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
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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.
USA
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.
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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