1
Similarity-based Traffic Reduction to Increase Battery
Life in a Wireless Process Control Network
Deji Chen, Mark Nixon, Tom Aneweer,
Rusty Shepard, Terry Blevins,
and Greg McMillan
Emerson Process Management
12301 Research Blvd. Building III,
Austin, TX 78759
Aloysius K. Mok
Dpt. of Computer Sciences
University of Texas at Austin
Austin, TX 78712
KEYWORDS
Process control, wireless network, similarity, data traffic, battery life
ABSTRACT
In a process control loop, the control algorithm is executed periodically at a rate such that
process values are sensed timely into the algorithm and output values are sent timely to
the actuators. Data is transmitted through wire between the controller and the devices the sensors and actuators. When wireless transmission is used among the controller and
devices, energy consumption by the wireless transmitters becomes a concern. In this
paper, we visit the trend of the latest development in both the controllers and the devices,
and present the need for energy conservation for ever proliferating wireless devices. We
introduce the concept of similarity distance to reduce the data traffic, hence reduce the
device energy consumption. Similarity distance is defined for the difference between two
values. Two values are considered similar in certain context if they are exchangeable in
that context. After discussing the applications of the similarity concept, we proceed to one
general concept where a similarity interface is placed between the controller and its
communications subsystem.
We then present a detailed analysis of a closed loop control with some experimental
results. One assumption for this work is that the added complexity could be handled by
the ever increasing computation power.
Copyright 2005 by ISA - The Instrumentation, Systems and Automation Society.
Presented at ISA EXPO 2005; http://www.isa.org
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INTRODUCTION
Process loop control must have fast and reliable data communication between the
controller and the devices in order to achieve optimum control. As is always the case, the
controller executes control algorithms periodically at a rate fast enough that unwanted
deviation in the process is quickly corrected. It is strongly discouraged to execute the
control algorithms only when data changes [5]. In real-time terms, we should apply time
triggered rather than event triggered mechanism.
This rule of thumb is slowly challenged by the latest development on two fronts, the
controller side and the device side.
On the controller side, the exponential growth of raw computation power makes complex
real-time control a reality. Moore's law applies equally to the embedded processors. A
current embedded processor has enough computation power as past mainframes. For
example, the latest MPC5500 family of 32-bit microcontrollers from Freescale
Semiconductor has speed up to 600MHz [7]. In comparison, the CPU of PlayStation 2
runs with a clock speed of 300MHz [8].
With increased controller power improved control opportunities are possible. For
example, we have seen the introduction and maturity of fuzzy control, neural network
control, model predictive control, etc. We are seeing continued improvement over
traditional control logic such as PID. With increased controller power we could also
move the computational load from devices and other parts of a control system to the
controller.
On the device side, we see ever faster pace of new product introductions. More and more
process variables are being monitored and controlled. Normally, a device requires wire to
draw power and transmit data. As more and more devices packed into a control system,
wireless devices start to show up. The latest we hear is “Wireless HART”. Wireless
devices rely on power sources other than main power and communicate with the
controller wirelessly. Most of them are battery powered, some draw solar power, some
pilfer ambient energy such as vibration, heat, pressure, etc. For these devices, energy
consumed for data transmission comprises a sizable portion of total energy consumption.
This is even more prominent for large-scale sensor networks where tiny sensors monitor
environment data. By putting the sensors sleeping most of the time, we could extend the
sensor lifetime for years.
Let’s look at the energy usage of a wireless device, this include the energy to sense,
actuate, compute, and transmit. With main power support, sensing and actuating
consumes most of the energy while data transmission is negligible. However, sensing and
actuating could be improved but wireless data transmission is difficult. For many sensor
types such as temperature sensors and piezoelectric pressure sensors, the reading can be
made very quickly, possibly as quickly as 50uS for the reading at about 1mA at 3V. A
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fixed energy amount is required for the radio to reach a certain distance. In a wireless
device, data transmission will constitute a bigger percentage.
It is with above background that we present this work. Although the change is not
significant yet, it helps if we study the trend and look into the future. We explore the
ways to reduce a wireless device's energy consumption and hence, prolong its lifetime or
maintenance cycle. We look at the ways to reduce the amount of data communication
with computation power from the controller. The concept we introduce is called similarity
distance. With this concept, we eliminate the transmission of similar data while not
degrading control performance.
