Opensense @ LCAV · 2014. 1. 23. · Eﬃcientdatacollec-onin par-cipatorysensing Runwei’Zhang,’ jointwork’with’Zichong’Chen, LCAV,EPFL Friday, May 24, 13

Efficient data collec-on in par-cipatory sensing

Runwei Zhang,

joint work with Zichong Chen,LCAV,EPFL

Friday, May 24, 13

LCAV – AudioVisual Communica1ons Laboratory

Contents

‣ Introduc<on

‣ Our 2-‐step adap<ve algorithm

‣ Simula<on results

‣ Problem formula<on

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Background in par-cipatory sensing

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‣Sensors equipped

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‣Sensors equipped

Cameras

GPS

Microphones

Compass

Gyroscope

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Thermometer

Hygrometer

Barometer

CO,CO2,NO,O3

‣Sensors equipped

Cameras

GPS

Microphones

Compass

Gyroscope

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Thermometer

Hygrometer

Barometer

CO,CO2,NO,O3

‣ How it works‣Sensors equipped

Cameras

GPS

Microphones

Compass

Gyroscope

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Thermometer

Hygrometer

Barometer

CO,CO2,NO,O3

‣ How it works‣Sensors equipped

Cameras

GPS

Microphones

Compass

Gyroscope

Mobile sensor

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‣ Achieve certain overall objec<ve‣ Reconstruct noise/temperature map

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‣ Cost per sensor reading‣ Energy consump<on

‣ Network bandwidth‣ Privacy leakage


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‣ Cost per sensor reading‣ Energy consump<on

‣ Network bandwidth‣ Privacy leakage


Reduce unnecessary sensor readings?

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Why can we turn off some sensors?

Sensor Coverage range

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All sensors always ac<ve


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All sensors always ac<ve ANer turning off


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All sensors always ac<ve ANer turning off


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Objec-ve & Challenge

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‣ Objec<ve

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‣ Objec<ve‣ Exploit the spa6al correla6ons

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‣ Objec<ve‣ Exploit the spa6al correla6ons‣ Reduce data readings

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‣ Objec<ve‣ Exploit the spa6al correla6ons‣ Reduce data readings‣ Maintain coverage (small reconstruc6on error)

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‣ Achieve fairness

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‣ Challenge

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‣ Challenge‣ Sensors’ moving trajectories unknown

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‣ Challenge‣ Sensors’ moving trajectories unknown‣ Decisions should be made online

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Contents

‣ Introduc<on




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The sta-c sensor networks

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‣ Maximum coverage problem

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Reduce

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Objec<ve: Maximize number of covered cells

Constraint: Number of ac<ve sensor

Reduce

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s6

s7

y7

y8



s1

s2

s3

s4

s5

y1y2

y3

y4

y5

y6

Sensors Cells

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s6

s7

y7

y8



s1

s2

s3

s4

s5

y1y2

y3

y4

y5

y6

Sensors Cells

A B: A can sense B

Friday, May 24, 13

Max

X

ej2E

yj

s.t.

X

i

si k

X

ej2Ai

si � yj

0 yi 1, si 2 {0, 1}.s6

s7

y7

y8



s1

s2

s3

s4

s5

y1y2

y3

y4

y5

y6

Sensors Cells

A B: A can sense B

Friday, May 24, 13

Max

X

ej2E

yj

s.t.

X

i

si k

X

ej2Ai

si � yj

0 yi 1, si 2 {0, 1}.s6

s7

y7

y8



s1

s2

s3

s4

s5

y1y2

y3

y4

y5

y6

Sensors Cells

A B: A can sense B

k=5?

Friday, May 24, 13

Max

X

ej2E

yj

s.t.

X

i

si k

X

ej2Ai

si � yj

0 yi 1, si 2 {0, 1}.s6

s7

y7

y8



s1

s2

s3

s4

s5

y1y2

y3

y4

y5

y6

Sensors Cells

A B: A can sense B

k=5?

