statistical modelling of wind effects on signal...

STATISTICAL MODELLING OF WIND

EFFECTS ON SIGNAL PROPAGATION

FOR WIRELESS SENSOR NETWORKS

Praditio Putra Trenggono

B. Eng (Hons)

Submitted in fulfilment of the requirements for the degree of

Master of Engineering (Research)

School of Engineering Systems

Built Environment and Engineering

Queensland University of Technology

2011

ii <Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks>

Abstract

A wireless sensor network system must have the ability to tolerate harsh

environmental conditions and reduce communication failures. In a typical outdoor

situation, the presence of wind can introduce movement in the foliage. This motion

of vegetation structures causes large and rapid signal fading in the communication

link and must be accounted for when deploying a wireless sensor network system in

such conditions.

This thesis examines the fading characteristics experienced by wireless sensor

nodes due to the effect of varying wind speed in a foliage obstructed transmission

path. It presents extensive measurement campaigns at two locations with the

approach of a typical wireless sensor networks configuration. The significance of this

research lies in the varied approaches of its different experiments, involving a variety

of vegetation types, scenarios and the use of different polarisations (vertical and

horizontal).

Non–line of sight (NLoS) scenario conditions investigate the wind effect based

on different vegetation densities including that of the Acacia tree, Dogbane tree and

tall grass. Whereas the line of sight (LoS) scenario investigates the effect of wind

when the grass is swaying and affecting the ground-reflected component of the

signal. Vegetation type and scenarios are envisaged to simulate real life working

conditions of wireless sensor network systems in outdoor foliated environments.

The results from the measurements are presented in statistical models involving

first and second order statistics. We found that in most of the cases, the fading

amplitude could be approximated by both Lognormal and Nakagami distribution,

whose m parameter was found to depend on received power fluctuations. Lognormal

distribution is known as the result of slow fading characteristics due to shadowing.

This study concludes that fading caused by variations in received power due to

wind in wireless sensor networks systems are found to be insignificant. There is no

notable difference in Nakagami m values for low, calm, and windy wind speed

categories. It is also shown in the second order analysis, the duration of the deep

fades are very short, 0.1 second for 10 dB attenuation below RMS level for vertical

<Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks> iii

polarization and 0.01 second for 10 dB attenuation below RMS level for horizontal

polarization. Another key finding is that the received signal strength for horizontal

polarisation demonstrates more than 3 dB better performances than the vertical

polarisation for LoS and near LoS (thin vegetation) conditions and up to 10 dB better

for denser vegetation conditions.

iv <Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks>

Contents

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

Contents ................................................................................................................................... iv

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

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

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

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

Acknowledgments ................................................................................................................... xi

Chapter 1: Introduction ............................................................................................................ 1

1.1 Motivation ...................................................................................................................... 3

1.2 Purposes ......................................................................................................................... 3

1.3 Significance .................................................................................................................... 4

1.4 Thesis outline ................................................................................................................. 5

Chapter 2: Literature Review ................................................................................................... 7

2.1 Factors affecting radio transmission .............................................................................. 7

2.2 Small-scale fading .......................................................................................................... 9

2.2.1 Types of small scale fading .................................................................................. 10

2.3 Related works ............................................................................................................... 13

2.3.1 Related works in fixed wireless links ................................................................... 13

2.3.2 Recent works in wireless sensor networks field ................................................... 16

2.4 Summary and implications........................................................................................... 17

Chapter 3: Research Design ................................................................................................... 19

3.1 Measurement equipment .............................................................................................. 19

3.1.1 Waspmote ............................................................................................................. 19

<Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks> v

3.1.2 Waspmote performance characterisation ............................................................. 22

3.1.2.1 Transmission distance test ............................................................................ 22

3.1.2.2 Attenuation by foliage tests ........................................................................... 24

3.1.3 Anemometer ......................................................................................................... 27

3.2 Methodology ................................................................................................................ 28

3.2.1 First phase: Measurement .................................................................................... 28

3.2.2 Second phase: Statistical analysis ........................................................................ 31

3.3 limitations .................................................................................................................... 32

Chapter 4: Results .................................................................................................................. 35

4.1 Statistical quantities ..................................................................................................... 35

4.2 First order statistics ...................................................................................................... 40

4.3 Second order statistics ................................................................................................. 50

4.4 Summary ...................................................................................................................... 55

Chapter 5: Discussion and Conclusion .................................................................................. 57

5.1 Discussion .................................................................................................................... 58

5.2 Implication and future work ........................................................................................ 60

References .............................................................................................................................. 63

vi <Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks>

List of Figures

Figure 2.1. Received power decay with distance at different frequencies (Friis Equation) ..... 8

Figure 2.2. Types of small-scale fading [21] .......................................................................... 11

Figure 3.1. Waspmote board [48] ........................................................................................... 20

Figure 3.2. Xbee radio transceiver stands alone (left) and with Waspmote board (right) ...... 20

Figure 3.3. Scatter plot of measured data and corresponding path loss exponent for different

polarisation ............................................................................................................................. 23

Figure 3.4. Type of vegetation at Samford; (a) Grass, (b) Apocynaceae Dogbane Family Tree

and (c) Forest .......................................................................................................................... 25

Figure 3.5. Signal strength performance due to different types of foliage ............................. 26

Figure 3.6. Kestrel 4000 pocket weather meter and PC interface [55] .................................. 27

Figure 3.7. Aerial map of the measurement locations; (a) Samford (b) Lake Cibeureum at

Tambun (taken from Google Maps) ....................................................................................... 29

Figure 3.8. Type of vegetation at Tambun; (a) Acacia trees, (b) Tall grass, (c) Grass. ......... 30

Figure 4.1. Received power and wind speed profile for vertical (top) and horizontal (bottom)

polarisation in Tambun Acacia tree scenario ......................................................................... 37


polarisation in Tambun tall grass scenario ............................................................................. 38


polarisation in Tambun grass scenario ................................................................................... 38


polarisation in Samford Dogbane tree scenario ..................................................................... 39


polarisation in Samford grass scenario ................................................................................... 39

Figure 4.6. PDF and CDF of Acacia tree scenario at Tambun ............................................... 43

Figure 4.7. PDF and CDF of tall grass scenario at Tambun ................................................... 44

Figure 4.8. PDF and CDF of grass scenario at Tambun ......................................................... 45

Figure 4.9. PDF and CDF of Dogbane tree scenario at Samford ........................................... 46

Figure 4.10. PDF and CDF of grass scenario at Samford ...................................................... 47

Figure 4.11. CDF of measured data at Tambun in low wind condition ................................. 49

<Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks> vii

Figure 4.12. CDF of measured data at Tambun in calm wind condition ............................... 49

Figure 4.13. CDF of measured data at Tambun in windy condition ...................................... 50

Figure 4.16. LCR results with respect to local mean for vertical polarisation measurements at

Tambun .................................................................................................................................. 51

Figure 4.17. LCR results with respect to local mean for horizontal polarisation measurements

at Tambun .............................................................................................................................. 51

Figure 4.18. LCR results with respect to local mean for vertical polarisation measurements at

Samford .................................................................................................................................. 52

Figure 4.19. LCR results with respect to local mean for horizontal polarisation measurements

at Samford .............................................................................................................................. 52

Figure 4.20. AFD results with respect to local mean for vertical polarisation measurements at

Tambun .................................................................................................................................. 53

Figure 4.21. AFD results with respect to local mean for horizontal polarisation

measurements at Tambun ...................................................................................................... 53

Figure 4.22. AFD results with respect to local mean for vertical polarisation measurements at

Samford .................................................................................................................................. 54


measurements at Samford ...................................................................................................... 54

viii <Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks>

List of Tables

Table 2.1. Frequency and wavelength band designators [20] .................................................. 9

Table 2.2. PDF representation of common distribution ......................................................... 13

Table 3.1. Xbee range of specification ................................................................................... 21

Table 3.2. Comparison of existing wireless nodes ................................................................. 21

Table 3.3. Received signal strength result from transmission distance test ........................... 23

Table 3.4. Received signal strength from attenuation by foliage test ..................................... 25

Table 3.5. Received signal strength comparison at 12 m distance ......................................... 27

Table 3.6. List of vegetation ................................................................................................... 30

Table 4.1. Statistical quantities of measurements in Tambun ................................................ 36

Table 4.2. Statistical quantities of measurements in Samford ................................................ 36

Table 4.3. Log likelihood value for Tambun measurements .................................................. 41

Table 4.4. Log likelihood value for Samford measurements ................................................. 41

Table 4.5. Nakagami m value for Tambun measurements .........................................42

Table 4.6. Nakagami m value for Samford measurements ........................................42

Table 4.7. Nakagami m value for different wind conditions at Tambun ...................48

Table 4.8. Nakagami m value for different wind conditions at Samford ...................49

<Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks> ix

List of Abbreviations

AFD Average Fade Duration

BER Bit Error Rate

CDF Cumulative Distribution Function

CW Carrier Wave

FSPL Free Space Path Loss

ISM Industrial, Scientific, and Medical

LCR Level Crossing Rate

LoS Line of Sight

MLE Maximum Likelihood Estimation

NLoS Non–Line of Sight

PDF Probability Density Function

QoS Quality of Service

QUT Queensland University of Technology

RSS Received Signal Strength

RMS Root Mean Square

SERF Samford Ecological Research Facilities

WSNA Workshop on Sensor Networks and Applications

x <Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks>

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: _________________________

<Statistical Modelling of Wind Effects on Signal Propagation for Wireless Sensor Networks> xi

Acknowledgments

As my journey through this Master Degree has finally come to an end, I would

like to express my gratitude to all those who gave me the possibility to complete this

thesis. First and foremost, I would like to thank my principal supervisor, Dr Karla

Ziri-Castro for her constant support, stimulating suggestions and encouragement in

all of the time of research, without which the completion of this thesis was not

possible. I also wish to give special thanks for my co-supervisor Professor Peter

Corke and Professor Nunzio Motta for their exceptional support and helpful advises.

I acknowledge the support of Ms. Annabelle Ramsay who helped me giving

access to conduct the measurement at Samford Ecological Research Facility (SERF).

