antenna used here could only

reduce a portion of the direct-path signal.

The received signal can be calculated using a free space model that

includes the Fresnel reflection coefficient (equation 7) [11]. Fresnel reflection

coefficient is a function of materials dielectric properties and also angle of incidence,

polarisation and frequency [5, 8, 9, 12, 22, 24].

(7)

Equation 7 [11] is similar to the free space equation and includes

variables of transmit power ( ), wavelength (λ) and gains of transmitting and

receiving antennas ( , , respectively). Distances and are the total distances

travelled by the reflected wave as it is reflected from the medium. The final value of

received power depends on the Γ value of the medium.

2.3 RELATIONSHIP BETWEEN DIELECTRIC PROPERTIES OF SOIL

AND ITS REFLECTION COEFFICIENT

Studies have shown that soil moisture affects the dielectric properties of soil

(Figure 6) [10], and this relationship has been used to measure and estimate soil moisture

by others [10, 23, 25-29]. Curtis (2001) [10] used a coaxial transmission method to

gather data on the relationship between soil moisture and its dielectric properties and

concluded that there is a dependence of soil dielectric properties on its volumetric

moisture content.



Soil dielectric properties are also dependent on its composition and

characteristics [10, 23, 25, 26], such as its water holding capacity, salinity and organic or

metallic content. Therefore, calibration of the measurement method to the specific type

of soil is important in soil moisture measurement.

Figure 6 Relationship between soil moisture and its dielectric properties [10]

Radio wave frequency’s effects on a soil’s dielectric properties at a given

moisture content largely occur at very low or very high frequencies (Figure 7) [23]

and relatively constant at frequencies in the middle. although the soil’s type and

characteristics can alter this relationship, albeit a little [23].



Figure 7 Soil relative permittivity at different soil moistures and frequencies [23]

There are many studies on methods to measure soil moisture using the

relationship between soil’s dielectric properties and the Fresnel reflection coefficient.

This relationship is the basic concept in TDR [30-34] and in EM wave [8, 12, 13, 16,

35, 36] methods of measuring soil moisture.

The SREM wave method detects changes in soil moisture by reflecting

an EM wave from the soil and measuring the strength of the reflected wave. The

concept behind this method is related to the Γ value of the mediums as explained in

section 2.2.3.

Using a ground reflection model [14] the received power from WSNs

radio transmissions can be calculated with different values of Γ. As variation in soil

moisture in turn affects Γ, the difference in the radio transmission’s received power,

in terms of soil moisture content, can be characterised.

One of the problem is that Γ is very dependent on the angle of incidence.

Rappaport (2001) [9] said that as the angle of incidence approaches zero, the ground

will behave much like a perfect conductor with Γ = 1. Small angle of incidence )

values, for example less than 10°, exhibit small changes in Γ values with variations

in the soil’s dielectric properties.



This research proposes a method that includes a second transmitter

located close to (a distance of less than five metres) the main WSNs node in order to

achieve a high θ value (45°). The second transmitter’s purpose is only to transmit a

signal to the node. This angle is sufficient to achieve the highest possible

differentiation of Γ, as described in section 2.2.3. By recording the RSS values, the

relationship between them and different levels of soil moisture can be depicted.

2.4 RECENT STUDIES ON METHODS OF MEASURING SOIL

MOISTURE

Here, how soil moisture content is traditionally measured is defined.

There are basically two definitions for soil moisture content: gravimetric and

volumetric (Figure 15). Gravimetric soil moisture (Equation 8) is based on the ratio

between the water contained and the dry soil mass in that soil. Volumetric content

(Equation 9) is based on the ratio between the volume of water in a sample of soil

and the volume of both the soil and the contained water. Both are measured in per

cents (WMO, 2008).

(8) (9)

There are five common methods of measuring soil moisture content [5]:

1. Gravimetric direct measurement

This method directly measures the difference between the soil sample

before and after its water content is removed. The soil sample is removed from the

ground then placed in a container while carefully minimising disturbance to the soil.

The container and the soil are then weighted. The soil in the container is then dried in

an oven until the mass is stabilised at a constant value, indicating that there is no

remaining water content. The ratio between the mass before and after drying is the

gravimetric soil moisture content.



This method, while arguably accurate, presents several constraints. It is

invasive as the soil is disturbed and may be impractical. Oven temperature is crucial

since there are cases where the soil contains organic matter that burns during the

drying process, resulting in inaccurate measurements. As the soil’s capacity to hold

water might change when it is disturbed, this direct soil moisture measurement

method is not ideal in applications when soil disturbance is undesirable.

2. Indirect measurement—Radiological method

Two common radiological methods for measuring soil moisture content

utilise neutron scatter and gamma rays attenuation. The neutron scatter method

observes the interaction between high-energy (fast) neutrons and the hydrogen atoms

from the water content in the soil. The gamma ray method measures the attenuation

of gamma rays as they pass through the soil. These two methods have higher risk

since use radioactive materials and are usually only performed at specialised

laboratories or research facilities.

3. Indirect measurement—Soil water dielectrics

Measuring the dielectric properties is widely used when measuring the

moisture content of soil. Studies have showed that water content in the soil changes

its dielectric permittivity. Moreover, this method is fast, reliable, non-destructive and

has significantly lower risk than radiological methods. The most commonly applied

method is TDR.

The WMO guide [5] states that TDR is a ‘method which determines the

dielectric constant of the soil by monitoring the travel of an EM pulse, which is

launched along a waveguide formed by a pair of parallel rods embedded in the soil’.

In TDR, EM wave propagation velocity is measured and related to the soil’s

dielectric permittivity.

Cataldo (2009) [31] studies method of measuring soil moisture using

TDR method without with a microstrip antennas that eliminates the need of sensor

probe.

TDR has been studied extensively [33, 34, 37] for obvious reasons and

because it presents the opportunity for continuous, real-time measurements in an area

of interest. Despite its advantages the TDR method requires several probes, usually



ranging from 10–50 cm in length [5] to be placed undersoil. In addition, cables are

needed to connect the probes to the measurement sensor.

