On the Way to WSN Collaboration with Robots
Maria Charalampidou, Konstantinos Papakeipis, Aristotelis Tsimtsios and Spyridon G. Mouroutsos
Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Keywords: Wireless Sensor Networks, WSN, Robots, Robotics, Collaborative WSN Robots, Review WSN Robots.
Abstract: Over the past years Wireless Sensor Networks (WSNs) have been applied to a range of fields from military
applications to medical and multimedia ones. Meanwhile, robots have managed to prove their necessity in
cases where actuation is needed, therefore, achieving high accuracy tasks indeed. New prospects of
collaboration for these two avant-garde technologies have already emerged and have been put into practice.
In this paper a min-review of this collaboration and the prospects are analysed and discussed.
1 INTRODUCTION
In the past decades, Wireless Sensor Networks
(WSNs) have been tested on a large number of
scenarios and applications and hold the focus of
research worldwide. WSNs consist of sensor nodes,
with embedded microprocessor, and form a smart
network, using wireless technologies (Akyildiz et al,
2002). WSNs represent a significant improvement
over traditional wired data acquision networks since
they can collect measurements from inaccessible
areas of interest. These areas can vary from a battle
field to an area in the middle of a jungle or inhabited
areas. WSNs can deliver physical quantity
measurements successfully and with very low power
demands since they run on common batteries and
they do not need to be replaced for several years.
In addition the advantages of autonomous robots
have already been proved. An autonomous robot can
be considered as a mechanical artificial entity that is
able to perform tasks without human intervention,
and regardless of its workplace. Regarding the way
in which robots approach the area of interest we can
categorize them as ground robots, aerial robots
(drones or Unmanned Aerial Vehicles- UAVs) or
underwater robots.
From the aspect of applications that have been
developed both WSN and robots can be found in
very divergent fields. For example WSNs
applications can be deployed for health and medical
diagnosis, multimedia and video streaming,
industrial monitoring, military, security and border
surveillance applications.
Since the evolution of robots has reached the
point of developing applications with acceptable
accuracy, researchers are focusing their interest on
developing more complex robotic systems that can
be integrated to other technologies such as WSN.
Collaborative WSN and Robotic systems can be a
very promising perspective. In this paper a min-
review of this collaboration and its prospects are
analysed and discussed.
2 WSN COLLABORATION WITH
ROBOTS
Recently multimodal applications which adopt
collaborations of different but supplementary
technologies are gaining ground. An aspect of this
concept explores the idea of using autonomous (or
semi-autonomous) agents (sensor nodes and robots)
in order to meet the requirements with a potential
reduction of cost and an increase in the overall
reliability (Agmon et al, 2008). Under this scope, in
this paragraph a literature review of WSN-Robot
collaboration defines the context and the main areas
of collaboration.
1. Network deployment, maintenance and
connectivity repair
One of the most compelling WSN/Robot
scenarios is to let the Robots deploy and manage the
sensors of the network in the field. For example in
(LaMArca et al, 2002), the PlantCare project is
presented, which is an autonomous indoor system
for managing the health of houseplants. The authors
219
Charalampidou M., Papakeipis K., Tsimtsios A. and Mouroutsos S..
On the Way to WSN Collaboration with Robots.
DOI: 10.5220/0005095702190224
In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),
pages 219-224
ISBN: 978-989-758-038-3
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
demonstrate how a single robot as a central system
administrator can be used to deploy and calibrate
sensors, detect and react to sensor failure, deliver
power to sensors, and maintain the overall health of
the WSN.
In addition, in ((Corke et al., 2004a), (Corke et
al., 2004b)) a sensor network deployment method
along with a repairing network connectivity
algorithm with the use of UAVs is presented. The
authors describe how the deployment algorithm of
the nodes is based on predetermined placement
positions. Thus, when the UAV is within a radius of
1.5 meters from the deployment location a “drop”
command is issued to deploy the motes.
Another example of multi-robot systems that
include sensor nodes and aerial or ground robots
networked together is cited in (Suzuki et al, 2008).
The authors present a sensor network deployment
method by the use of autonomous aerial vehicles.
The deployment algorithm is based on a given
desired network topology for the deployed network
and a deployment scale. The resulting locations are
the (x, y, z) coordinates where the sensors need to be
deployed. Subsequently, these coordinates are given
as way-points to the helicopter controller. The
helicopter then flies to each of these way-points
autonomously, hovers over each one of them and
then deploys a sensor at the specified location.
