Introduction of Mizuno lab s research projects - …mineno/research/20060605-MERL-Introduction-mineno.pdfIntroduction of Mizuno lab’s research projects Assistant Professor, Dr. Hiroshi

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Introduction of Mizuno lab’sresearch projects

Assistant Professor, Dr. Hiroshi MinenoDepartment of Computer Science,

Shizuoka University, Japan

Assistant Professor, Dr. Hiroshi MinenoDepartment of Computer Science,

Shizuoka University, Japan

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Campus location(Photos presented by www.pref.shizuoka.jp/)

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Shizuoka Prefecture(Photos presented by www.pref.shizuoka.jp/)

To create a pleasant industrial society that provides a perfect balance between natural beauty and high technologyTo create a pleasant industrial society that provides a perfect balance between natural beauty and high technology

- is blessed with four lovely seasons- temperate climate- almost never snows in flatlands

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Some Shizuoka industries are No.1

•Green tea（National share ofoutput 46% / 2003）

•Canned tuna fish（National share ofproduction 85% / 2003）

•Hothouse melon（National share ofproduction 36% / 2003）

•Motorcycles（National share ofproduction 66% / 2004）

•Plastic toy-model kit（National share ofshipment 89% / 2003）

•Pianos（National share ofshipment 100% / 2003）

（From Shizuoka prefecture’s website）

in Japan

The largest shareholders for their markets in JapanThe largest shareholders for their markets in Japan

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Outline of Shizuoka University

（As of May 1.2005)Hamamatsu campus

Shizuoka campus

•Faculties 6

•Graduate schoolsMaster’s course 5Doctor’s course 2 Professional degree course 1

•Research institute 1•Students

Under graduate 9,567Post graduate master’s course 1,315 Doctor’s course 162Professional degree course 31International students 295

•Staff 1,228

•Budget About ¥18.8 billion

Technical faculties

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Number of staff and students(As of May 1.2005)

371

132

731

268

358

92

7

Officials, etc

Boards

Professors

Associate Professors

Academic staff

Lecturers, Assistants

Teachers of Attached Schools

Total 1,228

7107%

2,45426%

8689%

95910%

1,69618%

2,88030%

Faculties9,567

82155%

1148%

17111%

15510%

513%

16511%

312%

GraduateSchools1,508

Agriculture

Electronic Scienceand Technology

Education

Humanities and Social Sciences

Informatics

Science and Engineering

Shizuoka Law SchoolAgriculture

Education

Engineering

Humanities and Social Sciences

Informatics

Science

<Staff>

<Enrollment>

Support experimental classes, lead research of students, promote and implement joint

research

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Prof. Tadanori Mizuno

1968: B.E. in industrial engineeringfrom Nagoya Institute of Technology1968-1993: Mitsubishi Electric Corp.1987: PhD in Computer Science from Kyusyu Univ.1993-1995: Prof. in Faculty of Engineering, Shizuoka Univ., Japan1995-: Prof. in Faculty of Informatics, Shizuoka Univ., Japan2006-: Concurrently, Dean of Graduate School of Science & Technology, Shizuoka Univ., JapanIEEE, ACM, IEICE, IPSJ Fellow

IEICE: The Institute of Electronics, Information and Communication EngineersIPSJ: Information Processing Society of Japan

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Assistant Prof. Hiroshi Mineno

1997, 1999: B.E. and M.E. from Shizuoka Univ.Incentive award for student of IEICE Tokai affiliate

1999-2002: NTT Service Integration LaboratoriesAchievement award from the director of NTT SI lab.

