shibo he 、 jiming chen 、 xu li 、, xuemin (sherman) shen and youxian sun state key laboratory...

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Shibo He 、 Jiming Chen 、 Xu Li 、 , Xuemin (Sherman) Shen and Youxian SunState Key Laboratory of Industrial Control Technology, Zhejiang University,

China 、 Department of Electrical and Computer Engineering, University of Waterloo, Canada

INRIA Lille - Nord Europe, Univ Lille Nord de France

IEEE INFOCOM 2012

Cost-Effective Barrier Coverage by Mobile Sensor Networks

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Outline Introduction Goal Assumption Problem formulation Periodic Monitoring Scheduling algorithm Coordinated Sensor Patrolling algorithm Distributed CSP Simulation Conclusion

Introduction Wireless sensor networks have received a lot of attention

due to their potential applications in various areas Environmental monitoring

The placement of sensors related to coverage issues is intensively studied in the literature, and can be divided into three categories. Target coverage Full coverage Barrier coverage

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Introduction The target coverage problem (Points of Interest, PoI)

aims at monitoring specific points in the field of interest.

Museum Campus Military

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Introduction The full coverage problem (Areas of Interest, AoI)

aims at covering the whole area. Sensors are deployed to maximize the covered area.

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Introduction The barrier coverage problem

Aim at detecting intrusion on a given area. Sensors have to form a dense barrier in order to detect each

event that crosses the barrier.

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Intruder

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Introduction Existing solutions to barrier coverage in mobile sensor

networks implicitly assume the availability of sufficient sensors. K-barrier One-barrier

K-Barrier One-Barrier

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Introduction These solutions will fail to work when sensor scarcity and

budget limitation. the performance of detecting intruder decreasing

K-Barrier One-Barrier

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Goal In the case of sensor scarcity, this paper proposed two

algorithms to Improve the probability of detecting intruder Decrease sensor’s moving distance

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Assumption The belt region of interest Ω with two long parallel

boundaries. m sensors are needed to guarantee full barrier coverage

but there are only n mobile sensors available (n < m).

l

Ω

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Problem formulation max γ while min L

Average intruder detection probability

Average sensor moving distance

： the state of intruder arrivaltia

tiu ： the state of sensor presence

at point i

t

t

0 0 1 1

1

0 0 1 1

0 0 1 0 0 0 1

0 0

1 0

tjL the distance that sensor j moves

in time t

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Patrolling algorithms Periodic Monitoring Scheduling Coordinated Sensor Patrolling

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Periodic Monitoring Scheduling The basic idea of PMS is to let the sensors monitor each point

periodically. there are m points, but only have n (n<m)mobile sensors to

monitor. sensor at point j moves to point mod(j + n, m) and sensing the point

for T time slots.

0 1 2 3 4

A B C

A

A

A

A

B

B

B

B

C

C

C

C

t0

t1

t2

t3

t4

0 1 2 3 4

0 1 2 3 4

0 1 2 3 4

0 1 2 3 4

1T B

2 mod(2+3,5)=0mod(0+3,5)=3 mod(3+3,5)=1 mod(1+3,5)=4

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Periodic Monitoring Scheduling The basic idea of PMS is to let the sensors monitor each point

periodically. Presenting PMS algorithm to solve barrier coverage problem

formulated Average intruder detection probability

Average sensor moving distance

m

n

Tm

nnnmmnrsL

'

)'2''(2

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Periodic Monitoring Scheduling Average intruder detection probability

m

n

m

n

pm

pn

:proof

the steady-state probability of intruder arrival at each slot

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Periodic Monitoring Scheduling Average sensor moving distance

Tm

nnnmmnrsL

'

)'2''(2

proof ：

the minimum scheduling period

),gcd('

nm

mm

),gcd('

nm

nn ： How many time slots that each point is monitored by

sensors

： How many time slots in the monitoring period

sensor’s moving distance when j+n > m

sensor’s moving distance when j+n <= m

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Patrolling algorithms Periodic Monitoring Scheduling Coordinated Sensor Patrolling

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Coordinated Sensor Patrolling A centralized coordinated sensor patrolling algorithm.

Exploiting the temporal correlation of intruder arrival times to improve average intruder detection probability γ.