A novel approach of using autonomous mobile
robots to deploy a WSN in an unknown zone is cited
in (Tuna et al., 2014). During the deployment of
WSN, multiple mobile robots perform cooperative
Simultaneous Localization and Mapping (SLAM)
and communicate over the WSN. However, the
system needs to be designed carefully considering
battery life of nodes, detection range of PIR sensors,
communication range and performance of wireless
sensors, mobile robot exploration strategies and
cooperative SLAM.
Another issue that network deployment methods
must address is the problem of best network area
coverage, without sensing holes, when redundant
sensors are present. A solution to this problem is
given in (Li et al., 2013) where the authors propose a
family of localized robot-assisted sensor relocation
algorithms. Robots move within the network to
discover sensing holes and redundant sensors by
local communication, and transfer the discovered
redundant sensors to the encountered sensing hole
positions.
Likewise in (Erman et al, 2008), the authors
present an architecture which integrates WSN and
UAVs for disaster response setting, and provides
facilities for event detection, autonomous network
rearrangement and repair by UAVs. In particular the
connectivity repair algorithm is based on the fact
that every node that broadcasts a signal ends up with
a group indicator. Otherwise the node does not have
an indicator and the point ends up blind. New
sensor-deployment is needed whenever the
helicopter observes that there are gaps in the grid of
sensors.
Considering that the sensor nodes of a WSN
have heavy workloads and their energy is easily
exhausted in (Sheu et al., 2008) a WSN node
replacement application is presented. Precisely, a
group of smart mobile robots navigate towards low-
energy fixed sensor nodes and replace them
automatically with new ones. This node replacement
strategy can be used in sensor networks consisting of
toughly recharged battery-powered sensor nodes.
2. Life extension of the WSN
Because of the fact that the WSN in most
applications needs to manage a large amount of data
acquired by the sensor measurements, it requires
large amounts of energy. This fact induces
significant system constraints. To answer this
problem in (Rahimi et al, 2003) the lifetime
extension is held with the introduction of mobility of
the nodes of the WSN. As a result, a small
percentage of nodes is converted to autonomously
mobile nodes allowing them to engage the energy
hunting mechanism. Thus, the mobile nodes move in
search of energy, recharge, and deliver energy to
immobile, energy – depleted nodes.
An alternative approach is cited in (Tong et al,
2011) where the Node Replacement and
Reclamation (NRR) strategy is proposed to meet the
challenges of designing an efficient WSN for long-
term tasks. According to this strategy a mobile robot
or a human labor called mobile repairman (MR)
periodically traverses the sensor network, reclaims
nodes with little or no power supply, replaces them
with fully charged ones, and brings the reclaimed
nodes back to the energy station for recharging.
Additionally, in (Tekdas et al., 2009) a system in
which the measurements of the sensor network are
mulled by robots is introduced. A proof of concept
implementation demonstrates that this approach
significantly increases the lifetime of the system by
conserving energy that the sensing nodes would
otherwise consume for communication.
3.
Collect and aggregate data from WSN
In (Chen et al., 2011) the authors investigate the
navigation strategy of a robot in order to collect the
data of the sensor nodes. The data gathering can be
scheduled based on time and location by the use of
three scheduling strategies: time based, location
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based, and dynamic moving based. The strategies
are simulated with ns-2 simulator and showed
improved data-collecting performance in cases of
partitioned/islanded WSNs. Another example is
cited in (Vasilescu et al., 2005) where a robotic
submarine serves as a mobile base station to collect
information from a network of underwater sensors
(AquaFleck).
4. Closer and more accurate monitoring
There are applications in which the WSN
measurements have low accuracy because of the
topology or the range of the sensors. In these cases,
the collaboration with robots can achieve a more
reliable system. For example in (Freeman and Simi,
2011), the application of a hybrid WSN assisted by
Robots for the detection of explosives in a given
indoor environment is featured. The WSN consists
of static sensor nodes and nodes embedded in
mobile robots for close monitoring.
5. Robot navigation and path planning
All the previous categories focus on WSN
assistance by robots. On the other hand, the
operation of robots can be enhanced by the use of a
WSN. For example in (Freeman and Simi, 2011), the
WSN guiding of the mobile robots in order to find
their path to the desired static node is presented. An
alternative is shown in (Enriquez, 2013) where a
system for mobile robot navigation around static
obstacles with the use of RFIDs is given. This
system uses an RFID system, for precise navigation
around obstacles.