We shall emphasize here that this is a research paper, the ideas are exploratory. At present
process loop control is still designed based on periodical sampling of the instruments.
In the next section we briefly mention the current process control mechanism. Then we
shall expand on the concept of similarity distance and its application to the process
control in Section 3. Section 4 discusses one general design of such application. We
present a detailed loop control experiment in Section 5 and conclude in Section 6.
PROCESS LOOP CONTROL
In this section we provide basic background information on control architecture.
Sensor
Controller
Control Network
Actuator
Figure 1. A Process Control System
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Figure 1 shows a process control system. The controller is connected with the devices,
both sensors and actuators, via the control network. The control network is wired. The
controller reads data from the sensors and writes data to the actuators. Data is also read
from the actuators and written to the sensors for other purposes like feedbacks. To
maintain continuous control, the control algorithm is run strictly periodically. Error will
be declared when data is not received for one or more consecutive periods. Besides the
control data, configuration data could be written to the devices; diagnostic and alarm
information could be retrieved from the devices. Let’s call this data assistant data. The
assistant data is transmitted only when the data is changed or it is requested. During the
life time of the system, control data takes up the majority of the network traffic. The main
reason is that the process data is time triggered whereas the assistant data is event
triggered. The time triggered data strictly increases as time passes; the event triggered
data varies and do not occur most of the time in a steady system.
While we transmit control data every period regardless of the value, we do employ many
ways to reduce data size when archiving historical data [6]. For example, exception report
records only those data values that exceed a pre-specified data tolerance level or dead
band. Exception reporting can produce compression ratios of up to 20:1 for moderately
variable process industry data. A ratio of 10:1 is typical. The compression ratios of
adaptive data-compression algorithm have been measured to range from 10: 1 to as high
as 100:1 depending on the variability of the incoming raw data [6].
On one hand we must transmit control data periodically to have a good control of the
process; on the other hand we know the data transmitted could be greatly compressed.
The question is if we could combine both to save energy for wireless devices. In the next
section we shall digress to talk about the general idea of data transmission reduction and
come back to this topic in the subsequent sections.
SIMILARITY CONCEPT AND ITS APPLICATIONS
Similarity distance [2] is defined for the difference between two values. Two values are
considered similar in certain context if they are exchangeable in that context. Similarity
distance is a measurement of the difference between two values such that two values are
similar if their similarity distance is within a limit value. The limit value depends on the
context.
As an example, the temperature value used in a control algorithm is considered similar to
the actual real world temperature at the time the algorithm is executed and before the next
measurement, even though the value was measured by the temperature sensor some time
beforehand. The real world temperature value may change from the time it is measured to
the time it is used in the algorithm. But if we keep the time short enough, we could
confidently assume that the temperature value is within allowable precision requirement.
This requirement dictates the algorithm execution rate. The more precise is required, the
faster rate we have to run the algorithm. For example, the temperature control of a
chemical reactor has short time constant and must be run once a second, whereas the
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heating algorithm for a water boiler has long time constant and could be run every 10
second. We could define above similarity distance to be 1 second and 10 seconds
separately. In this sense, the concept of similarity is already used in process control.
The similarity distance measurement applies differently in different context. In the
previous example we used time for the similarity distance for temperature. In other
context, we could use the temperature difference as the similarity distance. In extreme
cases, we could consider any temperature value to be similar if the temperature does not
contribute to the execution of the algorithm. For example the temperature of water
usually does not matter much in its flow control through a pipeline. If the output of a
control algorithm is in fail safe state holding last value, any input value to the algorithm
has no effect on the output and could be considered similar to 0.
Another key point of similarity concept is that we could use similar value in place of the
actual value without even knowing what the actual value is. We use the sensed
temperature value in the control algorithm without knowing what the exact current real
world temperature is. The law of physics tells us how big the temperature will change
within one second. Under the same condition, the water temperature changes at a slower
rate than steel temperature; a higher viscosity fluid flows slower than a lower viscosity
one. Within a context, we could calculate the similarity distance from the actual value. A
controller, armed with all the information associated with the process in control, should
be able to calculate the maximum change of the sensor values. If the maximum change
since last read is within the similarity distance limit, the controller could use the last read
value in its processing instead of reading anew. The controller does not need the sensor to
tell it if the value is similar.
