An Efficient Grid-Based Approach for Dynamical Target Coverage in Hybrid Sensor networks

An Efficient Grid-Based Approach for Dynamical Target Coverage in Hybrid Sensor networks

混合式感測網路中針對移動目標物的覆蓋率問題提出一個有效率以格網為基礎的方法

22

Outline Introduction Related work Preliminaries

Network model Proposed approach

Forming the monitoring region Circle covering to detect coverage hole Collecting the demand and supply information for healing

coverage holes Minimum cost flow to make the movement plan

Simulation result Conclusion Reference

33

Introduction

Wireless sensor networks (WSNs)

Applications Structural monitoring

Buildings and Ships Healthy monitoring Vehicular applications Target tracking-based applications

Environment monitoring Habitat monitoring Traffic monitoring

Military applications

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Research Issue

Target tracking Existing researches about target tracking can be divided into two categories: data aggregation (fusion), and location estimation.

the data aggregation involves the acquisition, filtering, and correlation of the relevant data from multiple sensor nodes.

The location estimation is to estimate the location of a target in a sensor field based on the received signal intensities at a number of sensor nodes and a priori information about the locations of these sensor nodes.

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Mobile target Static sensor node Sink

66

Mobile target Static sensor node Mobile sensor node Sink

77

Motivation and Goal

Motive In order to provide more detail and precise representation about

the mobile target.

goal Proposing a distributed approach to achieving the complete

monitoring for a mobile target. Minimum number of mobile sensor nodes used. Minimum movement cost.

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99

Related work

G. Wang, G. Cao, P. Berman, and T. La Porta, “Bidding Protocols for Deploying Mobile Sensors,” IEEE Trans. Mobile Computing, Vol. 6, No. 5, pp. 515-528, May 2007.

W.Wang, V. Srinivasan, and K.C.Chua, “Coverage in Hybrid Mobile Sensor Networks,” IEEE Trans. Mobile Computing, vol. 7, no. 11, pp. 1374-1387, Nov. 2008.

1010






1111

Preliminary

Network model

Two-dimensional field divided into a number of grids based on GAF protocol.

Hybrid sensor (static and mobile sensor)

The ratio between these two radiuses is larger than or equal to 2.

Rc: communication range , Rs: sensing range

GPS attached.

( )c

s

R

R

1212

Preliminary - GAF protocol

Our routing problem is based on the GAF protocol, which divides sensor field into multiple grids ， and each grid has one head ， which can communicate to its neighboring heads directly.

Grid head

grid

Rc

r

5cR

r

The relationship between r and Rc is: Sensor node

1313






1414

Working flow

Forming the monitoring region (grids) whenever a mobile target appears in the hybrid network

Using the circle covering to detect coverage holes

Collecting the demand and supply information for healingcoverage holes

Using the minimum cost flowto make the movement plan

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Forming the monitoring region

Originalmonitoring region

Extended monitoring region

1616

Coordinator selection

Monitoring region

grid head

coordinator

Sensor node

1717

Working flow





1818

Circle covering

Minimum Number of Circles to Cover A Rectangle

3 sR

sR

1919

Circle covering

Virtual basic circleMonitoring grid

Step 1

2020

Sensing range union and polygon inclusion

Static coverage set

Outside the static coverage set

Inside the static coverage set

Outside the static coverage set

Step 2

Step 3

2121

Working flow





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Gathering Information

3

1 1

2

Number of the required mobile sensor nodes

Number of mobile sensors for the coverage support

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Finding the search region

d

dd

d

d = maximum moving distance

3

2

1

1

3

2424

Dispatching mobile nodes

32

3

3

1

1

2

1

2 1

2525

Working flow





2626

Preliminary- Minimum cost flow Minimum cost flow problem:

Given a flow network with costs, find the feasible flow f in G that minimizes cost(f) among all feasible flows f in G.

s t

v1 v2

v3 v4

Input flow=5

( 7, 5 )( 7, 3 )

( 7, 4 )

( 5, 3 )

( 3, 1 )(6, 3 )

( 3, 1 )

( 5, 3 )( 5, 5 )

cost: 12

cost: 10

cost:11

( capacity, cost )

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Minimum cost flow (1)

Weighted bipartite graph

S T

D6

D10

D11

S6

S9

S12

S2

Flow network

Demand grid Supply grid

Transformation based on the grid unit

1

2

3

4

5

6

7

8

.

.

.

