Wireless Sensor/Actuator Network Design for Mobile Control

Applications
Feng Xia 1,2, Yu-Chu Tian 2, Yanjun Li 1 and Youxian Sun 1
1 State Key Laboratory of Industrial Control Technology, Zhejiang University,
Hangzhou 310027, China
E-mail: f.xia@ieee.org.
2 Faculty of Information Technology, Queensland University of Technology,
GPO Box 2434, Brisbane QLD 4001, Australia
E-mail: y.tian@qut.edu.au.
Abstract: Wireless sensor/actuator networks (WSANs) are emerging as a new generation
of sensor networks. Serving as the backbone of control applications, WSANs will enable
an unprecedented degree of distributed and mobile control. However, the unreliability of
wireless communications and the real-time requirements of control applications raise great
challenges for WSAN design. With emphasis on the reliability issue, this paper presents an
application-level design methodology for WSANs in mobile control applications. The
solution is generic in that it is independent of the underlying platforms, environment,
control system models, and controller design. To capture the link quality characteristics in
terms of packet loss rate, experiments are conducted on a real WSAN system. From the
experimental observations, a simple yet efficient method is proposed to deal with
unpredictable packet loss on actuator nodes. Trace-based simulations give promising
results, which demonstrate the effectiveness of the proposed approach.
Keywords: wireless sensor/actuator network, sensor network, control application, link
quality, packet loss.
1. Introduction
Recent advances in pervasive computing, communication and sensing technologies are leading to
the emergence of wireless sensor/actuator networks (WSANs) [1,2]. A WSAN is a distributed system
of sensor nodes and actuator nodes that are interconnected over wireless links. Sensors gather
information about the physical world, e.g., the environment or physical systems, and transmit the
collected data to controllers/actuators through single-hop or multi-hop communications. From the
received information, the controllers/actuators perform actions to change the behaviour of the
environment or physical systems. In this way, remote, distributed interactions with the physical world
are facilitated. Depending on the type of the target application, nodes in a WSAN can be either
stationary or mobile. In many situations, however, sensor nodes are stationary whereas actuator nodes,
e.g., mobile robots and unmanned aerial vehicles, are mobile. Sensor nodes are usually low-cost, lowpower,
small devices equipped with limited sensing, data processing and wireless communication
capabilities, while actuator nodes typically have stronger computation and communication powers and
more energy budget that allows longer battery life [3]. Regardless, resource constraints apply to both
sensors and actuators.
WSANs are not just an enhancement or complement to the intensively-investigated wireless sensor
networks (WSNs) [4-8], but go beyond. They are a new generation of sensor networks [2,3]. While
WSANs and WSNs share many common considerations concerning network design, such as
reliability, connectivity, scalability and energy efficiency, the coexistence of sensors and actuators in
WSANs causes substantial difference between these two types of networks. Applications in which
some actions are introduced for the purpose of enhancing the monitoring capability of the sensor
networks do not embody the essential characteristics of WSANs. On the contrary, actuators in a
WSAN should be an integral part of the network and perform actions interacting with the physical
world. As a consequence, WSANs have the ability to change the physical world, but WSNs do not. In
WSNs, power consumption is generally the primary concern; however, this may not be the case in
some WSANs where meeting the real-time, reliable communication requirements may be more
important [9].
Although there are many situations in which only WSNs are required, for example, environment
monitoring, product quality monitoring, and the like, there are an increasing number of applications
that necessitate the use of actuators along with sensors [10-12]. That is, the network system needs to
interact with the physical system or environment. Examples of application areas of WSANs include
disaster relief operations, intelligent building, home automation, smart spaces, pervasive computing
systems, and cyber-physical systems.
Because of the use of both sensors and actuators, WSANs, by definition, exploit the methodology of
feedback, which has been recognized as the central element of control systems [13]. The advent of
WSANs has the potential to revolutionarily promote existing control applications. It can be envisioned
that WSANs will become the backbone of many control applications enabling an unprecedented
degree of distributed control. The use of WSANs in control applications has many advantages
compared to wired solutions, which are dominant at the moment [14,15]. For instance, WSANs allow
more flexible installation and maintenance, fully mobile operation, and monitoring and control of
equipments in hazardous and previously difficult-to-access environments. Another important factor
that instigates the deployment of WSANs is their relatively low costs [11].