Similarity could also be defined for output value to actuators. Two identical output values
could be considered similar; two different values could be considered similar if the
physical results are the same or the difference could be ignored. For some actuators, the
value change has to be big enough to cause physical change to the device. The values
within a deadband could be considered similar as they do no cause any output change.
One of the report-by-exception methods is sandbox (or deadband). For a data source, be it
sensor or outputting controller, if the current value is within a certain range from the last
sent value, it is considered sandboxed and not sent. The sandbox could be interpreted as
the similarity distance; and the value within the sandbox is similar to the last sent value.
One way to increase the lifetime of a wireless device is to reduce its active cycle and put
it sleep most of the time. Similarity distance could be used to calculate how long a device
could be put into sleep.
The design problem is to define where to place the rules for deciding what is similar. In
some cases we need to always send the value, in others sending a value when it changes is
enough. In still others the issue will be to only send the value when it changes by some
amount. The opportunity here could be realized if we told the loop controller that it was a
pressure loop, temperature, or mass-flow, etc.
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Now let's look at a few examples.
Flow control
Figure 2 is a flow control unit. The controller reads the flow speed from the flow
transmitter and sends control data to the valve actuator to adjust its position. This
algorithm is run repeatedly to maintain the flow at a constant speed set by the user, the
setpoint (SP). The algorithm is configured to run at a period fast enough to maintain good
control during SP changes. However, most of the time the flow runs at SP in a steady
state. If we assume minor measured disturbances, the input and output values of the
control algorithm almost do not change in a steady states. Let's define the similarity
distance to be the longest time allowed between two controller executions, during SP
change, the similarity distance must be at most τ/3 to smoothly handle SP changes, where
τ is the time constant. In steady state, the similarity distance could be bigger, that is, we
could run the control algorithm at a lower rate. The drawback is the added complexity,
which means additional computation requirement.
Controller
Flow Transmitter
Valve
FT
Figure 2. Flow control
Level control
Figure 3 is a water tank level control unit. The input pipe fills the tank at a rate based on
the upstream process operation. The output flow is controlled by the valve. The periodic
control algorithm reads the tank level from the level transmitter and adjusts the valve.
The goal is to maintain the water level between a high limit (HI_LIM) and a low limit
(LO_LIM).
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Let's define the similarity to be the level difference, the similarity distance should be
smaller when the level is close to the limits and could be bigger otherwise. In other
words, we do not care much about the precise tank level when it is in the middle. Based
on the input flow, last measured level, and the last output valve position, the controller
could derive the maximum rate of water level change with time. The controller can delay
the control algorithm as long as the maximum change is within HI_LIM and LO_LIM.
Again, with more computation, the controller can reduce communications with the
devices.
Controller
LT
Level Transmitter
Valve
Figure 3. Level control
Skipped write
In a loop control, a controller output is periodically written to the actuator even if the
value is the same. For wirelessly communication, we could skip sending identical writes
as long as it does not jeopardize the communication condition status. Further more we
could also skip similar values. For device with deadband and limit cycle, similarity
distance could be defined to be the deadband. For example, let’s suppose in Figure 3 the
valve is sticky and only makes a move when the target value differs from current value by
1 unit. Armed with this knowledge, the controller will not transmit the output value
unless it is outside the 1 unit similarity distance of the last sent value. With high precision
measuring devices, the output value will be different each time because the input value is
not exactly the same at each period. For this situation, the similarity check will eliminate
many unnecessary transmissions.
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A SIMILARITY LAYER WITH WIRELESS NETWORK
In this section we describe a general design that makes use of existing process control
with a similarity interface.
The design is applied in a process control system where the wired control network is
replaced by a wireless network whose energy consumption for data transmission should
be as small as possible. The goal is to reduce the data traffic in the wireless network while
maintain the same level of process control.