My gratitude goes also for the direct or indirect support given by the professional

support staff of the Research Portfolio Office including Ms. Elaine Reyes, Ms. Judy

Liu, and the rest of the staff that I can’t possibly mention one by one. My heartfelt

thanks goes to Dr Lisa Lines for the help of thesis editing process. Thank you also to

the people in NIRAP sensor groups who have given me a broader knowledge in

research skills.

Especially, I would like to thank with all my heart for my family for their

constant support, devotion, and never stop believing in me. To my mother and father

who always encourage me, thank you for all the advices and comfort when the things

are rough. My love goes with you all.

Last but not least, I would like to take the opportunity to thank all my friends

and associates that I failed to mention. I thank you all, sincerely for your friendship.

Chapter 1: Introduction 1

Chapter 1: Introduction

Wireless communication, simply defined, is conveying information over a

distance to communicate using radio propagation as a medium. In order to achieve

good quality of communication, it requires minimal variations in the link; otherwise

fading in the receiver can be observed. One of the causes of temporal variations are

environmental conditions such as wind speed variation. For a wireless

communication system deployed in a rural environment, wind speed variations can

induce movements in the foliage resulting in temporal variations in the received

signal strength. A thorough understanding of these temporal variations is needed to

maintain a reliable link, and thus provide Quality of Service (QoS).

This research will investigate the effects of wind, focusing on wireless sensor

network systems. The wireless sensor network system arose from the development of

an ad hoc network that incorporated devices called nodes, which have the ability to

communicate with one another to pass messages to the base station. Based on this

ability, it is possible to set up a large network using low power transceivers. Further

development of the ad hoc network involves the use of sensing devices and low cost

microprocessors put together to establish a wireless sensor network.

Furthermore, wireless sensor networks have emerged as one of the latest

research trends in the wireless technology discipline due to the extensive range of

real world applications. Environmental monitoring is one example application that

requires a system deployed in rural areas. A typical environment in rural areas

includes dense vegetation, which can cause variations in wireless transmission.

Recent studies found in the literature regarding attenuation in signal

propagation due to windblown foliage movement show that temporal variations can

cause fades in the received signal. The influences of these factors have been

examined previously, including the relationship between wind speed and the Rician

K factor in the foliated fixed wireless links [1]. Statistical analysis of fading rate

found that the wind-induced temporal variation over a short period of less than 60

seconds is Rician distributed. However, the relationship of the Rician K factor

referring to the wind speed is not conclusive as it seems to depend on the operating

2 Chapter 1: Introduction

frequency and most of the studies focused on millimetre wave frequencies (3–30

GHz) [2, 3].

A wireless sensor network is a fixed wireless link that operates in the

Industrial, Scientific, and Medical (ISM) frequency bands and there are very limited

studies that investigate the relationship between wind speed and temporal variation in

the received signal in terms of wireless sensor network system characteristics.

A wireless sensor network has certain characteristics that make it a unique

system. One of the most well known characteristics is that energy consumption has

been a limitation in the design of the devices to achieve the idea of minimal human

intervention. The nodes have to operate for a very long time (1 or 2 years) and use

energy efficient schemes such as going into sleep mode. Depending on the

application, it only requires communicating with the base station periodically. As a

result, typical wireless sensor nodes use a low power microprocessor and transceiver

while maintaining minimal costs. While there are many studies focused on energy

saving schemes to increase efficiency, it is also important to study the fading in the

radio transmission to increase the robustness of the system. Moreover, most of the

studies found in the literature investigated the effect of windblown foliage using the

approach of cellular system, which cannot be applied directly in wireless sensor

networks system. As an example, they place one of the transceiver at 30-50 m height,

which rarely happens in wireless sensor networks system. Modulation and bandwidth

of the system also quite different from other fixed wireless links, where wireless

sensor networks system usually used IEEE 802.15.4 standards.

This research will investigate the effects of wind on the wireless sensor

network system at 2.4 GHz in rural environments by the use of statistical modelling.

Outdoor experiments have been conducted to measure received signal strengths

using a commercially available wireless sensor nodes device called Waspmote in two

different polarisations. The temporal variation of the received signal strength has

been statistically analysed and discussed to find the characteristic of the system in

windy conditions. Findings of this research are important to fill the knowledge gap

regarding the influence of wind on the temporal variation of the wireless sensor

network communication system. This knowledge can be used to predict additional

loss introduced by different wind conditions in rural environments.


1.1 MOTIVATION

Research studies on wireless sensor networks are mainly focused on

networking protocols [4-8], topology control [9, 10], application [11, 12] and energy

saving schemes [13, 14]. Energy saving strategies are vital factors in wireless sensor

networks as sensor nodes work unattended and rely heavily on efficient energy

consumption [15]. Other studies focus on the investigation of long-range propagation

models to communicate over a long distance and further extend the possible

application of wireless sensor networks [16, 17]. One study investigated windblown

foliage fading in a near-ground narrowband communication link at 400 MHz [18].

The researchers used a sinusoidal carrier wave (CW) as a signal source and wireless

sensor nodes as the receiver. They stated that fading due to windblown foliage can be

modelled as a Nakagami m distribution with the m shape factor a function of both

wind speed and excess path loss. It is also found that m is affected by the foliage type

and the different seasons. However, there is not a great deal of research about the

wind effects on the wireless sensor networks communication link.

Majority of the studied found in literature investigate the problem using fixed

wireless link approach such as cellular and wi-fi systems. This thesis aims to address

that gap by studying the effects of wind-induced movement on the transmission link

of the wireless network system by constructing a statistical model. The main research

questions that motivated this thesis are:

How do wireless sensor network systems perform in windy, foliated

environments?

What are the characteristics of the received power temporal variation?

What are the differences of the results compared to findings in related studies

of other fixed wireless links?

What are the effects of different polarisations? Which one is better?

1.2 PURPOSES

A fundamental issue in designing a wireless sensor network in a rural outdoor

environment is fading caused by the surrounding vegetation and weather conditions.

4 Chapter 1: Introduction

The experimental study that has been carried out in this thesis aims to investigate the

temporal variation caused by the surrounding environment.

The main objective of this research is to develop a statistical model based on

the received signal strength and wind speed recorded from the measurements. The

measurements have been conducted at 2.4 GHz with equal antenna height of 1.75 m.

Measurements have been categorised in different scenarios according to vegetation

type, which will be explained further in the methodology section. Different

polarisations have also being considered to compare the performance against

different wind conditions. A comprehensive data analysis presented in first and

second order statistics will aim to identify additional losses introduced by different

wind conditions in the rural environment for wireless sensor networks.

1.3 SIGNIFICANCE

The effect of wind in a foliated environment is found to be causing temporal

variation of the relative phases of the multipath components resulting in fast

variations of the received signal. The variation often results in attenuation that can

reduce the QoS in the system. Although studies suggest that it is frequency

dependent, it is very useful to predict additional loss introduced by different wind

conditions in terms of different polarisation and characteristics unique to a wireless

sensor network.

This thesis provides a statistical analysis of fading in wireless sensor network

system performance affected by varying wind speed in terms of different foliage and

polarisation conditions. The findings of this research will contribute to further studies

in the field, especially with a wireless sensor network approach. In addition, it could

also contribute as a reference to predict attenuation in the wireless sensor network

transmission related to different wind condition thus creating a protocol to

accommodate the best routing algorithm. It will increase the efficiency of the system,

as it would save more energy by carefully selecting the routes or changing the

sampling frequency based on the distance of the nodes and its environmental factors.


1.4 THESIS OUTLINE

This thesis is organised as follows:

Chapter 2 discusses the theory behind radio propagation including a fading

analysis. A review of related work in the same field is also established. These

discussions provide a fundamental foundation to develop the methodology used in

this thesis.

Chapter 3 presents a detailed description of the research design. It incorporates

the measurement equipment’s description, locations, measurement scenario and

procedure. This chapter also describes the experiments conducted to determine the

performance characterisation of the wireless sensor nodes used in this research prior

to investigating the effect of wind. Results from this experiment are important as an

illustration of the expected performance of the node in terms of transmission distance

and foliage attenuation. Several limitations derived from the methodology are

explained in the last section of this chapter.

Chapter 4 is dedicated entirely to presenting the result and analysis of the

experiments. This chapter presents the results in terms of first and second order

statistics. Temporal variations of the received signal are investigated in terms of

Probability Density Function (PDF) and Cumulative Distribution Function (CDF).

Comparisons with commonly known distribution have also been included. In

addition, Level Crossing Rate (LCR) and Average Fade Duration (AFD) have been

calculated and shown.

In Chapter 5, the conclusions of the measurement campaign are presented

along with the discussion about the findings. The discussions of the results

concerning the research questions stated in Chapter 1 are also included. Finally, the

implications of this thesis are derived and future research directions are presented.

Chapter 2: Literature Review 7

Chapter 2: Literature Review

This thesis investigates the relationship between varying wind speeds and

temporal variations that cause fading in the received signal. A literature review is

conducted to determine the current state of reported work about the effect of

windblown foliage in wireless communication. This chapter also contains an

overview of background theory in signal propagation.

The literature review commences with a discussion on how a signal propagates

in free space including factors that affect radio transmission. This is followed by a

discussion about the theory of fading. The chapter finishes with a review of previous

research related to the wind effects on fixed wireless links.

2.1 FACTORS AFFECTING RADIO TRANSMISSION

In order to achieve a successful radio transmission, several factors have to be

considered. Friis’ Radiation Formula [19] describes signal propagation in free space.

Pr = 𝐺𝑟𝐺𝑡𝑃𝑡 𝜆

4𝜋𝑑

2

Where Pr is the received power, Pt is the transmitted power, Gr is the received

antenna gain, Gt is the transmitted antenna gain, λ is the wavelength, and d is the

distance. This expression shows that in a radio transmission with a distance (d) apart

between two points, we can find the received power (Pr) in watts by characterising

transmitting and receiving antenna assuming in free space propagation.

