Context-aware Services in Ubiquitous Network

Chiung-Ying Wang

22009/04/10

Agenda

IntroductionPervasive ComputingContext-aware

Context-aware ServiceUbiquitous Phone System (UPS)Context-aware Path Planning (UbiPaPaGo)

Conclusion

32009/04/10

Introduction

With the advancement of technology and the rapid development of mobile device, we are entering the ubiquitous computing (UbiCom) era advocated by Mark Weiser

A ubiquitous network aims to provide users intelligent human-centric context-aware services at anytime anywhere.

Context-aware is the key feature for human-centered services as it enables services adaptive to human needs.

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Pervasive Computing

Mark Weiser’s[1] 1991 paper that described his vision of ubiquitous computing (ubicomp) , now also called pervasive computing Ubiquitous computing is the method of enhancing compu

ter use by making many computers available throughout the physical environment, but making them effectively invisible to the user

Ubiquitous environment Full of smart equipments and context-aware information Invisible computing

[1] M. Weiser, “The Computer for the Twenty-First Century,” Scientific American, pp. 94-104, September 1991

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Pervasive Computing (Cont.)

Smart space Various interconnected devices

Wearable and embedded computer, Smart phone, PDA, Notebook, PC, Server

Various connectives Peer-to-peer, client-server, MANET, WLAN, 3G and 4G

Seamless integration of networking and computing capabilities within human environments

Pervasive computing is about making our lives simpler

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Ubiquitous Environment

Internet

RFID Antenna

3G Base Station

Wireless AP

GPS Receiver

RFID Tag

RFID 資訊

GPSinformationGSM / 3G

signal

APsignal

Servers

GPSinformation

GSM / 3Gsignal

APsignal

RFIDinformation

GPS Satellite

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Ubiquitous Environment

Internet

Agents

RFID Antenna

3G Base Station

Wireless AP

RFID 資訊

GPSinformationGSM / 3G

signalNetworkStatus

Locationinformation

APsignal

ServersGPS

information

GSM / 3Gsignal

APsignal

RFIDinformation

GPS Satellite

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Scenario-1

五月天的下午，開著冷氣您乘坐在舒適豪華的 Jaguar 愛車裡，優雅舒適地徜徉於信義計劃區網狀的市區道路中，意氣風發的您不必為了惱人的單向、雙向行車問題而迷路或為了工作分派的問題而煩惱，因為您的車載電腦更勝於您美麗的女助理。“哈囉”，隨著熟悉的聲音，它提示您： 30 分鐘後您在台北 101 世界超高摩天大樓將有一個約會，並顯示從目前位置前往的路線圖，車程時間為 20 分鐘。您告訴它說“瞭解”，並結束對話。

“ 哈囉”，它又提醒您，走另一條路，車程可能會多個 10 分鐘，但您可以順道買一杯您喜歡的 StarBuck 拿鐵咖啡。“謝謝您”，您爽朗的回答，您喜歡這個超人性化的提議。但您可能會因此而遲到，怎麼辦呢？不用擔心，它 ( 您的夥計 ) 早就為老哥您設想週到地送出一個簡訊給您的約會對象，您可以從容不迫地到達目的地就好！不必為了些微的耽擱而焦慮。畢竟，台北市長曾說過：行車安全最重要的啦！

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Scenario-2

一大早，電腦會知道你起床了，於是啟動咖啡機

你一進入浴室，蓮蓬頭就自動開啟，調到你最喜歡的水溫

等你開始走下樓，事先裝好土司的烤麵包機就開始加熱，把早餐依你喜歡的方式煮好

打開冰箱，機器會提醒你，沒有牛奶了，而且罐子裡的涼拌包心菜絲已經過期，應該扔掉了

http://h50007.www5.hp.com/enterprise/member/info/0403_rfid/rfid01-02.asp

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Pervasive Computing (Cont.)

How to access information?