Despite many advantages, WSANs also raise challenges for control applications. Wireless channels
have adverse properties, such as path loss, multi-path fading, adjacent channel interference, Doppler
shifts, and half-duplex operations [16]. WSANs are known to be notoriously unpredictable and
inherently unreliable. This is especially true in the case of low-power communications and in the
presence of node mobility. With these characteristics, the quality of service (QoS) of the network
cannot be always guaranteed. A natural result is that control applications will suffer from time-varying
delay and packet loss, both of which could significantly degrade the control performance, or even
cause system instability. Therefore, WSANs must be well designed when deployed to control
applications.
The design of WSANs featuring node mobility is investigated in this paper for control applications.
The overall goal is to enhance the reliability of WSANs so that the required performance of control
applications is guaranteed in dynamic, lossy environments. In particular, our focus is on dealing with
unpredictable packet loss caused by unreliable link quality in mobile WSANs, without considering the
effects of time-varying delay. The reasons behind this choice of focus can be briefly explained as
follows. Firstly, since a packet loss can be equivalently regarded as a delay with a magnitude of
infinity, from the viewpoint of control, packet loss is a factor that often has more significant impact on
the resulting control performance than delay. Secondly, several methods have been presented in the
literature to cope with time-varying delay in control loops closed over wireless sensor networks [17-
21], while the packet loss problem that arises in WSANs is yet to be investigated.
An application-level design methodology will be developed for WSANs based on an experimental
study of the link quality properties and a compensation method for packet loss. The main contributions
of this paper include:
• The link quality of WSANs is characterized in terms of packet loss rate through experiments on
a real deployment. The experimental results provide important insight into how the WSAN
should be designed from the application point of view. Moreover, they are also of great value to
the design and evaluation of sensor network protocols and algorithms.
• A simple yet efficient method is developed to deal with unpredictable packet loss at actuator
nodes. It can significantly improve the QoS of WSANs under unreliable channel conditions, and
facilitates the implementation of (mobile) control applications over WSANs.
• The proposed approach is evaluated and verified using trace-based simulations that extract data
from the real experiments. In this way, real characteristics of the wireless links are taken into
account in performance evaluation. Promising results are presented and analyzed.
The proposed design methodology is a generic solution in the sense that: 1) It does not require any
modification to low layers such as physical layer, MAC layer, and transport layer within the network
protocol stack. Only the application layer is involved. Therefore, it is independent of the underlying
communication protocols, for example, MAC and routing protocols, utilized in the WSAN. 2) It does
not require any knowledge about the models of the physical systems to be controlled or the design of
the control algorithms. It is suitable for a wide range of control applications. 3) The proposed
algorithm is computationally cheap yielding only a small runtime overhead. This meets well the
general WSAN design requirements stemming from the constraints on data processing capacity and
energy consumption in actuator nodes, thus making the proposed approach applicable to various
WSAN platforms subject to resource constraints.
This paper is organized as follows. Section 2 briefly reviews related work with respect to WSAN,
control over WSNs, link quality analysis, and packet loss handling. Section 3 discusses the architecture
and design challenges of WSANs from an application perspective. In Section 4, the properties of link
quality are captured in terms of packet loss rate through experiments on a real WSAN. Aiming to
improve the reliability and QoS of WSANs, Section 5 proposes a method to handle packet loss.
Section 6 evaluates the performance of the proposed approach using trace-based simulations. Finally,
Section 7 concludes the paper.
2. Related Work
While significant effort has been made in research and development of WSNs in recent years and
tremendous advancements have been achieved with respect to deployment, localization, MAC
protocols, power control, topology control, routing, distributed signal processing, and security [22],
WSANs are a relatively new research area with limited progress. Akyildiz and Kasimoglu [1]
described research challenges for coordination and communication problems in WSANs. Rezgui and
Eltoweissy [2] discussed the opportunities and challenges for service-oriented sensor/actuator
networks. Ngai et al. [23] studied the route design problem for mobile actuators and developed a
practical algorithm to reduce the waiting time of sensors. Melodia et al. [3] presented a sensor-actuator
coordination model based on an event-driven partitioning paradigm. Sikka et al. [24] deployed a large
heterogeneous WSAN on a working farm to explore sensor network applications that can help manage
large-scale farming systems. A power-aware many-to-many routing protocol can be found in [9].