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The par-cipatory sensing scenario

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The par-cipatory sensing scenarioAt 6me slot n=1:

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12

3

45

6

At 6me slot n=1:

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12

3

45

6

At 6me slot n=1:

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12

3

45

6

At 6me slot n=1:

Reduce

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12

3

45

6

At 6me slot n=1:

Reduce

12

3

45

6

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12

3

45

6

At 6me slot n=1:

At 6me slot n=2:

Reduce

12

3

45

6

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12

3

45

6

At 6me slot n=1:

1

2

3

4 56

At 6me slot n=2:

Reduce

12

3

45

6

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12

3

45

6

At 6me slot n=1:

1

2

3

4 56

At 6me slot n=2:

Reduce

12

3

45

6

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12

3

45

6

At 6me slot n=1:

1

2

3

4 56

At 6me slot n=2:

Reduce

Reduce

12

3

45

6

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12

3

45

6

At 6me slot n=1:

1

2

3

4 56

At 6me slot n=2:

Reduce

Reduce

12

3

45

6

1

2

3

4 56

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At 6me slot n=1

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Sensor

12

3

45

6

At 6me slot n=1

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s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

Sensor

12

3

45

6

At 6me slot n=1

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s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

Sensor

12

3

45

6

At 6me slot n=1

b(n) = A(n) · s(n)

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

A B: A resides in B

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s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

Sensor

12

3

45

6

At 6me slot n=1 y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells...

...

B C: B can sense C

b(n) = A(n) · s(n)

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

A B: A resides in B

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s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

Sensor

12

3

45

6

At 6me slot n=1 y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells...

...

B C: B can sense C

b(n) = A(n) · s(n)

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

A B: A resides in B

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Friday, May 24, 13



At 6me slot n=2

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Sensor

1

2

3

4 56

At 6me slot n=2

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Sensor

1

2

3

4 56

At 6me slot n=2s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

s(n)2

Sensors

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Sensor

1

2

3

4 56


s(n)3

s(n)4

s(n)5

s(n)6

s(n)2

Sensors

...

A B: A resides in B

...b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

b(n) = A(n) · s(n)

Sensor loca6ons

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Sensor

1

2

3

4 56


s(n)3

s(n)4

s(n)5

s(n)6

s(n)2

Sensors

...

B C: B can sense C

...y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells

...

A B: A resides in B

...b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

b(n) = A(n) · s(n)

Sensor loca6ons

Friday, May 24, 13



Sensor

1

2

3

4 56


s(n)3

s(n)4

s(n)5

s(n)6

s(n)2

Sensors

...

B C: B can sense C

...y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells

...

A B: A resides in B

...b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

b(n) = A(n) · s(n)

Sensor loca6ons

Friday, May 24, 13

Max

0

@lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j ,� lim sup

n!1V (✓(n)

)

1

A

s.t. eT · s(n) k, 8n 2 N+

A(n) · s(n) = b(n), 8n 2 N+

X

ej2Ai

s(n)i � y(n)j , 8n 2 N+

✓(n)=

1

n

nX

t=1

s(t), 8n 2 N+

0 y(n)i 1, s(n)i 2 {0, 1}, 8n 2 N+.


Op-mal schedule with known trajectories

13

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Max

0

@lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j ,� lim sup

n!1V (✓(n)

)

1

A

s.t. eT · s(n) k, 8n 2 N+

A(n) · s(n) = b(n), 8n 2 N+

X

ej2Ai

s(n)i � y(n)j , 8n 2 N+

✓(n)=

1

n

nX

t=1

s(t), 8n 2 N+

0 y(n)i 1, s(n)i 2 {0, 1}, 8n 2 N+.