Then, it is convenient to take the 10𝑙𝑜𝑔10 of the Friss’ formula to give

10𝑙𝑜𝑔10 Pr = 10𝑙𝑜𝑔10𝐺𝑟 + 10𝑙𝑜𝑔10𝐺𝑡 + 10𝑙𝑜𝑔10𝑃𝑡 − 20𝑙𝑜𝑔10

4𝜋𝑑

𝜆

Or

Pr 𝑑𝐵𝑚 = 𝑃𝑡 𝑑𝐵𝑚 + 𝐺𝑟 𝑑𝐵𝑖 + 𝐺𝑡 𝑑𝐵𝑖 − 20𝑙𝑜𝑔10

4𝜋𝑑

𝜆

8 Chapter 2: Literature Review

This expression show the Friss’ equation expressed in decibels based on a 1-

milliwatt reference. It represents the loss of signal reaching the receiver as a result of

the spreading of the transmitted beam in an inverse square law fashion. To present

the expression in words:

𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑝𝑜𝑤𝑒𝑟 = 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑 𝑝𝑜𝑤𝑒𝑟 + 𝑎𝑛𝑡𝑒𝑛𝑛𝑎 𝑔𝑎𝑖𝑛𝑠 − 𝑓𝑟𝑒𝑒 𝑠𝑝𝑎𝑐𝑒 𝑙𝑜𝑠𝑠𝑒𝑠

While the Friss’ radiation formula applies on free space only, the above

expression allows a general approximation (best case scenario) for the received

power. Other factors affecting signal propagation are reflections (i.e. ground

reflection), diffraction (i.e. from nearby hills) and scattering (i.e. due to vegetation or

different weather conditions).

Figure 2.1. Received power decay with distance at different frequencies (Friis

Equation)

Moreover, different frequency bands result in different characteristics against

losses. This is due to the relationship between frequency and wavelength. A short

wavelength results in higher possibilities for the signal to be reflected or scattered by

obstacles that have the same or longer length. In free space, wavelength and

frequency are related to velocity according to [19]

𝑣 = 𝑓𝜆

Based on the value for the velocity of light, the expression can be written as

𝑓 𝑀𝐻𝑧 = 300

𝜆(𝑚)

433 MHz

MHzMHz

900 MHz

MHz

2.45 GHz

GHz


By using the above expression, wavelength for different frequencies can be

determined and shown in Table 2.1. Note that this research is focused on 2.4 GHz

frequencies that lie in the UHF band.

Table 2.1. Frequency and wavelength band designators [20]

2.2 SMALL-SCALE FADING

Small-scale fading is used to illustrate rapid fluctuations of the amplitudes,

phases, or multipath delays of a radio signal over a short period of time or travel

distance, so that large-scale path loss effects may be neglected. Fading is caused by

interference between two or more replicas of transmitted signals that arrive at

slightly different times at the receiver. These radio signals resulted from multipath

propagation that combine at the receiver antenna to give a resultant signal, which can

vary widely in amplitude and phase depending on the distribution of the intensity and

relative propagation time of the waves and the bandwidth of the transmitted signal.

There are many physical factors in the radio propagation channel influence

small-scale fading. These include the following [21]:

Multipath propagation

Multipath propagation occurs because of the presence of physical objects that

lead signals to be reflected and scattered. It can contribute to a constantly changing

environment in the channel that dissipates signal energy in amplitude, phase and

time. These multiple replicas of the transmitted signal that arrive in the receiver

produce random phase and amplitude, which result in fluctuations in signal strength.


Speed of the mobile

Due to the relative motion between the mobile and the base station, each

multipath wave experiences an apparent shift in frequency. The shift in received

signal frequency due to motion is called the Doppler shift, and is directly

proportional to the velocity and direction of motion of the mobile with respect to the

direction of the arrival of the received multipath wave. This phenomenon occurs

during a transmission where a receiver moves towards or away from a transmitter.

Speed of the surrounding objects

Objects in the radio channel will induce a time varying Doppler shift on

multipath components if they are in motion. This means that if the surrounding

objects move at a greater rate than the receiver, the effect dominates the small-scale

fading and otherwise the motion may be ignored and only the speed of the receiver

need to be considered. The coherence time defines the ‘staticness’ of the channel,

and is directly impacted by the Doppler shift [21].

The transmission bandwidth of the signal

The bandwidth of the channel can be quantified by the coherence bandwidth,

which is related to the specific multipath structure of the channel. It is a measure of

the maximum frequency difference for which signals are still strongly correlated in

amplitude. If the transmitted signal has a narrow bandwidth as compared to the

channel, the amplitude of the signal will change rapidly, but the signal will not

distort in time. Therefore, the statistics of small-scale signal strength and the

likelihood of signal smearing appearing over small-scale distances are very much

related to the specific amplitudes and delays of the multipath channel, as well as the

bandwidth of the transmitted signal.

2.2.1 TYPES OF SMALL SCALE FADING

The relationship between signal parameters (such as bandwidth and symbol

period) and channel parameters (such as rms delay spread and Doppler spread) will

determine different types of fading for different transmitted signals. The time

dispersion and frequency dispersion mechanism in a radio channel leads to four

possible distinct effects, which are evident depending on the nature of the transmitted


signal, the channel and the velocity. Figure 2.2 shows a tree of the four different

types of fading.

Figure 2.2. Types of small-scale fading [21]

A channel may be classified either as fast fading or slow fading depending on

how rapidly the transmitted signal changes as compared to the rate of change of the

channel [21]. Fast fading occurs if the channel impulse response changes rapidly

within the symbol period of the transmitted signal. This results in frequency

dispersion due to Doppler spreading, which leads to signal distortion. In the case of a

frequency selective, fast fading channel, amplitudes, phases and time delays of any

one of the multipath components vary faster than the rate of change of the

transmitted signal.

Fast fading only compromise with the rate of change of the channel due to

motion. Therefore, wind-induced movement in vegetation can be characterised in

terms of fast fading analysis. For example, in a radio propagation environment, we

can characterise the complex signal path gain of a narrowband wireless channel by a

frequency-flat (but possibly time varying) response [22].

𝑔 𝑡 = 𝑉 + 𝑣(𝑡)

Where V is a complex constant and v(t) is a complex, zero-mean random time

variation caused by vehicular motion or windblown foliage. This description applies


in term of coherent bandwidth and coherent time where the signal is experiencing flat

fast fading.

Statistical analyses in first and second order are constructed to help

characterise the fast fading components in the received signals. The constructed

fading amplitude distribution then compared to the commonly known distributions

associated with radio channel such as Rayleigh, Rician, Nakagami and Lognormal.

In a radio channel, Rayleigh distribution is typically used to describe the

statistical time varying nature of the received envelope of a flat fading signal, or the

envelope of an individual multipath component. Moreover, Rayleigh distribution is

known to describe when the condition of propagation is mainly dominated by the

random multipath component.

Conversely, when there is a dominant signal component present, such as a line

of sight (LoS) propagation path, the small-scale fading envelope distribution is

Rician. As the dominant signal becomes weaker, the composite signal resembles a

noise signal that has an envelope that is Rayleigh. In this fashion, Rician distribution

degenerates to a Rayleigh distribution when the dominant component fades away.

Rician distribution is often represent in terms of a parameter K factor and

defined as a ratio between the deterministic signal power and the variance of the

multipath. In a mathematical expression [21],

𝐾 = 𝐴2

2𝜎2

If K = 0, then the PDF tends to be a Rayleigh distribution and if K >> 1 the

PDF becomes Gaussian distribution. In Nakagami distribution, parameter m defines

the amount of fading with smaller m models the worst case fading, while m equal

infinity models the flat channel. The equivalent Rice distribution for Nakagami m is

[23],

𝑚 = 1 + 𝐾 2

1 + 2𝐾

Table 2.2 shows the mathematical representation of the common PDF

distributions.


Table 2.2. PDF representation of common distribution

Model Expression

Rayleigh distribution 𝑃 𝑟 =

𝑟

𝜎2𝑒−

𝑟2

2𝜎2

σ: Standard deviation

Rician distribution

𝑃 𝑟 = 𝑟

𝜎2𝑒−

𝑟2+𝐴2

2𝜎2 𝐼𝑜

𝐴𝑟

𝜎2

A: Amplitude of the dominant component

σ2: Variance of either of the real or imaginary

terms of the random multipath component

Io: Modified Bessel function of the first kind

and zero-order.

Nakagami distribution

𝑃 𝑟 = 2𝑚𝑚

Γ(𝑚)Ω𝑚 𝑟(2𝑚−1)𝑒−

𝑚𝑟2

Ω

m: Shape parameter,

Ω: Scale parameter, and

Γ: Gamma function.

Lognormal

distribution

𝑃 𝑟 = 1

𝑥 2𝜋𝜎2𝑒− ln 𝑥−𝜇 2

2𝜎2

𝜇: Location parameter,

σ: Scale parameter.

2.3 RELATED WORKS

This section will discuss previous research conducted in the related fields of

study into fixed wireless links in ISM frequencies and the wireless sensor networks

discipline. It will provide a brief overview of each field.

2.3.1 RELATED WORKS IN FIXED WIRELESS LINKS

Contributions to the investigations of the wind effect in the literature are

mainly focused on the fixed wireless links. This is because in a fixed wireless link,


the transmissions are mainly affected by the surrounding environment. One of the

main important environment factors that can influence the transmission is wind.

Wind can induce the foliage medium to move and therefore results in temporal

variations of the received signals, which may lead to unexpected deep fades and QoS

degradation in spite of a fade margin. Many empirical works have been conducted to

contribute to the investigation [1, 2, 24-35]. However, this section focuses on the

literature that studies fixed wireless links operating in the ISM frequencies especially

2.4 GHz frequency.

Hashim et al. [25, 34] examined that signal behaviour have a wind dependency

characteristics and the Rician K factor could be modelled by an exponential curve.

The measurement was conducted in indoor environment using artificial wind and

outdoor environment at frequencies of 0.9 GHz, 2 GHz, 12 GHz and 17 GHz. They

set up the height of the transmitter at 49 m and the receiver at 1.8 m for outdoor

measurement. Received signals from the measurements then analysed in terms of

first order and second order statistics. They found that their measured received power

showed a better fit to the Rician than Rayleigh distribution for high frequencies.

Pelet et al. [31, 36] studied the signal fading caused by the wind-induced

motion of trees at 2.45 GHz frequency and reported that wind impinging on the trees

as low as 15 km/h can cause significant fading. Fading rates under windy conditions

ranged from 0.5 to 2 fades/second and the slope occasionally reached 50 dB/second.

Perras et al. [26] compared the various temporal characteristics of radio

channels for frequencies of 2.45, 5.25, 29, and 60 GHz in various foliage conditions.