We use the similarity concept to reduce the data traffic. Instead of sending the input and
output data each time the control algorithm is executed, we only transmit them when they
are no longer similar to the last transmitted ones. In the mean time, the quality of the
control system is maintained.
In a process control system, the control algorithm, once configured, will run at a constant
rate. In the design a similarity layer is placed between the controller and its
communications subsystem. Such a layer may also be placed in smart devices depending
on if extra handling is required to skip data transmission. Such a layer does not exist in
devices that simply respond to controller request. This layer only handles control data. It
does not interfere with how assistant data is transmitted.
Figure 4 is the wireless process control system with the similarity layer.
Upon a reading request, the similarity layer calculates the similarity distance between the
current input value and the last read value. If similar, the last read value will be returned
directly; if no similar, the read request will be forwarded.
Upon a writing request, the similarity layer calculates the similarity distance between the
current output value and the last sent value. If similar, return success back without
actually sending the value; if no similar, the write request will be forwarded.
Smart devices have advanced software running inside. Their input or output function
blocks will declare communication failure if expected read or write request is not
received from the controller. For these devices, the similarity layer will also be installed
in them, but at a reduced computation load. The majority of similarity calculation will be
done at the controller side. Pre-calculated result could be downloaded from the controller
to the device during system configuration. In cases as described in the next section, no
enhancement to the device is required. In other words, this scheme could be applied to
devices currently on the market.
The similarity layer will be configured at the same time when the control algorithm is
configured. The configuration engineer will determine the similarity distance and the
algorithms to be executed in the similarity layer. Rules can be included in the
configuration system to automatically suggest what the similarity value is based on the
type of loop, loop performance, and the criticality of the loop. Note the actual algorithm
must be modified to account for the measurement not being available in a periodic basic.
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Similarity Layer
Sensor
Controller
Wireless Network
Actuator
Figure 4. A wireless process control system
Examples
For the application in Figure 2, the similarity layer in the controller will not interfere with
the algorithm execution but the algorithm must be designed to use non-periodic update.
Assume the modified controller runs once every 1 second. In steady state, the similarity
layer will skip most of the input/output requests; let's say it is nine out of ten. This results
in 90% reduction of data traffic in the network. Assume 10% of the time the process is
not in steady state, the similarity layer reduces totally 90% * (1 - 10%) = 81% of traffic.
For the application in Figure 3, the similarity layer will skip some of the periodic read
requests as follows. Based on the input flow and last set valve position, it derives the
maximum rate of water level change. On a read request, the similarity layer calculates the
upper and lower bound of the water level based on the maximum change rate and the last
read water level. If the bounds are well within HI_LIM and LO_LIM, the read request is
skipped. The closer the water level is to HI_LIM or LO_LIM, the less skips occur. Most
of the skips happen when the water level is in the midpoint between HI_LIM and
LO_LIM.
Assistant data
The similarity layer will not interfere with assistant data. The alarms, events, diagnostic
information, and user inquiries will be handled timely by the controller as if the similarity
layer does not exist. The majority of the control unit’s life time will be in a steady state
without assistant data traffic. Assume in Figure 2 10% of the data traffic is for assistant
data, the traffic reduction by the similarity layer is 81% * (1 - 10%) = 73%.
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Communication failure detection and elimination of offset
The similarity layer should also detect possible communication failure in time. In the
typical control system, loss of communication will be declared if read or write request do
not get through the network. The similarity distance calculation will take the possibility
of bad communication into account. Assume in Figure 2 a communication failure should
be caught within 5 seconds. We know the algorithm is run every one second. To detect
communication failure in the steady state, the similarity distance will be defined to be 5
seconds instead of 10 seconds. So the similarity layer could only skip four out of five
input/output requests from the algorithm. This results in an 80% * (1 - 10%) = 72% of
control data traffic reduction, and 72% * (1 - 10%) = 65% of total traffic reduction.
The use of a default refresh communication rate also has the benefit of eventually
removing the offset of the used value from the actual value.
Another communication failure detecting method is based on consecutive communication
failures. This should be adjusted in the similarity layer. Depending on the application
context, the similarity layer could further reduce the similarity distance to increase
transmission frequency, and/or resume regular transmission whenever one
communication failure occurs.
