Spatial Queries in Wireless Broadcast Environments

沈俊宏 助理教授亞洲大學資訊傳播學系

台中市霧峰區http://infocom.asia.edu.tw

大綱• 無線資料廣播相關技術

• 結合無線廣播與空間查詢

• Neighbor-Index Method

• 結論

2

無線環境

FIXED NETWORK

PDA

FIXEDHOSTBASE

STATION

BASESTATION

BASESTATION

Mbps to Gbps

MOBILE HOST

WIRELESS LAN CELL2Kbps - 15Mbps

WIRELESS RADIO CELL9Kbps - 14Kbps

BASESTATION

PDA

3

無線系統的特性• 傳輸的非對稱性

• 經常斷線

• 電力的限制

• 螢幕的尺寸

4

傳輸的非對稱性• Wireless information channels consist of

two distinct sets of channels:– uplink (upstream) channels:

• from clients to servers

– downlink (downstream) channels:

• from servers to clients

5

• In the environment under consideration, the downstream communication capacity is relatively much greater than the upstream one.

• In such an environment, the push-based data broadcast provides a good performance.

6

Push-based Data Broadcast System

7

Wireless channel

Mobile devices

屏東孔廟屏東大學 阿緱城門 孫立人將軍行館

Server

為什麼要使用無線資料廣播• Scalability ( 可擴充性 ):

– The server only broadcasts data items on the channel which can be accessed by the clients, no matter the number of the clients.

– For example, the servicing cost of the server for 10 clients is the same as that for 100 ones.

8

Microsoft® DirectBand™

• A wide-area wireless data broadcast network built and operated by Microsoft.

• Unused FM radio spectrum.

9

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無線系統的特性• 傳輸的非對稱性

• 經常斷線

• 電力的限制

• 螢幕的尺寸

13

選擇性聽頻道技術• Active mode ( 耗電模式 )

– More power consumption.

• Doze mode ( 省電模式 )– Less power consumption.

• Selective tuning is to remain the doze mode most of the time and go into the active mode only when the relevant information is present.

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: Index : Data

Time

: Desired Data

: Period of the active mode

15

效能評比參數• Access Time ( 存取時間 ):

– The client waiting time for accessing the requested data.

• Tuning Time ( 實際聽頻道時間 ):– The amount of time spent by a client listening to the

channel.– This determines the client’s power consumption.

• There is a trade-off between the access time and the tuning time.

16

T0 T1 T2 T3 T4 T5 T6 T7

Time

: Access Time : Tuning Time

• Access Time = (T7 – T0)

• Tuning Time = (T7 – T6) + (T5 – T4) + (T3 – T2) + (T1 – T0)

17

空間查詢• Location-Dependent Spatial Query (LDSQ) in

the wireless environment is that mobile users query the spatial data items dependent on their current location.– Nearest Neighbor Query– k-Nearest Neighbors Query– Window Query

18

Nearest Neighbor Query

• Show me the nearest convenient store.

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k-Nearest Neighbors Query

• Show me 2 nearest convenient stores.

20

Window Query

• Show me the restaurants around me.

21

Continuous Window Query

• Where are gas stations within this region along this segment?

22

Linear Access on the Wireless Channel

23

Neighbor-Index Method

Jun-Hong Shen, Ching-Ta Lu and Ming-Shen Jian, "Neighbor-Index Method for Continuous Window Queries over Wireless," Applied Mechanics and

Materials, Vol. 284-287, pp. 3295-3299, Jan. 2013. (EI)

24

Motivation

• For a continuous window query, answer spatial objects may be neighbors of each other.

25

• Therefore, we propose a neighbor-index method, NI, to efficiently support the continuous window queries over wireless data broadcast.

26

System Architecture

27

Mobile clients

Spatial objects

Spatial index

+

Server

A broadcast cycle

Wireless downlink channel Broadcasting

Update access patternsvia the uplink channel

Tune in & retrieve

Neighbor-Index Method

• Spatial objects are broadcast once in a cycle in the sequence of the Hilbert curve of order n.

• The spatial objects covered by the same value of the Hilbert curve of order (n-1) are put in the same group.

• An index bucket containing information about neighbors and the objects in this group is allocated before each group.