Coordinated Sensor Patrolling Intruder arrival analysis

t=1 11 pq

t=22

122 ppq

τ τ+1 τ+2

one intruder arrives at slot τ +2

two intruders arrive,one at slot τ +1 and the other is τ +2

the probability that the next intruder arrival is at slot τ +t given the last intruder arrival time is τ.

)(

1)( ),1()(x

t exFtFtFp

Cumulative Distribution Function

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Coordinated Sensor Patrolling Intruder arrival analysis

After an intruder arrives at a point, the probability that an intruder will arrive again at the same point in the next few time slots is very small.

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Coordinated Sensor Patrolling Point selection step Coordinated movement step

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Coordinated Sensor Patrolling Point selection step

Three principles A sensor should move to another point if it detects an intruder at the point

in the previous time slot. A sensor should not leave its current point until it detects an intruder.

0 1 2 3 4

A A Available

sensors availble ofnumber :n

B

B Unavailable

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Coordinated Sensor Patrolling Point selection step

Three principles A sensor should move to another point if it detects an intruder at the point

in the previous time slot. A sensor should not leave its current point until it detects an intruder The points with highest qt should be selected if a sensor wants to find a

point to monitor.

jI ： the number of time slots that there is no sensor at point j

2jI jIq &

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Coordinated Sensor Patrolling Coordinated movement step

In order to reduce the total moving unavailable sensors do not necessarily stay at their previous points distance of each sensor.

jIq

n

C

'C t0

0 1 2 3 4

0 1 2 3 4t1

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Distributed CSP Distributed variants

Simple DCSP

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Distributed CSP Simple DCSP

Initialization phase Dynamic movement phase

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Simple DCSP Initialization phase

The leader Indicating how the sensor likes to monitor the points. distribute the preference level of each sensor among the points. Assign a preference level to points

0 1 2 3 4

A B CA

1 ,m

1j

ji

ji plpl

m

nplpl

n

i

ji

j 1

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Simple DCSP Initialization phase

0 1 2 3 4

1 ,m

1j

ji

ji plpl

m

nplpl

n

i

ji

j 1

A

m=5,n=3

1ˆ AlpA B C1ˆ Blp 1ˆ Clp

5

2ˆ Alp

5

3

m

n

},min{, ji

ji

j plm

nplpl

m

npl

0 0jpl1 2 3 4

A

5

3}0

5

3,1min{,0 00 Aplpl

0

A 1

5

2}0

5

3,

5

2min{,0 11 Aplpl

0ˆ Alp

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Simple DCSP

0,5

2,

5

3 43210 AAAAA plplplplpl

0,5

1,

5

3,

5

1 40321 CCBBB plplplplpl

0,5

3,

5

2 21043 CCCCC plplplplpl

Initialization phase

}1,0{AMS

}3,2,1{BMS

}4,3{CMS0 1 2 3 4

A B CA

B

B

C

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Simple DCSP Dynamic movement phase

A sensor should not leave its current point until it detects an intruder.

Sensor moves to the new point with high Each sensor moves between points in MSi

Collision problem

ijIj

i qpl *

}1,0{AMS }3,2,1{BMS }4,3{CMS

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Simple DCSP Dynamic movement phase

Collision problem it will set Iij= 0 and recalculate

Sensor i and sensor i+1 generate random number from

and exchange their number

}1,0{AMS }3,2,1{BMS }4,3{CMS

0 1 2 3 4

B CA

ijIq

A

j

ij

i

ji

ji

ji

ji

plpl

pl

plpl

pl

1

1

1

,0,,0

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Simulation Using MATLAB to perform the simulation The network operation time is divided into time slots,

each with 1 unit simulated time.

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Simulation Performance of PMS

Average intruder detection probability v.s. T slots

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Simulation Performance of CSP

Average intruder detection probability v.s. number of sensor

Performance γ for different n and m when β = 4.

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Simulation Average intruder detection probability v.s. number of sensor

when β = 2 when β = 6

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Conclusion In the case of sensor scarcity , this paper proposed

Periodic monitoring scheduling algorithm Coordinated sensor patrolling algorithm

reduce the application budget. provides a new cost-effective approach to achieve barrier coverage in

large-scale mobile sensor networks.

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Thank you