13

coverage

Friday, May 24, 13

Max

0

@lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j ,� lim sup

n!1V (✓(n)

)

1

A

s.t. eT · s(n) k, 8n 2 N+

A(n) · s(n) = b(n), 8n 2 N+

X

ej2Ai

s(n)i � y(n)j , 8n 2 N+

✓(n)=

1

n

nX

t=1

s(t), 8n 2 N+

0 y(n)i 1, s(n)i 2 {0, 1}, 8n 2 N+.



13

coverage fairness

Friday, May 24, 13

Max

0

@lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j ,� lim sup

n!1V (✓(n)

)

1

A

s.t. eT · s(n) k, 8n 2 N+

A(n) · s(n) = b(n), 8n 2 N+

X

ej2Ai

s(n)i � y(n)j , 8n 2 N+

✓(n)=

1

n

nX

t=1

s(t), 8n 2 N+

0 y(n)i 1, s(n)i 2 {0, 1}, 8n 2 N+.



13

coverage fairness

Number of ac6ve sensors

Friday, May 24, 13

Max

0

@lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j ,� lim sup

n!1V (✓(n)

)

1

A

s.t. eT · s(n) k, 8n 2 N+

A(n) · s(n) = b(n), 8n 2 N+

X

ej2Ai

s(n)i � y(n)j , 8n 2 N+

✓(n)=

1

n

nX

t=1

s(t), 8n 2 N+

0 y(n)i 1, s(n)i 2 {0, 1}, 8n 2 N+.



13

coverage fairness

Number of ac6ve sensors

Adap6ve algorithms?

Friday, May 24, 13


Contents

‣ Introduc<on




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Step 1: choose subsets of cells

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Sensor

12

3

45

6



Friday, May 24, 13

Sensor

12

3

45

6



Friday, May 24, 13

Sensor

12

3

45

6



y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells...

...

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

Friday, May 24, 13

Sensor

12

3

45

6

Max lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j

s.t. eT · b(n) k, 8n 2 N+

X

ej2Ai

b(n)i � y(n)j , 8n 2 N+

A(n) · e � b(n), 8n 2 N+

0 y(n)i 1, 8n 2 N+

b(n)i 2 {0, 1}, 8n 2 N+.



y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells...

...

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

Friday, May 24, 13

Sensor

12

3

45

6

Max lim inf

n!1

1

n

nX

t=1

X

ej2E

y(n)j

s.t. eT · b(n) k, 8n 2 N+

X

ej2Ai

b(n)i � y(n)j , 8n 2 N+

A(n) · e � b(n), 8n 2 N+

0 y(n)i 1, 8n 2 N+

b(n)i 2 {0, 1}, 8n 2 N+.



y(n)1

y(n)2

y(n)3

y(n)4

y(n)5

y(n)6

y(n)7

Cells...

...

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...{A(n)}n2N+{b(n)}n2N+ is decided by

Friday, May 24, 13


Step 2: choose ac-ve sensors within cells

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Sensor

12

3

45

6

Friday, May 24, 13



Sensor

12

3

45

6

s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...A(n)

Friday, May 24, 13



Sensor

12

3

45

6

s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...A(n)

Friday, May 24, 13



Sensor

12

3

45

6

Min lim supn!1

V (✓(n))

s.t. A(n) · s(n) = b(n), 8n 2 N+

✓(n) =1

n

nX

t=1

s(t), 8n 2 N+

s(n) 2 {0, 1}M , 8n 2 N+.

ORIGINAL-‐OPT:s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...A(n)

Friday, May 24, 13



Sensor

12

3

45

6

Min lim supn!1

V (✓(n))

s.t. A(n) · s(n) = b(n), 8n 2 N+

✓(n) =1

n

nX

t=1

s(t), 8n 2 N+

s(n) 2 {0, 1}M , 8n 2 N+.

ORIGINAL-‐OPT:s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...A(n)

Adap6ve algorithms?