Computer-centric

Human-centric

Context-aware

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Context-aware

Context-aware means that one is able to use context information

A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use

Definition of context: Context is any information that can be used to characterize the

situation of an entity An entity is a person, place, or object that is considered relevant to the

interaction between a user and an application, including the user and application themselves

Context-Aware Applications Survey: http://users.tkk.fi/~mkorkeaa/doc/context-aware.html

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Example of Context identity spatial information - e.g. location, orientation, speed, and

acceleration temporal information - e.g. time of the day, date, and season of the

year environmental information - e.g. temperature, air quality, and light

or noise level social situation - e.g. who you are with, and people that are nearby resources that are nearby - e.g. accessible devices, and hosts availability of resources - e.g. battery, display, network, and

bandwidth physiological measurements - e.g. blood pressure, hart rate,

respiration rate, muscle activity, and tone of voice activity - e.g. talking, reading, walking, and running schedules and agendas

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Agenda

IntroductionPervasive ComputingContext-aware

Context-aware ServiceUbiquitous Phone System (UPS)Context-aware Path Planning (UbiPaPaGo)

Conclusion

Ubiquitous Phone System (UPS)

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Motivation Dual-mode feature-rich business smart phone h

as more and more popular. Besides tri-band UMTS/HSDPA and quad-band

GPRS/EDGE/GSM, most of these smart phones also support WiFi, Bluetooth, and GPS connectivity.

However, use of these feature-rich smart phones still relies on users’ attention with complicated configuration, either priori or on-demand.

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Motivation (Cont.)

For general naïve users, more intelligent, human-centered services and simplified user interfaces are expected

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Scenario

老闆突然有事要取消meeting,希望能自動撥號給實驗室任何一位研究生有空接聽電話的人費率最低

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Problem Definition

How to immediately achieve users’ requirements by context information

How to efficiently help users make explicit decisions from varieties of communication ways

How to help the caller contact with the callee whose personal smart device is out of use when emergency call is incoming

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Solution Approach Use context digest model and inference model t

o achieve users’ requirements by context-awareness

Use inference model to explicitly connect the most adaptive communication way for users

Use social model to help the caller contact with the callee whose personal smart device is out of use

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Social Network

Small-world phenomenonThe six degrees of separation concept

Social network is defined as a social structure made of nodes which are generally people or groups

Social network can reflect the relation between two individuals

212009/04/10

Architecture of UPS

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Models Context Digest Model

Ontology tree Inference Model Social Model

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Context Digest ModelContext Digest Model

Clients

Context Information Management Module

Context Acquisition Module

PersonalData

SensorData

StatusData

LocationData

Context Information

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Context Information Static context

Static context is the fundamental attribute users’ profile and preference, device’s profile

Dynamic context Dynamic context presents the instant information

user’s presence status and location

Auditing contextAuditing context is the statistical output by operating

phone call logs, location logs and event logs

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Inference Model Pre-define intelligent communication policies

with SPARQL (recursively, SPARQL Protocol And RDF Query Language)Event-orientedRule-based engine

Reach personalized service by the preference setting of users

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Social Model (1/2) Social model is designed to assist the caller c to contac

t with the user u through the rely node r when u’s phone is out of use.

Social network is built by the phone call recordsωu,bi

is the weight of relation between two individuals ω is weight of two-individual relation. R is the number of phone record Ru,b is indicated as the record of u’s calling to bi

bi is indicated as the u’s buddy

)( ,,

,,,

uxxu

ubbubu RRMax

RRii

i

ibx ,

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Social Model (2/2)Prelation is a rank of social relation between two individuals

ωu,bi and degree of relation between u and gj (gj: the group in u’s

buddy list)

Ptarget is an output of social model for contact with the person by computing the whole statistical data

Ptarget is the probability of c communicating with u by r

Plocation is the probability of u and bi being in the same location

Prelation is the rank of social relation between bi and u

αlocation is the weight of Plocation to affect Ptarget

αrelation is the weight of Prelation to affect Ptarget

relationrelationlocationlocationtarget PPP 1 relationlocation Where

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Intelligent Service of UPS

Ubiquitous Call Service AnyCall Service Emergent-contact Service

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Intelligent Service of UPS - Ubiquitous Call Service Ubiquitous call service is aimed to provide a novel

human-centric phone call service. When a user desires to call a buddy from his

contact list, he just clicks on the buddy’s name. The call agent on the user’s PDA phone will

communicate with the service providing server and provide the most appropriate service to the userVoIP call, a GSM call, or a short message

