intech-autonomous deployment and restoration of sensor network using mobile robots
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8/3/2019 InTech-Autonomous Deployment and Restoration of Sensor Network Using Mobile Robots
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International Journal of Advanced Robotic Systems, Vol. 7, No. 2 (2010) ISSN 1729-8806, pp. 105-114
105
Autonom ous Deployment and Restora t ion
of Sensor Netw ork us ing Mobi le Robots
Tsuyoshi Suzuki, Ryuj i Sugizaki , Kuniak i Kawabata,
Yasushi Hada and Yoshi to Tobe
Department of Information and Communication Engineering, Tokyo Denki University, Tokyo, Japan
Graduate School of Information and Communication Engineering, Tokyo Denki University, Tokyo, Japan
Kawabata Intelligent System Research Unit, RIKEN, Saitama, Japan
Disaster Management and Mitigation Group, National Institute of Information and Communications Technology
Tokyo, Japan
Department of Information Systems and Multimedia Design, Tokyo Denki University, Tokyo, Japan
Corresponding author E-mail: [email protected]
Abstract: This paper describes an autonomous deployment and restoration of a Wireless Sensor Network (WSN)
using mobile robots. The authors have been developing an information-gathering system using mobile robots and
WSNs in underground spaces in post-disaster environments. In our system, mobile robots carry wireless sensor
nodes (SN) and deploy them into the environment while measuring Received Signal Strength Indication (RSSI)
values to ensure communication, thereby enabling the WSN to be deployed and restored autonomously. If the
WSN is disrupted, mobile robots restore the communication route by deploying additional or alternate SNs to
suitable positions. Utilizing the proposed method, a mobile robot can deploy a WSN and gather environmental
information via the WSN. Experimental results using a verification system equipped with a SN deployment and
retrieval mechanism are presented.
Keywords: Robot Sensor Network, Sensor Node Deployment, Sensor Network Restoration, Disaster Information
Gathering, Mobile Robot
1. Introduction
In disasters such as large-scale earthquakes, it is
important to undertake disaster-mitigation activities for
reducing damage and for early post-disaster
rehabilitation and reconstruction (Kawata, Y., 1995).
During such activity, the extent of damage should be
determined promptly and accurately by gathering
information on the disaster area. In particular, there is an
increased need for information-gathering systems to
confirm the post-disaster status of underground spaces
such as subway stations and underground malls, because
such spaces, owing to their relative structural solidity, are
expected to be used as evacuation shelters and for storingemergency supplies. Disaster information-gathering
systems in the form of artificial satellites, UAV
(Unmanned Aerial Vehicles), balloon flights, etc., are
mainly used to gather disaster information over a wide
area. However, it is difficult to use these in underground
spaces, and useful alternative methods for gathering
information in such spaces have not been proposed yet.
Moreover, many cases have been reported in which
problems have actually been caused by existing disaster-
prevention equipment that failed to work during a
disaster. At present, rescue workers such as fire crews
and rescue teams enter post-disaster underground spacesto determine the extent of damage due to the disaster.
However, in such uncertain situations, because the rescue
activities are limited compared to that above the ground
level, personal suffering of rescue workers is high.
Fig. 1. Conceptual sketch of MRSN for gathering disaster
information in underground spaces
Based on the above scenario, the authors have been
developing a Multi-Robot Sensor Network System
(MRSN), in which multiple mobile robots such as high-
mobility rescue robots deploy a Wireless Sensor Network
(WSN) to gather disaster information in post-disaster
underground spaces (Fig. 1). A WSN, consisting of a large
number of small devices called Sensor Nodes (SN) (each
equipped with wireless communication functionality,
various sensors, a processor, and a power source) is anetwork system that can communicate and use sensing
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data gathered mutually by each spatially distributed SN.
An ad-hoc network connecting each SN, one by one, can be
constructed only by deploying a large number of SNs, and
such a network can be enhanced very easily compared
with wired and fixed networks. WSNs can provide various
services by collecting and processing the information
acquired by the SNs. WSN technology is expected to beapplicable in many fields such as the cooperative
monitoring of environmental conditions over a large area,
sites where the construction of communication
infrastructure is difficult, and in disaster-relief support.