The radio channels were statistically analysed and the CDF and PDF results were

compared against existing models. They observed that Extreme Value and

Lognormal Distributions best represent the data collected. It is also reported in their

observation that the amount of RF signal attenuation through trees is larger when the

size of obstruction and the wavelength are in similar size.

Another measurement was carried out by Dal Bello et al. [32] in Rio de Janeiro

at a tropical park for mobile cellular communication system. This measurement was

conducted using different heights for the transmitter, from 12 m to 54 m and fixed

receiver height of 2.4 m. At 0.9 and 1.8 GHz frequencies, they found dependency of

signal variability with the height of base station antenna. Dominant signals are

observed to increase as the height increases, while conversely, path loss is


decreasing. Rice and Nakagami distribution are found to fit the measured distribution

of the time fading received signal in most cases. For 0.9 GHz frequencies, there is

nearly always a dominant ray with distribution tending to be a Gaussian. Moreover,

vertical polarisation is also found to be 3 dB better than the horizontal polarisation in

0.9 GHz frequencies. Dal Bello’s work was adapted to be used in ITU-R P.833-6

[33]. Although Dal Bello’s work focused on the attenuation caused by vegetation, it

is related because the measurement was performed in an outdoor environment, in

which wind occurs.

Shukla et al. [37] developed a generic model specifically for attenuation in

vegetation supported by a set of outdoor measurements taken at 12 locations in

England and involves eight different tree species. Their model includes calculations

in terms of diffraction, reflection and scattered component to be accounted when

predicting losses in the transmission. They reported that their model is capable of

reducing significant errors in predictions, but suggest further validation covering

more species.

Cuinas et al. [38] used artificial wind at different controlled speeds and

directions to perform experiments for isolated trees in three different frequencies: 0.9

GHz, 1.8 GHz, and 2.1 GHz. They found that attenuation and scattering are the two

main phenomena as a result of leaf movement and they analyse the results of long-

and short-term effects. For short-term effects, the power samples measured along the

time are less concentrated around the median power than those measured in static

conditions. Long-term effects resulted in attenuation increment in the shadowed zone

of the travelled arc while the interaction between the tree and the radio waves

generates a scattering pattern around the tree that modifies the power received in free

space conditions.

Hajime Suzuki [39] conducted an investigation on diurnal signal fading

characteristic of IEEE 802.11b outdoor fixed link. His research shows that the fading

distribution was well approximated by the Rician distribution with Rician K factor as

a function of wind speed. In addition, he analysed the correlation between climate

parameters (temperature, relative humidity, rainfall and wind speed) and Rician

factor, standard deviation of the signal level, and median signal level. A strong

correlation between wind speed and Rician factor/standard deviation was found.

Another study by Hajime Suzuki et al. [40] investigated temporal signal variation


observed by wireless broadband channels in urban and suburban areas. They reported

that no apparent correlation was found between the signal variation and local wind

speed. Their results show characteristics of LoS paths, with smaller temporal signal

variation, and of multipath effect, with a small-scale local fading.

2.3.2 RECENT WORKS IN WIRELESS SENSOR NETWORKS FIELD

There are very few studies that specifically focus on the effect of wind in

wireless sensor network systems. One of the studies was conducted by Poh Kit

Chong et al. [18]. They aimed to characterise fading experienced by ground-surface-

level wireless communication caused by movement in the surrounding environment.

They measured the temporal fading characteristics experienced by antennas located

just 1.5 cm above the surface of the ground due to windblown foliage or human

movement in the environment for a narrowband channel in the 400 MHz frequency

band. They stated that fading due to windblown foliage can be modelled as a

Nakagami m distribution with the m shape factor a function of both wind speed and

excess path loss. It is also found that m is affected by the foliage type and the

different seasons.

Although the fact that the aim of this thesis is similar to that of Poh Kit Chong

et al., the methodology of the research is quite different, notably in terms of ground-

surface measurement setup, frequency used and environment. Chapter 3 will describe

the research methodology in more detail. Further research in the wireless sensor

network field that is related to this research includes that on multipath fading and

packet delivery performance in an outdoor environment using various types of

experiments and methodologies.

Anastasi et al. [15] performed an extensive experimental analysis to investigate

the performance of sensor networks in 433 and 916 MHz frequency. They found that

the transmission range of sensor nodes significantly decreases in the presence of fog

and rain. In addition, there is a minimum distance from the ground at which sensor

nodes should be set. They reported that only when the distance from the ground is

1m or beyond can the percentage of packet losses be considered negligible.

Experimental work on temporal characteristics of outdoor sensor networks is

reported by Zennaro et al. [41] in 2.4 GHz and Sun et al. [42] in 950 MHz frequency.

They analysed their experiment in terms of packet reception rate. Zennaro et al. also


analysed in terms of energy characteristic. Zennaro et al. found that link quality in

wireless sensor network is related to the motes location by distance. Both of the

studies suggest the use of opportunistic energy-aware routing protocol to mitigate the

effect of varying channel conditions. Similar work and suggestions also reported by

Zhao et al. [43] in 433 MHz frequency, they report a systematic medium-scale

measurement of packet delivery in three different environment (office building, a

habitat with moderate foliage, and open parking lot). Their findings quantify the

prevalence of ‘gray areas’ within communication range of sensor radios and indicate

significant asymmetry in realistic environments.

Pucinelli et al. [44] examined the effect of multipath fading in 433 MHz and

2.4 GHz wireless sensor networks in physical layer analysis. They emphasise that

fading is a spatial phenomenon and does not directly depend on time but only

depends on the position of the nodes and the topology of their surroundings. Their

experiments showed that wideband radios’ supposed immunity to multipath fading is

not true. Conversely, Bao Hua Liu et al. [45] examined the multipath fading effects

on the performance of different MAC protocols in 2.4 GHz frequency. They showed

that fading and shadowing can have a significant influence on network performance.

Nandi et al.’s [46] study dedicated on the optimal transmit power in the

presence of Rician fading. It is observed that the optimal transmit power required to

maintain network connectivity satisfying a given maximum acceptable bit error rate

(BER) threshold value in multipath Rician fading channel is greater as compared to

that in absence of fading. The optimal transmit power is increased in severity of

multipath Rician fading. Moreover, they used 2.4 GHz carrier frequency in their

simulation. In terms of antenna performance, Buckley et al. [47] performed a

qualitative assessment of various antenna performances within a 2.4 GHz frequency

wireless sensor network node. From the experiment results, it is seen that antenna

performance is directly related to the type and size of the chosen antenna. In

addition, the performance of antenna can be affected to a large degree depending on

the manner in which it is configured within the wireless sensor network.

2.4 SUMMARY AND IMPLICATIONS

This chapter provided an explanation of the background theory and previous

studies related to this research. Sufficient theory and a literature review are important


as the foundation to support the analysis of the results and the development of the

most effective methodology to answer the research questions. Moreover, several

implications are derived from the discussions in this chapter.

First, characteristics of temporal variation in received signals caused by wind-

induced foliage movement are not only determined by the frequency of the signal,

but also by foliage type, the topology or geographic condition of the environment,

and the configuration of the wireless system used in the link. Second, the relationship

of specific theoretical distribution model referring to the wind effect is not

conclusive. Third, there are a limited number of studies in the wireless network

sensors field that focus on the specific topic of this thesis.

Therefore, the findings of this research will contribute to further studies on the

effect of wind on wireless sensor networks. A further description of the configuration

of the research will be provided in the next chapter.

Chapter 3: Research Design 19

Chapter 3: Research Design

This chapter describes the design adopted by this research to achieve the aims

and objectives stated in Section 1.3. The design of this research was developed based

on key conclusions drawn from the literature review. Anastasi et al. [15] suggest that

the minimum distance from the ground is 1 m to minimise the effect of Fresnel zone,

thus neglecting the packet loss percentage. For 2.4 GHz frequency and 12 m distance

as to be used in configuration of the measurements, the minimum distance from the

ground is 0.6 m [21]. Dal Bello reported that at 0.9 GHz frequencies, horizontal

polarisation is 3 dB better than the vertical polarisation. This thesis aims to determine

whether similar results can be established for the 2.4 GHz frequency band.

This chapter is divided into three main sections. Section 3.1 details the

instrument used in the study; Section 3.2 outlines the methodology used in the study,

including the procedure and location of the measurements; and Section 3.3 discusses

the ethical considerations of the research and its potential problems and limitations.

3.1 MEASUREMENT EQUIPMENT

3.1.1 WASPMOTE

Waspmote [48] is a sensor board developed by Libelium [49], a Spanish-based

company that designs and produces hardware for the implementation of wireless

sensor networks, mesh networks and communication protocols for all sorts of

distributed wireless networks. Waspmote is using the 8 MHz ATmega 1281

microcontroller with 32 kHz RTC clock and support SD card for data logging.

Typical energy consumption when the board is in the ON state is 9 mA and it is

capable of switching into sleep and hibernate state if not in use for a certain period of

time. In sleep and hibernate state, energy consumption is decreased by 62 µA and 0.7

µA, thus it is possible to achieve one year of operation without recharging. The board

also offers a solar panel socket as a backup energy source.

20 Chapter 3: Research Design

Figure 3.1. Waspmote board [48]

Waspmote offers different performance depending on the required criteria,

such as range, frequency, protocol and transmitted power. This is achievable using

Xbee, a set of radio transceivers made for Waspmote.

Figure 3.2. Xbee radio transceiver stands alone (left) and with Waspmote board (right)

The Xbee radio transceiver ranges in protocols and frequency. For example,

with a combination of Waspmote and Xbee-868 radio transceiver, Libelium stated

that a 40 km transmission range can be achieved given LoS and 5dBi dipole antenna.

Libellium offers various kits that include a Waspmote sensor board, preferable Xbee

radio transceiver, and optional accessories such as batteries and GPS module.

21


Table 3.1. Xbee range of specification

This study used Waspmote with the XBee-802.15.4-Pro and 2 dBi

omnidirectional antennas. The table below shows the comparison between other

currently commercially available nodes. Maximum ranges shown in the table are

determined on different standard depending on LoS or NLoS conditions and antenna

gains set by the manufacturer and provided as an approximation.