Despite their contributions in WSAN, none of the networks designed in these works are particularly
for real-time control applications.
Sensor networks have started to attract the attention of control engineers. Kumar et al. [10]
developed distributed ad-hoc network algorithms to facilitate executing control procedures in a
distributed manner. Li [11] prototyped a light monitoring and control application as a case study of
WSANs. Oh et al. [25] illustrate the main challenges in developing real-time control systems for
pursuit-evasion games using a large-scale sensor network. A mixed model for design, analysis, and
synthesis of control algorithms within sensor networks has been presented in [26]. Korber et al. [27]
dealt with some of the design issues of a highly modular and scalable implementation of a WSAN for
factory automation applications. Considering networked control systems (NCSs) over WSNs,
Nikolakopoulos et al. [17] developed a gain scheduler to cope with time-varying delay induced by
dynamic changes in the number of hops in multi-hop communications. Witrant et al. [18] also
considered the effect of time-varying delay caused by multi-hop communication, and proposed a
predictive control scheme with a delay estimator. Various design challenges associated with control
over wireless networks have been addressed in these papers, but the impact of packet loss as a result of
unreliable communications in WSANs, particularly those with mobile nodes, on the performance of
the control applications remains an open issue, and needs to be investigated systematically.
Experiments have been conducted for analysis of the link quality in sensor networks, e.g., [28-31].
The authors reported, respectively, their measurements of the packet delivery performance of sensor
networks of different sizes in different environments. Some important characteristics with respect to,
e.g., packet loss rate in WSANs, have been captured in these papers; but none of the analysis has
intended for real-time control applications. For example, the relationship between the resulting control
performance and the link quality has not yet been characterised. Also, no methods have been
developed in these papers to address the observed unreliable packet delivery.
In the control community, effort has been made for packet loss compensation. A recent survey on
this topic can be found in [32]. Despite their differences, most of existing packet loss compensation
methods have the common features that: 1) they depend heavily on the knowledge about the accurate
models of the physical systems to be controlled, and, possibly, the controller design; and 2) the
relevant algorithms are computationally intensive. Due to these reasons, they are impractical for real
systems lacking well-established mathematical models. In particular, they are not the desirable
solutions for resource-constrained WSANs because of too large computational overheads.
In summary, the field of WSANs is emerging; but the full potential of WSANs for control
applications is yet to be explored. For this purpose, the characteristics of the link quality of real-world
WSANs in terms of packet loss rate, which may significantly affect the performance of control
applications, should be analysed. Resource-efficient paradigms addressing the packet loss problem
need to be developed when designing WSANs for (mobile) control applications.
3. WSAN for Control Applications
This section describes the architecture of WSANs as a backbone for constructing control
applications. The main challenges in design of WSANs will also be discussed briefly.
In general, there are three essential components in a WSAN: sensors, actuators, and base stations.
The roles of sensors and actuators have been described previously, while the base stations are often
responsible for monitoring and managing the overall network through communications with sensors
and actuators. Depending on whether or not there are explicit controller entities within the network,
two types of architectures of WSANs for control applications can be distinguished, as shown in Fig. 1
and Fig. 2, respectively. These two architectures are called automated architecture and semi-automated
architecture, respectively, in [1].
In the first type of architecture as shown in Fig. 1(a), there is no explicit controller entity in the
WSAN. In this case, controllers are embedded into the actuators and control algorithms for making
decisions on what actions should be performed upon the physical systems will be executed on the
actuator nodes. The data gathered by sensors will be transmitted directly to the corresponding actuators
via single-hop or multi-hop communications. The actuators then process all incoming data by
executing pre-designed control algorithms and perform appropriate actions. From the control
perspective, the actuator nodes serve as not only the actuators but also the controllers in control loops.
From a high-level view, wireless communications over WSANs are involved only in transmitting the
sensed data from sensors to actuators; control commands do not need to experience any wireless
transmission because the controllers and the actuators are logically integrated, as shown in Fig. 1(b).