We assume the following scenario for deploying a WSN
via mobile robots in post-disaster underground spaces:
1. Under Japanese Building Standards Law, direct safety
stairs leading above ground level for escape during a
disaster are installed so that the walking distance in an
underground space is not above 30 m. In our system,
mobile robots linearly deploy WSN between such
gateways for confirmation of the approach path ofrescue workers (Fig. 2); the robots and WSN then
gather and transmit information on the disaster.
Fig. 2. WSN deployment scenario using mobile robots in
a MRSN
2. The robots loaded with SNs enter the underground
space. At first, the robots move while confirming the
communication status based on an operator's terminal,
and deploy a SN. The WSN is linearly deployed by the
robots repeating this movement and deploying each
SN one by one, confirming the communication status
between the SNs to the next gateway in the same way.
3. Environmental information gathered by the MRSN is
continually transmitted to the operators above ground
level, and the disaster status within the space is thusdetermined. The operators gather information
adaptively in a disaster situation by teleoperating the
robots via WSN when necessary.
4. The robots deploy additional or alternate SNs to
necessary sites to restore communication links because
of network disconnection due to unforeseen causes
and/or problems with the SNs themselves.
The authors have developed a SN by relating it to use in
post-disaster situations (Sato, H., et al., 2008; Sawai, K., et
al., 2008) and to the teleoperation method for multi-robots
via WSNs (Kimitsuka, Y., 2008). However, a number of
problems arise because the environmental state of thedisaster area is unpredictable when a WSN is set up by
mobile robots in these situations.
- It is difficult to ensure a communication route owing
to a change in environmental factors such as obstacles,
radio interference and radio wave conditions.
- The communication route may be disconnected
because of battery drain, SN failure, etc.
Therefore, in such unforeseeable circumstances, a WSN
should be deployed adaptively by mobile robots byensuring communication connection between SNs. In
addition, it is necessary to maintain the function of an
information-gathering network by continually
determining the state of the WSN and recovering the
communication route if necessary. Thus, in order to solve
the abovementioned problems and achieve the scenario,
this paper proposes an autonomous WSN deployment
and restoration method using mobile robots, including
the following strategies:
- The adaptive deployment of a WSN with
consideration of the communication status.
- The restoration of the communication route by
deploying additional and alternate SNs when the
network is disconnected.
Experiments verifying the proposed method by testing
WSN deployment, restoration, and environmental
information-gathering using the prototype MRSN system
are also described.
The rest of the paper is organized as follows: Section 2
describes related research. Section 3 explains the
autonomous deployment and restoration of WSNs by
mobile robots. Section 4 details a prototype system for
verification of the proposed method, and Section 5
evaluates system conditions and sensor properties viapreliminary experiments, before applying the proposed
method through the experiments described in Section 6.
Section 6 shows the experimental setup and the results of
autonomous WSN deployment and restoration using the
MRSN prototype system. Concluding remarks are given
in Section 7.
2. Related research
Studies of the manual deployment of SNs on building
ceilings, streetlights, etc., for preemptive disaster-area
information-gathering (Kurabayashi, D., et al., 2001) can
be found in rescue research projects (Tadokoro, S., et al.,2003). Preemptively deployed fixed SNs can gather
continuous information at any time before a disaster
occurs. However, their functionality following a disaster
cannot necessarily be guaranteed. Human resources are
often insufficient to deploy the SNs. Moreover, humans
could be hurt by secondary disasters during information-
gathering activity.
An autonomous construction of WSNs has been
previously discussed in conventional studies on SN
deployment. The methods proposed in these studies
consist mainly of randomly scattering many low-cost SNs,
constructing WSNs with mobile SNs, etc. (McMickell, M.,et al., 2003; Dantu, K., et al., 2005; Parker, L. E., et al., 2003).
However, in the scattering deployment method, there is a
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possibility that the SNs may not necessarily be deployed to
the desired locations. Although it may be possible to evade
such a problem by scattering a massive number of SNs, the
costs and increased communication traffic associated with
the deployment of a large number of SNs may prove
problematic. In WSNs composed of Mobile Sensor Nodes
(MSNs), these can be deployed to any position desired.However, MSNs are generally expensive. Moreover, the
SNs do not necessarily all need to move, depending on the
environment. In general, system cost is a significant
problem because a large number of SNs are needed to
gather information over a large disaster area. Therefore, it
is necessary to consider the costs of the SNs and thereby
reduce the energy costs for the entire system.
