cost-efficient sensor deployment in indoor space with obstacles

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Cost-Efficient Sensor Deployment in Indoor Space with Obstacles Nara Institute of Science and Technology * Tokyo University of Science, Yamaguchi Nanan Marc Thierry Kouakou, Keiichi Yasumoto , Shinya Yamamoto*, and Minoru Ito

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Presentation slides at WoWMoM 2012

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Page 1: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

Cost-Efficient Sensor Deploymentin Indoor Space with Obstacles

Nara Institute of Science and Technology*Tokyo University of Science, Yamaguchi

Nanan Marc Thierry Kouakou, Keiichi Yasumoto, Shinya Yamamoto*, and Minoru Ito

Page 2: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Overview

Indoor Wireless Sensor Network (indoor WSN) Monitor/Collect various information of indoor space

Human position, temperature, humidity, illuminance, etc Application

Human activity prediction, energy-saving appliance control, security, etc

ChallengesCoverage of target 3D spaceConnectivity among sensor nodes

Page 3: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Design of Indoor WSN

Characteristics of indoor WSN Target monitoring space is three dimensional

Constraints on installing positions (cost, defiling)Ex. Easy on ceiling/wall, but not easy on floor/in the air

Many obstacles Influence on sensing and wireless communication

Requirements for indoor WSN Minimize deployment cost Guarantee full coverage and wireless connectivity

take into account shape of target space, deployment cost, influence of obstacles

Page 4: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Organization

1. Related Work2. Problem Formulation3. Deployment Algorithms4. Evaluation5. Conclusion

Page 5: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Related Work: Coverage of 2D space with Obstacles

[1] proposed a method using Delaunay triangulation First apply the contour deployment (around obstacles),

then cover the remaining space by triangles

ProblemThe deployment is only considered in 2D space, leading to some

inaccuracies when applying to 3D spaceDeployment cost depending on position is not considered

[1] Wu et al., “A Delaunay Triangulation based method for wireless sensor network deployment”, Computer Communications, 2007

Delaunay triangulation

Page 6: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Related Work:Coverage/connectivity in 3D space without obstacles

[2] Bai et al., “Full-Coverage and k-Connectivity (k=14,6) Three Dimensional Networks”, Infocom 2009[3] Bai et al., “Low-Connectivity and Full-Coverage Three Dimensional Wireless Sensor Networks”, MobiHoc 2009

[2][3] showed optimal deployment patterns guaranteeing full coverage and wireless connectivity in 3D space Several different optimal deployment patterns depending on

relationship between sensing and communication radii rs and rc

Problem Not consider influence of obstacles and position-

dependent deployment cost

Page 7: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Human Body Shadowing Problem

• No approach focusing on the indoor WSN deployment problem that takes into account the human body shadowing effects

[4][5] discussed effects of the human body and its mobility on indoor communications

[4] Klepal et al., Influence of People Shadowing on Optimal Deployment of WLAN Access Points, VTC2004-Fall.[5] Collonge et al., Influence of the human activity on wide-band characteristics of the 60 GHz indoor radio channel, IEEE Trans. Wireless Commun., 3(6), 2004.

Page 8: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Contribution of this Work

Cost-efficient deployment methods for 3D WSNs in indoor environment taking into account obstacles Coverage of 3D space with static and mobile

obstacles (human body)

Page 9: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Organization

1. Related Work2. Problem Formulation3. Deployment Algorithms4. Evaluation5. Conclusion

Page 10: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Assumptions

Sensor nodes Shape of sensing range and communication range: sphere Sensing radius rs, communication radius rc (fixed)

Target space Deployable area

Sensor can be installed Cost of each point in area

given Monitoring space

Space to be monitored Obstacles (static and mobile)

exist

Deployable area

Obstacle

Monitoring space

Page 11: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Assumptions for Obstacles

Influence on sensing Sensor can NOT sense

Information from shadow area

Influence on wireless comm. Sensors can NOT

communicate when obstacle is on the line of sight

Sensor

Sensing range

Obstacle

Shadow area

s0

s1Wireless

communication range

rc

Obstacle

Page 12: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Problem Definition

Input Target space, monitoring space Deployable area with cost of each point Sensing and communication radii rs, rc

Output Number of sensors, sensor positions

Constraints Monitoring space is k-covered Wireless connectivity between sensors

Objective Minimize overall deployment cost

This is NP-hard problem (minimum set cover)

Any point of monitoring space is covered by at least k sensors

Page 13: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Assumption for Mobile Obstacles

ms

mobile obstacle po

s

mr

s

m

mobile obstacle

ceiling

groundpos

mh

Only human body considered as mobile obstacle Represented by cylinder: radius mr, height mh

Mobile obstacle obstructs monitoring point m from some sensors sensing ranges obstructed sensors change by mobile’s position

We assume each point is affected by only one mobile obstacle at one time

XX

XX

Top view Side view

X

XSensors

Page 14: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Mobile k-Coverage Problem

Target problem for mobile obstacleDetermine the number of sensor nodes and their installing positions to achieve mobile k-coverage with the minimal deployment cost

Mobile k-Coverage A monitoring point m is mobile k-covered if for

any location of the mobile obstacle, m is k-covered

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Organization

1. Related Work2. Problem Formulation3. Deployment Algorithms4. Evaluation5. Conclusion

Page 16: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Discretization of Problem