Base Station
Sensor
Actuator
Physical
Actuator System
Sensor
Controller
WSAN
(a) Network topology
(b) Abstraction of control application
Figure 1. WSAN Architecture without explicit controllers.
Fig. 2(a) shows the second type of architecture, in which one or more controller entities explicitly
exist in the WSAN. The controller entities could be functional modules embedded in the base stations
or separated nodes equipped with sufficient computation and communication capacities. With this
architecture, sensors send the collected data to the controller entities. The controller entities then
execute certain control algorithms to produce control commands and send them to actuators. Finally,
the actuators perform the actions. In this context, both the sensor data and control commands need to
be transmitted wirelessly in a single-hop or multi-hop fashion. A high-level view of the applications of
this architecture is depicted in Fig. 2(b).
Base Station
Sensor
Actuator
Physical
Actuator System
Sensor
Controller
WSAN
(a) Network topology
(b) Abstraction of control application
Controller
Figure 2. WSAN Architecture with explicit controllers.
In combination with the unique characteristics of WSANs, control applications pose the following
main challenges associated with the design of WSANs:
• Reliability. From the control perspective, packet loss degrades control performance and even
causes system instability. Because practical control applications can only tolerate occasional
packet losses with a certain upper bound of allowable packet loss rate, WSAN design should
minimize the occurrence of packet losses as much as possible. Ideally, every packet should be
transmitted successfully from the source to the destination without loss. However, due to many
factors such as low-power radio communication, variable transmit power, multi-hop
transmission, noise, radio interference, and node mobility, packet loss cannot be completely
avoided in WSANs. The challenge then becomes how to improve the reliability of the network
system in the presence of packet loss.
• Real-time constraint. Control systems are inherently real-time systems in the sense that control
actions must be performed on the physical systems by their deadlines. It is worth mentioning
that real-time does not necessarily mean ‘fast’. For real-time control applications, both delay
and its jitter should be limited and predictable in favor of control performance improvement.
However, the use of dynamic routing protocols and random MAC protocols (e.g., CSMA/CA),
as well as the mobility of nodes, makes the WSAN-induced delay time-varying and
unpredictable. The challenge here is how to guarantee the delay is sufficiently small and
deterministic with small jitter so that it will not significantly degrade the control performance.
As mentioned in Section 2, WSAN-induced time-varying delay has been addressed in [17,18]. In
the recent work by Tian and colleagues [19-21], a real-time queuing protocol has been developed that
can be used to tackle the second design challenge of WSANs. Therefore, in this paper, we concentrate
our attention on the first design challenge concerning the reliability of WSANs. The existence of
mobile nodes in the system undoubtedly makes this task even more difficult.
In the following, we will restrict our description to WSANs with the first type of architecture as
shown in Fig. 1, since it is more resource-efficient and is more representative of the next generation of
sensor networks. It is, however, noteworthy that our design method is applicable to a wide range of
WSANs with arbitrary architectures. Typical examples of (ongoing) real-world application scenarios
of the considered WSANs include the pollution source location problem and the fire in a road tunnel
scenario where mobile robots must be controlled in a WSAN to accomplish certain jobs [33]. When
illustrating our approach, we will exploit a high-level abstraction of the WSANs for various
application setups in order to maintain the application independency and wide applicability of our
solution.
4. Experimental Analysis of Link Quality
In order to address the challenge of unreliable communication in WSANs, it is necessary to
understand first how unreliable practical WSANs really are. That is, the packet loss behavior of the
network should be studied. This is done in this work through conducting experiments on a real WSAN
system and collecting quantitative data to capture the channel characteristics. This section reports our
experimental measurements that characterize the link quality in terms of packet loss rate of a practical
WSAN, since in this context the packet loss rate is a critical factor affecting the performance of the
control applications. Instead of an exhaustive study of link quality with respect to a lot of factors, e.g.,
platform, environment, deployment, time, etc., which has been done e.g. in [27-30], we focus on a
simple yet sufficiently illustrative characterization of the link quality that help make decisions
concerning WSAN design for mobile control applications.