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Hilbert Curve

0

1

0

2

3

0 1

23

4

5 6

7 8

9 10

11

1213

14 15

Order 1

Order 2

Good locality

0

1 2

3 4 5

67

8 9

101112

1314

15

16 17

1819

20

21 22

23 24

25 26

27

2829

30 31 32 33

3435

36

37 38

39 40

41 42

43

4445

46 47

48

4950

515253

54 55

5657

58 59 60

61 62

63

Order 3

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Spatial Objects

• Hilbert curve of order 3

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Grouping

• The objects having the same Hilbert-curve value of order (n – 1) are allocated to the same group.

31

5 6

4 7

9 10

8 11

3 2

0 1

13 12

14 15

8

29

0

1 2

48

49

21 41 42

32

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• Hilbert curve of order 2

Neighbor Index Construction

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1. procedure NeighborIndexConstruction(h(n-1))

2. current_h ←h(n-1) /*current_h is the current processing block.*/

3. for i ← (n-1), 1 do

4. Call procedure NeighborInformation(current_h)

/* Calculate the Hilbert-curve value of the previous order. */

5. current_h ←

6. end for

7. Call procedure IndexEntryShrink

8. Add the index entries pointing to those spatial objects in the same

group to the index bucket for this group

9. end procedure

4/_ hcurrent

Neighbor Index

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5 6

4 7

9 10

8 11

3 2

0 1

13 12

14 15

8

29

0

1 2

48

49

21 41 42

32

23

3

21

0 8

29

0

1 2

48

49

21 41 42

32

23

• Hilbert curve of order 2 • Hilbert curve of order 1

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o0 o1 o2 o29 o32 o41o8 o42o21 o23NI1 NI2 NI3 NI4 NI5

o48 o49NI6 NI7

Index bucket Data bucket

(29, 29, NI4)

(0, 2, NI1')

(32, 32, NI5)

(32, 42, NI5)

(48, 49, NI7)

(21, 29, NI3)

Neighbor indexes of order 2

Neighbor indexes of order 1

Index Entry Shrink

35

o0 o1 o2 o29 o32 o41o8 o42o21 o23NI1 NI2 NI3 NI4 NI5

o48 o49NI6 NI7

Index bucket Data bucket

(29, 29, NI4)

(0, 2, NI1')

(32, 32, NI5)

(32, 42, NI5)

(48, 49, NI7)

(21, 29, NI3)

Neighbor indexes of order 2

Neighbor indexes of order 1

Index entry shrink(29, 29, NI4)

(0, 2, NI1')

(32, 42, NI5)

(48, 49, NI7)

(21, 29, NI3)

A Broadcast Cycle with Neighbor Indexes

36

o0 o1 o2 o29 o32 o41o8 o42o21 o23

(29, 29, NI4)

(32, 42, NI5)

(48, 49, NI7)

(21, 29, NI3)

NI1 NI2 NI3 NI4 NI5o48 o49

NI6 NI7

(0, 2, NI1')

(29, 29, NI4)

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(8, 8, NI2')

(21, 23, NI3')

(8, 8, NI2)

(21, 29, NI3)

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(41, 42, NI6)

(48, 49, NI7)

(8, 8, NI2')

(21, 29, NI3')

(29, 29, NI4')

(48, 49, NI7)

(0, 8, NI1')

(21, 29, NI3')

(32, 32, NI5')

(0, 8, NI1')

(21, 29, NI3')

(32, 42, NI5')

Index bucket Data bucket

Continuous Window Query

• Targeted segments – (8, 11)– (31, 35)– (53, 53)

37

38

o0 o1 o2 o29 o32 o41o8 o42o21 o23

(29, 29, NI4)

(32, 42, NI5)

(48, 49, NI7)

(21, 29, NI3)

NI1

NI2

NI3 NI4 NI5o48 o49

NI6 NI7

(0, 2, NI1')

(29, 29, NI4)

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(8, 8, NI2')

(21, 23, NI3')

(8, 8, NI2)

(21, 29, NI3)

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(41, 42, NI6)

(48, 49, NI7)

(8, 8, NI2')

(21, 29, NI3')

(29, 29, NI4')

(48, 49, NI7)

(0, 8, NI1')