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Intelligent Service of UPS - Ubiquitous Call Service Ubiquitous call service automatically choose the rig

ht communication channel to connect to the callee based on context information like reachability, call rate, user preference

UbiCall also provides intelligent feedback service when the callee is not availablesuch as when to call back according to callee’s calendar,

whether to automatically re-dial when the callee becomes available.

312009/04/10

Intelligent Service of UPS- AnyCall Service AnyCall is similar to the anycast service in IP networks

It provides the user to call the most appropriate buddy within a group

Consider an emergent scenario where the user needs to call anyone in his family group to inform a

n accident. In such an emergent situation, any buddy from the family group will be able to help the caller.

AnyCall provides the user an interface to just a single click on the group name, it will connect the phone call to one of the group members who is available at that time to answer the phone.

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Intelligent Service of UPS- Emergent-contact Service The emergent-contact service helps the caller

contact with a third party from callee’s contact list or a phone at a location when the callee is not reachable. social, calendar and location information are used

to reason the most appropriate third party.

332009/04/10

Implementation Experimental Environment Middleware

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Experimental Environment

InternetInternet

Telecommunication Network

Telecommunication Network

Wireless Network

Wireless Network

Wireless Network

Wireless Network

Inference Server

Agents

Service Providing Servers

Context Management Server

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Middleware

Web ServiceSOAP

Ontology treeOWLRDF

Context Acquisition or Communication

XML, DTD, RDF Triple Storage, OWL Semantic Definition

Encapsulating SOAP Message

WSDL Agents

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Implemental achievement

InternetInternet

hushpuppy

Dormitory

Engineering College A

Lunsrot

In EA315

zqq

In EA105

Messaging Server

VoIP Server& Agents

Context Management Server

UbiCom Group

E-learning GroupWeb2.0

Group

Web2.0 Group

Septhiorth

Learn2.0 project members

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Ubiquitous Call Service

During Lunsrot’s surveying papers, he has new thoughts about Web2.0 and he wants to discuss with hushpuppy

hushpuppy

In the Library

Lunsrot

In EA315

? Inference

hushpuppy

In the Library

Lunsrot

In EA315

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AnyCall Septhiorth wants to discuss with anyone who j

oins Learn2.0 project

SepthiorthLunsrot

In EA315zqq

In EA105

hushpuppy

In the Library

Elearn2.0 ProjSepthiorthLunsrot

In EA315zqq

In EA105

hushpuppy

In the Library

Elearn2.0 Proj

?

Inference

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Verification of Social Model (1/7) Find 22 experimenters and select one who knows others of them

Set the relation degree of the chosen one from his preference setting

Record the phone call records of 22 experimenters during one month

Record the location logs of the chosen one during one month and calculate statistics of logs

Record locations of 22 experimenters during some week after that month

No. Nickname Relation (Group)

b1 masa Myself

b2 yuwen Graduate Mate

b3 puzi Graduate Mate

b4 calais Graduate Mate

b5 iamshyang Graduate Mate

b6 bluejam Graduate Mate/University Mate/Dorm Mate

b7 zqq Graduate Mate/University Mate

…

0 Myself

1 Family

2 Dorm Mate

3 Friend

4 University Mate

5 Graduate Mate

6 Younger Graduate Mate

7 Graduate Mate for Doctor's degree

8 Advisor

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Verification of Social Model (2/7) Record the phone call records of 22 experime