On the contrary in (Batalin et al., 2004) the
authors describe an algorithm for robot navigation
by the use of sensor nodes as signposts for the robot
to follow, thus obviating the need for a map or
localization on the part of the robot. Navigation
directions are computed within the network (not on
the robot) by applying value iteration.
In (Cheng, 2012), a novel approach to mini‐UAV
localization in a WSN is presented. According to the
method firstly the environmental adaptive RSS
parameters are employed given from the WSN in
order to estimate the range estimation model.
Afterwards a particle swarm optimization-based
method is proposed to solve the established
objective function.
6. Enhancing Operations / Delivering a task
Thereafter representative examples of WSN
collaboration with robots are given. For example in
(Merino et al., 2011) a human tracking system for
person guidance with WSN, fixed and robot onboard
cameras is presented. The information from the
aforementioned sources is fused in order to respond
to a guidance request.
In (Herrero and Martínez, 2011) a system for
mobile robot odometry relying on a WSN/Robot
system is proposed. The robot emits RF and
ultrasound signals at the same time which are
captured by the nodes of the WSN. These nodes
compute their distance from the robot and transmit it
back to the robot. The robot computes its location
based on these measurements by rejecting the
inaccurate ones.
In (Marantos et al., 2008) a method for mobile
robot localization in WSNs is presented. The
proposed method makes use of fuzzy logic for
modeling and dealing with uncertain (noisy and
unreliable) information from measurements of the
Radio Frequency Signal Strength of the nodes. The
method succeeds in handling highly uncertain
situations that are difficult to manage by well-known
localization methods such as the Monte Carlo
method.
In (Costa et al., 2012) the authors describe an
architecture based on UAVs which can be employed
to implement a control loop for agricultural
applications, where UAVs are responsible for
spraying chemicals on crops. The process of
applying the chemicals is controlled by means of the
feedback obtained from the wireless sensor network
(WSN) deployed on the crop field.
Many security applications have also been
developed. Amongst the latest systems, there are the
collaborative WSN and Robot security systems
which are equipped with appropriate sensors and
deployed in a variety of environments and
topologies. For example, in (Li and Parker, 2008),
the authors present an indoor intruder detection
system that uses a WSN and a ground mobile robot.
Upon the detection of an intruder, the mobile robot
travels to the position where the intruder is detected
in order to investigate.
Another example is given in (Lin et al., 2008)
where an intruder detection system which consists of
zigbee sensor modules that detect intruders and
abnormal conditions is presented. The sensor nodes
transmit intrusion alarm to the monitoring center. If
any possible intruder is detected, the robot moves to
that location autonomously and transmits images to
remote mobile devices of security guards, in order to
determine and respond to the situation in real time.
In addition in (Cho et al., 2006) the researchers
introduce a collaborative WSN and ground mobile
robots collaboration for indoors navigation at a
construction site (warehouses, office buildings,
manufacturing facilities) providing security and
safety.
OntheWaytoWSNCollaborationwithRobots
221
Another home security application is cited in
(Tian et al., 2009) where a WSN and ground robot
household security system is proposed. The system
is composed of the robot node and the sensor nodes
of the WSN for monitoring temperature, humidity,
gas leaking, fire and housebreaking. The robot node
undertakes the task of collecting and processing
sensor data from the WSN, and also acts as the
interface of the system with the other users.
Figure 1 shows the Comparison of key design
issues for collaborative WSN/Robot systems in
experimental works as it is evident from the
literature.
3 OPEN ISSUES
The WSN collaboration with Robots is a very
promising perspective, indeed. However, this
collaboration has already shown its drawbacks. At
first, the effort of organizing a team of autonomous
agents to best patrol the area is computationally
expensive, even for relatively small problem
instances (While et al, 2013).
In addition, from the literature review a number
of open issues to be investigated are revealed
considering the data gathering and processing. These
processes consume large amounts of the system’s
energy; therefore, the investigation of new methods
for energy-efficient data gathering protocols is
necessary. These protocols must be oriented towards
forwarding sensing data within a specified latency
constraint without sacrificing energy efficiency and
thus achieving longer life of batteries for WSN.
Another issue is the investigation of the overall
accuracy of the systems in high noise conditions. For
example the infrared and ultrasonic sensors of robots
often do not recognize thin-legged chairs- which
make robots get stuck in one place while wheels are
still running- or reflectivity of the floor surface,
which often deludes robots. Another solution is to
leverage techniques developed in the robotics
community to build spatial models from noisy
sensor information and to keep track of complex
dynamic systems.