Other methods for SN deployment have been proposed;
these have been based on achieving maximum
communication or sensing range using MSNs or mobile
robots (Batalin, M., et al., 2002; Miyama, S., et al., 2003;
Sugano, M., et al., 2006) and deployment by virtualinteraction between SNs based on physical models
(Howard, A., et al., 2002; Pac, M. R., et al., 2006). However,
these studies assume that it would be difficult to ensure
communication between SNs owing to environmental
factors such as obstacles and radio interference that could
block communication channels. Moreover, it is possible for
communication between SNs in WSNs to be interrupted
owing to SN battery drain or device failure. Therefore, it is
essential that the status of WSNs is known at all times, in
order to ensure communication between SNs and to
maintain their collective functionality as an adaptive
information communication network. In related research,the deployment and repair of the sensor network by UAV
was proposed (Corke, P., 2004). However, it is difficult for
an UAV to move in the rubble and in underground spaces.
3. Autonomous WSN Deployment and Restoration
based on Received Signal Strength Indication (RSSI)
value between SNs by Mobile Robots
In our approach, we assume that the SN cost problem is
solved by using low-cost SNs that can perform the
minimum functions necessary for environmental
information-gathering. Moreover, the energy cost
problem is expected to be solved by enabling high-mobility robots to deploy WSNs. Therefore, a WSN
deployment method using mobile robots is essential.
The robots manipulate fixed SNs and adjust the spatial
range of the WSN and network topology of the WSN
adaptively according to the conditions of the post-
disaster environment. In order to ensure communication
between SNs, the RSSI value is monitored while the
robot-deployed SNs are in transit to their designated
locations. The robots confirm whether the SNs are able to
communicate with one another, and deploy SNs while
ensuring communication channels between them. This
proposed method is expected to enable the deployment ofWSNs adaptable to changes in signal conditions caused
by environmental interference.
After this WSN is deployed, the circumstances under
which it would be unable to continue functioning are
estimated by assessing the amount of battery drain that
would result in the failure of a SN. The robots can then
specify the necessary details for a replacement SN by
using positional information recorded when the original
SN was deployed (odometric information, for instance).When a signal can be received from the SN, the accuracy
of the detection of its location is improved using the RSSI
value. Finally, the mobile robot moves to the location of
the target SN, the additional or alternate SN is deployed,
and the function of the WSN is maintained.
Figure 3 shows an algorithm for the deployment and
restoration of a WSN by mobile robots based on the
above proposal. Only the path of motion of the robots is
given by the operator, and the robots then deploy the
WSN, ensuring connection between SNs by measuring
the RSSI value autonomously. They also detect any
abnormalities in the SNs and resolve encounteredproblems by deploying additional or alternate SNs to
suitable positions. The robots distinguish between the
SNs by referring to their unique IDs.
Fig. 3. WSN deployment and restoration algorithm using
mobile robots1. A mobile robot begins to move to the given target
coordinates via the operator’s instructions.
2. The robot receives the SN status and sensor data via
the WSN while moving, and confirms the status of the
SN.
3. If the status of the SN is normal, the robot obtains the
RSSI value of the adjacent SN. The robot refers to the
status and the RSSI value from the operator’s terminal
for the initially deployed SN.
4. The robot stops in a location where the average of the
measured RSSI values falls below a previously
determined threshold, and the SN is then deployed.The robot stores the SN’s ID and position coordinates
obtained from odometric data.
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5. If information is not obtained or an abnormal state is
received from the SNs, the robot retrieves location data
corresponding to the IDs of the relevant SNs, and then
moves to the position of the SN.
6. The robot communicates with an SN evaluated as
being abnormal. If a signal from the SN can be
received and its status is normal, the robot determinesthat communication must have been disconnected by a
change in the environment. The robot thus deploys
additional SNs while measuring the RSSI values of
nearby SNs to repair the connection.
7. If a signal from the SN cannot be received or the SN
cannot maintain its function because of battery
draining, the robot deploys an alternate SN within the
communication range of nearby SNs while measuring
the RSSI values.
8. When some abnormal SNs are detected
simultaneously, the robot restores the communication
route from the nearest SN.
4. Prototype System Configuration for Verification of
Proposed Method
A SN deployment and retrieval mechanism was
developed to verify the proposed method, which was
applied to a mobile robot. This section describes the SN,
mobile robot system, and SN deployment and retrieval
mechanism used as a verification platform system.