Table 3.2. Comparison of existing wireless nodes

Wireless

Nodes

Processor/Radio

Chip Frequency Max Range Tx Power

Mica2 [50] MPR400/CC1000 868/916 MHz 500 ft/152.4 m -20 to

+5dBm

TelosB [51] MSP430/CC2420 2.4 – 2.483.5 GHz 100 m outdoor/30 m

indoor. -24 to 0 dBm

Imote2 [52] Intel PXA271

Xscale/ CC2420 2.4 GHz 30 m -24 to 0 dBm

JCUMote [16] ATmega 128L/

CC1020 40.66 MHz 13200 m 30 dBm

Fleck [53] ATmega 1281/

Nordic nRF903

433/868/916 MHz/2.4

GHz 1300 m 10 dBm

Waspmote ATmega 1281/

XBee 0.868/0.9/2.4 GHz 40 km 20 Bm


3.1.2 WASPMOTE PERFORMANCE CHARACTERISATION

Prior to use the nodes in the measurement, we developed a performance

characterisation of Waspmote nodes in terms of transmission distance and

attenuation by foliage. The objective is to investigate the relationship between

transmission distance and signal strength in a LoS condition and study the

attenuation expected with the presence of typical foliage found in outdoor

environments. Both tests are conducted in two different polarisations: vertical and

horizontal. The result from this experiment will provide insight into Waspmote’s

performance so we know the attenuation characteristic of the nodes based on the

received signal strength before we introduce wind as a new factor aside transmission

distance and foliage attenuation.

The experiments use 2.4 GHz Waspmote nodes with transmit power of 10 dBm

and 2 dBi omni directional antenna. Both transmitter and receiver were placed at

equal ground at height of 1.75 m. Note that the experiment’s terrain is relatively flat.

We used an empty football field (and LoS straight road for the 500 m test) for the

transmission distance tests. For the attenuation by foliage, we used the Queensland

University of Technology’s (QUT) research facility at Samford [54]. The transmitter

node sent 50 KB of data for each transmission with a sampling rate of an average

0.5-1 samples/sec. The RSS values then calculated and recorded based on DC

voltage value for every good packet received using the on-board ADC and then

provide the conversion into dBm by its absolute value.

3.1.2.1 Transmission distance test

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

decay rate as the distance between transmitter and receiver increases in a LoS

scenario. In this experiment, received signal strength at distances 0, 12, 25, 50, 100,

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

and horizontal. To ensure accuracy, 250 points of data from each measurement were

recorded and the result was then averaged.

It was expected that this experiment would show a decrease in received signal

strength as the distance between transmitter and receiver increased. As there is LoS,

the primary variable that affects the decrease in signal strength is ground reflection.

In Table 3.3, the average value from 250 points of data is demonstrated for each

23


measurement. The maximum (best signal strength) value of RSS recorded according

to the Waspmote specification is -36 dBm, which is shown in the 0 m distance test.

Table 3.3. Received signal strength result (dBm) from transmission distance test

Range/

0m 12m 25m 50m 100m 200m 400m 500m

Polarisation

Vertical -36 -44.4 -52.1 -54 -60.7 -66.7 -78.3 -89.9

Horizontal -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

Packet Loss

V 7.8% 5% 7.8% 4.7% 7% 7% 24.7%

H 9% 9% 4% 6.3% 3% 5% 44%

As expected, the signal strength decreased as the distance between the

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

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

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

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

strength. Figure 3.3 displayed the path loss (transmitted power – received power) in

different distance and corresponding path loss exponent derived from the data

including the standard deviation marked by vertical line adjacent to the data points.

Figure 3.3. Scatter plot of measured data and corresponding path loss exponent for

different polarisation


We can immediately see that horizontal polarisation exhibits better

performance than vertical polarisation. This is primarily because in vertical

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

horizontal polarisation. The ground reflection effect on signal strength diminishes as

the distance between transmitter and receiver increases. The effect occurs as a result

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

reflected signal approaches the direct path signal [21]. That is why as the distance

grew, the different signal strengths between the two polarisations became smaller.

Path loss exponent from the measured data is found to be n = 1.7 which is better than

the free space environment (n = 2) and equal to the value in indoor LoS environment

(n = 1.6 to 1.8) [21].

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

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

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

the signal quality drops significantly due to hardware limitations. The fact that

almost 40 per cent from 250 transmissions failed (packet loss) further strengthens

this phenomenon.

3.1.2.2 Attenuation by foliage tests

The objective of this experiment is to determine the attenuation from various

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

The experiment consisted of three scenarios based on types and height of the

vegetation. The grass experiment was situated at a LoS area where there was tall

grass at heights below the antenna. Two others scenarios were in the non–line of

sight (NLoS) condition.

25


(a) (b)

(c)

Figure 3.4. Type of vegetation at Samford; (a) Grass, (b) Apocynaceae Dogbane

Family Tree and (c) Forest

Table 3.4. Received signal strength (dBm) from attenuation by foliage test

Foliage/ Grass

(12m)

Apocynaceae Dogbane Family

Tree (12m) Forest (12m) Forest (25m)

Polarisation

Vertical -49.5 -69.3 -63.2 -65.3

Horizontal -46.5 -59.2 -58.5 -60.2

Difference 3 10.1 4.7 5


Figure 3.5. Signal strength performance due to different types of foliage

Obstruction in the form of vegetation between the transmission paths will

introduce attenuation in the 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 scatter. When it

confronts obstacles that are larger, it could reflect. Both of these phenomena

contribute to signal attenuation [21].

As with the transmission distance test, horizontal polarisation exhibits better

performance than vertical polarisation. The bush scenario shows more than 10 dBm

difference. One of the factors is that in relation to foliage condition, primary

obstructions are vertical tree trunks and branches with a length equal to or larger than

the 2.4 GHz frequency wavelength (12.5 cm). This is why horizontal polarisation

fared better than vertical polarisation.

When the vegetation is denser, attenuation is higher. The photos shown in

methodology demonstrate why the received signal strength in the forest environment

is higher than in the Dogbane family tree, even when the distance increased from 12

to 25m. The branches and leaves of the Dogbane family tree are simply too dense

compared with the forest scenario.

The presence of vegetation could contribute a significant amount of attenuation

to signal strength and in turn cause a severe decrease in transmission distance. A 12

m transmission distance the forest scenario, for example, exhibits a decrease in signal

-70

-60

-50

-40

-30

-20

-10

0

Free Space (12m)

Grass (12m) Dogbane (12m)

Forest (12m) Forest (25m)

Sign

al S

tre

ngt

h (

dB

m)

Signal Strength Performance Due to Different Type of Foliage

Vertical Polarisation

Horizontal Polarisation

27


strength almost the same as 200m LoS. Table 3.5 shows signal attenuation in 12 m

with and without the presence of foliage.

Table 3.5. Received signal strength (dBm) comparison at 12 m distance

Foliage/Polarisation Free Grass Apocynaceae Dogbane

Tree Forest (12m)

Vertical -44.4 -49.5 -69.3 -63.2

Horizontal -41.9 -46.5 -59.2 -58.5

Attenuation with

respect to foliage free

for vertical polarisation

5.1 24.9 18.8

Attenuation with

respect to foliage free

for horizontal

polarisation

4.6 17.3 16.6

3.1.3 ANEMOMETER

This study used a pocket weather meter produced by Kestrel to measure and

record wind speed. A Kestrel type 4000 is capable of measuring wind speed with an

operational range up to 60 m/s in ± 3 per cent accuracy. It is also capable of storing

the measured data with an interval of every two seconds and together with the PC

interface NK PN-0830 and given software, performs a data upload to the computer.

Figure 3.6. Kestrel 4000 pocket weather meter and PC interface [55]


3.2 METHODOLOGY

The research was divided into two phases. The first phase consisted of

measurement and the second phase was statistical data analysis based on phase one.

A detailed description of these phases is given below.

3.2.1 FIRST PHASE: MEASUREMENT

The research has approached the main problem by conducting experimental

measurements in outdoor environments. The idea is to simulate a condition of a

wireless sensor networks system communicating in the presence of wind and

vegetation. This section will be discussed in three important aspects: location, list of

vegetation and procedure of the measurement.

Location of the measurement

Measurements were carried out at two locations: Samford Ecological Research

Facilities (SERF), a QUT research facility in Queensland Australia; and Tambun,

which is a suburb near Jakarta in Indonesia with a rural environment along the lake

Cibeureum.

29


(a)

(b)

Figure 3.7. Aerial map of the measurement locations; (a) Samford (b) Lake Cibeureum

at Tambun (taken from Google Maps)

List of vegetation

This research categorised the measurements based on the type of vegetation.

There are five different types of vegetation, three of them located in Tambun and the

other two located in Samford. Table 3.6 provides the list of the vegetation and this is

followed by photographs. Note that the photographs of vegetation in Samford were

shown previously in Figure 3.4 (a) and (b).


Table 3.6. List of vegetation

Location Vegetation

Samford Grass and Apocynaceae Dogbane Family Tree

Tambun Grass, Tall Grass and Acacia Tree

(a) (b)

(c)

Figure 3.8. Type of vegetation at Tambun; (a) Acacia trees, (b) Tall grass, (c) Grass.

This thesis aimed to investigate the temporal variation caused by the movement

of leaves induced by varying wind speeds. It was expected that the vegetation chosen

in this research would simulate two scenarios. First, communication in LoS where

the grass below the antenna height is swaying, which means that ground-reflected

signal would be affected by wind. The second scenario is in the condition where the

movement of leaves that are equal height with the antennas affects the direct signal

of the nodes communicating in NLoS.

Acacia trees in this measurement have an average leaf dimension of 2.8 cm x

13.8 cm, while the Dogbane trees average leaf dimension is around 3.5 cm x 21 cm.

31


Procedure

A peer-to-peer connection was set up between two nodes; the transmitting node

sent 50 KB of data to the receiver node and then the receiver node sent its received

signal strength based on the connection before to the gateway connected to the

computer. The nodes (transmitter, receiver and gateway) were configured to transmit

with the power of 10 dBm or 10 mW in 2.4 GHz band. The sampling interval was

not fixed and a typical one sample every one or two seconds are recorded. In contrast

to previously reported measurements [18, 26, 32], in studies that used a continuous

wave, this measurement system used a wideband signal (5 MHz).