4. Indirect measurement—Soil water potential

Instruments used in this method measure the potential water in the soil

and return analogue readings unlike TDR readings that are measured electronically.

Tensiometers function in a manner similar to that of plant roots; usually the meters

include a porous container filled with water. If the water content in the ground

increases, water will seep into the tensiometers. Tensiometers tend to only work well

in wet soil and lose their accuracy in dry soil [5].

Electrical resistance blocks are used for dry soil environment. They

measure the relationship of water content in the soil and the soil’s electrical

resistance. Although relatively inexpensive, the blocks need to be calibrated

individually and routinely to produce a reliable measurement. Alkalinity in the soil

also decreases the accuracy of this method.

5. Indirect measurement—Remote sensing

When the area of measurement becomes too large to be practically

covered by other methods, a microwave method may be used [8, 12, 28, 36]. Using

the same principle as that in TDR, measurement is based on the dielectric properties

of the soil changing as its moisture content change. There are two methods of

sensing, passive (where satellites or airborne sensors monitor microwave emissions

from the earth) and active (where the measurement is taken from the satellite sent

signal). To cover wider area satellites [8, 13] and airborne platforms [16] have been

used.

This method is useful for measurement on broad geographic scales or to

assess inhomogeneous soil moisture content and to provide measurement from deep

soil layers. As it usually involves satellite and airborne platforms, this method is not

suitable for local measurement application and is associated with high costs.



Name Basic

Principal

Spot/

Continuous Price Picture

[38]Decagon

EC-5

&

EC-10

FDR/TDR Continuous

US$139

+

US850

(Data

Logger)

AquaSpy[39] Capacitan

ce Continuous N/A

Sentek

EnviroScan

[40]

TDR Continuous US$225

Spectrum

Watermark

6450

[41]

Resistance Continuous

US$35

(not

including

cable and

data

logger)

Vernier

[42]

TDR Continuous

US$95

(not

including

data

logger)



Stevens Hydra

Probe II

[43]

TDR Spot

US$395

Or

US$1995

w/ Data

Logger

Lutron

PMS714

[44]

FDR Spot AU$350

AquaPro

[45]

Capacitan

ce Spot

US$1034

w/logger

Table 2 Comparison between Several Commercially Available Soil Moisture Sensors

Table 2 lists several commercially available soil moisture sensors based

on their principal operation system, whether their capable of continuous monitoring,

and their prices. As best of the author’s knowledge current soil moisture sensing

solution still invasive to the soil as shown in table 2.

There are many soil-based factors that affect the accuracy and precision

of results from measuring soil moisture, especially affecting indirect or surrogate

methods of measurement [5]. These factors may include soil density, salinity,

temperature and metallic components of the soil. These sources of inaccuracy usually

can be reduced by calibrating the sensors for different types of soil. Moreover, the

water holding capacity of soil largely depends on its clay and sand content; clay

holds more water than sand [5].

Studies also show that some indirect methods work well on certain range

of soil moistures where others do not [5, 6]. It is advisable to investigate the type and



consistency of the soil to be measured to determine its water holding capacity and

then choose the appropriate method to measure its water content.

Other studies indicate that there have been efforts aimed at developing

non-invasive methods of measuring and estimating soil moisture with the majority

using EM waves [8, 12, 16] and TDR [31, 34].

2.5 SUMMARY

This review describes the factors involved in EM wave propagation that

are applicable to detecting changes in soil moisture. Changes in soil moisture content

will change the soil’s dielectric properties, which in turn will affect the soil’s Fresnel

reflection coefficient. By measuring differences in RSS in relation to variations in

Fresnel reflection coefficients, soil moisture can be estimated.

Using directional antenna that point ground-ward at an appropriate angle

of incidence, will minimise the energy from the direct-path EM wave. The received

signal will then consist largely of the ground-reflected wave. By using a free space

model, propagation that includes the Fresnel reflection coefficient will provide

different RSS values at different moisture levels.

There are many interesting and innovative studies in the field of soil

moisture measurement [11, 22, 23, 27, 30-33, 36, 37]. Adamo et al. [7, 46, 47]

studied the use of sound wave propagation for measuring soil moisture, although it

needed invasive probes for measurement.

The literature review indicates that although there are extensive studies in

the field of soil moisture measurement, there have not been reported non-invasive

methods based on WSNs radio wave propagation. The SREM wave method, with its

non-invasiveness and practical characteristics, provides significant advantages

compared to existing methods.

Chapter 3: Research Methodology Page 21


Chapter 3: Research Methodology

3.1. INTRODUCTION

This chapter describes the methodology used and the research design

developed to achieve the research objectives discussed in Chapter 1. It also describes

in detail the configuration of the SREM wave method and the hardware that is

needed to implement it. This chapter also describes the three types of experiments

undertaken in this research.

3.2. EQUIPMENT

The research uses Waspmote wireless sensor nodes operating in the 2.4

GHz ISM frequency band with the IEEE 802.15.4 standard protocol (Figure 10)[48].

Waspmote nodes can operate independently as they are powered by rechargeable

batteries. Single node consisting of the electronic board, rechargeable battery,

transceiver (IEEE 802.15.4), and antenna cost US$150 at the time of purchase. The

gateway is connected to a laptop computer to access the measurement data. The

objective is to make two WSNs nodes communicate while the gateway monitors the

process. The transmitting node sends data to the receiving node where the RSS will

be recorded.

Figure 8 Waspmote wireless sensor node with omni-directional antenna.



In this study, the Waspmote uses two types of antennae, a 2 dbi omni-

directional antenna at the receiver and a 6 dbi uni-directional board Yagi antenna at

the transmitter. This project also uses a 20 dB RF attenuator to reduce the transmit

power of Waspmote. This is because the Waspmote can only accurately detect signal

strength above −40 dBm. Two high-grade tripods with a maximum height of 1.7 m

and an integrated inclinometer ensure the stability and precision of the experimental

apparatus. The tripod used in this experiment cost AU$50 (personal property).