4.1. Sensor Node
In this system,a Mica2 MOTE with a MTS300 sensor
board by Crossbow Technology, Inc. was used as the SN(Fig. 4 (d) and Table 1). The initial MOTE packet structure
was “destination SN ID, data type, group ID, packet
length data, and sensor data” of maximum 29 bytes. We
restructured the packet as “source SN ID, destination SN
Fig. 4. Configuration of SN deployment and retrieval
system
Frequency 315/433/915 MHz
Radio communication
standardLow-power radio
Modulation method FSK
Transmission power −20 dBm
Receiver sensitivity −110 dBm
Sensors Light, temperature, etc.
Table 1. Specifications of MICA2 MOTE
ID, remaining battery level for SNs’ status evaluation,
and sensor data” by modifying the MOTE application.
Therefore, the robots could confirm the status of SNs and
their sensing data via the WSN. The robot could also
obtain the RSSI value between SNs by using the MOTE
application, deploying SNs as necessary by estimating the
communication status using this value.
4.2. Mobile Robot
An omni-directional mobile robot (Asama et al., 1995)was used as the mobile robot platform (Fig. 4 (a)). The
robot has a control PC, driving actuators, an omni-
directional vision camera, infrared range sensors and
batteries, and can move to the target coordinates while
obtaining odometric information. A MOTE with serial
interface board MIB510 was connected to the robot’s PC
to communicate with deployed the SNs.
4.3. Sensor Node Deployment and Retrieval Mechanism
A deployment and retrieval mechanism employing a
screw and nut configuration was developed for carrying
MOTE-sized SNs and deploying or retrieving them,
which was then mounted on the robot (Fig. 4 (a)). Thismechanism comprises of a “SN tray”, and a “guide and
screw part” for handling the SN tray (Fig. 4 (b)).
The SN tray is a resin (MC nylon) molded regular
octagonal product with a hollow that can store up to two
MOTE-sized SNs and that corresponds to the nut of the
lead screw mechanism. The center of the SN tray is tapped
according to the screw, and the SN tray can be moved
vertically according to the guide by installing the screw.
The lead screw is 20 mm in diameter, has a 10 mm pitch,
and is 200 mm in length and rotates via a stepping motor
(Motor 2 in Fig. 4 (b)). The SN tray moves up and down
by 10 mm (one screw rotation) so that the orientation ofthe SN tray is maintained by the guide. The height of the
SN tray is retained by stopping the screw. The number of
SN trays that can be loaded is decided according to the
length of the screw, and up to ten SN trays can be
installed in the prototype. The point of the screw is
designed so that it tapers and can be installed in the
screw hole of the SN tray. A vertical motion of the screw
and guide part can be adjusted by a translation actuator
driven by another stepping motor (Motor 1 in Fig. 4 (b)).
Figure 5 shows the sequence of SN deployment.
The SN tray is retrieved by the reverse procedure of Fig.
5. Accuracy of position and orientation control is requiredfor such retrieval tasks in general. However, in this
mechanism, the SN tray is pushed and positioned by the
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Fig. 5. Sequence of SN deployment
guide even if the robot experiences some movement
errors. Therefore, the screw can be installed in the
position of the screw hole, and the robot can execute a
retrieval task smoothly.
5. Determining Threshold of RSSI and Evaluating SN
Optical Sensing Property
5.1 Determining Threshold of RSSI taking into account
Communication Status between SNs
In the WSN deployment and restoration method
proposed in this paper, it is essential to decide on the
threshold for the RSSI as a critical value with which the
robot determines the deployment position necessary for
stable communication between SNs in the environment.
Therefore, typical communication between the SNs was
investigated in the below-mentioned experimental
environment. Here, the RSSI value between two SNs
stored in the SN trays was measured. Figure 6 shows an
experimental result. The typical RSSI value of MOTE wasascertained as −100 dBm at about 0.5 m when stored in
the SN tray; packet loss was confirmed frequently at this
distance or greater. It was concluded that the
communication route could therefore be assured by
setting the threshold over −100 dBm when the mobile
robot deployed SNs. We decided on a threshold of −70
dBm, including a safety margin because the robot moves
while measuring RSSI values and deploys SNs at various
locations after this value is below the threshold. The
MOTE communication distance was shortened by storage
in the SN tray. This was due to deterioration in
communication by having stored the MOTE antenna inthe SN tray given that the robot must remain portable,
and having placed the SN tray directly on the floor.