4.1. Experimental Setup
The sensor nodes used in the experiments is the MICA2 motes from Crossbow [34]. The MICA2
mote, designed specifically for deeply embedded sensor networks, is based on the Atmel ATmega128L
microcontroller. Each sensor node contains 128KB program flash memory, 512KB measurement flash,
and 4KB configuration EEPROM. MICA2 uses the Chipcon CC1000 wireless transceiver, and
supports multiple channels (868/916 MHz), hardware encoding (Manchester), frequency shit keying
(FSK) modulation, and up to 38.4 kbps data rate. The RF power of MICA2 is programmable from -20
to +5 dBm, and the receive sensitivty is -98 dBm. The MICA2 51-pin expansion connector supports
analog inputs, digital I/O, I2C, SPI and UART interfaces. These interfaces make it easy to connect to a
wide variety of external peripherals. Any MICA2 Mote can function as a base station when it is
connected to a standard PC (personal computer) interface or gateway board. A base station allows the
aggregation of sensor network data onto a PC or other computer platform.
MICA2 runs TinyOS, an open-source embedded operating system developed at UC Berkeley. It
provides basic system services, such as communication and simple process scheduling, and access to
hardware components such as sensors and actuators. The MAC layer implements a simple CSMA/CA
protocol. A link-level acknowledgement can be sent by the receiver for each successful packet.
Base Station
MICA2
Motes
PC
Figure 3. Experimental deployment.
The experiments are conducted on an open ground, as shown in Fig. 3. Nodes are placed
equidistantly along a line with a spacing of 0.5m. One PC is connected with the base station using a
MIB510 mote interface board. The PC is used to configure network parameters and collect
experimental data via the base station. In the experiment, the first node, i.e. the mote on the position
marked as ‘1’ in Fig. 3, transmits continuously a 13 byte data packet at a rate of 8 packets per second.
The packet loss rates associated with the remaining nodes with different distances from the first node
are measured.
4.2. Observations
In a WSAN with mobile (actuator) nodes, the distance between the mobile actuator and the sensor
that are involved in transmitting the sensed data will change over time, thus aggravating the variability
of link quality. This is why we pay a special attention to examining how the link quality varies over
distance.
Fig. 4 shows the packet loss rates on different nodes when the transmit power is set to 0dBm.
Within the distance of 7m, the packet loss rates remain less than 10%. Beyond 30m, the packet loss
rates are fairly close to 100%, implying that almost all packets sent to the nodes are lost. This indicates
that the radio range is approximately 30m. For links with distances ranging from 7 to 30m, the packet
loss rates could vary drastically. For example, the packet loss rates vary nearly from 0% to 100% in
the area between 9 and 13m. It can be observed that nodes far away from the transmitter may possibly
undergo smaller packet loss rates than those near the transmitter.
0 5 10 15 20 25 30 35
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance (m)
Packet Loss Rate
Figure 4. Packet loss rate versus distance (0dBm).
Fig. 5 plots the recorded data when the transmit power is set to be -5dBm. Similarly, the packet loss
rates show great variability and irregularity both over different distances and at a given distance.
Compared to the higher power case shown in Fig. 4, almost 100% packet loss rates are observed at a
smaller distance, and the radio range decreases to 26m.
0 5 10 15 20 25 30
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Distance (m)
Packet Loss Rate
Figure 5. Packet loss rate vs. distance (-5dBm).
We have observed that the packet loss rates over the wireless channel in real WSANs are highly
variable and irregular. The assumptions that link quality is exclusively based on distance, which is
often made when modelling the link quality of sensor networks [35,36], simplify system analysis but
can be practically questionable. This could be further justified by the fact that the link quality of
WSANs depends also on many factors other than distance and transmit power, such as the
environment, noise, radio frequency, modulation scheme, and (hardware) platform in use, just to
mention a few. Due to the unpredictability of the link quality in terms of packet loss rate and the
impracticability to model it accurately, it is now imperative to develop a platform-independent
paradigm to enhance the reliability of WSANs under lossy conditions. A desirable solution should be
widely applicable to diverse application scenarios with different system and environment setups.
