InTech-Autonomous Deployment and Restoration of Sensor Network Using Mobile Robots

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



International Journal of Advanced Robotic Systems, Vol. 7, No. 2 (2010)

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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



Tsuyoshi Suzuki, Ryuji Sugizaki, Kuniaki Kawabata, Yasushi Hada and Yoshito Tobe: Autonomous Deployment and Restoration of Sensor Network using Mobile Robots

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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)




113

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.

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InTech-Autonomous Deployment and Restoration of Sensor Network Using Mobile Robots

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