Fig. 6. RSSI value between SNs
Therefore, it will be necessary to modify the tray
configuration and deployment method to improve
communication. However, the aim of this paper is to
verify the method of WSN deployment and restoration by
mobile robot, and improvements in the communication
performance of the system will be attempted in future
work.
5.2 Evaluation of SN Optical Sensing Property
Illuminance data was gathered by the WSN for use as
sample data of the system. This is because fire fighting
and prevention of fire spreading, which occurs after a
disaster, are important, and rescue workers must record
the fire incident in addition to undertaking rescue efforts
in a post-disaster environment. Thus, illuminance is used
as data for the detection of fire symptoms.
In this experiment, illuminance in the environment was
measured by an onboard MOTE optical sensor, CdSe
photocell. Moreover, illuminance was also measured for
use as comparison data using a digital illuminometer
(silicon photodiode mounted on LX-1332D of CUSTOM),
which is able to measure up to 200,000 lx. A lamp was
used as the light source.
The relative position of the light source and the onboard
MOTE optical sensor was adjusted depending on the
deployment orientation of the SN tray when the MOTE
was stored in the SN tray. Thus, the relation between the
value of the MOTE optical sensor when stored in the SN
tray and the deployment orientation of the SN tray was
examined in the experimental environment (described
below) with the light fixture turned off.
Figure 7 shows the result of averaging 40 data sets of
sensor output at various orientation angles. It was
confirmed that the illuminance data could be used as
environmental data because the data was attenuated in
Fig. 7. SN optical sensor property
Mobile robot stops at
deployment location of
SN. The guide and screw
with held SNs then move
down using Motor1.
Guide and screw with
held SN move up using
Motor1.
Robot moves to the next
deployment location.
One SN tray is placed onthe floor by rotating thescrew of Motor2; the nexttray is then held at the endof the screw by stoppingthe screw rotation.
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proportion to the distance between the SNs; however, a
difference in data was observed according to the position
of the SN tray over a short distance. Moreover, the typicaldistance between optical sensors for each position of the
SN tray was determined.
6. WSN Deployment and Restoration using Mobile
Robots and Environmental Information-Gathering by
MRSN
6.1 Autonomous Adaptive WSN Deployment and Restoration
using Mobile Robots
The proposed method was installed on the above-
mentioned robotic system, and the WSN deployment and
restoration experiment was executed in an environment
consisting of an indoor passage (height: about 2.24 m;width: about 1.77 m; total length: about 40 m) of a
ferroconcrete building. The experiment was carried out at
nighttime, assuming that the light fixture fails in a post-
disaster environment. In this experiment, five SN trays
each with a MOTE were used, and the communication
routing path was decided statically when SN trays were
deployed.
An underground space has earthquake resistance because
of its structural solidity (Godard, J. P., 2004). Therefore, it
is assumed that the extent of structural collapse is limited
and that the environmental structure is maintained. The
focus of the experiments was the WSN deploymentmethod based on environmental conditions in terms of
communication. Therefore, it was assumed that the
environmental structure had not collapsed, and only
lighting-equipment damage was assumed in the
experiments.
Figure 8 shows an experimental scenario. Motion path of
the robot is given by an operator. A robot deploys the
initial SN (SN1 in Fig. 8) after receiving instructions
regarding WSN deployment. Then, the robot moves
along this path while measuring the RSSI value of SN1,
and SN2 is deployed at a location at which the measured
RSSI value is below the threshold of−
70 dBm. The robotdeploys the WSN, repeating the placement of SNs on the
floor, one by one, based on the threshold, while
measuring the RSSI value of the nearest SN. Furthermore,
the functional decline of a deployed SN by battery drain
is simulated in order to verify the restoration function bywhich communication is maintained. When the robot
detects a disruption to the network or abnormality in an
SN of the deployed WSN, the robot identifies the target
SNs through their IDs and localizes them using
odometric information recorded when they had been
deployed. If a signal can be received from the target SN,
the accuracy of the detection of its location is improved
using the RSSI value. Then, the mobile robot moves to the
location of the target SN and deploys an additional or
alternate SN within the range of communication of
nearby SNs while measuring RSSI values for maintenance
of the WSN.Based on the above scenario, experiments on WSN
deployment and restoration by mobile robot were carried
out. Figure 9 shows an experimental example. The
pictures from (a) to (d) in Fig. 9 show the robot deploys
SN1 to SN4 for WSN deployment while moving along the
route instructed by the operator (straight line trajectory).
