Content uploaded by Wendy Wu
Author content
All content in this area was uploaded by Wendy Wu on Nov 30, 2018
Content may be subject to copyright.
Feasibility Study on Wireless Passive SAW Sensor in
IoT enabled Water Distribution System
Zhaozhao Tang
Faculty of Arts and Creative Technologies
Staffordshire University
Stoke on Trent, ST4 2DE, United Kingdom
zhaozhao.tang@research.staffs.ac.uk
Wenyan Wu
School of Engineering and the Built Environment
Birmingham City University
Birmingham, B4 7XG, United Kingdom
wenyan.wu@bcu.ac.uk
Jinliang Gao
School of Municipal and Environmental Engineering
Harbin Institute of Technology
Harbin, 150090, P. R. China
gjl@hit.edu.cn
Po Yang
Faculty of Engineering and Technology
Liverpool John Moores University
Liverpool, L3 3AF, United Kingdom
p.yang@ljmu.ac.uk
Abstract—Internet of Things (IoT) technology has recently
been widely utilized into a variety of industrial applications.
Wireless Passive Surface Acoustic Wave (SAW) sensors have
attracted great attention in numerous IoT enabled applications.
The sensor nodes are not directly supplied by the power supply
as it absorbs the energy from the interrogating Radio Frequency
(RF) pulses to excite the SAW. Temperature and pressure are
two imperative parameters to monitor whether the pipelines in
water distribution system are properly functioning. In this study,
the wired and wireless response signals of the passive SAW delay
line temperature and pressure sensor is compared. There is a 1/5
attenuation on the response signal when wireless interrogation
and response signal receiving process is applied. A wireless
passive SAW delay line temperature and pressure sensor was
planted in a framework simulating the environment of the water
pipelines, and the response signals of the sensor node in different
ambient environments are analysed and compared. Compared to
the air environment, there is only a sight attenuation of the
response signal when the sensor node is planted in the si mulating
water pipeline environment framework.
Keywords—IoT; SAW; sensor; water; interrogation; response
I. INTRODUCTION
Internet of Things technology has recently been widely
utilized into a variety of industrial applications, including
healthcare [11][12][13][14], manufacture [15], energy
consumption [16], etc. Smart water meters [17][18] are a form
of IoT, a network of technologies which can monitor the status
of physical objects, capture meaningful data, and communicate
that data over a wireless network to a computer in the cloud for
software to analyze in real time and help determine action
steps. Technologies are capable of monitoring objects such as
smart water meters and other electronic devices, organisms, or
a natural part of the environment such as an area of ground to
be measured for moisture or chemical content. A smart device
is associated with each object which provides the connectivity
and a unique digital identity for identifying, tracking, and
communicating with the object. A sensor within or attached to
the device is connected to the internet by a local area
connection (such as RFID, NFC, or BTLE) and can also have
wide area connectivity. Typically, each data transmission from
a device is small, but the number of transmissions can be
frequent. IoT involves many, many things interacting with each
other to produce actionable information.
The temperature and pressure sensors are essential elements
for monitoring ambient temperature and water pressure in
water distribution system. Conventionally water temperature
and pressure is measured by the so-called active sensors
because they need direct power supply e.g. batteries. Energy
harvesting technologies, such as solar panel and vibration
energy harvesting, are developed to charge up the batteries
[1][2]. This method is unreliable since the risk of damaging of
the batteries is high especially in harsh environment e.g.
extreme weather conditions. Moreover, the design complexity
of energy harvesting units is high which will lead to capital
expenditure for a water distribution company to invest.
However, the batteries used to power up the sensor nodes
are no longer required which can be replaced by using the
wireless passive SAW sensors. There are many advantages of
using the wireless passive SAW sensors apart from wireless
access and passive elements characters. The size of the sensor
node is small when compared with that the active sensors.
Kalinin et al.’s research indicated that the SAW sensor nodes
could be as small as the size of a coin [3][4]. They have high
sensitivity against the temperature and pressure variations.
They are ruggedized elements and suitable to use in harsh
environment [5]. Horsteiner et al., Mrosk et al., Wolff et al, and
Fachberger et al. investigated the substrate and metallisation
materials of the SAW sensors and their prospects for sensing
application in harsh environment [6][7][8]. In recent studies,
wireless passive SAW sensors have been widely used in many
applications e.g. temperature monitoring in extreme
environment, chemicals detection, tire pressure monitoring. In
2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom)
and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
978-1-5386-3066-2/17 $31.00 © 2017 IEEE
DOI 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.127
830
2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom)
and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
978-1-5386-3066-2/17 $31.00 © 2017 IEEE
DOI 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.127
830
water distribution system, active sensors are still widely used
even though some energy harvesting systems are developed for
charging the batteries for powering active sensor nodes, which
is complicated, costly and not fully reliable.