The reported received signal strength value of the nodes has accuracy between

-40 to -100 dBm. The nodes were separated at a distance of 12 m. This distance was

set up with the idea of reducing interference factors in the link aside from wind and

vegetation. It is also easier to record an accurate wind speed on site when the

distance is short. An omnidirectional whip antenna with 2 dBi gain was used for the

transmitter and receiver nodes with the height of 1.75 m from the ground. The height

is sufficient to minimise the Fresnel zone effect as suggested by Anastasi et al [15].

Two different polarisations were used for the measurement: vertical and horizontal.

Horizontal polarisation was achieved by rotating the antenna by 90 degrees and

while the omni-directionality of the antenna would be lost using this setup, it would

be interesting to see the result compared to vertical polarisation.

Wind speed was measured with a sampling rate of 0.5 sample/sec. In addition,

wind speed was categorised in three different states of condition: low (0–2 m/s),

calm (2–5 m/s) and windy (above 5 m/s). The received signal strength data from the

links and wind data were measured simultaneously. All data were recorded over

intervals of 30 minutes with around 1800 received signal strength data for one

measurement.

3.2.2 SECOND PHASE: STATISTICAL ANALYSIS

The received signal strength value collected from all the measurements was

combined and sorted according to wind speeds, scenarios and polarisations to

represent the possible effect of wind. Temporal variations in the received signal were

analysed and presented in terms of their first order and second order fast fading


statistics using MATLAB PDF and CDF for the first order statistics, then LCR and

AFD for the second order statistics.

A fast fading component was extracted by normalising the received signal

values to its mean. The distribution functions of fading amplitude composed from

measured data were compared to the commonly known distributions such as Rician,

Rayleigh, Nakagami and Lognormal.

It was expected that in a rural environment, the dominant signal component is

assumed to be the sum of an attenuated direct signal and the contributions of

diffracted signals around the tree structure as well as the ground reflection

component. Therefore, the PDF and CDF is thought to follow the Rician distribution

[25] and one of the statistical characteristics of the fast fading can be described by

the K-factor, which will be found in the analysis. High K-factor represents a strong

mean component relative to multipath and low value represents relatively high

multipath contributions. In Nakagami distribution, parameter m defines the amount

of fading with smaller m models the worst case fading, while m equal infinity models

the flat channel. The equivalent Rice distribution for Nakagami m is [23],

𝑚 = 1 + 𝐾 2

1 + 2𝐾

3.3 LIMITATIONS

This section discusses the limitations of the study based on the methodology

used in this research. It should be noted that this research used an empirical approach

to answer the research question. Several aspects are considered in this discussion on

limitations in terms of wind speed, distance and vegetation type.

Wind speed characteristics as a weather element are one of the environmental

factors that are very specific to the location. This is the key reason for using two

different locations in this measurement. Although SERF provides a perfect

simulation of an outdoor environment with its grassland and forest, the wind speed

recorded was very low. During a one-month period, typical maximum wind speed

recorded at the time of measurement was under 5 m/s. Conversely, Tambun

measurements recorded a much higher wind speed (maximum wind speed of 8.9 m/s)

with an average speed of 2–3 m/s for the 30-minute time period. Nevertheless, we

33


consider the results of the measurements to be a valid representation of the systems

performance in windy conditions.

Depending on the application, the 12 m distance between nodes could be

considered very short for wireless sensor network environmental monitoring. It could

affect the received signal, as the dominant signal could still be much stronger than

those multipath signals. However, a short distance provides greater accuracy for the

wind speed measurement, the anemometer was placed near the receiver node and

with the short distance thus wind meter reading is the same for the whole

transmission distance. The relatively short distance also minimizes other sources of

interference in the vicinity that might affect the link. Another important note is the

fact that it’s impossible to estimate RSS values for the lost packets, which can distort

the RSS estimates at larger distances, packet loss for all the experiments was below

10 %, which makes these effects negligible.

In spite of the fact that the methodology used a specific vegetation type, the

types used were considered typical of vegetation encountered in a rural outdoor

environment. Different vegetation densities would simulate different path loss.

Moreover, the result should give an idea of the performance of the wireless sensor

network nodes relative to the size of the foliage and frequency used.

Chapter 4: Results 35

Chapter 4: Results

Movement from the foliage due to varying wind speeds can introduce fading

in the received signal. Nevertheless, the severity of the fades depends on the

obstruction in the transmission path as well as the wind speed [18]. Types of

vegetation chosen in the experiment are also envisaged to represent a different path

loss, where denser vegetation means higher path loss. Minimum path loss would

refer to the average received power in a 12 m LoS condition in transmission distance

test result shown in Section 3.1.2.1 (-44.4 dBm for vertical polarisation and -41.9

dBm for horizontal polarisation). Recorded received powers are statistically

modelled using first and second order statistics. Analyses of the result are explained

together with the presentation of the entire measured data in this chapter.

The chapter begins with the description of statistical quantities of the received

envelope and wind speed along with its profile in a period of 30 minute in Section

4.1. Section 4.2 shows the PDF and CDF of the received envelope. In addition, it

demonstrates the CDF of the received envelope during different wind speeds. Section

4.3 presents second order statistics in terms of LCR and AFD. Finally, Section 4.4

summarises the analysis of the measurements.

4.1 STATISTICAL QUANTITIES

Tables 4.1 and 4.2 show the list of statistical quantities for the received

envelope and wind speed in both measurement locations. The mean received power

for horizontal polarisation demonstrates consistent higher result than the vertical

polarisation, showing an average of 3.5 dB better for a LoS and near LoS (thin

vegetation). However, there is a significant increase of performance in the NLoS

denser vegetation condition (Acacia and Dogbane tree scenario), showing 8.11 dB

and 10.19 dB better than the vertical one. Worst-case scenario is found in the vertical

polarisation Acacia tree scenario showing the highest loss in average RSS value (-

66.75 dBm), high packet loss (7.88 %), and highest standard deviation (4.33 dB).

Highest average mean RSS value is found in horizontal polarisation grass scenario

showing -48.15 dBm. In addition, highest mean wind speed (3.76 m/s) also recorded

in horizontal polarisation grass scenario.

36 Chapter 4: Results

Table 4.1. Statistical quantities of measurements in Tambun

STATISTICAL QUANTITIES

ACACIA TREE TALL GRASS GRASS

V H V H V H

Mean RSS (dBm) -66.75 -58.64 -51.56 -48 -51.69 -48.15

Standard deviation (dB) 4.33 2.82 1.62 1.04 0.69 0.84

Max wind speed (m/s) 8.4 7.3 7.9 6.8 8.9 7

Mean wind speed (m/s) 2.25 1.92 2.96 3.05 3.57 3.76

Packet loss (%) 7.88 2.24 6.17 2.37 6.29 5.32

Correlation coefficient -0.0122 -0.0791 0.0608 0.0140 -0.0443 0.0451

Table 4.2. Statistical quantities of measurements in Samford

STATISTICAL QUANTITIES

DOGBANE TREE GRASS

V H V H

Mean RSS (dBm) -69.32 -59.13 -49.94 -46.46

Standard deviation 3.91 1.22 0.61 1.01

Max wind speed (m/s) 2.4 3 4.5 2.4

Mean wind speed (m/s) 0.54 0.82 1.53 0.55

Packet loss (%) 8.63 2.60 4.89 7.12

Correlation coefficient -0.0432 -0.1206 0.0453 -0.0055

Standard deviation is closely related as a measurement of variability to show

the characteristic of how much variation the received power from the mean. In all of

the measurements, horizontal polarisation showed less variation of received power

than the vertical polarisation except for the grass scenario. This means that the

ground reflected components are found to contribute more fluctuation in received

power in LoS condition when using horizontal polarisation.

37


Packet loss percentages in these measurements are calculated by the ratio of

unsuccessful packet received in the transmission. Highest packet losses are found in

the dense vegetation in both Tambun and Samford (7.88 % and 8.63 %) when using

vertical polarisation. As with the average received power, packet loss percentages for

horizontal polarisation exhibit a better result than the vertical polarisation with the

exception of grass scenario in Samford. This anomaly occurs because at the time the

measurement was taken, the gateway that connected to the laptop was misplaced by

putting it on the ground for a while with extra foliage obstruction (thick high grass)

from the receiver than in the previous measurement. This justifies the additional

degradation in packet loss performance in the link.

Correlation coefficient values between the received power and wind speed

are shown to be very low for all the measurements even with higher average wind

speed in Tambun. This means that wind speed variations are found to be

insignificantly affecting the received power. Figures 4.1 to 4.5 show the received

power and wind speed for all measurements for 30 minute time period in both

locations and polarisations. From visual inspection, it can be seen that there is no

apparent correlation between the received power and wind speed when it varies from

low to windy conditions.

Figure 4.1. Received power and wind speed profile for vertical (top) and horizontal

(bottom) polarisation in Tambun Acacia tree scenario



(bottom) polarisation in Tambun tall grass scenario


(bottom) polarisation in Tambun grass scenario

39



(bottom) polarisation in Samford Dogbane tree scenario


(bottom) polarisation in Samford grass scenario


4.2 FIRST ORDER STATISTICS

In the case of the wireless propagation channel, the received signal is a

product of the summation of several multipath components that arrive from different

directions as a result from reflections, diffractions and scattering of the transmitted

signal. Further, depending on the characteristic of the channel, the received signal is

also affected by fading of the channel, and is thus the product of slow and fast

fading.

The terms ‘slow fading’ and ‘fast fading’ refer to the rate at which amplitude

and phase change imposed by the channel. In slow fading, amplitude and phase

change imposed by the channel could be assumed as constant over time and it

typically occurs when there are obstructions in the main path between the transmitter

and receiver.

Moreover, since the transmitter and receiver position is stationary, the slow

fading component was assumed to be constant. Therefore, the fast fading component

was extracted by normalising the received power values to its mean, as in [26]. The

distribution functions of fading amplitude composed from measured data were

compared to commonly known distributions associated with wireless communication

systems.

Figures 4.6 to 4.10 illustrate the PDF and CDF of all the received power (in

mW) recorded in the two locations with its fitted distributions. Since Rayleigh,

Rician and Gaussian distributions can be approximated by the Nakagami distribution,

it should be noted that all the theoretical distributions fitted in the results have a

Nakagami equivalent with suitable m parameter. Low Nakagami m value

corresponds to the worst case fading or similar to Rayleigh distribution with high

fluctuations of received power while high m value models a flat channel with smaller

fluctuations.