In this example, simulated data regarding battery drain
was transmitted from SN1. Figure 9 (e) shows the robot
detects an abnormality in SN1 by communication via
WSN, before then moving to a nearby location using
odometric information recorded when SN1 was
deployed. The robot then deploys an alternate SN, SN5,
within the communication range based on RSSI value of
SN2 to restore the WSN ((f) in Fig. 9). The trajectory of the
motion path for WSN restoration is given by the operator.
However, each SN’s location and the location of alternate
SNs to be deployed are recorded and executed by the
robot autonomously.
Figure 10 shows the distance between deployed the SNs.
A difference of up to 0.1 m (about 20% of the average
deployment distance between the SNs) was observed
between each SN. The distance between the SNs differed
in each experiment. It is believed that the deployment
position of the SNs was adaptively decided by the robotaccording to environmental factors, the individual
characteristics of the SNs, etc. Moreover, the WSN
Fig. 8. Scenario of actual experiment
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Fig. 9. Actual experimental example of autonomous
adaptive WSN deployment and restoration using
prototype MRSN system
restoration function was confirmed by detecting the
status of the SNs via the WSN and by deploying alternate
SNs when abnormal states occurred in a part of the WSN
after deployment. Thus, the effectiveness of the proposed
WSN deployment and restoration method that takes into
account the communication status using mobile robots
was verified.
In this experiment, the average speed of the robot when
moving was 0.52 cm/sec, the average deployment time
was 4.8 min, and the average time until restoration (by
deploying SN5) was 7.5 min, because the robot had to
stop for RSSI value measurement. Therefore, it is
necessary to improve the motion speed of the robot using
a sampling period of RSSI measurements based on thecommunication property of the SN in order to enable the
WSN to be deployed over a long section more quickly.
Fig. 10. Example of distance between SNs deployed by
mobile robot
Fig. 11. Experimental setup for measuring illuminance
6.2 Information Gathering by Managed WSN
Illuminance data for use as environmental information
was detected and gathered by applying the WSN
deployed and restored by the mobile robot. Figure 11
shows the experimental situation. The experiments were
carried out in both the deployed WSN ((a) in Fig. 11) and
the restored WSN ((b) in Fig. 11) for verifying the
effectiveness of the WSN deployment and restoration.
The light source (lamp) was placed 0.2 m from SN1 in
each WSN. When an illuminance data was gathered by
WSN, the light fixture in the passage and the lamp were
turned off at first. WSN then started to detect and gather
illuminance data. The lamp was lit in 30 sec after the start
of detection, and light was held for 30 sec. The lamp was
turned off for another 30 sec. Therefore, an illuminance
data was measured for 90 sec in total in each WSN. Datafrom each SN was transmitted to the robot via the WSN.
Figure 12 shows the result obtained by data detected
using the deployed WSN. This result shows that the
change in illuminance and relative position between the
light source and SNs can be detected by taking into
account the typical distance between optical sensors;
however, a detection delay and error margin of a few
seconds were found in SN2. Figure 13 shows the result
obtained from data detected using the restored WSN. The
communication and sensing function of SN1 was
supplemented by the alternate SN, SN5; however, in
contrast to Figure 12, a measurement error was found atthe position at which SN5 was deployed. Thus, the
effectiveness of the WSN restoration was confirmed.
SN3 and SN4 were placedin the same manner asSN2 while maintainingconnection with thenearest SN.
An alternate SN, SN5,was placed within thecommunication range ofSN2. Then, the robotreturned to its deploymenttask.
Mobile robot stopped at
deployment location of
SN1.
SN2 was placed based on
the RSSI value to maintain
communications with
SN1.
Then a simulated low- battery signal was sentfrom SN1. The robotregistered the status ofSN1 via WSN and movedto a nearby location.
SN1 was placed on thefloor. Then, the robotmoved to the nextlocation to place SN2while measuring RSSI.
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Fig. 12. Result of gathering illuminance information by
deployed WSN (Fig. 11 (a))
Fig. 13. Result of gathering illuminance information by
repaired WSN (Fig. 11 (b))
7. Discussion and Future Direction
This paper has focused on a WSN deployment and
restoration method based on communication conditions.