Figure 9 WaspMote’s node with the yagi (right) and omni (left) antennas attached

Ground moisture content is measured with a Lutron PMS714 soil

moisture sensor (Figure 11). This sensor measures the conductive ability of the soil

and converts the reading to a per cent moisture content. It has five per cent accuracy,

which is sufficient for this research as a more accurate sensor costs five times more

and lacks portability. The Lutron sensor used in this study costs AU$ 350.

Figure 10 Lutron PMS714 soil moisture sensor



3.3 EXPERIMENTAL SET UP

Experiments were performed at three different locations in Brisbane,

Australia: one is a soccer field and the other two are public parks. The levelness of

the ground is determined by making sure that all three tripod legs are fully extended

and the inclinometer shows that both tripods are level. This is to make sure the angle

of incidence calculation is accurate.

ASRIS [49] indicates that the soil in each of these locations comprises

ten to twenty per cent clay. At each site, six individual spots representing six

different levels of soil moisture were examined. These six levels of soil moisture are

8, 10, 15, 18 and 20% according to the portable soil moisture. The different levels are

chosen to clearly differentiate the measurement results.

Figure 11 Three test sites at Brisbane, Queensland, Australia (Courtesy of Google Map)



The objective of these field tests is to measure RSS and its relationship to

soil moisture content. The experimental set up consists of two nodes (transmitter and

receiver) mounted on a tripod at a height of 1.3 m and one gateway connected to the

laptop to record the measurements. The first node transmits data to the second node

and the receiving node sends confirmation signals and also forwards the RSS data to

a laptop computer connected via a gateway. The distance from each node to the

measurement location is 1.3 m, resulting in a 45° angle of incidence (Figure 14). This

angle is deemed the best angle since it provides the best direct-path signal attenuation

and the simplest configuration for this experimental set up; a higher angle means the

distance between the two nodes would be too close to each other.

Figure 12 ASRIS data on Australian surface soil texture [49]



Figure 13 Experimental set up

The set up for all experiments is as follows:

a. The transmitter uses a Yagi directional antenna with a 20 dB RF

attenuator and the receiver uses an omni-directional antenna.

b. Both transmitter and receiver are placed on their respective tripod at a 1.3

m height.

c. The spot to be measured is located.

d. Distances from transmitter and receiver to the measured spot are 1.3 m,

achieving a 45° angle of incidence.

e. The transmitter antenna is angled 45° downward, to coincide with the

angle of incidence calculated from the antenna height and distance to the

measurement spot.

f. Tx node sends signal to Rx node

g. Rx node sends the measured RSS to Waspmote gateway connected to a

laptop. Software records these measurements and converts them to a

manageable text file.

h. RSS is measured 200 times and the average RSS value determined.

i. Soil moisture level is measured with the portable sensor

j. Repeat these steps at different locations or under different levels of soil

moisture.



Figure 14 SREM wave method configuration.

Two hundred measurements were averaged because Waspmote’s sensitivity

is only 1 dBm according to the manufacturer’s specifications. Using this number of

individual measurements will increase the accuracy of each measurement average. A 45°

incidence angle is easy to set up because only the height of the antenna and its distance

to the measured spot need to be equalised.

Figure 15 WaspMote Gateway connected to a laptop

Soil

45° 45°

𝑇𝑥 (Yagi)

𝑅𝑥 (Omni)

1.3m 1.3m

1.3

m

1.3

m Measured

Spot



3.4 EXPERIMENTS

3.4.1 Performance Characterisation of Waspmote and Antennas

Before the field experiment phase, detailed knowledge of the Waspmote and

other hardware used in this project is needed. This is necessary to verify the

manufacturer’s specifications. Knowing the exact gains, polarisations and transmission

lobes of the antennae is especially vital to ensure subsequent measures are as accurate as

possible since the method relies heavily on the angle of incidence and the radiation

patterns of the antennae. There are two works done in achieving this, one consist of the

transmission distance and attenuation by vegetation test and the other one is directly

relevant to the study in this thesis is the attenuation by angle test.

3.4.1. a. Transmission Distance and Attenuation by Vegetation

A study done with fellow student, Mr. Praditio Trenggono, investigates the

attenuations from different types of vegetation to Waspmote. Although this study is not

directly related to the research presented in this thesis, it is important to further

investigate the performance characterisation of Waspmote. This study consists of two

stages, transmission test and attenuation by vegetation test.

The purpose of transmission distance test is to verify the signal strength

decay rate as the distance between transmitter and receiver increases in a line of sight

(LOS) scenario. In this experiment received signal strength at distance 0, 12, 25, 50,

100, 200, 400 and 500 m is recorded; two different polarisations were used, vertical

and horizontal. To ensure accuracy 200 points of data from each measurement are

recorded and the result is then averaged.

This experiment will expect decrease in received signal strength as the

distance between transmitter and receiver increases. As there is LOS the primary

variable that affects the decrease in signal strength is free-space propagation.



Figure 16 Three scenarios from left right and bottom, grass, bush, and forest

The next stage is the attenuation by foliage test (figure 15). The objective of

this experiment is to determine the attenuation to received signal strength from various

types of vegetation that obstruct the transmission between transmitter and receiver. The

experiment places the transmitter and receiver at 12 m with each types of vegetation

between them. The types of vegetation chosen are grass, tall grass, dense grass, bush and

forest/tall trees. Received signal strengths of each vegetation scenarios are compared to

the free space values at the same distance to figure their total attenuation.

3.4.1. b. Attenuation by Angle

The first step is a distance test to determine the Waspmote attenuation levels

at distances of three and five metres. The transmitter node uses a Yagi directional

antenna pointed directly at the receiver, which has an omni-directional antenna. One

hundred RSS values for each range are taken, and then averaged to obtain the final

values. The range test phase also tests the RF attenuators to determine their exact

attenuation. The procedure is similar and involves capturing each RSS value at five

metres and ten metres. The designated range is important to verify that each result is

consistent with other results.