Friday, May 24, 13


“Let the laziest serve” (LLS)

At 6me slot n

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✓(n�1)m =

1

n� 1

n�1X

t=1

s(t)m , 8m

(1) Calculate the frac6on of ac6ve sensing 6mes for each sensor

At 6me slot n

Friday, May 24, 13



✓(n�1)m =

1

n� 1

n�1X

t=1

s(t)m , 8m


(2) Within each chosen cell,choose the ac6ve sensor

with the smallest

m

✓(n�1)m

At 6me slot n

Friday, May 24, 13



s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

✓(n�1)m =

1

n� 1

n�1X

t=1

s(t)m , 8m



with the smallest

m

✓(n�1)m

At 6me slot n

Friday, May 24, 13

✓(n�1)3 < ✓(n�1)

2



s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

✓(n�1)m =

1

n� 1

n�1X

t=1

s(t)m , 8m



with the smallest

m

✓(n�1)m

At 6me slot n

Friday, May 24, 13

✓(n�1)3 < ✓(n�1)

2

✓(n�1)6 < ✓(n�1)

5



s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

✓(n�1)m =

1

n� 1

n�1X

t=1

s(t)m , 8m



with the smallest

m

✓(n�1)m

At 6me slot n

Friday, May 24, 13

✓(n�1)3 < ✓(n�1)

2

✓(n�1)6 < ✓(n�1)

5



s(n)1

s(n)3

s(n)4

s(n)5

s(n)6

Sensors

s(n)2

b(n)1

b(n)2

b(n)3

b(n)4

b(n)5

b(n)6

b(n)7

Sensor loca6ons...

...

✓(n�1)m =

1

n� 1

n�1X

t=1

s(t)m , 8m



with the smallest

m

✓(n�1)m

At 6me slot n

Friday, May 24, 13


Gradient upda-ng direc-on

Friday, May 24, 13



@V (✓)

@✓i>

@V (✓)

@✓j✓i > ✓j

When the objec6ve func6on fulfills

if

Friday, May 24, 13



@V (✓)

@✓i>

@V (✓)

@✓j✓i > ✓j


if nega6ve entropy Lp-‐norm

Friday, May 24, 13



@V (✓)

@✓i>

@V (✓)

@✓j✓i > ✓j


if

s(n)LLS let be op6mal w.r.t the linearized problem

nega6ve entropy Lp-‐norm

Friday, May 24, 13



@V (✓)

@✓i>

@V (✓)

@✓j✓i > ✓j


if



Min r>V (✓(n�1)) · xs.t. A

(n) · x = b

(n),

x 2 [0, 1]M .

ORIGINAL-‐LINEARIZED:

Friday, May 24, 13



@V (✓)

@✓i>

@V (✓)

@✓j✓i > ✓j


if



Min r>V (✓(n�1)) · xs.t. A

(n) · x = b

(n),

x 2 [0, 1]M .

ORIGINAL-‐LINEARIZED:

integer constraints are changed into non-‐integer ones

Friday, May 24, 13


When trajectories are from markov chains

Friday, May 24, 13


When trajectories are from markov chainsSensor 1

Friday, May 24, 13


When trajectories are from markov chainsSensor 1 Sensor 2

Friday, May 24, 13



...

...

Friday, May 24, 13



A(n) 2 {A[1],A[2],A[3] · · · }

Trajectories of all sensorsare drawn from a large markov chain

...

...

Friday, May 24, 13



A(n) 2 {A[1],A[2],A[3] · · · }


Min V (⇥)

s.t. A[l] · q[l] = b[l], l = 1, · · ·L,

⇥ =LX

l=1

Pl · q[l], l = 1, · · ·L

q[l] 2 {0, 1}M , l = 1, · · ·L.

MARKOV-‐OPT:

...

...

Friday, May 24, 13



A(n) 2 {A[1],A[2],A[3] · · · }


Min V (⇥)

s.t. A[l] · q[l] = b[l], l = 1, · · ·L,

⇥ =LX

l=1

Pl · q[l], l = 1, · · ·L

q[l] 2 {0, 1}M , l = 1, · · ·L.

