proceedings ncact- 2011

PROCEEDINGS OF 4th NATIONAL CONFERENCE on ADVANCED COMPUTING TECHNOLOGIES(NCACT11) on FEBRUARY 2,2011 @ S.A.ENGINEERING COLLEGE

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2nd FEBRUARY, 2011


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Organized by

DEPARTMENT OF

COMPUTER SCIENCE AND ENGINEERING

S.A. ENGINEERING

COLLEGE

NBA ACCREDITED & ISO 9001:2008 CERTIFIED INSTITUTION

Poonamallee Avadi Road, Veeraraghavapuram,

Thiruverkadu, Chennai 600 077.

E-Mail: [email protected] Website: www.saec.ac.in

Phone Nos : 044 26801999, 26801499

Fax No: 044 26801899

Sponsored by

DHARMA NAIDU EDUCATIONAL AND CHARITABLE TRUST


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BOARD OF TRUSTEES

(Late) D. SUDHARSSANAM,

Founder

Shri.D. DURAISWAMY

Chairman

Shri.D.PARANTHAMAN

Vice Chairman

Shri.D. DASARATHAN

Secretary

Shri. S. AMARNAATH

Treasurer

Shri.S. GOPINATH

Joint Secretary


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PREFACE

Department of Computer Science & Engineering, S.A. Engineering College, Chennai,

organizes the 4th National Conference on Advanced Computing Technologies (NCACT-

2011) on 2nd February 2010. This National Conference NCACT-2011 aims:

The main objective of this conference is to create awareness and also to

provide a perfect platform for the participants to upgrade their knowledge and

experience and to discuss on the ways to disseminate the awareness of the

latest developments and advances in computing Technology

To reflect the current focus of global research, recent developments,

challenges and emerging trends in the field of Advanced Computing

Technologies..

The deliberation of this conference will be through presentation of papers.

Areas of the Conference

Cloud, Grid and Quantum Computing

Nano, Distributed and Parallel Computing

Wearable ,Ubiquitous Computing

Computer and Information Security

Wireless Networks

Multimedia Network and Applications

3G/4G Networks

E - learning Methodologies

Data Mining and Warehousing

Intelligent Web Services

Information retrieval


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SOA tools and Services

Computational intelligence

A total of about 172 Technical papers were received from post graduate student, Faculty

members and Research Scholars from R & D organizations covering a wide spectrum of

areas , viz. Cloud, Grid and Quantum Computing , Data Mining and DataWareHousing,

Network Security, Wireless Technologies, Operating Systems, Web Mining etc., These

papers were peer reviewed by technical experts and 78 papers have been selected for

presentation. This volume is a record of current research in the field of recent trends in

advanced computing technologies. We would like to express our sincere thanks to Shri.

D.Duraiswamy, Chairman, Shri.D.Dasarathan ,Secretary,Shri.D.Paranthaman, Vice

Chairman Shri. S. Amarnaath, Treasurer, Thiru P. Venkatesh Raja, Director, Dr.

S.Suyambhazhahan Principal providing us all the supports for conduct this 4 th National

Conference. We thank the various organizations that have deputed delegates to participate

in the conference. We wish to express our sincere thanks to all advisory committee

members for their cordiality and share their expertise during various processes of the

conference. Also we thank faculty members and students of Department of Computer

Science & Engineering for their co-operation in bringing out this conference in grand

success.

EDITORIAL BOARD

N. PARTHEEBAN (Ph.D).,

ASSISTANT PROFESSOR

COMPUTER SCIENCE AND ENGINEERING


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STEERING COMMITTEE

CHIEF PATRON

(Late)Shri. D. SUDHARSSANAM, Founder Shri.D. DURAISAMY, Chairman Shri.D.PARANTHAMAN, Vice Chairman Shri.D.DASARATHAN, Secretary Shri.S. AMARNAATH, Treasurer Shri.S. GOPINATH, Joint Secretary

PATRON Shri. P. VENKATESH RAJA, Director.

CO-PATRON

Dr. S.SUYAMBAZHAHAN, Principal.

ADVISORY COMMITTEE

Dr. C.CHELLAPPAN, Professor, Anna University

Dr.K.S.EASWARAKUMAR, Professor, Anna University,Chennai.

Dr. V. RHYMEND UTHARIRAJ, Director, Ramanujam Computing Center, Anna University

Dr.A.KANNAN, Professor , Anna University

Dr.S.VALLI,Associate Prof, Anna University

Dr. V. UMA MAHESHWARI, Associate Prof, Anna University.

Dr. A.P. SHANTHI, Professor, Anna University.