2002-: Assistant Prof. in Faculty of Informatics, Shizuoka Univ.Outstanding paper awards of IPSJ national workshops, ’03,’04,’05Best conversant award of IPSJ national symposium (3/300), 2005Appeared in news paper in Melbourne, Australia, 2005

2006: PhD in Information Science and Electrical Engineering from Kyusyu Univ., Japan2006-: Concurrently, Assistant Prof. in Graduate School of Science & Technology, Shizuoka Univ., Japan

Since 2002, I’ve supported Prof. Mizuno and promoted joint research.IEEE, ACM, IEICE, IPSJ

IEICE: The Institute of Electronics, Information and Communication EngineersIPSJ: Information Processing Society of Japan

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Member of Mizuno Lab.

Prof. Dr. MizunoAssistant Prof. Dr. MinenoResearcher: Dr. Chen (Zhejiang Univ., China) (’05.10-’07.9)

Graduate students: 7 (16)(Adult doctoral course: 16)Doctoral course: 1 (D3, works for NiCT* as an internship)Master’s course: 4 (M2), 2 (M1)

Under graduate students: 5

<Publications> 2003 2004 2005 2006Journals 0 (4) 2 (6) 3 (11) 3 (+1)

Intl. conferences 2 (13) 2 (8) 3 (10) 5 (+4)

National workshops 11 (36) 18 (37) 18 (34) 4 (+6)

*NiCT: National Institute of Information and Communications Technology

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Research areaMobile computing group (with NTT DoCoMo)1. Concurrent multiple paths communication (SHAKE)

• Delay, jitter, bandwidth-aware IP packet distribution for TCP• Real-time communication support with FEC for UDP flows• Policy-based traffic distribution for link aggregation system

2. Mobile personal area networks (mPAN)• Remote Plug & Play of USB devices for mPAN• Streaming mobility within mPAN toward FMC extension

Ubiquitous computing (with Renesas Solutions, Mitsubishi)3. Home appliance translator (HAT)4. Indoor location tracking system using mobile

detectors (MobiTra)5. Meta-data based data aggregation in clustering

WSNs6. Localization for irregular sensor networks

※ representative example

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SHAKESHAKE enables the sharing of multiple heterogeneous links.

Internet

●

User A

User B

User C

Data-1

Data-2

Data-3

Data

User D

Ad hoc cluster network

Long-range wireless link( 2G , 3G cellular, PHS, etc.)

Short-range wireless link( 802.11x, Bluetooth, etc.)

Data flow

Correspondingnode

-High throughput-High reliability-High connectivity

1. Concurrent multiple paths communicationSHSHaring multiple paths procedure for a cluster networKK EEnvironment

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Mobile IP SHAKE

InternetAL (Alliance Leader)

CN

HA

Support all protocols on IP layer Downlink traffic at MNs via HA (no optimization)Uses HA of Mobile IPv4 for dispersing trafficWithout providing a special function for CN

MN

Cluster Network

AM (Alliance Member)

MN moves out of its home network, registers CoA to its HA.MN forms alliance network with vicinity nodes, registers CoA of AM.HA distributes packets to registered CoAs. AM forwards to AL.

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Packets distribution – DQ method

⑤⑤

Path-1

①

③

d1[ms]

D1 [ms]

Path-2Src Dst

②

④ QQitit = = QQiiττ -- BBii・・((t t --ττ) + ) + SS

DDii == ・・1000 + 1000 + ddi i

τ [ms]

S [bit]S [bit]

BBii

QQitit

τ: Start time t : Current timed : Delay, S : Packet sizeQin: Stored data in path-i on nBi : BandwidthD : Expected arrival

Src Dst

Q2tQ1t

t [ms]

d2

D2

Distribute packets to path with shortest Di (DQ)Multiple paths and delay-jitter promote out-of-order packets

Large delay-jitter caused by error recovery in lower layerCongestion control by TCP limits volume of packet transmission (Fast Retransmit Algorithm)

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Packets distribution – DQJ methodOut-of-order packets should be limited to three

Fast Retransmit Algorithm starts when the source receives more thanthree of the same duplicate-ACKs.