Complexity Modified problem still NP-hard

Discretization Deployable area Deployable points Target monitoring space Monitoring

points

Heuristic algorithms to achievea near-optimal solution in a reasonable

amount of time

Page 17: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Algorithm for Minimal Costk-Coverage (only static obstacles)

per-cost volume: how many monitoring points are covered by the deployable point per unit deployment cost

Places sensor node on the grid point with the highest per-cost volume

Repeats until all the monitoring points are sufficiently covered

per-cost volume =

Number of monitoring points covered

Deployment cost of the deployable point

0.65 0.75

0.150.350.35

0.25

0.2 0.65 0.25

0.45

0.050.250.35

0.25

0.2 0.150.55

0.050.250.35

0.25 0.40

0.1 0.05

Page 18: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Influence of the Mobile Obstacle

Vertical plane Δ tangent to the monitoring point orthogonal to the mobile

obstacle Shaded area: half-space

divided by Δ that contains the mobile obstacle Nodes in the shaded area

cannot sense the monitoring point

(Δ)

rs

shaded area

monitoring point

(Δ )

Sufficient condition for mobile k-coverage:For arbitrary position of the mobile obstacle, the half-sphere that is not in the shaded area, contains at least k sensor nodes

X X

X

X

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Sensor Placement forMobile k-Coverage (1)

Basic Idea Consider sphere with radius: rs centered at the monitoring point Divide it into 2k equivalent portions (spherical wedges) Put one sensor in each wedge

k2

2

Spherical wedge

wedge

sensor

(Δ)

(Δ)

??

sensor(Δ)

4

obstacle

(Δ)4

Dividing into 2k wedges(k=4)

Dividing into 2k+1 wedges(k=2)

Page 20: Cost-Efficient Sensor Deployment in Indoor Space with Obstacles

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Sensor Placement forMobile k-Coverage (2)

Covering spherical wedge Divide sphere into 2k+2

wedges angle: , radius: rs

)1(2

2

k

covering wedge

monitoring point

sensor node(Δ)

Spherical wedge Covering spherical wedge (k=3)

4

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Heuristic Algorithm for Minimal Cost Mobile k-Coverage

per-cost volume: for a deployable point, the number of covering wedges in which it is located per unit deployment cost

For each monitoring, compute covering spherical wedges

monitoring points

deployable points

deployed nodes

1. For each monitoring point, determine its covering wedges2. Set a node on the deployable point with the highest per-cost volume3. Repeat until each wedge contains at least one node

k=1

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Organization

1. Related Work2. Problem Formulation3. Deployment Algorithms4. Evaluation5. Conclusion

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Evaluation

Purpose1. Understand to what extent the deployment

cost can be reduced2. Investigate the effectiveness of the computed

deployment for obstacles

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Evaluation on Deployment Cost

Three deployable regions region 1 (cost=1): on the ceiling region 2 (cost=5): in the “air” (h = 2m)

region 3 (cost=2): on the partition walls

Target monitoring space Horizontal plane (h = 1.5m)

Side view

floor

Top view of the indoor environment

Method # of nodes

Deployment cost

Proposed Method

14 19

Triangular lattice [6]

7 35The deployment cost is 45% smaller

[6] Bai et al., “Complete optimal deployment patterns for full-coverage and k-connectivity (k≤ 6) wireless sensor networks”, 9th ACM Mobihoc, 2007

ceiling

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Evaluation of Mobile 3-Coverage

sinktag node

sensor node

beaco

n

(node_id, rssi)

Purpose: investigate if beacon sent by tag node is received by at least 3 sensors with sufficient RSSI for arbitrary position of user

1. The tag node broadcasts a beacon at some monitoring point

2. Sensor node which receives the beacon sends the RSSI with its ID to the sink

3. The message with (node_id, rssi) is logged with the timestamp at the sink① ②

user

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Coverage and Sensing Radius

rssi0 : average RSSI of a packet sent from a ZigBee device placed at a distance 5m

rssi0 (d=5m) = -60dBm

Distance (m) 3 4 5 6RSSI value (dBm)

-56 -60 -60 -63

If a sensor node receives a beacon sent from monitoring point with RSSI greater than rssi0, then this point is covered by the node.

ZigBee DeviceRSSI measurement without obstacle

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Monitoring Area and User Position

Target monitoring area 2.5m x 2.5m, horizontal plane at height 1m above

the floor For each target point (P1…P4), the user stands at 4

positions around the tag node at distance of 5 to 10 cm

UP1

UP2

UP3

UP4tag

node

Monitoring points User’s positions

5-10cm

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Sensor Deployment

Installed 9 sensors based on computation result

Seminar room at NAIST

S1S2S3S4

S5

S6

S7 S8 S9

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Result of Mobile 3-Coverage

UP1 UP2 UP3 UP4-70.0

-60.0

-50.0

-40.0

rssi0

At least 3 sensors received beacon with RSSI more than -60dbM for any point P1—P4 and any user position UP1— UP4

mobile 3-coverage is achieved

P1

-70.0

-60.0

-50.0

-40.0

rssi0

P2

S1

S2

S3

S4

S5

S6

S7

S8

S9

UP1 UP2 UP3 UP4

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Conclusion

Cost-efficient sensor deployment method for indoor Defined problem taking into account position-

dependent installing cost and obstacle influence Devised algorithm which places one sensor in

each 1/2(k+1) spherical wedge for mobile k-coverage Evaluated mobile 3-coverage on ZigBee testbed

Future work Integrating more accurate model of radio signal

diffraction and fading effect