The next step is to verify the attenuation resulting from different angles of

the directional antenna by angling it at upward angles of 30°, 45°, and 60°, thereby

avoiding any signal reflection. As with the previous steps, measurements are taken at

five and ten metres to improve the consistency of the results. This phase is important as

it determine whether the captured RSS value when the antenna is pointed towards the

ground is from the soil-reflected wave.

3.4.2 Proving Concept and Field Measurements

Using the configuration shown in Figure 19, measurements are taken under

three different soil conditions: very dry, moist and very wet. The objective of this

experiment is to determine whether different soil moisture conditions will result in

different RSS readings. The soccer field at St. Lucia, Brisbane was selected for this

experiment because it has a large, flat unobstructed terrain.

After the previous steps have determined the validity of the concept of using

soil-reflected EM wave to detect changes in soil moisture, this step is undertaken to

derive a curve relating different soil moisture levels with obtained RSS values. This is

achieved by using the process of calibration [5] and recording different RSS values at

different soil moisture levels. This calibration method is required for different soil types

and compositions since reflected EM wave methods of detecting changes is soil moisture

are highly dependent on soil properties.

Eighteen spots were measured at three different locations in Brisbane to

provide the data needed for analysis. Because at each spots two hundred measurements

are taken, there are 3600 data points in total. Typical text file data size of this experiment

is approximately 50kb. after At the analysis each on two hundred measurements are

averaged into one value to increase accuracy because Waspmote can only record signal

strength in 1 dB steps.

3.5 SUMMARY

This chapter describes in detail the experimental approach used to detect

changes in soil moisture using WSNs signals and the methodology in assessing that

concept in a real life situation. The chapter starts by explaining the steps taken to acquire



performance characterisation of the Waspmote sensor and other hardware used in this

project. It also presents the proposed method to calibrate the apparatus for use in

different soil types and composition.

All experiments in this research are conducted at an open field where there

are no unwanted signal reflections from the surroundings vegetation or objects. The

method of determining antenna radiation pattern explained in section 3.4.1.b usually

needs to be done in an anechoic chamber. But since there are no unwanted signal

reflection the research equipment did not detect any Wi-Fi signal at the test sites,

interference from other 2.4 GHz transmitting devices are minimal or non-existent. As

with other devices such as cordless phones, their range is limited and should not reach

the experiment location.

The next chapter presents the result obtained in each of the experiments

described in this chapter.

Chapter 4: Results Page 31


Chapter 4: Results

4.1 INTRODUCTION

Chapter 4 presents the results of this research including field experiments

and the analysis that accompanies them. The field experiments are the main focus of this

study and consist of three parts:

1. Characterising hardware performance.

2. Developing a curve that depicts the relationship between soil moisture

and received signal strength.

3. Generating a polynomial function based on the data points taken from

field measurements.

4.2 TEST RESULTS

4.2.1 Transmission Distance and Attenuation by Vegetation

Range/ 1 m 12 m 25 m 50 m 100 m 200 m 400 m 500 m

Polarisation

Vertical

(dBm) −36 −44.4 −52.1 −54 −60.7 −66.7 −78.3 −89.9

Horizontal

(dBm) −36 −41.9 −44.1 −49.9 −55.1 −63.9 −75.4 −91.9

Difference 0 2.5 8 4 5.6 2.7 2.9 2

Figure 17 Transmission distance test results in dBm

As expected the signal strength decrease as the distance between

transmitter and receiver increases. By observing the decay rate from the graph we

can see the performance characteristic of this particular WSNs device. Although the

performance characteristic is specific to Waspmote, it could also give a guideline on

how a WSNs device performs in terms of relationship between range and signal

strength.



Figure 18 Comparison between horizontal and vertical polarisation

We can immediately see in Figure 16 that horizontal polarisation exhibit

better performance than vertical polarisation. This is primarily because in vertical

polarisation the signal is more prone to interference from ground reflection than

horizontal polarisation. Ground reflection effect to signal strength diminishes as the

distances between transmitter and receiver increase. The effect happens as a result of

the angle of incidence between them approaches zero and the strength of the

reflected signal approaches the direct-path signal [50]. That is why as the distance

grew the different at signal strength between the two polarisations is getting smaller.

0 50 100 150 200 250 300 350 400 450 500-100

-90

-80

-70

-60

-50

-40

-30Signal Strenght at Various Distance and Different Polarization

Distance (m)

Sig

nal S

trength

(dB

m)

Horizontal Polarization

Vertical Polarization

0 50 100 150 200 250 300 350 400 450 5002.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9Path Loss Exponent For Vertical Polarization

Distance (m)

Path

Loss E

xponent

Path Loss Exponent

PLE Trend Line



Figure 19 Path Loss Exponent Calculation from Vertical and Horizontal Polarization

Using Equation 10 [9] the Path Loss Exponent (PLE) showed in Figure

18 from the received signal strength (Pr) values at each distances (d) are calculated.

Variables and are the reference distance of 1m and -36 dBm respectively. In

free space the expected path loss exponent is n = 2, and in an outdoor urban

environment it usually n > 2.7 [9]. Because the PLE values are below 2.5 it agrees

with the theory since the measurement took place at line of sight (LOS) environment

that is approaching free space condition. The trend lines shows that the range of the

path loss exponent values are approximately from 2.38 – 2.52 for vertical

polarization, and horizontal polarization from 2.03 – 2.55. This also confirms that

horizontal polarization exhibit relatively better performance than vertical

polarization.

(10)

At 500 m, the signal drops below −90 dBm. There is significant decrease

in signal strength compared to 400 m, 11.6 dBm for vertical and 16.5 dBm for

horizontal polarisation. This is because there is a threshold at certain range where the

signal quality drops significantly due to hardware limitation. The measurements and

0 50 100 150 200 250 300 350 400 450 5001.8

2

2.2

2.4

2.6

2.8

3Path Loss Exponent For Horizontal Polarization

Distance (m)

Path

Loss E

xponent

Path Loss Exponent

PLE Trend Line



path loss exponent calculation shows the performance characteristic of WaspMote

WSNs node in a Line of Sight outdoor environment.