1

2

3

4

5

6

7

Demand node Supply node

Dummy source node

Dummy destination node

1 2 3 4

5

9

6 7 8

10 11 12

11 12 13 14

Mobile sensor node

Search grid

Coverage hole

Monitoring grid

1

2

2

3

41

34

8

5

676

7

5

2828


Linear equation

(1)

(2)

(3)

(4)

(5)

_ _

( , ) ( , )1 1

s grid d gridN N

i j i jj i

CMinimize F

subject to

( , ) min ( , )i j i jL h m

_ _

( , )1 1

s grid d gridN N

i j jj i

F m

j

_ _ _

( , ) ( , )1 1 1

d grid s grid d gridN N N

S i i ji j i

HF F

( , ) ( , )i j i jF L

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Minimum cost flow (3)(6,2) (6,6) (6,9) (6,12) (10,2) (10,6): 1 0 2 3 2 1Minimize F F F F F F

(10,9) (10,12) (11,2) (11,6) (11,9) (11,12)1 2 3 2 2 1F F F F F F (1)

(2)

(4)

(6,2) (6,6) (6,9) (6,12)1, 1, 1, 1L L L L

(10,2) (10,6) (10,9) (10,12)1, 2 , 2 , 3L L L L

(11,2) (11,6) (11,9) (11,12)1, 2 , 2 , 3L L L L

(3)

(5)

(6,2) (6,6) (10,6) (10,9) (11,12)0 , 1, 1, 2 , 3F F F F F Solution

(6,2) (10,2) (11,2) 1subject to F F F

(6,6) (10,6) (11,6) 2F F F

(6,9) (10,9) (11,9) 2F F F

(6,12) (10,12) (11,12) 3F F F

(6,2) (6,6) (6,9) (6,12)1, 1, 1, 1F F F F

(10,2) (10,6) (10,9) (10,12)1, 2 , 2 , 3F F F F

(11,2) (11,6) (11,9) (11,12)1, 2 , 2 , 3F F F F

(6,2) (6,6) (6,9) (6,12) (10,2) (10,6)F F F F F F

(10,9) (10,12) (11,2) (11,6) (11,9) (11,12) 7F F F F F F

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1 2 3 4

5

9

6 7 8

10 11 12

11 12 13 14

Mobile sensor node

Search grid

Coverage hole

Monitoring grid

1

2

2

3

41

34

8

5

676

7

5

9

6

10 11 12

2

2

3

41

34

8

5

676

7

5

3131



Collecting the demand and supply information for healing coverage holes


Time Complexity (1)

(1)O

Working Stage Time Complexity

2

2( ( ) )ci

s

RO s

R

(1)O

3.5_ _(( ) )s grid d gridO N N

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Collecting the demand and supply information for healing coverage holes


Time Complexity (2)

Working StageCommunication

Complexity

( )i iO s m

None

( )gridgrid

dO N

E

( )gridgrid

dO N

E

3333






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Simulation result

Grid size 17 (m)

Sensing field size 500 x 500

Node number 1500 for sparse setting

Mobile node ratio 0.1 ~ 0.5

Sensing range 10 (m)

Target size 100 x 100 (m)

Simulation setting

2( )m

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The following three metrics are concerned:

Average coverage ratio

Average normalized movement cost

Total unified energy consumption

Simulation result

Simulation setting (cont.)

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3 approaches to be compared:

Simulation result

Simulation setting (cont.)

Proposed 1 approach

Ideal approach

Proposed 2 approach

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Circle covering

Distributed circle covering: 81 Centralized circle covering: 72

Proposed approach 1 & 2 Ideal approach

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Initial target coverage rate

Average target coverage rate : 32.28 %

Coverage rate of sensor field:43.789444 %

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Performance – (1)

Average coverage ratio

(%)

0.7

0.75

0.8

0.85

0.9

0.95

1

10% 20% 30% 40% 50%

Percentage of mobile sensors

Ave

rage

cov

erag

e ra

tio Ideal approach

Proposed approach 1

Proposed approach 2

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Performance – (3)

Average normalized movement cost (m)

25

30

35

40

45

50

55

60

10% 20% 30% 40% 50%

Percentage of mobile sensors

Ave

rage

nor

mal

ized

mov

emen

t cos

t

Ideal approach

Proposed approach 1

Proposed approach 2

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Performance – (3)

Total unified energy consumption

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

10% 20% 30% 40% 50%Percentage of mobile sensors

Tot

al U

nified

Ene

rgy

Con

sum

ptio

n

Ideal approach

Proposed approach 1

Proposed approach 2

710

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Conclusion This paper has presented a distributed approach to improving

the coverage of a mobile target in the hybrid sensor network.

The proposed approach can assist the execution of the data aggregation and location estimation with more precise computation results.

Simulation results showed that the performance of the proposed approach has small differences with the ideal solution.

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Thanks for your attention

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Wireless sensor network (WSN)

SensorEnvironment

Wireless Communication link

Sensor node

Base station (BS)or

Sink

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Proposed 1 approach

coordinator

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Ideal approach

Super powerful node

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Proposed 2 approach

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An Efficient Grid-Based Approach for Dynamical Target Coverage in Hybrid Sensor networks