In this paper, we aim to provide a better solution for
monitoring temperature and pressure in water distribution
system. Active sensors are no longer used in this study. A
‘wireless passive SAW delay line temperature and pressure
sensor’ is adopted into a two-layer glass pipe experimental
framework to simulate the water pipe environment. The
adopted SAW sensor node was interrogated by using a vector
signal generator, and a signal analyser was used for receiving
the response signals and the subsequent processing steps.
Temperature changes were applied by the water bath, and
pressure changes were controlled by the liquid pressure
transmission platform. First, following the initial
characterisation experiment for the SAW sensor nodes [9], the
wired and wireless response signals of the passive SAW delay
line temperature and pressure sensor is compared. Then the
response signals of the sensor node in different ambient
environments are analysed and compared. The influences to the
wireless passive SAW sensor interrogation and response
signals by the pipeline environment are obtained and discussed.
II. SAW SENSOR
Generally, wireless passive SAW sensors have two
alternative types –resonator and reflective delay line.
Kalinin et al. did remarkable research and development on
the SAW resonators. SAW resonators consist of an IDT and
shorted electrode gratings on both sides to form a resonant
cavity. The surface acoustic waves are excited by the IDT
placed in the cavity and are reflected by the electrode gratings
to form a standing wave pattern. The shift in the resonance
frequency of the resonator generated when an external pressure
or temperature is applied which allows the observer to estimate
the respective parameter [3][4].
The reflective delay line uses a piezoelectric substrate with
an interdigital transducer (IDT) and multiple reflectors
deposited over piezoelectric substrate. The interrogation unit
(reader) sends an electromagnetic signal to the IDT which
converts the received signal into a SAW that propagates on the
piezoelectric crystal surface towards the reflectors. The
reflectors are placed in a specific pattern which reflects part of
the incoming wave; these reflections are reconverted into an
electromagnetic signal by the IDT and are transmitted back to
the interrogator unit. The ambient interferences could change
the propagation velocity of the SAW, resulting in phase shifts
and time delays of the reflected waves [5].
Shown in Fig. 1, the fundamental structure of the wireless
passive SAW temperature and pressure sensor consists of a
SAW delay line with an antenna connected with the IDT. The
SAW sensor obtains the necessary energy from the RF signal
received through the antenna. In this case, the SAW sensor
contains a minimum number of elements and there are no
power supply components, such as batteries, in this design. The
outside pressure applied through the action point is different
from the reference pressure inside the sensor. The pressure
differences act on the left part of the substrate, and the
temperature affects the whole substrate at the same time by the
bonding basis on the right of the substrate. The RF signal can
be received and sent through the connected antenna and
converted by the IDT. Reflectors are manufactured in parallel
with the IDT on the substrate to reflect the SAW. Two are
located at the right side of the IDT (R2and R3) and one is on
the left (R1). Such a design can fully use the two opposite
directions SAW energy propagating from the IDT [10].
The dimensions of the top surface of the substrate change
with the change of temperature and pressure. Consequently, the
time delay of the SAW changes and the phase shift of the
reflected pulse will be different. In this SAW sensor design, the
interrogator transmits a burst signal, the sensor responds with a
chain of bursts depending on the position of the reflectors
arranged on the substrate's surface. The different delay time
between two or more response signals is evaluated, then the
current pressure and temperature information are acquired from
the response signals through the subsequent signal processing.
Fig. 1. The structure of the wireless passive SAW temperature and pressure
sensor node.
III. EXPERIMENTAL PROTOCAL DESIGN
Fundamentally, the reader for the wireless passive SAW
sensor node is simulated by the signal generator and the
oscilloscope. The water pipeline environment is simulated by
the two-layer glass pipe. The water bath can control the
temperature of the inner glass pipe, and the oil pressure
platform can control the pressure of the inner glass pipe. The
entire experimental framework is shown in Fig. 3. In our
experiments, shown in Fig. 2 and Fig. 3, the interrogation
pulses were generated from a Function/Arbitrary Waveform
Generator (Agilent 33220A) and an ESG Vector Signal
Generator (Agilent E4438C) and analysed by a Spectrum
Analyser (HP 8563E). The response signals were received and
analysed by a 1 GHz Mixed Signal Oscilloscope (Agilent
MSO6104A). Wired and wireless interrogation approaches for
the sensor nodes were undertaken respectively. Based on the
831831
studies result in [9], the centre frequency of these sensor nodes
is 425 MHz, and the best interrogation pulse width is 1 μm.
The following experiments are undertaken by the above
settings.
Fig. 2. shows the processes of the interrogation, response,
and analysis. The Function/Arbitrary Waveform Generator
generates the pulse signal to mix with the standard 425 MHz
sine wave generated by the ESG Vector Signal Generator. The
result of the signals multiplication is transmitted out as the
interrogation signal to the sensor node. The Mixed Signal
Oscilloscope receives both the interrogation signal and the
response signals.