Foliage/

Polarisation Free Grass

Tall

Grass

Dense

Grass Bush Forest

Vertical −44.4 −49.5 −49.6 −56.7 −69.3 −65.3

Horizontal −41.9 −46.5 −46.7 N/A −59.2 −58.5

Diff. Vert. 5.1 5.2 12.3 24.9 20.9

Diff. Hor. 4.6 4.8 N/A 17.3 16.6

Table 3 Waspmote’s Signal Strength Attenuation Because of Foliage

Obstruction in the form of vegetation between the transmission paths will

introduce attenuation in signal strength. We have to consider the wavelength of the

frequency we are using in this experiment, 2.4 GHz has 12.5 cm wavelength. If the

wave confronts obstacles smaller than its length, it could scatters. When it confronts

obstacles larger, it could reflect. Both of these phenomena contribute to signal

attenuation. [50]

As with the transmission distance test, horizontal polarisation exhibit

better performance than vertical polarisation. Bush scenario even shows more than

10 dBm difference. One of the factors is in foliage condition primary obstructions are

vertical tree trunks and branches of which the length is equal or larger than the 2.4

GHz frequency wavelength (12.5 cm) [50, 51]. This is why horizontal polarisation

fared better than vertical polarisation. Furthermore, when the vegetation is much

denser, the attenuation becomes higher. The branches and leaves of the bush are

simply too dense compared with the forest scenario [52].

When compared to result in transmission distance test, at 12 m LOS

condition the present of vegetation could contribute to attenuation as low as 5 dBm

and as high as 15 dBm. The presents of vegetation could contribute significant

amount of attenuation to signal strength and in turns cause severe decrease in

transmission distance. Just 12 m transmission distance at forest scenario for example

exhibit decrease in signal strength almost the same as 200 m LOS.



4.2.2 Attenuation by Angle

The objective of this part of the study is to determine the performance of the

hardware used in the experiments. Signal strength is recorded at distances of five and ten

metres, both with and without an RF attenuator. Rather than perform tests at one

distance, tests at two different distances are done to help confirm the results. As

described in Chapter 3, the RF attenuator is vital because Waspmote is only accurate in

detecting signal strength at levels above 40 dBm and has maximum sensitivity of 36

dBm. Therefore, a 20 dBm attenuator is used to reduce the signal strength below 40

dBm to ensure detection accuracy.

Determination of the attenuation caused by the directionality of the Yagi

antenna is also required. By knowing the Yagi antenna’s radiation pattern it is possible to

determine the best angle of incidence for the next step. This pattern is obtained by

pointing the Yagi antenna upward at three different angles, 30°, 45° and 60° and then

recording the RSS (Table 3). The angle with the highest attenuation is then chosen as the

angle of incidence.

Angle RSSI Without Attenuator (dBm) RSSI With Attenuator (dBm)

5 m 10 m 5 m 10 m

0° −36 −36 −53 −55

30° −37 −41 −57 −61

45° −46 −48 −65 −67

60° −42 −45 −62 −64

Table 4 Hardware Performance Characterisation Results

The results in Table 3 show that, in the absence of an attenuator, even at 5 m

distance the signal strength is too high to be detected accurately by Waspmote.

Considering that measurement of soil moisture is done at distances of less than three

metres to achieve sufficient angle of incident the use of an attenuator is necessary. The

difference in signal strength between five and ten metres is 2 dBm. The attenuator

decreases the signal strength by approximately 20 dBm as its specification stated.

With the 0° angle value used as a reference, we conclude that a 30° angle

gives 4 dBm attenuation, 45° angle gives 12 dBm attenuation and 60° angle gives 9 dBm



total attenuation. Therefore, the angle of incidence for subsequent experiments in this

research will be 45°, as it provides the highest attenuation to the direct-path signal.

This approach is a simple yet reliable method of determining the radiation

pattern of an antenna. More precise methods require an anechoic chamber to eliminate

signal reflections that might interfere with the test results. Here, since the antenna

characterisation test is done in an empty, open field location and as the antenna in

pointed up to the sky, there will be no interference from unwanted reflected signals.

4.2.3 Field Experiments

In this step, the concept that using soil-reflected electromagnetic waves

emanating from a WSNs node to differentiate levels of soil moisture value is tested.

Studies have shown that different values of soil moisture will affect the reflection

coefficient of the soil; thereby affecting the amount of energy from electromagnetic

waves that are be reflected from it. The experiments also provide a fundamental

configuration for subsequent test of hardware, positioning, programming code and the

overall methodology.

To successfully prove the concept, the received signal strength from three

different soil conditions (wet, moist and dry) should be differentiable. Chapter 2

describes reports showing that higher soil moisture levels produce higher soil reflection

coefficients and, as a result, better levels of RSS; the opposite is also true.

Figure 20 Theoretical relationship between RSS and reflection coefficient

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-52

-50

-48

-46

-44

-42

-40

-38

-36

-34

-32

-30

Reflection Coefficient

Receiv

ed S

Ignal S

trength

(dB

m)

Relationship Between RSS and Reflection Coefficient



The graph in Figure 19, derived from equation [7] with reflection coefficient

values from 0.1 to 1, depicts the theoretical relationship between the reflection

coefficient of a surface and the signal strength reflected from that surface. It shows a

non-linear curve with increasing steepness as the reflection coefficient decreases. Higher

soil moisture values produce higher reflection coefficients and hence higher signal

strength values. Moreover, differences in signal strengths are higher at lower reflection

coefficient values. In this research, those relationships mean that the differences in signal

strength at higher values of soil moisture are smaller than the signal strength differences

at lower values of soil moisture.

