signal propagation on a railway wireless condition monitoring system
TRANSCRIPT
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IET Colloquium on Antennas, Wireless and Electromagnetics
27 May 2014, Ofcom, London, UK
Signal Propagation on a Railway
Wireless Condition MonitoringSystem
Ahmadreza Faghih,Costas Constantinou, Edward Stewart
School of Electronic, Electrical and Computer
Engineering, The University of Birmingham
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Railway Research Centre
University of Birmingham
Acknowledgments: Thanks to Motorail Logistics, owners of LongMarston railway test track
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Introduction
Railway condition monitoring:
Wheel Profile Monitoring, Bogie Performance Detector, Tread
Condition Detectors, Hot Axle Bearing, Acoustic bearing Defect
Detectors and
Reduce running and maintenance costs by using a train-wide
wireless sensor network (WSN) Need to characterise wireless channel reliability on moving
train, in different environments and geographic locations
Quantify reliability of railway-WSN
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Time-Varying Channel
Multipath channel with significant time variability due to
Relative movement of linkages and
changing surrounding environment train speeding past embankments,trees, tunnels, station platforms, etc.
Because of train movement, vibration, travelling around bendsand proximity of scatterers in different environments, both
shadowing and multipath fading are highly variable Receiver signal strength, BER , packet loss and data rate are
stochastic functions of time and sensor locations
Propagation time is much lower than frame transmission time intarget railway systems MAC protocol operation is efficient
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Channel Signal Statistics For WSN
A static train configuration in situated in an open area isamenable to electromagnetic modelling of the radio channel
between nodes Moving train through a variety of environments is too
complex to model, so we adopt a statistical signal description
approach Emphasis of this preliminary study is slow channel path loss
variability using a 2.4GHz COTS transceiver pair
We present a simple statistical characterisation of the timevarying channelon a real train in a test track environment
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TRC104NI
DAQ
Excel
File
PIC
MCU
2.4GHz
transceiver
RSSI
ValueTRC104
2.4GHz
transceiver
SPI
Data Acquisition Method I
Pair of wireless Transceivers: TRC104 2.4-2.52GHz , 128
channel with 2.16 dBi monopole antennas
Sample and digitise RSSI at a sampling rate of 104samples/s
Transmitter operates in continuous mode
System calibration to convert RSSI to path loss done inanechoic chamber
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Data Acquisition Method II
Test Route at Long Marston train test track
Divide route in different zones to evaluate channel for
straight track, bends, and/or objects beside track, open areas
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Nodes Location
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Nodes Location (cont.)
Checked for free channel using spectrum analyser and set
channel: 2.520GHz Ptx= 2 dBm
Receiver kept in one location
Transmitter location moved as shown in config110
~ 10 m
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Statistical Analysis of Results
First order statistics presented only
Construct histogram/PDF (Probability Density Function) and
CDF (Cumulative Distribution Function) for received power
and hence path loss
Evaluate the median path loss and IQR for each configuration
during train movement and stop conditions in various
sections of track as well as around entire circuit
The number of significant energy transport paths are related
to separation and nodes location
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Transmitter and receiver were installed on opposite sides of a
tanker wagon with NLOS, so bends do not have noticeable effect
on signal strength
Estimated that dominant paths go around as well as below wagon Buildings and parked trains beside track have little influence on
spread of signal values
First example of observed Prx
PDF
0
0.02
0.04
0.06
0.08
0.1
-95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41
Frequency
Prx (dBm)
Config3
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Transmitter installed on passenger wagon, receiver on tankerwagon and on opposite sides of train
The bottom of passenger wagon was lower compared to tanker
wagons so the path(s) under the wagon are weaker
There was a big building beside track on the transmitter side for
part of this railway track
Bends have a marked effect on signal strength giving rise to two
peaks in PDF
Second example of observed PrxPDF
0
0.01
0.02
0.03
0.04
0.05
-95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41
Pr
obability
Prx (dBm)
Config2
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CDF for static train
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-100 -80 -60 -40 -20 0
Config4
Config5
Config1
Config6
Config2
Probability
Prx dBm
dTx-Rx
(m)
M
(Loss)
IQR
(Loss)
Config1 10.5 79 dB 2.5 dB
Config2 456.2
dB1 dB
Config3 4.75 55 dB 0.7 dB
Config4 1170.3
dB0.8 dB
Config5 19 77 dB 0.8 dB
Config6 11 73 dB 3.4 dB
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CDF for moving train along whole track
dTx-Rx
(m)
M
(Loss)
IQR
(Loss)
Config1 10.584.2
dB
10.7
dB
Config2 4 70 dB13.2
dB
Config3 4.75
62.5
dB 7.5 dB
Config4 11 77 dB10.5
dB
Config5 1979.3
dB 8.9 dB
Config6 1178.5
dB
10.8
dB
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-100 -80 -60 -40 -20 0
Config3
Config4
Config5
Config1
Config6
Config2
Prx dBm
Probability
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Future work
Next steps are to:
Calculate BER from SNR statistics
Calculate PER from packet length and error correcting code Determine appropriate system thresholds (minimum acceptable PER
for sensing application)
Calculate WSN outage probabilities assuming transmit node power
control characteristics
Calculate signal transmission delay distributions
Compute second order statistics to establish fade durations
and level crossing rates
Translate to WSN outage duration statistics
Examine mitigation strategies and associated WSN protocols
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Summary
Preliminary measurement results on real trains presented
Limited first order statistics considered only in this work
Distance dependence is not a reliable indicator for path loss Most paths are non-line of sight
Train movement results in 5-14 dB of increase in median pathloss
Train movement results in 7-13 dB increase in IQR of path loss
Scatterers in immediate vicinity to track (parked trains,building, etc.) as well as bends in track have a strong effect on
path loss variation and often observed to give rise to multi-modal path loss distributions
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References
A. Goldsmith (2005) Wireless Communication, Cambridge University
H. Chuan, M.S. Alouini (2011) Order Statistics in WirelessCommunication, Cambridge University
E. Biglieri, S. Benedetto (1999) Principle of Digital Transmission withWireless Application, Prentice-Hall
W. Stallings (2007) Data And Computer Communications, 7thEdition,Prentice-Hall
N. Yaakob, I. Khalil and J. Hu (2010) Performance Analysis of OptimalPacket Size for Congestion Control in Wireless Sensor Networks, IEEE
K. Doddapaneni, E. Ever (2012) Path Loss Effect on Energy Consumptionin a WSN, International Conference on Modelling and Simulation
T. Stoyanova, F. Kerasiotis (2009) A Practical RF Propagation Model forWireless Network Sensors, International conference on SensorTechnologies and Applications