Fig. 3. shows the actual experiment framework set up. The
castor oil pressure control platform is connected to the strictly
sealed inner chamber of the glass tube, so that the pressure
applied via the platform is equal to the air pressure sensed by
the sensor node according to Boyle’s Law. The temperature is
controlled by the water bath, which is connected to the outer
layer of the glass tube, which makes the water with specific
temperature circulate between the water bath container and the
outer layer of the glass tube.
The actual temperature and pressure change can be read
from the temperature and pressure control platforms, while
they can also be analysed and calculated through the data of the
response signals received by the oscilloscope. Therefore, the
calculated values can be compared against the actual values to
evaluate the sensor performance.
250 kHz – 6 GHz ESG
Vector Signal
Generator
30 kHz – 26.5 GHz
Spectrum Analyser
20 MHz Function/
Arbitrary Waveform
Generator
Wirless Passive SAW
Temperature and
Pressure Sensor
Pressure Control
(Liquid Pressure
Transform)
Temperature Control
(Water Bath)
1 GHz Mixed Signal
Osilloscope
Fig. 2. Wireless interrogation, response and signal analysis with temperature
and pressure control.
Fig. 3. The picture of the experiment framework set up.
IV. EXPERIMENTAL RESULT
Wired and wireless interrogation approaches for the sensor
nodes were undertaken respectively. The results and analysis
are compared and shown in Fig. 4. The attenuation of the
wireless response signals is high against the wired ones.
Furthermore, the attenuation increases with the increase of the
signal amplitude. There is an average 1/5 attenuation on the
response signal when wireless interrogation and response
signal receiving process is applied.
Fig. 4. The attenuation of the received wireless signal against the wired
signal.
The response signals reflected by R2, R3and R1between air
and water are compared and analysed (Fig. 5-7) respectively.
These three figures indicate that generally the response signals
when the sensor node is in the experiment container simulating
the water pipeline environment just have slight attenuation
against the ones when sensor node is in the air. Therefore, the
attenuation caused by water could be ignored in the practical
engineering work.
832832
Fig. 5. The comparison of the response signals reflected by R2 when the
sensor node was put in the air and in the simulating water pipeline condition.
Fig. 6. The comparison of the response signals reflected by R3 when the
sensor node was put in the air and in the simulating water pipeline condition.
Fig. 7. The comparison of the response signals reflected by R1 when the
sensor node was put in the air and in the simulating water pipeline condition.
V. FUTURE WORK AND DISCUSSION
All the characterisation and initial testing works have been
done by [9] and this study. The next step is to use the
temperature and pressure control systems in this experiment
framework to obtain the corresponding data of the response
signals. Through the analysis of the data and the sensor design
theory, the feasibility of the wireless passive SAW sensor used
in water distribution system can be evaluated.
In future applications, one benefit of utilizing SAW sensors
in IoT enabled distributed water system is to potentially help
water savings in landscape irrigation in parks, medians, and
elsewhere. This is a major use of water in cities. Nationwide, it
is estimated to be nearly one-third of all residential water use
and as much as half of this water is wasted due to runoff,
evaporation, or wind. Landscape irrigation systems, which
apply sophisticated data analytics to a wide variety of objects,
are available in the market.
Also, in the water infrastructure, a utility can use an IoT
network for predictive information to remotely determine the
status and working condition of equipment (open or closed, on
or off, full or empty, etc.). The information can be actionable,
for example; a water gate can be opened or closed or a pump
turned on or off remotely to adjust the flow of water through a
water supply system. Pumps, gates, and other equipment with
moving parts in the water infrastructure can be monitored for
predictive maintenance alerts based on vibration and other
indications of failure to prevent equipment malfunctions. If a
water pump is about to fail, the utility can be prompted to
repair or replace it. An IoT-enabled water treatment plant can
report if its filters are clean and functioning properly. The IoT
can measure water pressure in pipes to find leaks faster in the
water transportation system or the presence of certain
chemicals in the water supply.
833833
VI. CONCLUSION
The wireless passive SAW sensor is introduced and the
related studies are reviewed. The wired and wireless response
signals of the passive SAW delay line temperature and pressure
sensor is compared. There is a 1/5 attenuation on the response
signal when wireless interrogation and response signal
receiving process is applied. A wireless passive SAW delay
line temperature and pressure sensor was planted in a
framework simulating the environment of the water pipelines,
and the response signals of the sensor node in different ambient
environments are analysed and compared. Compared to the air
environment, there is only a sight attenuation of the response
signal when the sensor node is planted in the simulating water
pipeline environment framework.