Table 4 shows the measurements of received signal strength at three

different soil conditions: wet, moist and dry. The soils were measured under the driest

and wettest conditions possible and the soil moisture measurements are taken with the

portable soil moisture sensor. It should be noted that the probe of the sensor cannot

penetrate the hard dry soil; therefore, the dry soil moisture measurement could not be

obtained; even measuring moisture levels in moderately moist soil is difficult with that

portable sensor. This shows one of the advantages of developing a non-invasive method

of measuring soil moisture, a method in which no probe and no soil penetration is

required.

Dry Moist Wet

RSS (dBm) −55.1 −52.7 −49.3

Moisture (%) N/A 10.2 20

Table 4 Received Signal Strength at Different Soil Conditions

The results show that differentiation of those three soil moisture conditions

in terms of RSS reflected from the soil is possible. The signal strength is higher under

wetter conditions and lower under drier soil conditions. These results agree with the

theory of reflection coefficient that higher soil moisture values have higher reflection

coefficient and therefore will reflect more electromagnetic wave energy.



Table 6 presents soil moisture and their corresponding reflection

coefficient (εr) values from [23] for soil classified as silty sand containing 14% clay,

77% sand and 9% silt. That soil is chosen because it is similar to the test site’s soil

type (10–20% clay per ASRIS [49]). Using equation [7], the expected RSS values for

each soil moisture level can be calculated (Table 4).

εr Moisture (%) RSS

4 8 −42.0209

6.5 13.2 −39.2932

9 18.4 −37.9154

14 29.1 −36.0604

Table 5 Theoretical Signal Strength Values for 10–20% Clay Soil

Figure 21 RSS values based on reflection coefficients from Table 4 (εr = 4 – 14)

5 10 15 20 25 30

-42

-41

-40

-39

-38

-37

-36

Soil Moisture (%)

RS

S (

dB

m)

Relationship Between Soil Moisture and Signal Strength for Soil Containing 14% Clay



Table 6 shows that for soil moisture levels of 8–29%, the corresponding

relative permittivity values are εr = 4–14. The reflection coefficient can be

calculated from equation [5] and, based on those values, the relationship between the

signal strength and soil moisture value can be plotted using equation [7] (Figure 20).

This graph agrees with the results from the field measurement data obtained using

the SREM method.

The data from Table 6 and Figure 20 are from a study by Curtis (1995)

[23]. That study measured the electrical properties of different types of soil using an

FDR method. That study’s results are chosen as a reference because they represent

the closest soil type to the one used for measurement in this research. The results

confirm that further research into the development of a new method of measuring

soil moisture is needed, especially with regard to different types of soil.



Figure 22 Measurement data from three test sites

Figure 21 shows the results of measurements obtained using the SREM

method at three different test sites. It depicts the relationship between soil moisture

in per cent and RSS in dBm. The blue curve shows the theoretical relationship (see

Figure 20). The red lines show the mean ⁄ 1 values. Only the mean values are

showed because the typical data range from the measurements is 3 – 4 dB due to

WaspMote 1 dB sensitivity limitation. For the purpose of analysis and comparison

the theoretical values shown in each blue curve are shifted by 13 dB from those in

Figure 20 in order to be closer to the measured values.



As the measurements use a 20 dBm RF attenuator and as the direct-path

wave cannot be completely eliminated, a gap between the two results is expected.

The shifting process does not compromise the analysis as it is just used to show how

the trends compare. Regardless, the two results follow the same trend and agree with

each other.

Table 7 shows measurement from each test sites. The levels of soil

moisture assessed are approximately 8, 10, 13, 15, 18 and 20% resulting in the total

of 3600 data points. Figure 21 shows that each measurement’s values are in the

expected range and they follow the trend expected from the theoretical values. Thus,

the experiments’ results support the established theory that higher soil moisture

means higher signal strength.

Table 6 Mean Signal Strength Values from Soil Moisture Measurements

The RSS data for each soil moisture level are averaged to get the values

presented in Table 5. Averaging is done to increase accuracy because the sensitivity of

Waspmote WSNs is only 1 dBm. The driest possible soil that can be measured with the

portable soil moisture probe is 7.8% (dryer soil is too hard to penetrate with the probe),

and the wettest soil measured is 21% (in situ measurement of soil moisture rarely

exceeds 20% [31]).



As explained in chapter 3.3, the Lutron soil moisture sensor measurements

are used as the benchmark in the experiments. Therefore the results shown in the thesis

are from the Lutron sensor. These results then compared with the theoretical calculation

from references.

The signal strength range from the 7.8–21% soil moisture data is 4.4 dBm;

the theoretical signal strength for a 8–29% range is 6 dBm. This RSS difference is

because the experimental set up cannot completely eliminate the direct-path signal

received, therefore affecting the soil-reflected signal. However, the experiments showed

clear separation of RSS between the highest and lowest soil moisture levels; supporting

the theoretical data.

The difference between signal strengths is larger at low soil moisture than at

high soil moisture. There is a clear 1 dBm difference between 8% and 10% compared to

a difference of 0.3 dBm between 18.4% and 20% soil moisture. This agrees with

previous studies [24, 31].

Although the accuracy of the measurements is consistent when recording

relatively similar values at each determined values (8, 10, 13, 15, 18 and 20%), its

precision suggests the need for improvement; especially at higher soil moisture levels

where the signal strength difference is relatively small. This lack of precision is largely

due to the sensitivity of the hardware itself and not due to the method as the theoretical

values in Figure 19 show that this is the case with soil moisture measurement method

that utilise soil relative permittivity values [5, 30].