Give a decision by using the measured delay-jitter whether the path selection is useful or not

6

45

0Path-2Src Dst

3

Path-1

1

Src Dst Path-3Src Dst

2jitter

expectationexpectation

Packet 0 could possibly change placeswith packets 1, 2 due to the jitter on path-1

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Performance evaluation on emulated networks

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2. Mobile personal area network (mPAN)The appearance of dual-mode (cellular/WLAN) cellular phones provides the potential for new IP-based multimedia communications.Recent consumer electronics have been media devices networked through the broadband Internet access service, and they have much greater capabilities than mobile phones.

Bandwidth, display size, audio quality, computation power

Combining advantages of both into a single virtual device complements the capabilities of mobile phones and provides seamless interaction between consumer electronics, mobile phones, and PC devices. => mPAN

Keyword: IMS, FMC, SIP, home network, signaling overhead

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Streaming mobility within mPANCP controls I/O devices for A/V streaming within mPANindividually by combining SIP REFER and 3PCC methodsFrom EtoE-type to Indirect-type to overcome NAT traversal and reduce transfer time between devicesAdditional handover header cuts down the retrieval time

SIP UA: SIP User Agent SIP SM: SIP Streaming Mobility (SIP Extension)B2BUA: Back to Back User Agent SDP: Service Discovery Protocol

SIP UA B2BUA SIP UASIP SM

SDPSIP SM

SDP

SIP UASIP SM

SDPSIP SM

SDP

CN GW CP Device

display

speaker

FixedMobile

Corresponding node (CN),as well as mPAN

Cellular (CP)

VoIP tel.

Home network

mPAN

GW

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Call flows for streaming mobility

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Streaming transfer time

0

500

1000

1500

2000

2500

3000

ＡＢ（再調整あり）Ｂ（再調整なし）

所要

時間

（m

s）

ALL

Audio

Video

A：basic transferB：transfer between devices

ALL：Audio and VideoAudio：uLawVideo：MJPEG

Tran

sfer

tim

e (m

s)

B (Require renegotiation)

B (No renegotiation)

A

The indirect approach with B2BUA reduced transfer time when renegotiation between parties was unnecessary.

The indirect approach with B2BUA reduced transfer time when renegotiation between parties was unnecessary.

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Streaming retrieval time

0

1000

2000

3000

4000

5000

6000

Video: H.263 All: H.263 Video: MJPEG All: MJPEG

Ret

rieval

tim

e (

ms)

Non-supported SIP UA

Handover-header-supported SIP UA

The handover supported SIP UA dramatically reduced retrieval time compared with non-supported SIP UA that took a few seconds.

The handover supported SIP UA dramatically reduced retrieval time compared with non-supported SIP UA that took a few seconds.

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3. Home appliance translator (HAT)

Home networks cannot be constructed easily.

Conventional appliances cannot be used!User needs to replace to network appliances.User needs to lay down the communication infrastructure.User needs to set complex settings for network.

Spread of home networks will be slow if these problems are not solved.

Because…

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Home appliance translator (HAT)Enable conventional appliances to participate in home networks

HAT transmits information on an appliance’s state to HAT-Sub and controls appliances based on the received instructions from HAT-Sub.HAT-Sub infers the states of appliances using information from HATs and transmits operation instructions to HATs.

HATHAT-Sub

Conventionalapplianceinformation

TV is turned on

instruction

Please turn off TV

I try turning off TV

control

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Architecture of HAT

Notification service

ReceptionSignal

CurrentAmpere

Adjustmentmodule

“Propertychanged

Message”

Control serviceApplianceControl

set

set

“Invokeaction

Message”

IRtransceiver

module

Measurementmodule

PLC

mod

em m

odul

e

control

HATHAT-Sub

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Inference of appliance states

HAT-Sub infers the state based onamount of current consumed by the applianceIR signal that the appliance received

HAT HAT-Sub

Measurementmodule

PLC

mod

em

PLC

mod

em

IR signaldatabase

Inferencemodule

Displayinterface

IR signalreceiver

Information on the

appliance

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

時間(s)