5. Dealing with Packet Loss on Actuators
To meet the above requirement, we attempt to develop an application-level design methodology for
WSANs in mobile control applications. The principles in our development are: 1) to modify only the
application layer of the networks without exploiting any application-specific (lower layer) network
protocols, 2) not to use any statistic information about the distribution of packet loss rate in any
specific WSAN, and 3) not to use the knowledge about the models of the controlled physical systems
and the controller design of the target control applications.
We propose to use a simple yet efficient method on the actuator nodes to cope with packet loss
occurring in WSANs. The basic idea is: whenever a sensor data packet is lost, the actuator will still
produce a control command (usually called control input in control terms) by means of prediction from
previous control command values.
For a control loop within a (possibly large-scale) system shown in Fig.1, suppose that the k-th
sensor data is lost. In this case the actuator (to which the last data should be sent) will calculate an
estimate of the control command using the PID (proportional-integral-derivative) algorithm, the most
popular control algorithm in the control community, as follows:
ˆ( ) ( 1) 1 ( ) ( ( 1) ( 2))
k
P I D
i k m
u k K u k K u i K u k u k
m
−
= −
= 􀀄 − + 􀀄 Σ + 􀀄 − − − (1)
where û(k) is the estimate of the k-th control command u(k), P K
􀀄
, I K
􀀄
, D K
􀀄
and m are user-specified
parameters. Using (1), the actuator predicts u(k) based on the previous m consecutive control
commands (which are also possibly predicted values) in the case of packet loss and performs the
actions corresponding to the value of û(k). Given that the accuracy of the prediction of control
commands is sufficiently high, proper actions will be performed on the controlled physical system in
every sampling period, regardless of the loss of the sensor data. In this way, the effect of packet loss on
the performance of the control applications can be substantially reduced. In other words, the reliability
of the WSAN is improved, from the application point of view.
The PID algorithm is here used to predict the unavailable control commands that result from sensor
packet loss. In the control community, in contrast, it is typically used in control system design, as will
be shown later in Section 6. Varma et al. [37] have used a similar method called nqPID to predict CPU
workload in dynamic voltage scaling systems. It proved quite effective and insensitive to parameter
changes.
The work flow of the actuator can be illustrated as follows:
Input: Sensor data
Output: Control command
Begin
If the sensor data is lost then
Compute û(k) using (1)
Else
Compute u(k) using pre-designed control algorithm(s)
End if
Store u(k) or û(k) in memory
Discard u(k-m) in the memory
Perform actions corresponding to u(k) or û(k)
End
It can be seen that this design method is quite simple. The major overhead is a small fraction of
memory to temporarily store the previous m control commands. Despite this, it does not depend on any
knowledge about the underlying platform, environment, link quality characteristics, models of the
controlled systems, or controller design. Furthermore, only a very limited amount of computations
have been introduced, which fulfills well the general requirements of WSANs concerning the
constraints on computational capacity and energy expenditure. In addition, it is worth mentioning that
although zero delay is assumed in this paper, the proposed design method can be easily combined with
the real-time queuing protocol developed in [19-21] to deal with simultaneously time-varying delay
and packet loss.
6. Performance Evaluation
In this section, we conduct trace-based simulations using Matlab to evaluate the performance of the
above-proposed design methodology for WSANs.
6.1. Control Application Overview
In the simulations, a commonplace control system design is used to keep the results as general as
possible. The model of the controlled physical system is given below, which may represent an inverted
pendulum system, a common benchmark problem in the control field [38]:
3 2
( ) 4.546s
0.182 31.182 4.454
G s
s s s
=
+ − −
The controllers use the PID control law, the most popular control law in practical control
applications, with the following parameters: KP = 120, KI = 1000, and KD = 5. The PID control
algorithm is implemented in the actuator as follows [14]:
( ) ( )
( ) ( )
( ) ( 1) ( ( ) ( 1)) 2
( ) ( ( ) ( 1))/
( ) ( ) ( ) ( )
( )
P
I
D
r k y k
P k K e k
I k I k K h e k e k
D k e k e k h
u k P k I k D k
e k
K
−
=
= − + + −
= − −
= + +
=
/
where r(k) is the desired system output (i.e., reference input or set-point), y(k) is the sensed value of
the system output (i.e., measurement), h is the sampling period of the sensor. In the simulations, h is
set to 20ms.