Figure 23 Comparison between a fourth degree polynomial curve for measured data and

theoretical values with RSS measured as dBm

Figure 22 shows a plot of 18 mean values obtained from 3600

measurements. It shows the relationship between RSS in dBm and soil moisture. The red

curve is for the theoretical reference values from Figure 19 that are shifted 13 dBm for

the purpose of comparison and analysis. The green triangles are for each of the 18

measurement mean values and the blue curve is a fourth degree polynomial curve fitting

of those data. Figure 20 show that the fourth order polynomial curve trend closely

matches the theoretical curve, especially in the 11–13% soil moisture range and only

shows large differences from the theoretical at 8–10% soil moisture. There is a relatively

small error in the measurement at 8% moisture. A possible reason to explain this is that

the reference and measured soil types and relative permittivity values are not exactly a

match. The error shows more here because the influence of the relative permittivity to

the reflection coefficient is more pronounced at lower soil moisture values compared to

higher values.

The measurements values consisted of the direct path reflected path signals

that influence each other depending on their phase and time arrived. Reflected wave

signals arrive after the direct path signals as it travel at a longer path. As in this

experiment the direct path wave signals cannot be completely eliminated it influence the

reflected signals as explained in section 2.2.2. Equation 7 only calculates the pure



reflected signal values and do not account the influence of the direct path wave. There

are 13 dB differences between the theoretical and the measurement values where

logically it should have 20 dB differences because of the 20 dB attenuator. This is

because the measured values are the addition between the two signals strength.

The value of 13 dB is limited to this experiment’s data set because of the

particular hardware used. In other words different experimental set up and hardware

might yield different value. There are two ways to solve this problem; the first one is by

further reducing the direct path wave using hardware, for example using antenna with

smaller lobe. The second way is to differentiate the direct path and soil reflected wave by

using the fact that the soil reflected wave will arrive later because it travels in a longer

path. The second solution is more precise because the direct path signals can be

completely eliminated but require more computing power in the receiver.

Goodness of Fitness (GoF) determines the order of polynomial to best fit the

curve. It has four components: sum of squares due to error (SSE), R2, adjusted R

2 and

root mean squared error (RMSE). Obtaining a best fit means obtaining SSE and RMSE

values as close to zero (0), and obtaining the two R2 values as close to unity (1) as

possible. The goodness of fit parameters of fourth, seventh and ninth order polynomials

are shown in Table 8.

Goodness of Fit

Order SSE R2 Adjusted R

2 RMSE

4th 0.4853 0.989 0.9863 0.1932

7th 0.3946 0.9915 0.9856 0.1986

9th 0.3632 0.9922 0.9834 0.2131

Table 7 Goodness of Fit of the Degrees of Polynomial



Figure 24 Seventh and ninth degree polynomial curve of measured data and theoretical

values

8 10 12 14 16 18 20 22-56

-55

-54

-53

-52

-51

-50

-49

Soil Moisture (%)

RS

S (

dB

m)

7th Degree Polynomial Curve

Theoritical Value

Measurement Data

7th degree

8 10 12 14 16 18 20 22-56

-55

-54

-53

-52

-51

-50

-49

Soil Moisture (%)

RS

S (

dB

m)

9th Degree Polynomial Curve

Theoritical Value

Measurement Data

9th degree



The fourth degree polynomial fitting is chosen because it most accurately

represents the theoretical curve (see Figure 22). The order polynomial that will result in

best fit highly depends on the nature of the data itself can be determined through

experimentation. Using an excessively high order will invariably result in worse fit.

Figure 23 shows the seventh and ninth degree polynomial fittings; they less accurately

represent the theoretical curve. Table 8 shows the best SSE, RMSE and R2 values in

bold. The adjusted R2 and RMSE value of fourth degree is better than that for the

seventh and ninth degrees, indicating a better fit. The RMSE and adjusted R2 values are

reported to be better in determining GoF than SSE and R2 [9]. Moreover, R

2 can be

misleading as the R2

value increases as the number of fitted coefficients increase;

therefore it is not the best variable to determine best fit. Adjusted R2 is used to overcome

this problem and is a better variable to determine best fit. On that basis, the fourth order

polynomial is selected as the best fit for the presented data.

4.2.4 Polynomial Equation

Figure 25 Curve fitting to generate an empirical polynomial function

The objective of this step is to construct a curve using signal strength values

from several measurements of soil moisture to produce a polynomial equation that can

-54 -53.5 -53 -52.5 -52 -51.5 -51 -50.5 -50 -49.5

8

10

12

14

16

18

20

RSS (dBm)

Soil

Mois

ture

(%

)

Relationship Between Signal Strenght and Soil Moisture

Measurement Data

Soil Moisture Curve



be used to accurately estimate values of soil moisture from recorded signal strength. For

that purpose, derivation of an equation for values of soil moistures from measured signal

strength is needed. To that end, the x and y axes from the fourth order polynomial curve

are reversed so that the y values (soil moisture in per cent) can be predicted as the

function of the x values (RSS). The GoF data for the reversed curve indicates that the

fourth order polynomial provides a best fit (Table 9). The curve fitting and calculation

are done by MatLab software.

Goodness of Fit

SSE R2 Adjusted R

2 RMSE

0.2767 0.9917 0.9891 0.4614

Table 8 Goodness of Fit of the Fourth Degree Polynomial Curve

The fourth order polynomial curve is similar to the one depicting the

theoretical relationship between signal strength and reflection coefficient (see Figure x).

This further indicates that it is the best fit to represents the relationship between soil

moisture levels and received signal strengths.

Equation 11 is the fourth order polynomial equation used to estimate soil

moisture values from RSS values. By substituting obtained RSS values (in dBm) for the

x values in equation, the corresponding values of soil moisture (in per cent) can be

calculated.

(11)

4.3 SUMMARY

The first conclusion from the experiments results is that the concept of

estimating soil moisture using soil-reflected signals from wireless sensor networks is

viable. When subjected to three different soil conditions (wet, moist and dry), three

different signal strength levels were obtained, agreeing with the results expected from a

theory based on reflection coefficients.



These experiments obtained 3600 signal strength measurements at different

soil moisture values and those are compared with measurements from a Lutron portable

soil moisture sensor. Using these data points other values were interpolated to construct a

curve representing the relationship between the recorded signal strengths and the

measured soil moistures.