REFERENCES
[1] N. Metje, D. N. Chapman, D. Cheneler, M. Ward and A. M. Th omas,
“Smart Pipes—Instrumented Water Pipes, Can This Be Made a
Reality?” Sensors, vol. 11, pp. 7455–7475, 2011.
[2] A. M. Sadeghioon, N. Metje, D. N. Chapman and C. J. Anthony,
“SmartPipes: Smart Wireless Sensor Networks for Leak Detection in
Water Pipelines,” Journal of Sensor and Actuator Networks, vol. 3, pp.
64–78, 2014.
[3] V. Kalinin, “Passive wireless strain and temperature sensors based on
SAW devices,”Proc. IEEE Radio and Wireless Conf., Atlanta, USA, pp.
187−190, 2004.
[4] V. Kalinin, “Modelling of a wireless S AW system for multiple
parameter measurement,” Proc. IEEE Ultrason. Symp., pp. 1790−1793,
2001.
[5] D. C. Malocha, M. Gallagher, B. Fisher, J. Humphries, D. Gallagher and
N. Kozlovski, “A Passive Wireless Multi-Sensor SAW Technology
Device and System Perspectives,” Sensors, vol. 13, pp. 5897–5922,
2013.
[6] J. Hornsteiner, E. Born, G. Fischerauer, and E. Riha, “Surface acoustic
wave sensors for high temperature applications,” IEEE Int . Freq. C ont rol
Symp., pp. 615-619, 1998.
[7] J. M. Mrosk, C. Ettl, L. Berger, P. Dabali, H. J. Fecht, G. Fischerauer, J.
Hornsteiner, K. Riek, E. Riha, E. Born, M. Werner, A. Dommann, J.
Auersperg, E. Kieselstein, B. Michel, and A. Mucha, “SAW sensor for
high temperature applications,” Proc. 24th Annu. Conf. Industrial
Electronics Society IEEE, pp. 2386-2390, 1998.
[8] U. Wolff, F. L. Dickert, G. K. Fischerauer, W. Greibl, and C. C. W.
Ruppel, “SAW sensors for harsh environment,” III Sens. J., vol.1, no.1,
pp. 4-13, 2001.
[9] Z. Tang, W. Wu, and J. Gao, “Water pressure sensing based on wireless
passive SAW technology,” 13th International Conference on Computin g
and Control for the Water Industry, CCWI2015, Procedia Engineering,
vol.119, pp. 892-900, 2015.
[10] T. Li, “Passive SAW temperature and pressure sensor based on tire
pressure moniyoring system,” Dissertation for the Doctoral Degree in
Engineering, Harbin Institute of Technology, 2009.
[11] P. Yang, D. Stankevicius, V. Marozas, Z. Deng, A. Lukosevicius, F.
Dong, E. Liu, L. Xu, and G. Min, “Lifelogging Data Validation Model
for Internet of Things enabled Personalized Healthcare,” IEEE
Transactions on Systems, Man and Cybernetics: Systems, early access,
2016.
[12] J. Qi, P. Yang, M. Hanneghan, and S. Tang, “Multiple density maps
information fusion for effectively assessing intensity pattern of
lifelogging physical activity,” Neurocomputing, vol.220, pp. 199-209,
2017.
[13] J. Qi, P. Yang, M. Hanneghan, D. Fan, Z. Deng, and F. Dong, “Ellipse
fitting model for improving the effectiveness of life-logging physical
activity measures in an Internet of Things environment,” IET Networks,
vol.5, Issue. 5, pp. 107-113, 2017.
[14] J. Qi, P. Yang, D. Fan, and Z. Deng, “ASurveyof Physical Activity
Monitoring and Assessment using Internet of Things Technology,”IEEE
International Conference on Pervasiven Intelligence and Computing, pp.
2357-2362, 2015.
[15] D. Georgakopoulos, P. P. Jayaraman, M. Fazia, M. Villari, and R.
Ranjan, “Internet of Things and Edge Cloud Computing Roadmap for
Manufacturing,” IEEE Cloud Computing, vol.3, issue 4, pp. 66-73,
2016.
[16] F. Renna, J. Doyle, V. Giotsas, and Y. Andreop oulos, “Media Query
Processing for the Internet-of-Things: Coupling of Device Energy
Consumption and Cloud Infrastructure Billing,” IEEE Transactions on
Multimedia, vol.18, Issue: 12, pp. 2537 - 2552, 2016.
[17] N. Cherukutota, and S. Jadhav, “Architectural framework of smart water
meter reading system in IoT environment,” 2016 International
Conference on Communication and Signal Processing (ICCSP), pp. 791-
794, 2016.
[18] J. Lloret, J. Tomas, A. Canovas, and L. Parra, “An Integrated IoT
Architecture for Smart Metering,” IEEE Communications Magazine,
vol.54, Issue: 12, pp. 50-57, 2016.
834834