nters during one month

masamasa

calaiscalais

puzipuzi bluejambluejam

yuwenyuwenzqqzqq

iamshyangiamshyang

hopperhopper

sigwasigwa

des33des33

septhiorthsepthiorth

hushpuppyhushpuppy

LunsrotLunsrot

yanminyanmin

NickNick

rhhwangrhhwang

MeiMei

BrianBrian

DadDad

yuanyuan

hsghsg

suesue

Classmate in graduate school Roommate

Classmate in university

Younger classmate in graduate school

Senior classmate in graduate school

Advisor

Family Friend

: No records during a period: No records during a period: User: User :Group Name:Group Name

masamasa

calaiscalais

puzipuzi bluejambluejam

yuwenyuwenzqqzqq

iamshyangiamshyang

hopperhopper

sigwasigwa

des33des33

septhiorthsepthiorth

hushpuppyhushpuppy

LunsrotLunsrot

yanminyanmin

NickNick

rhhwangrhhwang

MeiMei

BrianBrian

DadDad

yuanyuan

hsghsg

suesue

Classmate in graduate school Roommate

Classmate in university

Younger classmate in graduate school

Senior classmate in graduate school

Advisor

Family Friend

: No records during a period: No records during a period: User: User :Group Name:Group Name

412009/04/10

Experimental

Successful case Masa attends group meeting in EA105 from 16:00

to 18:00 on every Tuesday. It is a quite regular activity and he attends the meeting as usual on the fifth week.

Table shows the possibility of suggesting each contact as well as a location if that location has a phone.

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432009/04/10

Experimental

Fail case On the Friday of the fifth week, masa attends a BB

Q party from 18:00 to 22:00 which is not a regular activity.

The BBQ party was held in Orange Grange where masa never been there during the past four weeks.

Furthermore, masa does not put this event in his calendar.

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Experimental

Puzi is the emergent contact suggested by the system due to his high intensity of social relation with masa.

However, Puzi is preparing a presentation in EA315 at that time.

Bluejam has the second highest possibility and is actually also in the BBQ party. Therefore, if the system provides a list of possible candidates instead of just one, the user could have chance to find masa through bluejam.

462009/04/10

Agenda

IntroductionPervasive ComputingContext-aware

Context-aware ServiceUbiquitous Phone System (UPS)Context-aware Path Planning (UbiPaPaGo)

Conclusion

472009/04/10 47

ScenarioScenario Mary has a class at 13:30 PM but she is late. She

leaves library and uses PDA to connect online e-learning system and walks to classroom as soon as possible She needs to hand in an hard copy assignment in class, so she

also needs printer to print out her assignment on her moving way. Therefore, Mary hopes to find the shorter path

without breaking off online e-learning system and access printer service.

Application Path plan in Navigation System

Requirement Signal strength, Bandwidth, shorter path, printer service

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Problem Definition

Based on user’s requirement and available context, how to plan path?

Path planning problem Find a optimal path that maximizes the user-required fitness

function Goal

Maximum bandwidth, signal strength Minimum distance Available service

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Design Idea

Phase 1 ： Create a trajectory map using Spatial Conceptual Map (SCM)Spatial Conceptual Map (SCM) Modify the parameters of SCM

Modify the partition rule of Way Element Areas (WEA) Characteristic Function

Phase 2: Plan optimal path using Genetic Algorithm Initialization

Coding or Representation Fitness function

Parent selection Reproduction

Crossover Mutation

Convergence When to stop

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CCU Campus Map

http://www.ccu.edu.tw/eng/GIS/ccumap.swf

512009/04/10

Spatial Conceptual Map (SCM) Spatial Conceptual Map (SCM) RepresentationRepresentation

CCU Campus

O1

N

S

EW

O2

O6

O5

O4

O3

O7

O8

O9

α1

α2

α3

α4 α5α6

α10

α29

α30

α31 α55

α56

α57

α11

α12

α13

α32

α33

α34

α24

α25

α26

α48

α49

α50

α51

α52

α53

α70

α71

α72

α73

α74

α75

α76

α77

α78α58

α59

α18 α19 α20

α21 α22 α23 α35 α45 α46 α66 α67 α68 α79

α63 α64

α39 α40

α36 α37

α42 α43

α27

α28

α54

α69α62

α61α60α38

α41

α44

α47α14α9

α8α7

α15

α16α17

α65

AP1

AP2

AP4

AP3

AP5 AP7

AP8

AP9AP6

522009/04/10

MOAC Representationα18 α19 α20 α21 α22 α23 α24 α25 α26 α27 α28 α29

α18 Co (α18) E

α19 W Co (α19)Co(O2)