Moreover, an issue that must be successfully
addressed is false alarms. The increased rates of
false alarms not only affect the accuracy of the
system but also the overall energy consumption of
the system and make it very energy intensive. An
idea that could be explored is the continuously
distributed calibration of the sensed quantity in order
not to be sent for further processing unreliable data.
In general terms what is missing from the
literature is to conduct long-term experiments. In
most of the cases the experimental test-beds are
conducted only to execute a series of experiments
for different conditions. This prevents the system
from being simulated in real work conditions and
from bringing all the points for improvement to the
surface.
4 CONCLUSIONS
While WSNs are perfect in monitoring the
environment and in detecting any kind of
abnormalities, they are very limited in reacting to
what they detect. Robots, on the other hand, can
reduce the workload and enhance the capabilities of
WSN. Some of the important benefits that robots can
provide to WSNs are sensor deployment, calibration,
power management, closer monitoring and active
reaction to the deviations of the system. This robot
assistance advocates the augmentation of the robots’
communication and interaction capabilities with
those afforded by the WSN and services embedded
within the environment. Meanwhile WSNs can
contribute to more accurate localization, navigation
and path planning of the robots. In this paper a min-
review of this collaboration and its prospects are
analysed and discussed.
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APPENDIX
Figure 1: Comparison of key design issues for collaborative WSN and robot systems in experimental works.
Work Sensor platform Nodes
Sensors used in
WSN
Robots Robots’ sensors
Type of
collaboration
Indoor /
Outdoor
evaluation
LaMArca et al.,
2002
Mica / TinyOS N/A
Photoresistor,
thermistor,
irrometer,
current
1 On ground Pioneer 2-DX
Laser scanner,
recharging,
environmental
Bi-directional Indoors
Corke et al.
2004a
Mica / Tiny OS 50 N/A 1 Aerial
USC’s
autonomous
helocopter
GPS Bi-directional Outdoors
Tuna et al. ,
2014
N/A N/A
PIR, light,
acoustic
4 On ground N/A
laser and
ultrasonic range
finders, cameras,
infrared, bumber
WSN
Robot Outdoors
Freeman and
Simi 2011
MicaZ /TinyOS N/A
Environmental
and ultra sound
1 On ground Pololu 1060
Gyro,
accelerometer
Bi-directional Indoors
Batalin et al.,
2003
Mica 2 /TinyOS 9 - 1 On ground Pioneer 2DX - WSN
Robot -
Enriquez, 2013 MICAz N/As No extra sensors 1 On ground
Pioneer 3-DX
(Chamuko),
RFID reader
WSNc and
RFID system
Robot
Indoors
Li and Parker,
2008
Crossbow motes N/A Light, sound 1 On ground Pioneer 3-2DX Camera WSN
Robot Indoors
Sheu et al., 2008
MICA2,
MICA2DOT /
TinyOS
12 - 3 On ground Custom - WSN
Robot Indoors
Caballero et al.,
2008
Mica2 / TinyOS 25 - 1 On ground
Romeo (4-
wheel)
DGPS,
gy ros cop e,
compass
WSN
Robot Outdoors
Merino et al.,
2011
Mica2 /TinyOS 30 - 1 On ground
Romeo (4-
wheel)
Cameras, laser
range-finders,
localization,
navigation and
perception
WSN and
camera network
Robot
Outdoors
Herrero and
Martínez, 2011
Custom ( IEEE
802.15.4)
/Contiki OS
N/A N/A 1 On ground UPAT‘s Rover
Compass,
encoders
WSN
Robots Indoors
Cho et al., 2006
Ultra Wide Band
sensors
N/A
Motion, sound,
light,
temperature,
smoke, humidity
5 On ground N/A
Encoders,
infrared
WSN
Robot Indoors
Mester, 2009,
iMote2 /
TinyOS
N/A
Accelerometer,an
alog and digital
temperature,
humidity, light
1 On ground Pioneer P3-DX N/A WSN
Robot N/A
Tian and Geng,
2009
Custom (Zigbee) N/A
Temperature,
humidity,
infrared, smoke,
gas
1 On ground Custom N/A WSN
Robot Indoors
Qiao et al., 2013 Custom N/A
Camera, PIR,
temperature,
light
N/A On ground
Transmote
module
Camera. PIR,
temperature,
light
Bi-directional Indoors
Lin et al., 2008 Custom (Zigbee) 3
Pyro,
microphone,
accelerometer
1 On ground UBOT
Encoders,
ultrasonic,
infrared, camera
WSN
Robot Indoors
Type of Robots
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