In this paper, the key result is that autonomous WSN
deployment, restoration, and information-gathering can
be achieved adaptively according to the communication
conditions regardless of the communication distance of
the SNs. However, it is difficult to apply this prototype
system to actual post-disaster situations. Therefore, we
are now developing new spherical SNs and crawler
robots for WSN deployment and gathering information in
post-disaster situations based on the findings obtained
from the experimental results (Sato, H., et al., 2008; Sawai,
K., et al., 2008) (Fig. 14). A passive pendulum mechanism
for maintaining the position of sensors and an impact-
resistance capability achieved via a soft outer shell are
installed in the SNs to be employed in the disaster areas.
This SN can communicate over a distance of 10–30 m inindoor spaces by using EEE802.11b. Moreover, the
amount of information gathered is increased by installing
Fig. 14. (a) Crawler robot, (b) SN with passive pendulum
mechanism, (c) Soft outer shell for impact-resistance
capability
an omni-directional fish-eye camera sensor. The crawler
robot can move over a rough terrain by using its crawler
and flippers, and its speed when moving is about 12
m/min. In the assumed environment shown in Fig. 1,
taking into account the range of mounted sensors, we
estimate that this robot can connect between stairs 30 m
by deploying about five SNs. It is believed that the timerequired for the deployment is about five minutes. The
proposed method in this paper will be applied to new
robots and SNs. These component technologies are
integrated, and the performance of the MRSN must be
further developed and improved to enable it to be
applied practically to gather information on the effects of
disasters in underground spaces.
8. Conclusion
This paper has proposed an autonomous deployment and
restoration method for WSNs, deployed by MRSNs,acting as post-disaster information-gathering systems in
underground spaces. A SN deployment and retrieval
mechanism was developed and mounted on a mobile
robot for verifying the proposed method. In this method,
the robot deploys the SNs based on the RSSI values
between the SNs. Therefore, it is possible to deploy a
WSN that adapts to communication conditions in the
environment. Moreover, the robot restores the
communication route by deploying additional or
alternate SNs to suitable positions when the WSN is
disconnected for any reason. It was experimentally
confirmed that the robot could deploy and restore theWSN and gather environmental information using the
proposed method.
Linux computer, Fish eye camera,Wireless LAN, Battery, etc.
250 mm
(a)
(b) (c)
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It will be necessary in future work to discuss appropriate
operational methods for the MRSNs that consider the
characteristics of multi-robot systems and WSNs, for
example, reductions in network traffic, routing
approaches, sensor data processing, multi-robot
cooperation, etc.
9. Acknowledgement
This work was partially supported by the Research
Institute for Science and Technology of Tokyo Denki
University, Grant Number Zb06-01 / Japan.
We are grateful Takayuki Tsubonuma who was an
undergraduate student of Tokyo Denki University in
2005. He developed the prototype of the Sensor Node
Deployment and Retrieval Mechanism.
10. References
Asama, H. (1995). Development of an Omni-Directional
Mobile Robot with 3 DOF Decoupling Drive
Mechanism, Proceedings of IEEE International
Conference on Robotics and Automation , pp.1925-1930, 0-
7803-1965-6, May 21-17
Batalin, M. A. & Sukhatme, G. (2002). Sensor coverage
using mobile robots and stationary nodes, Proceedings
of the SPIE, volume 4868 (SPIE2002) , pp. 269-276,
Boston, MA, USA, August 2002
Corke, P., Hrabar, S., Peterson, R., Rus, D., Saripalli, S.
and Sukhatme, G. (2004). Autonomous Deployment
and Repair of a Sensor Network using an UnmannedAerial Vehicle, Proceeding of IEEE International
Conference on Robotics and Automation , pp. 3602-3608,
0-7803-8232-3/04, New Orleans, LA, April 2004
Dantu, K.; Rahimi, M., Shah, H., Babel, S., Dhariwal, A. &
Sukhatme, G. S. (2005). "Robomote: enabling mobility
in sensor networks, Proceedings of Fourth International
Symposium on Information Processing in Sensor
Networks , pp. 404-409, 0-7803-9201-9, UCLA, Los
Angeles, California, USA, April 2005
Godard, J. P. (2004). Urban Underground Space and
Benefits of Going Underground, Proceedings of World
Tunnel Congress 2004 and 30th ITA General Assembly ,pp.1-9, Singapore, 22-27 May 2004
Howard, A.; Matari'c, M. J. & Sukhatme, G. S. (2002).
