A Location-determination Application in
WirelessHART
Xiuming Zhu1, Wei Dong1,Aloysius K. Mok1,Song
Han1, Jianping Song1, Deji Chen2,Mark Nixon2
1University of Texas at Austin
2Emerson Process Management
Outline

Location Awareness In WSN

WirelessHART

Localization in WirelessHART

Implementation

Experimental Results

Conclusion

Future Work
Location Awareness in WSN

Three kinds of distance indication information
– RSSI (Received Signal Strength Indicator)
It makes use of signal strength decay models to estimate the
distance
– TDOA(Time Difference of Arrival)
It makes use of signal (usually sound) propagation speed
– AOA(Angle of Arrival)
It determines the direction with antenna array

Our solution is based on RSSI because we do not
want to add extra devices to hardware
WirelessHART (I)

The first open wireless
standard for the process
control industry

Four Types of devices
–
Network Manager: control
center
–
Gateway: similar to AP
–
Field Devices: Sensors
–
Handheld Device (Badge):
Carried by workers
A Typical WirelessHART Network
WirelessHART (II)

WirelessHART Architecture
– Physical Layer : IEEE
802.15.4-compatible DSSS
radios
– MAC Layer : A time
synchronized and secure
layer
– Network Layer: Supports
mesh and star topology
– Application Layer:
Command-oriented
WirelessHART (III)

WirelessHART devices will be widely installed
in thousands of factories. And it is valuable to
know the locations of workers and assets
because of the hazardous conditions in
industrial environment, for example, a chemical
leak, a tornado.

However, there is no location awareness
support in WirelessHART
Localization in WirelessHART : Question

All field devices are attached to fixed locations

We need to locate the handheld device

The handheld device can sense the signal
strength from neighboring field devices

Also, field devices can also sense the signal
strength of the handheld device

However, two devices can not communicate
with each other for security issues

Then, how to compute the location?
Localization in WirelessHART : Solution

Both field devices and the handheld device send
neighbor health reports to the network manager
periodically, which contain receive signal strength of
their neighbors.

Thus, the network manager gets pairs of receive signal
strength .e.g. field device 1 reports RSS of the handheld
device is -50dbm and the handheld device report RSS
of field device 1 is -51 dbm.

Then, the network manager can choose most trustable
pairs of receive signal strength by comparison and
compute the location of the handheld device by
trilateration
Localization in WirelessHART: Example
Implementation : Hardware Platform

Hardware : JM128 Board
–

48MHz 32-bit CodeFire V1
processor with a programmable
128KB flash and 16KB RAM
Three commands provided by
WirelessHART
–
Command 780 : neighbor
receive signal strength report
–
Command 787 : neighbor
health report
–
Command 797 : setting the
transmitting power
A demo field device
Implementation : Propagation Model (I)

Floor Attenuation Factor Model
P(d)[dBm]= P(d0 )[dBm]-10nlog(d/d0) - nw * WAF

n is the rate at which the path loss increases with
distance, P(d0) is the signal power at certain
reference distance d0 and d is the distance, nw is
number of walls and WAF is wall attenuation factor

Except P(d0), every other parameter can be derived
empirically. However, it is not realistic to get one
value for all cases even within the same test
scenario. Then,how to set these parameters?
Implementation : Propagation Model (II)

We formulate it as an optimization problem.

Before the localization, we can collect enough
data about received signal strength and
distance. These data can be used to train the
model to get the best parameter tuple
(n,nw,WAF). Later on, we use these parameters
to estimate the distance.
Implementation: Trilateration

Trilateration
p1
p2 r1
p0
r0
p3
r2

We have to calculate the point p0 that minimizes
 (| p  p | r )
n
2
0
i
i
i
Implementation: Threshold (C)

Choosing Threshold (C)
C  10nlog(d)  nw * WAF
In our experiments, d is 1.5 meters. That is, if
the difference of two signal strength values will
cause a distance error more than 1.5 meters,
the pair will be discarded.
Experimental Results: Noisy, indoor(I)

Indoor, noisy with obstacle
A noisy office (Black dots are the possible locations for
handheld devices)
Experimental Results: Noisy, indoor(II)
Table 1
n
2.91
nw
0
WAF
0
Table 2
Error: (m)
Max
Min
11.72
0.39
Average Median
4.56
3.96
It is a little surprising to see both nw (
number of walls)and WAF (wall attenuation
factor) are zero, although there are
obstacles. A possible explanation is that the
fminsearch in matlab balances the effect of
obstacle attenuation (WAF) with optimized
attenuation factor (n) .
Figure4 Distance Error CDF(noisy)
Experimental Results: Quiet, indoor(I)

Indoor, quiet, no obstacle
– A Discussion Area(4m*6m)
– Light-of-sight connection
between devices
Experimental Results: Quiet, indoor(II)
Table 3
n
nw
WAF
4.12
0
0
Table 4
Error: (m)
Max
Min
2.49
0.26
Average Median
1.52
1.67
Compared to the noisy case, it is much
better . This can be attributed to the fact
the training data and the test data are
under the same uniform environment.
Compared with the noisy case, there is
much less uncertainties.
Figure 5 Distance Error CDF(quiet)
Experimental Results: Quiet, outdoor(I)

Outdoor, no obstacle
– A Parking Area(10m*25m)
– Close to a busy road
Experimental Results: Quiet, outdoor(II)
n
3.58
Table 5
nw
0
WAF
0
Table 6
Error: (m)
Max
Min
11.87
1.00
Average Median
5.03
3.16
Compared to the quiet indoor case, the
distance errors are larger. This is
because the training and testing for this
experiment were done in different
environments. However, the result is still
promising. The median is below 4
meters, which is quite accurate for
industrial use.
Figure 6 Distance Error CDF(outdoor)
Conclusion

Our application is purely software-based and applicable
to all WirelessHART networks.

It is the responsibility of the network manager to
compute the location of the handheld device because it
can make use of all information in the network.

Accuracy is improved by the careful use of two
techniques: comparison of two-way sensing distance
and training parameters before localization.

Experimental results are very promising. All
experimental results shows that median errors are blow
4 meters , which is quite enough for industrial use (10
meters)
Future Work

Use fingerprint information : A thorough
investigation RSSI fingerprint can be done
beforehand. Later on, a pattern-matching method
will be employed to locate the device

Consider the historical location information and
then, we can use it to estimate the present
location

Finally, we hope we can deploy our application in
real process control plants to validate our
implementations
Thank you and Q&A