E

α20W Co (α20)

α21 Co (α21) E

α22 W Co (α22) E

α23 W Co (α23)

α24 Co (α24) S

α25N Co (α25) S

α26 N Co (α26)Co(O3)

S

α27 N Co (α27)Co(O4)

S

α28N Co (α28) S

α29N Co (α29)

532009/04/10

Characteristic Function

Co(Oi)={c1=id c2=name, c3=location,…}Ex: Co(O1)={c1=B1, c2=library, c3=α8}

Co(αi)= {c1=distance, c2=AP list, c3=available service…}Ex: Co(α2)= {c1=5, c2={AP1 , AP2}, c3={printer, fax}}

Co(APi)= {c1=SSID, c2=available bandwidth, c3=signal strength,…}Ex: Co(AP1)= {c1=3com, c2=4, c3=5}

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Step 2: Plan optimal path using Genetic Algorithm

Initialize chromosomes Crossover Mutation Evaluate fitness function

α1

α3

α5

α10

α12

α14

α19 α33

α35

α29

α31

α24

α57

α59

α53

α55

α48

α46

α51

α69

α70

α79

α64 α75

Node: Object and crossover

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Initial population

Initial population is very important because it affects the convergence speed and the quality of solution.

Random initialization possible leads to large computing overhead and loop path

Heuristic initialization is hard to find global optimal.

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Initial population

We adopt a probability network approach which combines the features of random and heuristic initializations to generate initial candidate paths in the gene pool.

This process is repeated from the starting node to the goal node A gene in a locus randomly selected from the set of forward direction nodes

which are connected with the node of the locus number and not visited before Easily to achieve destination node Avoid loop problem

The selected gene n put in the mth locus of the chromosome The nodes x which are not passed through

randomly select one node from the set of nodes which are connected with x and put it in the xth locus

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Initial population

GoalRandomly select k pathsGuarantee to destination nodeAvoid loop problem

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Example-1

Ex: Route 19 -> 51 (O5-> O7)

locus numbers

Chromosome

10 12 14 19 24 29 31 33 35 46 48 51 53 55 57 59

Route 19-> 33 -> 57 -> 55 ->53 ->51

29

α10

α12

α14

α19 α33

α35

α29

α31

α24

α57

α59

α53

α55

α48

α46

α51

33 57 55535114 35 29 53 59 4846 24 57

612009/04/10

Example-2

Ex: Route 19 -> 51 (O5-> O7)

locus numbers

Chromosome

10 12 14 19 24 29 31 33 35 46 48 51 53 55 57 59

Route 19-> 33 -> 31->29->24->48->51

α10

α12

α14

α19 α33

α35

α29

α31

α24

α57

α59

α53

α55

α48

α46

α51

33 312448 5112 19 35 29 46 59 53 55 57 33 57

622009/04/10

Fitness Function Fitness function of the chromosome measures the

objective cost function Signal strength (S) Available Bandwidth (B) Path Length (D) Available Service (S)

The relative values of ws,wb,wd, ,wa represent the aggressiveness of the cost function