The estimation of the parameters for all the theoretical distributions used to

compare the measured data was performed using the Maximum Likelihood

Estimation (MLE) method, in which the set of parameters that maximises the

likelihood function are determined [25]. The log likelihood value (function) can be

used to compare the fit of multiple distributions and the theoretical distribution with

the largest value is the best fit for the measured data statistically. Tables 4.3 and 4.4

41


show the log likelihood value for each distribution compared to measured data at two

locations.

Table 4.3. Log likelihood value for Tambun measurements

LOG LIKELIHOOD

VALUE LOGNORMAL NAKAGAMI RICIAN RAYLEIGH

Acacia Tree

V -1130 -1170 -1665 -1665

H -1344 -1195 -1196 -1196

Tall Grass

V -590 -537 -569 -813

H 122 46 -8 -634

Grass

V 349 344 336 -262

H 369 318 281 -492

Table 4.4. Log likelihood value for Samford measurements

LOG LIKELIHOOD

VALUE LOGNORMAL NAKAGAMI RICIAN RAYLEIGH

Dogbane Tree

V -931 -831 -956 -956

H -95 -174 -237 -582

Grass

V 790 780 766 -464

H 112 165 179 -458

It was found that the measured data can be represented by both Lognormal and

Nakagami distribution reasonably well based on log likelihood value over different

scenarios and locations. In general, Nakagami shows better fit than Rician and

Rayleigh when using the log likelihood value. Therefore the results will be presented

and compared to the Nakagami distribution (that includes Rician and Rayleigh as

particular cases).

In wireless communication, Lognormal distribution is found to represent

attenuation caused by shadowing or slow fading from random objects which have the

same transmitter-receiver separation [21]. This means that the fading characteristics


for all of the measurements are the result of attenuation due to vegetation

obstruction. Therefore, Nakagami m parameters are the indication of transition

between low path loss and high path loss in terms of the variations of the received

power. In addition, the effect of shadowing is prevailing the fast fading component

caused by varying wind speed in these experiments. Table 4.5 and 4.6 below show

the Nakagami m value for Tambun measurements.

Table 4.5. Nakagami m value for Tambun measurements

TAMBUN

ACACIA TREE TALL GRASS GRASS

V H V H V H

Nakagami m value 1.8 1.9 3.7 5.3 7.2 6.1

Table 4.6. Nakagami m value for Samford measurements

SAMFORD

DOGBANE TREE GRASS

V H V H

Nakagami m value 1.05 4.5 7.7 5.9

In the Acacia tree scenario measurement shown in Figure 4.6, the multipath

component is dominant in the received envelope. PDF figures shown that there are

large spread of received power from the mean. Low Nakagami m values are found

for the Acacia tree scenarios, which yield 1.8 and 1.9 for vertical and horizontal

polarisation respectively. Significance of multipath contributions is evident due to

the foliage characteristics that introduce higher path loss than the tall grass and grass

scenario.

43


Figure 4.6. PDF and CDF of Acacia tree scenario at Tambun

Accordingly, as the path loss is decreasing, the received power dominant

component is stronger than the multipath. Less variation of received signal strength

is recorded during these measurements as seen in the PDF figures shown in Figure

4.7 and 4.8; the received power is concentrated around the mean. As the foliage

density is decreased and LoS condition is more apparent in the link, the m values are

increased. Lower m value (3.7) is evidence of a more significant multipath

contribution in vertical polarisation, whereas higher m (5.3) in horizontal polarisation

shows less influence of multipath.


Figure 4.7. PDF and CDF of tall grass scenario at Tambun

In LoS scenario the influence of multipath also shows a minimal effect in the

grass scenario. However, horizontal polarisation is found to exhibit lower m value

(6.1) than the vertical polarisation (7.2). This means that the ground reflected

components are more affected by the horizontal polarisation due to the similarities

with the ground plane and also the grass, which are in laying position due to wind.

45


Figure 4.8. PDF and CDF of grass scenario at Tambun

Received envelope measured data from the Samford measurements are

displayed in Figures 4.9 and 4.10. In Figure 4.9, Nakagami with a small m value

(1.05) fits the whole vertical polarisation measurement curve, which means a

significant amount of fading in the received envelope. Conversely, the received

envelope for the horizontal polarisation for the Dogbane tree shows that a more

dominant component exists (higher m value of 4.5) than the vertical polarisation and

showed a tendency of less multipath impact.


Figure 4.9. PDF and CDF of Dogbane tree scenario at Samford

Figure 4.10 shows a similar flat fading characteristic for the grass scenario as

with the measurement in Tambun. The Nakagami distribution is found to give the

best representation of the curve. High m values are found to represent both curve,

which showed 7.7 and 5.9 for vertical and horizontal polarisation. Furthermore,

similar trend is found as in grass scenario measurement at Tambun where horizontal

polarisation is showing lower m value than the vertical polarisation. Subsequently,

horizontal polarisation experiences higher multipath fading in grass scenario than

vertical polarisation.

47


Figure 4.10. PDF and CDF of grass scenario at Samford

In all the figures, the measured data can be fitted to Lognormal and Nakagami

distribution reasonably well. Similar results were also found in the studies by Perras

et al. [26], Chong et al. [18] and Hashim et al. [25]. Perras found that in the

frequencies ranging from 2 to 60 GHz, Lognormal are best to represent the start of

their measured data curves while Extreme Value distribution fits the tail end. In

addition, they also found that the variance of the received signal at 2 GHz is the

smallest compare to the higher frequencies. Chong et al. observed that from their

measurements at 400 MHz using wireless sensor nodes as the receiver, Nakagami

distribution gave the best fit for both first and second order statistics, using the m

shape factor as a function of wind speed and excess path loss. Hashim et al. studied

the effect of wind influence on radiowave propagation through vegetation and set up

experiments in the two different environments from 0.9 to 17 GHz frequencies. They

found that at 2 GHz frequency, Gaussian distribution is the most suitable distribution

to describe the fast fading amplitude distribution during all wind speed conditions. It

is known that Gaussian distribution results from the summation of positive random

variables and the Lognormal distribution results from the product of several positive


random variables. Therefore, if a variable has a Gaussian distribution, the same

variable expressed in logarithmic units will have a Lognormal distribution [23].

In the beginning, it was envisaged that the fading amplitude distribution would

shift from Rician to Rayleigh distribution or in other word the K factors would

decrease with increasing wind speed. However, Nakagami distribution with similar

m values is found to characterise all the CDF of measured data during low (0–2 m/s),

calm (2–5 m/s) and windy (above 5 m/s) wind speed conditions.

Table 4.7 shows the Nakagami m value sorted in different wind categories for

every scenario. It can be seen that wind is not significantly affect the received power

as there is diminutive different for m value between low, calm, and windy conditions.

Moreover, there isn’t any definitive trend that can be found in both polarisations of

which we can expect m value would notably decrease as the wind speed increase.

There is a slight decrease shown in the vertical polarisation for Acacia tree when it

shifts from calm to windy condition. Then also shown in horizontal polarisation for

both tall grass and grass scenario at Tambun. Furthermore, another similar trend was

shown in every scenario at Samford other than the Dogbane tree vertical polarisation

scenario, which shows 0.02 increases when it shifts from low to calm condition.

Table 4.7. Nakagami m value for different wind conditions at Tambun

NAKAGAMI M VALUE LOW CALM WINDY

Acacia Tree

V 1.8 1.9 1.5

H 1.9 2.0 2.1

Tall Grass

V 3.79 3.8 3.9

H 5.7 5.1 4.8

Grass

V 7 7.1 7.3

H 6.5 6.16 6.11

49


Table 4.8. Nakagami m value for different wind conditions at Samford

NAKAGAMI M FACTOR LOW CALM WINDY

Dogbane Tree

V 1.05 1.07 -

H 4.5 4.2 -

Grass

V 7.7 7.6 -

H 5.9 5.7 -

Figures 4.11 to 4.13 display the visual representation of the CDF for

measurements at Tambun. Measurements at Samford are not shown due to the low

wind speed recorded.

Figure 4.11. CDF of measured data at Tambun in low wind condition

Figure 4.12. CDF of measured data at Tambun in calm wind condition


Figure 4.13. CDF of measured data at Tambun in windy condition

One of the reasons similar m values are found in every scenario could explain

because windy conditions lasted for a shorter time than low and calm condition did at

the time of measurement. Given the slow sampling period of the link, it may not be

affect the increase of multipath influence in the received power. Another reason

would be due to the random movement of branches and leaves that may varies for

every wind speed. The size of branches and leaves in Acacia tree and Dogbane tree

are more comparable to the wavelength of the signal hence likely contributing to

more attenuation.

4.3 SECOND ORDER STATISTICS

Second order statistics in the form of LCR and AFD have been derived from

the measured data. LCR and AFD are two important statistics that are useful to

characterise the severity of the fading since it relates the time rate of change of the

received signal to the signal level and velocity of the mobile [21]. Results for LCR

and AFD during measurements at both locations and for both polarisations are

presented in Figures 4.16 to 4.23. In addition, the results were shown for every

scenario instead of different wind speed conditions, as it has been showed in the

previous section that differences in wind speed showed no significant differences in

the received signal strength. The results were then compared to theoretical

Lognormal and Nakagami distributions.

51


Figure 4.16. LCR results with respect to local mean for vertical polarisation

measurements at Tambun

Figure 4.17. LCR results with respect to local mean for horizontal polarisation


LCR is defined as the expected rate at which fading signal envelope,

normalised to the local root mean square (RMS) signal level, crosses a specified

level in a positive going direction. Figures 4.16 to 4.19 show that irrespective of

polarisation, with denser foliage the signal variations below the RMS value (deeper

fading) is more significant and occur more often as can be seen from the larger

spreads of the LCR curves for Acacia tree and Dogbane tree.


Figure 4.18. LCR results with respect to local mean for vertical polarisation

measurements at Samford

Figure 4.19. LCR results with respect to local mean for horizontal polarisation


AFD is defined as the average period of time for which the received signal is

below a specified level. It is shown in Figures 4.20 to 4.23 that fade occurs for longer

durations for denser foliage and vertical polarisation. The shapes of the curves for the

horizontal polarisation tall grass and grass scenarios are quite similar, showing

similar fading performance. The z-shape curves suggest that the RF signal tends to

fluctuate constantly in a small range in these conditions.