As the major perturbations on the controlled system, the reference input changes over time as a
square wave with a period of 2s. To measure the performance of the control application, the integral of
absolute error (IAE), one of the widely used control performance metric defined as
0
( ) | ( ) ( )| t J t =∫ r τ −yτ dτ , is recorded. The bigger the IAE value the worse the control performance.
6.2. Simulation Results and Analysis
Because of the mobility of the actuator node, the distance between the sensor and the actuator varies
during runtime according to Fig. 6. The RF power of the sensor is assumed to be 0dBm. Accordingly,
the packet loss rates with respect to different distances will be randomly extracted from the data set
reported in Fig. 4. For instance, when the distance is 10m from t = 16 to 20s, the packet loss rates will
take random values out of {0.7160, 0.2716, 0.6790, 0.9136, 0.9259, 0.6543, 0.3827, 0.6543, 0.4691,
0.3333, 0.2963, 0.1358, 0.5062, 0.6790, 0.5802}.
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
0
5
10
15
20
25
30
Time (s)
Distance (m)
Figure 6. Variable distance in simulations.
The performance of the control application is compared with respect to two different WSAN design
methods: 1) traditional design method without packet loss handling mechanism on the event-triggered
actuator; and 2) the application-level design methodology proposed in this paper. Some relevant
parameters are set as follows: K􀀄 P = 0.3, 0.2 I K􀀄 = , 0.5 D K􀀄 = , and m = 3.
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
0
10
20
30
40
50
60
70
80
IAE
Time (s)
Traditional Design Method
Our Design Method
Figure 7. Control performance in terms of IAE.
Fig. 7 shows the control performance in terms of the IAE associated with different design methods.
The cumulated IAE value of the system designed using our method is only 19.4% that of the system
using traditional design method. The rapid increase in IAE from time t = 16s to 20s implies that the
system becomes unstable during this period of time. This can also be seen from Fig. 8 (the upper part),
where the measured/sensed system output is depicted. The instability is mainly caused by the large
packet loss rates at the distance of 10m, which has been shown in Fig. 4.
16 16.5 17 17.5 18 18.5 19 19.5 20
−100
−50
0
50
100
System Output
Traditional Design Method
16 16.5 17 17.5 18 18.5 19 19.5 20
−2
−1
0
1
2
3
System Output
Our Design Method
Time (s)
Figure 8. System output.
In contrast, the system remains stable all the time, when the design method proposed in this paper is
employed. This is justified by the quite slow increase in IAE throughout the simulation, see Fig. 7. As
also shown in Fig. 8 (the lower part), the performance of the control application is satisfactory even
when the system may encounter considerably severe packet loss. Since no nodes are allowed to work
beyond its radio range (i.e., 30m in this case) in practice, the above results demonstrate that the method
proposed in this work is effective in mobile control applications where the communication distance
may change over time.
7. Conclusion
This paper deals with the design of WSANs for control applications. The related design challenges
have been discussed with respect to reliability and real-time constraints. With focus on improving the
reliability of WSANs to provide control applications with network QoS guarantees, a generic
application-level design methodology has been presented. The link quality of WSANs has been
examined in terms of packet loss rate through experimenting on a real WSAN system. From the
experimental observations, a simple yet effective method has been developed to deal with
unpredictable packet loss on the actuator nodes. The proposed design methodology has also been
verified through trace-based simulations. It enables mobile control applications over WSANs since it
can guarantee satisfactory control performance even in the presence of significant packet loss.
The design methodology proposed in this paper is independent of the computation and
communication platforms upon which the WSAN is built, and the environment in which the WSAN is
deployed. Also, it does not rely on the system models and controller design of the target control
applications. Furthermore, it is computationally cheap since only a small overhead is introduced.
Therefore, the proposed design methodology can be applied in a wide range of WSAN-based control
applications.
Acknowledgements
Authors Xia and Tian would like to thank Australian Research Council (ARC) for its support under
the Discovery Projects Grant Scheme (grant ID: DP0559111). The authors are grateful to Jing Yu at
Zhejiang University for her assistance in collecting experimental data.
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