消費

電流

量(m

A)

0

0.2

0.4

0.6

0.8

1

1.2

動作

状態

(0:停

止状

態 1

:動作

状態

)

閾値

消費電流量

状態

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0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

時間(s)

消費

電流

量(m

A)

0

0.2

0.4

0.6

0.8

1

1.2

動作

状態

(0:停

止状

態 1

:動作

状態

)

閾値

消費電流量

状態Receive the instruction

Send the current and the IR signal

Prototype of HAT and HAT-Sub

HAT-BHAT-A

Infer the state and send the instruction

Turn on TV and turn the channel to video mode

Play the HDDrecorder

HAT-Sub

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4. Indoor goods tracking system

Indoor Positioning Systems (IPS)Active badge:

• Infrared signal, room level granularity

Active bats, cricket:• TDoA between RF and ultrasound

RADAR:• RSSI of 802.11 RF, several meters

RF based proximity:• The location is given as a centroid.

Reader

Tag

Large Expensive Hard to install

Requiring special devices to track the targetTrade-off between position accuracy and the number of sensing devices

Requiring special devices to track the targetTrade-off between position accuracy and the number of sensing devices

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Solution is RFID tags

Active RFID tags Passive RFID tagsMerit Long read range Small, lightweight, no battery

Demerit Need battery exchange, expensive

Short read range

Categorized by existence of battery

Tag readers will be commonly mobileSmall and lightweight have been developed

Ubiquitous Communicator byYRP Ubiquitous Networking Lab.

2004.9.15 2005.3.2Trial model

Cell phone with tag reader by KDDICompact Flash type

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Hierarchical tracking of MobiTra

UserUser

ObjectsObjects

Mobile tag readers (MTRs)Mobile tag readers (MTRs)

MobiTra serverNetwork

PassiveRFID tags

Hierarchicaltracking

Sensing environmentSensing environment Traditionalresearch

Users who have MTR need not be aware of detecting objects.MobiTra server does not need to know where MTRs are.

MTR’s location and detected object’s ID (no MTR’s ID for privacy)

Target location is estimated by using detection histories.

Users who have MTR need not be aware of detecting objects.MobiTra server does not need to know where MTRs are.

MTR’s location and detected object’s ID (no MTR’s ID for privacy)

Target location is estimated by using detection histories.

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204738C10:15

204020C10:15

204532A10:13

203030A10:18

202512A10:13

203433B10:09

205050C10:15

205050C10:15

201525A10:17

204040B10:16

201010A10:18

……………

Rangey-coordinate

x-coordinate

Object ID

Time

Detection histories (i)

(ii)

(iii)

(iv)

Estimated area

Position estimation algorithmsearch a target's history from the latest oneinitialize Qi to the circleiterate if there are detection histories

if the target's history is detectedif the area is overlapped with the Qi

update Qi using conjunction methdelse

exit from a loopend if

elseif the area is overlapped with the Qi

update Qi using negative method end if

end ifend for loop

(a) Circle

AB

(c) Negative

B

(b) Conjunction

AApproximate the range by a circleAlthough this approximation overestimates the uncertainty of the detected object’s position, it guarantees that the actual position is always within the area.

Approximate the range by a circleAlthough this approximation overestimates the uncertainty of the detected object’s position, it guarantees that the actual position is always within the area.

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Simulation results

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250

Applied detection histories

Dec

rease

of

est

imate

d a

rea (

%) Square on a side: 3m

Square on a side: 4m







0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250


Dec

rease

of

est

imate

d a

rea (

%) Square on a side: 3m








Estim

ated

are

a (%

)

Estim

ated

are

a (%

)

Applied detection histories Applied detection histories

All tags are within the estimated area because there are no detection errors in simulation.Negative method is effective for reducing the estimated area.Applying 100 detection histories are enough for reducing the estimated area to 1/5 from the starting value.