53


Figure 4.20. AFD results with respect to local mean for vertical polarisation





Figure 4.22. AFD results with respect to local mean for vertical polarisation




Theoretical Lognormal distributions shown in the figures are constructed with

the mean and standard deviation of 1. When compared to the measured data, it

visually tended to represent denser foliage (Acacia and Dogbane tree) measurements

better than the other scenarios due to the similarity of the normalised standard

deviation value. Thus, it should not be misleading, as Nakagami with a different m

value is able to represent all the results better, as in the first order statistics.

55


4.4 SUMMARY

The effect of wind on a 2.4 GHz outdoor wireless sensor link was studied using

different types of vegetation in two locations. Fading was relatively slow given the

duration of the deep fades are very short, 0.1 second for 10 dB attenuation below

RMS level for vertical polarization and 0.01 second for 10 dB attenuation below

RMS level for horizontal polarization. Although the measured data is specific to the

configuration and environment condition of the measurements, the results are valid

given the limitations specified in the methodology.

Most of the measured received signal strength statistically follows the

Lognormal distribution. Nakagami with different m parameters are also able to

represent the entire measured data curve with lower m means higher fading and

higher m means lower fluctuations of the received power. There is also no prominent

m value difference found for every scenario at low, calm, and windy wind speed

categories for each scenario which supplement the finding of the insignificant effect

of wind on received power. Another key finding is that the received signal strength

for horizontal polarisation demonstrates more than 3 dB better performances than the

vertical polarisation for LoS and near LoS (thin vegetation) conditions and up to 10

dB better for denser vegetation conditions.

Chapter 5: Discussion and Conclusion 57

Chapter 5: Discussion and Conclusion

This thesis studied the effect of wind on the signal propagation of wireless

sensor networks, especially at 2.4 GHz. Wind speed variation is considered one of

the typical environmental conditions encountered when deploying a wireless sensor

network in outdoor situations that can introduce fading. Further, wireless sensor

networks are designed to work independently to record and monitor environmental

conditions with minimal human interference. It has to have an ability to withstand

harsh environment conditions and minimise communication failures. This thesis

presented an extensive measurement campaign to study fading characteristics of the

wireless sensor network link in outdoor environments. Chapter 3 proposed the

methodology to be used in this thesis derived from the literature review presented in

the previous chapter. Two different locations with different wind characteristics were

chosen with different types of foliage to simulate a typical working condition for

environmental monitoring applications. In addition, we investigated the performance

of the nodes using vertical and horizontal polarisation.

A novel finding in this thesis is that there is insignificant correlation between

wind speed and the recorded received signal strength. Chapter 4 presented the

complete empirical results and analysis in terms of the statistical model. First and

second order statistics are constructed from the received power distributions and then

compared to known theoretical distributions commonly used in wireless

communication systems. Lognormal and Nakagami distributions are found to fit the

measured distribution of the time fading received power, thus reinforcing the novel

finding. Nakagami m value can be used as a function of received power fluctuation

with lower m means higher fading and higher m means lower fluctuations of the

received power. It also found that there is diminutive difference of m values with

increasing wind speed. With the unique characteristics of wireless sensor networks,

in applications in which the communication between the nodes do not frequently

exist, fading caused by temporal variation due to wind speed could be neglected, as

shown in the AFD analysis the duration of deep fades below RMS level are very

short. Another key finding is that the received signal strength for horizontal

polarisation demonstrates more than 3 dB better performances than the vertical

58 Chapter 5: Discussion and Conclusion

polarisation for LoS and near LoS (thin vegetation) conditions and up to 10 dB better

for denser vegetation conditions.

The results from this thesis are measured in relation to the limitations detailed

in Chapter 3. Three key factors must be considered in this thesis. First, a wind speed

characteristic is deterministic of the weather condition and location of the

measurement. Second, transmission distance is adjusted to minimise interference

other than the foliage and local wind speed can be accurately measured between the

transmitter and receiver. Third, types of vegetation are chosen with different

densities to simulate different path loss. A LoS grass scenario investigated the effect

of swaying grass on the ground-reflected signal. The average dimension of the leaves

of Acacia trees and Dogbane trees are comparable with the size of the wavelength for

2.4 GHz. Further, we believe that the results of the measurement were a valid

presentation of the 2.4 GHz wireless sensor network’s performance in windy

conditions.

To the best of the author’s knowledge, such detailed analysis considering the

effect of wind on 2.4 GHz wireless sensor network systems and its fading

characteristics is the first to be reported in the literature.

The next section presents the discussion of the results based on the research

questions stated in Chapter 1. This is followed by a discussion of the implications

and suggestions for further works that have arise from the analysis conducted in this

thesis.

5.1 DISCUSSION

This section presents a discussion on the results from the measurement

campaign with reference to the literature. It will propose solutions to the research

question stated in Chapter 1, which are repeated below:

How do wireless sensor network systems perform in windy, foliated

environments?

What are the characteristics of the received power temporal variation?

What are the differences of the results compared to findings in related studies

of other fixed wireless links?

59


What are the effects of different polarisations? Which one is better?

Measured data demonstrates that in a windy, foliated environment, wireless

sensor nodes operating at 2.4 GHz undergo a slow fading in the received powers.

This means that the received powers are attenuated due to the effect of foliage

obstruction in the transmission path and varying wind speed is found to have

minimal effect. Different types of vegetation are proven to introduce different levels

of attenuation where denser foliage introduces higher attenuation in the received

power. In addition, comparable size of the physical component of the vegetation

(leaves and branches) and the wavelength of the frequency will also affect the

attenuation. In a 2.4 GHz, the size of the wavelength is equal to 12.5 cm, which is

comparable to the size of leaves in the Acacia tree (2.8 x 13.8 cm) and Dogbane tree

(3.5 x 21 cm).

The characteristics of the measured received power temporal variation

distribution are statistically found to follow Lognormal and Nakagami distribution.

Lognormal distribution is known to represent the attenuation caused by shadowing or

slow fading in a wireless communication [21]. Conversely, different Nakagami m

value can be defined as the parameter for the received power fluctuations due to

multipath influence. Moreover, Nakagami can approximate both Rician and

Lognormal distribution under certain conditions [32]. It also found that there is

diminutive difference of m values with increasing wind speed.

The results from the measurement campaign in this thesis are similar to some

of the studies found in the related literature. The comparison will be discussed in

terms of similarity frequency used in the studies except for the work of Chong et al.

[18]. Using a wireless sensor node as a receiver in their measurements, they found

that Nakagami distribution is also best to fit the measured received power

distribution for 400 MHz narrowband frequency in ground-surface communication

with m parameter as a function of wind speed and excess path loss. Perras [26] found

that in the frequencies ranging from 2 to 60 GHz, Lognormal are best to represent the

start of their measured data curves while Extreme Value distribution fits the tail end.

In addition, they found that the variance of the received signal at 2 GHz is the

smallest compared to the higher frequencies. Hashim [25] found that at 2 GHz

frequency, Gaussian distribution is the most suitable distribution to describe the fast

fading amplitude distribution during all wind speed conditions. Gaussian distribution

60 Chapter 5: Discussion and Conclusion

results from the summation of positive random variables and the Lognormal

distribution results from the product of several positive random variables. Therefore,

if the log of the mean of the envelope is Gaussian, the envelope mean follows the

Lognormal distribution [23].

Suzuki [29, 39] investigates the characteristics of 1.9 and 2.4 GHz wireless

broadband channels in suburban and urban area. While an apparent correlation

between the signal variation and the local wind speed was observed in the suburban

site, no such correlation was found in the urban site. Moreover, the differences in the

correlation results in this thesis and the work of Suzuki for suburban area conditions

are due to the different configuration used, higher average wind speed, and type of

foliage in the measurement. Suzuki set up the measurement link using 18 m 12 dB

gain transmitter antenna height with the distance of 130 m from the 21 dB gain

receiver that was positioned inside a hut. The higher possibility of swaying at the top

part of the tree than the lower part will increase the fluctuation of the received signal.

In addition, the average wind speed recorded was higher for the same period and the

different type of vegetation encountered in the measurements must be considered.

Horizontal polarisation is proven to exhibit better fading performance than the

vertical polarisation. One of the interesting findings is that the results for 2.4 GHz

show much better performance with denser foliage obstruction even with a higher

wind speed condition. As stated in Tables 4.1 and 4.2, horizontal polarisation is

found to be approximately 8–11 dB better in the Acacia and Dogbane tree compared

to 3 dB better in the LoS and near LoS condition. This finding supports Dal Bello’s

studies [32] at 0.9 GHz frequency and suggest the use of horizontal polarisation for

future wireless sensor networks deployed in a dense foliage environment.

5.2 IMPLICATION AND FUTURE WORK

Several implications can be derived based on the findings of this thesis for the

deployment of wireless sensor networks. The key implication is the use of horizontal

polarisation when implementing a wireless sensor network system in a thick foliage

environment. However, the typical vegetation characteristics in the environment

should be noticed beforehand. In very dense foliage where the leaf or branch

dimensions are comparable with the wavelength of the frequency and the angle is

relatively horizontal, then horizontal polarisation advantage will falter.

61


Energy consumption is a limited and important resource for wireless sensor

networks as it determines the lifetime of the system. Algorithm and routing protocols

need to address the link with high fading characteristics such as high path loss and

variation due to vegetation obstruction. One possible example is an algorithm to

automatically adjust the transmit power to compensate attenuation caused when the

surrounding foliage is thick. Instead of repeating the transmission and increasing the

risk of high packet loss, higher transmit power for short period would compensate

attenuation and minimise wasted energy. Another example is to choose the best

routing based on the position of the nodes in the most efficient route with minimum

foliage.

As the results are dependent on the environmental conditions, we recommend

further studies on the effect of wind for different types of vegetation and in

environments with higher average wind speeds. We also suggest further investigation

into the changes of the fading characteristics of wireless sensor networks in heavy

rain fall where the electromagnetic waves of the signal interacts with the rain

particles and absorbs part of the energy causing a signal attenuation [15].

References 63

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statistical modelling of wind effects on signal...

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