Once loss of communication is detected, the similarity layer should no longer skip
network request until communication is back in normal.
Some control data is broadcasted without confirmation requirement. If the sender uses
similarity layer to skip broadcasts, the receiver, if it has communication failure detection
mechanism based on receiving the broadcasts, should also deploy the similarity layer to
handle skipped broadcasts.
Computation overhead
This design is aimed at deploying the similarity layer in the controller and the transmitter,
where the heavy part is put in the controller. The overhead could be very small as we
shall see in the next section. Existing wireless devices could be readily used for this
design. Similarity layer adds very little into transmitter computation. The power
consumption is more significant for transmission than computation. As controllers
become more powerful, the similarity layer could be more sophisticated and more
computation intensive in some complex context such as cascade controls, or better
similarity distance could be calculated with more complex methods.
DESIGNING CONTROL FOR WIRELESS COMMUNICATIONS
In this section we give a detailed analysis and design for closed loop control.
Utilizing wireless communication to provide a measurement used in closed loop control
presents many technical challenges. To reduce transmitter power consumption, it is
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desirable to minimize how often a measurement value is communicated. However, to
avoid the restrictions of synchronizing the measurement value with the control, most
multi-loop controller are designed to over-sample the measurement by a factor of 2-10X.
Also, to minimize control variation, the typical rule of thumb is that feedback control
should be executed 4X to 10X times faster that the process response time, process time
constant plus process delay. Thus, to satisfy these requirements, the measurement value is
often sampled much faster that the process responds as illustrated in Figure 5.
Process Output
O
63% of Change
Time Constant (
)
Deadtime (TD )
Process Input
I
Control Execution
New Measurement Available
Figure 5. Control data sampling rate
By synchronizing measure and control execution, as done in fieldbus devices based on
FOUNDATION Fieldbus, then it is possible to eliminate the need to over sample the
measurement. However, if the traditional approach is taken in scheduling control 4-10X
faster than the process response, then the power consumption associated with the
transmission of the measurement value may be excessive for all but the slowest types of
process. Slowing down the control execution to reduce the power consumption associated
with communication may increase control variability when the process is characterized by
frequent unmeasured disturbances.
Ideally the power consumption could be minimized by only transmitting the measurement
value only as often as required to allow control action to correct for unmeasured
disturbances or changes in operation point. For example, one approach to minimize the
power consumed in communicating new measurement values is to design the transmitter
and wireless communication according to the following rules:
Rules for Transmitting a New Measurement Value
1. The transmitter will periodically sample the measurement 4-10x faster than the
process response time.
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2. If the magnitude of the difference between the new measurement value and the
last communicated measurement value is greater that a specified resolution or if
the time since the last communication exceeds a refresh time then the new value
will be communicated.
However, the underlying assumption in the control design (using z transform, difference
equations) and digital implementation of the PID is that the algorithm is executed on a
periodic basis. When the measurement is not updated, then the calculated reset action
may not be appropriate. For example, if the control algorithm continues to execute using
the last measurement value, then the output will continue to move based on the reset
tuning and error between the last measured value and the setpoint. If control execution is
only executed when a new measurement is communicated, then this could delay control
response to setpoint changes and feedforward action on measured disturbances. Also,
when control is executed, then calculating the reset contribution based on the scheduled
period of execution or on the time since the last execution may result in changes that
increase process variability. To provide best control when a measurement is not updated
on periodic basis, the PID may be restructured to reflect the reset contribute for the
expected process response since the last measurement update. One means of doing this is
illustrated in Figure 6.
Traditional PI Controller
Setpoint
O (s )
E (s ) K
P
Process
1
1  sTRe set
Setpoint
Unmeasured
Disturbance
Traditional
Transmitter
PI Controller for Wireless
Process
KP
Newest
Communicated
Measurement
Value
New
Value
Flag
Modified
Filter
Unmeasured
Disturbance
Wireless
Transmitter
Communications
Stack
Figure 6. PID enhancement for wireless transmission
Traditionally, a PI controller may be implemented using a positive feedback network to
determine the reset contribution. Mathematically, it can be shown that the transfer
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function for this implementation is equivalent to the standard formulation for
unconstrained control i.e. output not limited.