All tags are within the estimated area because there are no detection errors in simulation.Negative method is effective for reducing the estimated area.Applying 100 detection histories are enough for reducing the estimated area to 1/5 from the starting value.

(a)Use only conjunction method (b) Use additional negative method

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Prototype of MobiTra

Cricket 3m

3m

MobiTra Server

Client WLAN AP

TargetsMTR

Mobile tag reader(Vaio + Spider IIIA)

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Results of prototype

00.10.20.30.40.50.60.70.80.9

1

0 10 20 30 40 50 60 70 80 90 100

Not using negation method (prototype)

Using negation method (prototype)Not using negation method (simulator)

Using negation method (simulator)

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50 60 70 80 90 100

Dis

tanc

e fr

om R

F ta

g to

est

imat

ed a

rea

(cm

)Es

timat

ed a

rea

(%)



Estimation error was about 120 cm.Estimation error was about 120 cm.

352006/6/5

Estimated area in detailReal locationEstimated area

The reasons of the error are thought of as: Positioning error of the CricketDetection lag of RFID tag reader

The reasons of the error are thought of as: Positioning error of the CricketDetection lag of RFID tag reader

(a)Use only conjunction method (b) Use additional negative method

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5. Meta-data based data aggregation

Meta data

Source node

selection msg

Sensing data

Sink node

Sensing data

CH

CM

Sensing range (S)

Source node

Aggregated data

CH: Cluster HeadCM: Cluster Member

Sensing range (S)

Sink node

CH

CM

in clustering WSNs

Traditional data aggregation Meta-data based data aggregation

To prolong network lifetime

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Basic energy consumption model

Transmit k-bit message to distance d:

Receive k-bit message:

211 **),()(),( dkEkdkkEdkE TT

t ∗+=+= εε

422 **),()(),( dkEkdkkEdkE TT

t ∗+=+= εε

RRr EkkEkE *)()( == Transmitter electronics (ET)

Receiver electronics (ER)

ε1Transmit amplifierε2

T. Rappaport, “Wireless Communications: Principles and Practice,” Second Edition, Prentice Hall, 2002.

CH:

CM:

382006/6/5

Simulation Results - Network Lifetime

8221494

S=20m, k=4, Lm=8 bytes, Ld=64 bytes

Num

ber o

f sen

sor n

odes

stil

l aliv

e

LEACH-traditional

LEACH-meta

Round (CP:4%)

The network lifetime increased by 182% with our proposed meta-data based data aggregation method.The network lifetime increased by 182% with our proposed meta-data based data aggregation method.

LEACH: Low Energy Adaptive Clustering Hierarchy, 2002

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Simulation Results - Impact of α

S=20m, k=4

LEACH-traditional

LEACH-meta

Rou

nds

of fi

rst s

enso

r nod

e w

as d

ead

α= Lm/Ld

α is a ratio of meta-data (Lm) to sensing-data (Ld).When α is smaller, the network lifetime will increase.

α is a ratio of meta-data (Lm) to sensing-data (Ld).When α is smaller, the network lifetime will increase.

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Research areaMobile computing group (with NTT DoCoMo)1. Concurrent multiple paths communication (SHAKE)

• Delay, jitter, bandwidth-aware IP packet distribution for TCP flows

• Real-time communication support with FEC for UDP flows2. Mobile personal area networks (mPAN)

• Remote Plug&Play of USB devices for mPAN• Streaming mobility within mPAN toward IP multimedia

subsystem

Ubiquitous computing (with Renesas Solutions, Mitsubishi)3. Home appliance translator (HAT)4. Indoor location tracking system using mobile

detectors (MobiTra)5. Meta-data based data aggregation in clustering

WSNs6. Localization for irregular sensor networks

Documents

Introduction of Mizuno lab s research projects - …mineno/research/20060605-MERL-Introduction-mineno.pdfIntroduction of Mizuno lab’s research projects Assistant Professor, Dr. Hiroshi