A fourth degree polynomial curve agrees with the theoretical relationship

between signal strengths and reflection coefficients. The polynomial has a similar

steepness that increases as the soil becomes dryer or the reflection coefficient smaller.

The differences between signal strength values increase as the soil moisture values

decrease. This means that estimating soil moisture becomes more precise in dryer soil.

An empirical equation to estimate soil moisture from measured signal

strength is derived using the fourth degree polynomial curve. Although this equation and

the results in this chapter are for a specific hardware and set up and for the soil type used

in this research, the results indicate that a methodology and set up that utilises the

telecommunication signal of a WSNs node can detect changes in soil moisture. This

approach can for the basis for further studies and development.

Chapter 5: Conclusions Page 49


Chapter 5: Conclusions

There are various soil moisture measurement methods available today,

including those based on TDR, FDR, EM wave, acoustic and radiological concepts.

Studies have been conducted into developing new methods of measurement using one or

several of those concepts. This research’s objective is to assess the concept of developing

an innovative method of detecting changes in soil moisture by using the

telecommunication signals of WSNs.

The SREM wave method utilises the strength of soil-reflected signals to

differentiate different levels of soil moisture. By using wireless signals and a simple

transmitter adjacent to WSNs nodes, an SREM wave method would be non-invasive,

simple to set up and low cost. The SREM wave method works on the principle that

different levels of soil moisture will affect the electrical properties of the soil and,

therefore, change its reflection coefficient based on angle of incidence. Higher reflection

coefficient means more energy that will be reflected back from the soil, hence higher

signal strength.

In the one year research timeline in this study, the methodology chosen to

achieve the research objective involved field experiments using Waspmote WSNs nodes

and a Lutron portable soil moisture sensor. Each WSNs signal strength measurement

was paired with a correspondent value of soil moisture measured by the portable sensor.

While the IEEE 802.15.4 standard protocol used in Waspmote has an ability to measure

and record RSS in dBm, the sensitivity is only 1 dBm. Therefore it cannot register RSS

differences of less than 1 dBm. To increase the accuracy level, each experiment contains

200 measurements at 18 different locations within three test sites in Brisbane; each

location comprises 200 measurements for the total of 3600 data points. This amount of

data is sufficient for the purpose of assessing and proving the SREM wave method

concept.

The first phase involved proving the concept by obtaining measurements

under three different soil conditions: very wet, moist and very dry. This phase

successfully recorded different signal strengths for each corresponding soil moisture



level, and the results agree with the theory that higher soil moistures means higher

received signal strengths.

The second research phase conducts measurements at the designated test

sites (see section 3.3). The 3600 data points support the findings in the first phase.

Higher signal strength corresponds to higher soil moisture values, and it decays as the

soil becomes dryer. The results confirm that the differences in signal strength at different

level of moisture are greater in dryer soil; therefore measurement is more accurate

compared to wetter soil. The highest soil moisture value (21%) was under the wettest

condition possible. This is because in situ soil moisture measurements higher that 20%

are rare. The soil portable soil moisture probe also can only record a level of 7.7% under

the driest soil conditions because the probe cannot penetrate the hardened dry soil. This

supports the advantage of using a non-invasive method that does not need a sensor

probe.

The last research phase generates an empirical equation using a fourth

degree polynomial developed from the experimental results. The equation can be used to

estimate soil moisture based on the measured signal strength value. Although this

equation is restricted to the specific hardware and soil type used in this research, the

methodology and set up, which utilises the telecommunication signal of a WSNs node to

detect changes in soil moisture, is innovative and can form a basis for further studies and

development.

The SREM wave method is heavily dependent on the amount of direct-path

wave strength that can be attenuated so that the receiver only captures the soil-reflected

wave. The project uses a Yagi directional antenna with small beam width to reduce the

2.4 GHz direct-path signal. The antenna is angled down to the soil, based on a selected

angle of incidence. The WSNs node then captures this soil-reflected wave and records its

signal strength. Although a 45° angle from the directional antenna provides significant

12 dBm attenuation to the direct-path wave, it does not completely erase it. Thus, there is

still unwanted interference to the reflected signal.

Soil type and structure is important to any method of soil moisture

measurement, especially one that measures the electrical properties of the soil. Different

types of soil have different water holding capacity; for example, higher clay content

means higher water retention capacity while higher sand content means lower retention.

The experiments in this study are done 10–20% clay content soils. It is common and



important to calibrate for different soil types and locations before installing any type of

sensors.

Two limitations in this research are the hardware used and the experimental

location. The experiment results rely heavily on the portable soil moisture sensor that

was selected and the performance characteristics of the Waspmote node and the Yagi

antenna, Moreover the study is limited to one soil type in Brisbane, Australia. Another

limitation is that the EM wave method becomes less precise at high levels of soil

moistures (see Chapter 4); however, as in situ measurements of soil moisture rarely

exceed 20% this may not be a significant problem. Despite these limitations the research

successfully assesses and proves the validity of the concept of using soil-reflected EMs

to differentiate soil moisture levels using WSNs. The results indicate that further

research and study is warranted.

Accuracy and precision of measurements are two main points that should be

addressed in further studies. There are two suggestions in achieving improvements in

these factors: one is the development of a more sensitive, dedicated sensor to measure

signal strength; the second is the use of a smaller beamwidth, more precise transmitting

antenna to eliminate interference from the direct-path wave. These hardware upgrades

might mean higher cost, but the advantages of non-invasiveness and simplicity of set up

are significant and warrant further studies. In-depth research and experiments that

involve more soil types and experimental locations will also contribute to the further

development of the SREM wave method.

In conclusion, despite the limitations of this research it has been proven that

the concept of an SREM wave method that can detect changes in soil moisture with soil-

reflected electromagnetic wave using WSNs is valid. From the data gathered a fourth

degree polynomial equation to estimate soil moisture value from measured RSS values is

presented (Equation 11). The results successfully answer the research questions posed in

Chapter 1.

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