Satisfy user’s requirement

632009/04/10

Fitness Function

0signal,

thresholdsignal),0signal(threshold

thresholdsignal,signal

signal

i

ii

ii

i

α

sααs

sαα

α

0bandwidth,

thresholdbandwidth),0bandwidth(threshold

thresholdbandwidth,bandwidth

bandwidth

i

ii

ii

i

α

bααb

bαα

α

available isserver required if ,1

availablenot isserver required if ,0i

S

642009/04/10

Fitness Function

ss

sii lu

usignals

|)(|

1)(*

dd

dii lu

lcedisd

|)(tan|

1)(*

bb

bii lu

ubandwidthavailableb

|)(_|

1)(*

1

)))(((w))(*)(*)(*

(1

a111

adbs

i

n

n

ii

d

n

ii

b

n

ii

s

wwww

SfMaxn

dw

n

bw

n

swF

i

652009/04/10

Fitness Function

Route 5-> 10 -> 29-> 31

O1

N

S

EW

O2O5

α5α6

α10

α29

α30

α31

α15

α16α17AP1

AP3

AP5

AP6

)(w))()()(

(1

a111

iS

n

dw

n

bw

n

swF

n

i

n

ii

d

n

ii

b

n

ii

s

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Reproduction

The best fitness chromosome is coped into the gene poolCrossoverMutation

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Crossover

Crossover operation combines two chromosomes (called parents) to produce a new chromosome (called offspring)

our crossover operation is carefully designed to avoid the path looping problem

Two parent chromosomes are randomly selected from the gene pool.

If the genes of the locus of two parents are the same, then just copy the gene to the offspring. Otherwise, we will prefer to select the gene (node) that has not been appeared (visited) before.

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Example -crossover

Ex: Route 19 -> 51 (O5-> O7)

locus numbers

Parent 1

10 12 14 19 24 29 31 33 35 46 48 51 53 55 57 59

29

α10

α12

α14

α19 α33

α35

α29

α31

α24

α57

α59

α53

α55

α48

α46

α51

33 57 55535114 35 29 53 59 4846 24 5733

Parent 2 33 312448 5112 19 35 29 46 59 53 55 57 33 57

33 31 5153 29

Offspring: 19-> 33 -> 31->29->53->51

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Mutation

Avoid local optima by preventing the population of chromosomes

Randomly select one node in the route path as the mutation gene

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Example - Mutation

Ex: Route 19 -> 51 (O5-> O7)

locus numbers

Chromosome

10 12 14 19 24 29 31 33 35 46 48 51 53 55 57 59

Route 19-> 33 -> 57 -> 55 ->53 ->51

29

α10

α12

α14

α19 α33

α35

α29

α31

α24

α57

α59

α53

α55

α48

α46

α51

33 57 55535114 35 29 53 59 4846 24 5733

Before

Route 19-> 33 -> 35 -> 46-59-57-55-53-51After

35

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Implementation -1

User connects to Service Provider and uses UbiPaPago Service

722009/04/10

Implementation -2

UbiPaPaGo Service Provider receives the request from user

732009/04/10

Implementation -3

The path planning result

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Experimental Results -1

Results of UbiPaPaGoExample map with optimal path

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Experimental Results -2

Average fitness value under various population sizes

0.82

0.84

0.86

0.88

0.9

Number of Generation

Ave

rage

Fit

ness

Val

ue

RandomUbiPaPagoOptimal

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Agenda

IntroductionPervasive ComputingContext-aware

Context-aware ServiceUbiquitous Phone System (UbiPhone)Context-aware Path Planning (UbiPaPaGo)

Conclusion

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Conclusion-1 UPS is a context-aware intelligent communicat

ion systemUnify various intercommunicated interfaces Intelligently and immediately help human achieve r

equired services Integrate a communication system with social net

work

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Conclusion-2

UbiPaPaGo is a context-aware path planning mechanism It takes into account multiple requirements and context

of users and environment when planning the best-fitting path

Experimental results show that the proposed UbiPaPaGo efficiently finds the best-fitting path which guarantees RSS, bandwidth, available service and shorter distance simultaneously

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Future Works-1

UPSHandover between telecommunication network and IP

networkEmotion for presence context can immediately

respond to users’ degree of satisfaction without users’ feedback and make UPS provide more adaptive services

Other social behaviors can help strengthen social network and tune up social model

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Future Works-2

UbiPaPaGoUnknown destinationVertical handoff between AP and BSResource reservation along the path in advance to

guarantee quality of service of the path Security and privacy issues

Q&A

Thank You