(21, 29, NI3')

(32, 32, NI5')

(0, 8, NI1')

(21, 29, NI3')

(32, 42, NI5')

Index bucket Data bucket

Tune in here

• After examining NI2– (8, 11) -> (8, 8)– (31, 35) -> (32, 35)– (53, 53) -> ()

• V = {o8, NI5} -> V = {NI5}

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o0 o1 o2 o29 o32 o41o8 o42o21 o23

(29, 29, NI4)

(32, 42, NI5)

(48, 49, NI7)

(21, 29, NI3)

NI1

NI2

NI3 NI4 NI5o48 o49

NI6 NI7

(0, 2, NI1')

(29, 29, NI4)

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(8, 8, NI2')

(21, 23, NI3')

(8, 8, NI2)

(21, 29, NI3)

(32, 42, NI5)

(48, 49, NI7)

(0, 8, NI1')

(41, 42, NI6)

(48, 49, NI7)

(8, 8, NI2')

(21, 29, NI3')

(29, 29, NI4')

(48, 49, NI7)

(0, 8, NI1')

(21, 29, NI3')

(32, 32, NI5')

(0, 8, NI1')

(21, 29, NI3')

(32, 42, NI5')

Index bucket Data bucket

Tune in here End here

• After examining NI5– (32, 35) -> (32, 32)

• V = {o32} -> V = {}

有效區間• The Minkowski region has the same size as the

query window and centers at the answered object.

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效能模擬比較• We compare our proposed method, NI, with

distributed spatial index (DSI).

B. Zheng, W.C. Lee, C.K. Lee, D.L. Lee, and M. Shao: A distributed spatial index for error-prone wireless data broadcast, The VLDB Journal, 18(4) (2009) 959-986.

空間資料• There are two cases of the data distributions:

(a) uniform and (b) the real map (Greece).

42

(a) (b)

42

Parameters

• The simulation parameters

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Parameter Description

WinSideRatio The ratio of the side length of a window query to that of the search space

QueryLengthRatio The ratio of the length of a query line segment to the side length of the search space

43

System Model

• The start position of a continuous window query is randomly picked, and its corresponding moving direction is randomly picked between 0 and π/2.

• In our simulation, 10,000 points are uniformly generated in a square Euclidean space (28 28).

• In our simulation, 10,000 queries with a square of 28*WinSizeRatio 28*WinSizeRatio are randomly issued.

HCDI - 44

存取時間

45

• Uniform

100

110

120

130

140

150

160

170

0.02*0.02 0.05*0.05 0.1*0.1 0.2*0.2

Acc

ess t

ime

x 10

0000

WinSizeRatio*QueryLengthRatio

NIDSI

30

32

34

36

38

40

42

44

0.02*0.02 0.05*0.05 0.1*0.1 0.2*0.2

Acc

ess

tim

e x

1000

00WinSizeRatio*QueryLengthRatio

NI

DSI

• Real

實驗聽頻道時間

46

-

5

10

15

20

25

0.02*0.02 0.05*0.05 0.1*0.1 0.2*0.2

Tu

nin

g ti

me

x 10

0000

WinSizeRatio*QueryLengthRatio

NIDSI

-

2

4

6

8

10

0.02*0.02 0.05*0.05 0.1*0.1 0.2*0.2

Tu

nin

g ti

me

x 10

0000

WinSizeRatio*QueryLengthRatio

NIDSI

• Uniform • Real

耗電程度的比較

47

-

1,000

2,000

3,000

4,000

5,000

0.02*0.020.05*0.05 0.1*0.1 0.2*0.2

Pow

er c

onsu

mpt

ion

WinSizeRatio*QueryLengthRatio

NI

DSI-

500

1,000

1,500

2,000

0.02*0.020.05*0.05 0.1*0.1 0.2*0.2

Pow

er c

onsu

mp

tion

WinSizeRatio*QueryLengthRatio

NIDSI

• Uniform • Real

LEA - 48

結論• To speed up the query processing and reduce

power consumption of mobile devices, our proposed method interleaves neighbor information of spatial objects among a wireless broadcast cycle.

• Experimental results show that our proposed method outperforms DSI on the average access time, tuning time and power consumption.

Thanks for your attention.

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