Mobile Sensor Network Deployment using Potential
Fields: A Distributed, Scalable Solution to the Area
Coverage Problem, Proceedings of Distributed
Autonomous Robot System 2002 , Fukuoka, Japan, June
25-27 2002
Kawata, Y. (1995). THE GREAT HANSHIN-AWAJI
EARTHQUAKE DISASTER: DAMAGE, SOCIAL
RESPONSE, AND RECOVERY, Journal of National
Disaster Science , Vol. 17, No. 2, pp.1-12, 1995.
Kimitsuka, Y., Sawai, K. & Suzuki, T., (2008).
Development of Mobile Robot Teleoperation System
utilizing Robot Sensor Network, Fifth International
Conference on Networked Sensing Systems (INSS2008) ,
p250, June 17-19, Kanazawa, Japan
Kurabayashi, D., Asama, H., Noda, K. & Endo, I. (2001).
Information Assistance in Rescue using Intelligent
Data Carriers, Proceedings of 2001 IEEE/RSJ
International Conference on Intelligent Robots and
Systems, pp. 2294-2299, 0-7803-7398-7, Maui, Hawaii,
USA, October 2001
McMickell, M. B.; Goodwine, B., Montestruque, L. A.
(2003). MICAbot: A Robotic Platform for Large-Scale
Distributed Robotics, Proceedings of the 2003 IEEE
International Conference on Robotics & Automation ,
pp.1600-1605, 0-7803-7736-2, Taipei, Taiwan,
September 2003
Miyama, S.; Imai, M. & Anzai, Y. (2003). Rescue Robotunder Disaster Situation: Position Acquisition with
Omni-directional Sensor, Proceedings of the 2003
IEEWRSJ International Conference on Intelligent Robots
and Systems , pp. 3132-3137, 0-7803-7860-1, Las Vegas,
Nevada, October 27-31, 2003
Pac, M. R.; Erkmen, A. M., Erkmen, I. (2006). Scalable
Self-Deployment of Mobile Sensor Networks: A Fluid
Dynamics Approach, Proceedings of International
Conference Intelligent Robots and Systems , pp. 14460-
1451, 1-4244-0259-X, Beijin, China, October 2006
Parker, L. E.; Kannan, B., Fu, X. & Tang, Y. (2003).
Heterogeneous Mobile Sensor Net Deployment UsingRobot Herding and Line of Sight Formations,
Proceedings of the 2003 IEEWRSJ International
Conference on Intelligent Robots and Systems , pp. 2488-
2493, 0-7803-7860-1, Las Vegas, Nevada, October 2003
Sato, H., Kawabata, K., Suzuki, T., Kaetsu, H., Hada, Y.
and Tobe, Y. (2008). Information Gathering by
wireless camera node with Passive Pendulum
Mechanism, Proceedings of International Conference on
Control, Automation and Systems 2008 , pp.137-140, Oct.
14-17, Seoul, Korea, 2008
Sawai, K., Suzuki, T., Kono, H., Hada, Y. and Kawabata,
K., (2008). Development of a Sensor Node with
Impact-Resistance Capability to Gather the Disaster
Area Information, 2008 International Symposium on
Nonlinear Theory and its Applications (NOLTA'08) ,
pp.17-20, 978-4-88552-234-5, Budapest, Hungary,
September 7-10, 2008, IEICE, Tokyo, Japan
Sugano, M.; Kawazoe, T., Ohta, Y. & Murata, M. (2006).
An indoor localization system using RSSI
measurement of a wireless sensor network based on
the ZigBee standard", Proceedings of the sixth IASTED
International Multi-Conference on Wireless and Optical
Communication , pp.503-508, Banff, Canada, July 2006
8/3/2019 InTech-Autonomous Deployment and Restoration of Sensor Network Using Mobile Robots
http://slidepdf.com/reader/full/intech-autonomous-deployment-and-restoration-of-sensor-network-using-mobile 10/10
International Journal of Advanced Robotic Systems, Vol. 7, No. 2 (2010)
114
Tadokoro, S., Matsuno, F., Onosato, M. & Asama, H.
(2003). Japan National Special Project for Earthquake
Disaster Mitigation in Urban Areas, First International
Workshop on Synthetic Simulation and Robotics to
Mitigate Earthquake Disaster , pp.1-5, Padova, Italy, July
2003