O( s)
1 

 K P 1 
E (s)
 sTReset 
where K P  Proportional Gain
TReset  Reset , seconds
One advantage of the positive feedback network is that the reset contribution is
automatically prevented from winding up when the controller output is high or low limit.
For control applications that involve getting their measurement from a wireless
transmitter, the positive feedback network may be modified to accommodate non-periodic
measurement update. Specifically, the filter used in this network can be modified to have
the following behavior:
1. Maintain the last calculated filter output until a new measurement is
communicated.
2. When a new measurement is received, calculate the new filter output based on the
last controller output and the elapsed time since a new measurement value was
communicated.
To account for the process response, the filter output may be calculated in the following
manner when a new measurement is received.
 T

TReset

FN  FN 1  ON 1  FN 1   1  e


where FN  New filter output




FN 1  Filter output last execution  filter output after last new measurement
ON 1  Controller output last execution
T  Elapsed time since a new value was communicated
For those processes that require PID control, it is necessary to apply the PI modifications
for wireless communication. In addition, the rate contribution to the PID output should be
recomputed and updated only when a new measurement is received. The derivative
calculation should use the elapsed time since the last new measurement.
EXAMPLE OF CONTROL PERFORMANCE
The closed loop response of the PI controller modified for wireless communication is
illustrated in Figure 7 for both setpoint and load disturbances. In this example, the
wireless transmitter follows the Rules for Transmitting a New Measurement Value. Also,
the response is shown for a standard PI controller where the measurement value is
communicated as frequently as the PI control algorithm executes.
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Lambda Tuning   1.0
Communication Resolution = 1%
Communication Refresh = 10 sec
PV for PI Control
with Wireless
Measurement
Setpoint
PI Control Output
with Wireless
Measurement
Unmeasured Load
Disturbance
Figure 7. The closed loop response of modified PI controller
For the lag process used for this example, the number of communications during the
duration of the test was reduced by over 96 % when the rules for wireless communication
as outlined above are followed. The impact of non-periodic measurement updates on
control performance is minimized through the use of the modified PI algorithm for
wireless communication. The difference in control performance is shown below in terms
of Integral Absolute Error (IAE) for periodic measurement update vs. non-periodic.
Table 1 – Control Performance Difference
Communications/Control
Number of
Communications
IAE
Periodic /standard PI controller
692
123
Update Using communication
Rules/ PI controller for Wireless
25
159
The power that must be supplied by the transmitter for data transmission can be
significantly reduced when the communication rules and the PI controller modifications
are used with wireless transmitters. This reduction in power requirement increases the
number of control applications that may be addressed using wireless transmitters.
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6. CONCLUSION
In a wireless process control network, the controller is usually powered from the grid, the
device may or may not be powered by the grid, and the traffic relay node is most likely
powered from the grid. In the case where a battery or fuel cell is required, although the
battery or fuel cell is providing an extended life, there is always the need to reduce the
energy consumption. By reducing the control data traffic, we could increase the lifetime
of the wireless nodes powered by batteries or fuel cells.
While current wired control loops should not report by exception, we speculate that future
wireless control systems will have to deal with the challenges. We introduced the concept
of similarity distance to support the idea of running control dynamically based on the
context and in the mean time not degrading the quality of control too much. Similarity
concept indicates that the control algorithm must extend for the measurement not being
periodically updated. Similarity calculation is context specific as described in the
previous sections. It could be computation intensive or very simple as in Section 5. The
ever increasing processor power could well handle this extra work. The detail analysis
and design with PID control proved that energy consumption could be greatly reduced for
wireless process controls.
REFERENCES
1. Caro, Dick, “Wireless Networks for Industrial Automation”, ISA - The
Instrumentation, Systems, and Automation Society, 2004.
2. Chen, Deji, “Real-Time Data Management in the Distributed Environment”, Ph.D.
Thesis, the University of Texas at Austin, August 1999.
3. Hieb, Brandon, “Developing a Small Wireless Control Network”, Master’s Thesis,
the University of Texas at Austin, December 2003.
Copyright 2005 by ISA - The Instrumentation, Systems and Automation Society.
Presented at ISA EXPO 2005; http://www.isa.org