MARKOV-‐OPT:

...

...

Pl is the sta6onary distribu6on A(n) = A[l]

Friday, May 24, 13



Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·



Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,



Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,



Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,



Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,

n1 n2 n3



Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,

n1 n2 n3



Ev(j)l =Pl

P3, The expected 6mes that

during the recurrent round obeys sta6onaryj

A(n) = A[l]

Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,

n1 n2 n3



n 2 [nj�1 + 1, nj ] A(n) = A[l]At each 6me with , is op6mal w.r.ts(n)

Ev(j)l =Pl



A(n) = A[l]

Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,

n1 n2 n3

Min r>V (✓(n�1)) · xs.t. A

[l] · x(n) = b

[l],

x 2 [0, 1]M .




Ev(j)l =Pl



A(n) = A[l]

Friday, May 24, 13

A(n) : A[3],A[1],A[2],A[4],A[2],A[3],A[1],A[4],A[3], · · ·

n : 1, 2, 3, 4, 5, 6, 7, 8,

n1 n2 n3

Min r>V (✓(n�1)) · xs.t. A

[l] · x(n) = b

[l],

x 2 [0, 1]M .

⇡Min r>V (✓(nj�1)) · xs.t. A

[l] · x(n) = b

[l],

x 2 [0, 1]M .




Ev(j)l =Pl



A(n) = A[l]

Friday, May 24, 13



Friday, May 24, 13



During each recurrent round , n 2 [nj�1 + 1, nj ]

Friday, May 24, 13

P1/P3Ev(j)l

Min r>V (✓(nj�1)) · xs.t. A

[1] · x = b

[1],

x 2 [0, 1]M .




Friday, May 24, 13

P1/P3Ev(j)l


[1] · x = b

[1],

x 2 [0, 1]M .

P2/P3


[2] · x = b

[2],

x 2 [0, 1]M .




Friday, May 24, 13

P1/P3Ev(j)l


[1] · x = b

[1],

x 2 [0, 1]M .

P2/P3


[2] · x = b

[2],

x 2 [0, 1]M .

P3/P3


[3] · x = b

[3],

x 2 [0, 1]M .




Friday, May 24, 13

P1/P3Ev(j)l


[1] · x = b

[1],

x 2 [0, 1]M .

P2/P3


[2] · x = b

[2],

x 2 [0, 1]M .

P3/P3


[3] · x = b

[3],

x 2 [0, 1]M .



...

...


Friday, May 24, 13

P1/P3Ev(j)l


[1] · x = b

[1],

x 2 [0, 1]M .

P2/P3


[2] · x = b

[2],

x 2 [0, 1]M .

P3/P3


[3] · x = b

[3],

x 2 [0, 1]M .



...

...

In all, is op6mal w.r.t the linearized version of MARKOV-‐OPT


Friday, May 24, 13

P1/P3Ev(j)l


[1] · x = b

[1],

x 2 [0, 1]M .

P2/P3


[2] · x = b

[2],

x 2 [0, 1]M .

P3/P3


[3] · x = b

[3],

x 2 [0, 1]M .

Min rTV (✓(nj�1)) ·LX

l=1

Plq[l]

s.t. A[l] · q[l] = b[l], l = 1, · · ·L,q[l] 2 {0, 1}M , l = 1, · · ·L.



...

...



Friday, May 24, 13

P1/P3Ev(j)l


[1] · x = b

[1],

x 2 [0, 1]M .

P2/P3


[2] · x = b

[2],

x 2 [0, 1]M .

P3/P3


[3] · x = b

[3],

x 2 [0, 1]M .

Min rTV (✓(nj�1)) ·LX

l=1

Plq[l]

s.t. A[l] · q[l] = b[l], l = 1, · · ·L,q[l] 2 {0, 1}M , l = 1, · · ·L.



LLS is asympto6cally op6mal

...

...



Friday, May 24, 13


Summary of the 2-‐step adap-ve algorithm

Friday, May 24, 13



‣ Totally memoryless and adap<ve

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‣ Does not know trajectories in advance

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‣ Achieves op<mal coverage in step 1

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‣ Achieves asympto<cally op<mal fairness in step 2

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‣ Achieves asympto<cally op<mal fairness in step 2

‣ Not necessarily op<mal when consider step 1 and step 2 jointly.

Friday, May 24, 13


Contents

‣ Introduc<on




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Simula-ons

Friday, May 24, 13


Simula-ons

‣ Bus trajectories‣ Synthesized from Lausanne bus <metables

‣ 327 buses, 400 bus stops, 18km*6km

Friday, May 24, 13


Simula-ons


‣ 327 buses, 400 bus stops, 18km*6km‣ Taxis trajectories‣ Real data from San Francisco

‣ 500 taxis, 30 days

Friday, May 24, 13


Simula-ons


‣ 327 buses, 400 bus stops, 18km*6km‣ Taxis trajectories‣ Real data from San Francisco

‣ 500 taxis, 30 days‣ We have not applied the reduc<on in step 1.

Friday, May 24, 13


Synthesizing Lausanne bus trajectories

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1 km

Friday, May 24, 13



1 km

‣ Acquire all the GPS loca<ons of 400 bus stops

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1 km

‣ Interpolate with <metables of 37 bus lines


Friday, May 24, 13



1 km

‣ Interpolate with <metables of 37 bus lines


‣ Add GPS noise, Poisson delay noise

Friday, May 24, 13


Energy efficiency

Friday, May 24, 13


Energy efficiency

‣ The obtained sensing schedule

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Energy efficiency

time / 10 minutes

bus

ID

100 200 300 400 500 600 700

50

100

150

200

250

300


Friday, May 24, 13


Energy efficiency

time / 10 minutes

bus

ID

100 200 300 400 500 600 700

50

100

150

200

250

300

Buses traveling to remote areas


Friday, May 24, 13


Energy efficiency

time / 10 minutes

bus

ID

100 200 300 400 500 600 700

50

100

150

200

250

300


Weekends


Friday, May 24, 13


Energy efficiency

time / 10 minutes

bus

ID

100 200 300 400 500 600 700

50

100

150

200

250

300


Weekends


‣ Energy savings

Friday, May 24, 13


Energy efficiency

time / 10 minutes

bus

ID

100 200 300 400 500 600 700

50

100

150

200

250

300

0.5 1 1.5 2 2.5 30

10

20

30

40

AI side length/km

Ener

gy sa

ving

s


Weekends


‣ Energy savings

Friday, May 24, 13


Energy efficiency

time / 10 minutes

bus

ID

100 200 300 400 500 600 700

50

100

150

200

250

300

0.5 1 1.5 2 2.5 30

10

20

30

40

AI side length/km

Ener

gy sa

ving

s


Weekends


‣ Energy savings

Friday, May 24, 13

−50 0 50 100 150 200 250 300 3500

0.05

0.1

0.15

0.2

Node idThe

ener

gy c

ost t

o th

e ba

selin

e

RandomOur algorithmOptimal


Fairness

Sorted bus id

Active time ratio

Friday, May 24, 13


Using San Francisco taxis data

‣ Ac<ve sensing loca<ons accumulated with <me

Without our scheme With our scheme

Friday, May 24, 13


Energy efficiency & fairness

Friday, May 24, 13


Future works

‣ Validate the reconstruc<on error‣ Consider temporal correla<ons

x

y

t

Friday, May 24, 13

Documents

Opensense @ LCAV · 2014. 1. 23. · Eﬃcientdatacollec-onin par-cipatorysensing Runwei’Zhang,’ jointwork’with’Zichong’Chen, LCAV,EPFL Friday, May 24, 13