Dr.B.VINAYAGA SUNDARAM, MIT, Chennai

Dr. S.R.BALASUNDARAM, Professor, NIT, Trichy

CONFERENCE CHAIRMAN

Mrs. P.N.JEBARANI SARGUNAR(Ph.D), HOD / CSE

CO-ORDINATORS Mr. N.PARTHEEBAN(Ph.D), Asst. Prof / CSE

Mrs. B.MURUGESWARI(Ph.D), Asst. Prof./ CSE


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ORGANIZING COMMITTEE MEMBERS :

Mrs. R.GEETHA, M.E, Asst.Professor

Mr. C. BALAKRISHNAN, M.E. Asst.Professor

Mrs. N.S. USHA, M.E. Asst.Professor

Mrs.D.CHITRA, M.E. Asst.Professor

Mrs. E. SUJATHA, M.Tech. Asst.Professor

Mrs. A. BHAGYALAKSHMI, M.E. Asst.Professor

Mrs. S. KALPANA DEVI, M.Tech., Asst.Professor

Mr. M. BALASUBRAMANIAN, B.E. Senior Lecturer

Mr.A.MANI,M.E, Senior Lecturer

Mrs.PAUL JASMINE RANI, M.E, Senior Lecturer

Mrs.NITHYA, M.E,Senior Lecturer

Mrs.VANITHA, B.E. Lecturer

Ms. K. RAMYA DEVI, B.E. Lecturer

Mr.G.THIAGARAJAN, B.E. Lecturer

Mr.S.PRABHU, B.E. Lecturer

Mrs.JOYCE JESIE, B.E. Lecturer

Mrs.R.SUDHA, B.E. Lecturer

Mr.MUTHU KUMARASWAMY, B.E. Lecturer

Ms.S.PREETHI,B.Tech ,Lecturer.


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VISION AND MISSION

To transform our institution into quality technical education center imparting updated technical knowledge with Character building.

To create an excellent teaching and learning environment for our staff and

students to realize their full potential thus enabling them to contribute positively to the community.

To significantly enhance the self-confidence level for developing creative skills

of staff and students.

ABOUT THE COLLEGE

The S.A. Engineering College was established by the Dharma Naidu Educational & Charitable Trust in the year 1998-'99.The college is approved by AICTE Delhi and affiliated to Anna University, Chennai, Tamilnadu. The college is well-planned and well-designed with spread over to 42 acres and has more than 2.91 lakhs sq.ft. of constructed area. In recognition of quality system of high calibre being implemented in the administration of the Institution and achievement of its goals, M/s TUV has accorded ISO 9001:2008 Certification. All the under graduate programs offered are accredited by National Board of Accreditation (NBA).The college offers following 6 U.G programmes and 5 P.G programmes.

B.E. - Computer Science and Engineering

B.E. - Electronics and Communication Engineering

B.E. - Electrical and Electronics Engineering

B.E. - Mechanical Engineering

B.E. - Civil Engineering

B.Tech. - Information Technology

M.E. - Computer Science Engineering

M.E. - Communication Engineering

M.E. - Embedded Systems Technologies

M.B.A. - Master of Business Administration

M.C.A. - Master of Computer Applications

The college maintains high standard of education by providing a wide array of

world class of academic facilities, employing highly qualified and experienced faculty

members and creating an ambience conducive of quality education.


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ABOUT THE DEPARTMENT

The department of Computer Science & Engineering was established in the

year 1998.The department is accredited by NBA. The department has grown in

strong over the years in terms of infrastructure facilities, experienced and dedicated

team of faculty strength, technical expertise, modern teaching aids, tutorial rooms,

well equipped and spacious laboratories. The department has separate library and

seminar hall with all latest equipment. The department has currently an intake of 120

students. The department also has 120 KVA power backup and 2 Mbps Leased Line

Internet connectivity for the benefit of the students.


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(NBA Accredited and ISO 9001:2008 Certified Institution) Poonamallee Avadi Road, Veeraraghavapuram, Chennai 600 077.

E-Mail : [email protected] Website: www.saec.ac.in Phone Nos : 044 26801999, 26801499 Fax No: 044 26801899

S. AMARNAATH, M.Com., CORRESPONDENT

MESSAGE

We at this institution constantly strive to provide an excellent academic environment

for the benefit of students and faculty so that they will acquire a technological competence

synonymous with human dignity and values.

We are dedicated to a continuous process through this 4th National Conference on

ADVANCED COMPUTING TECHNOLOGIES NCACT11 to enable upgrading academic

performance and managerial practices through infra-structure and technological facilities.

This commitment, will enable us to provide updated knowledge-inputs and practical support

to the participants in order to build their confidence level.

I am happy to know that our institution is maintaining the tradition set with respect to

the contents in Engineering & Technology, cultural and other activities of the organization

extending with another milestone of this Conference in this academic year 2010-2011,

organized by the Department of Computer Science and Engineering.

I congratulate and offer my best wishes to the Principal and committee members who

have involved them in this conference towards the academic development for the benefit of

students community.

S.AMARNAATH


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P. VENKATESH RAJA, B.E.,M.S., DIRECTOR

MESSAGE

This institution is a tribute to the great organizing genius of its Founder. Without his

initiative and inspiration it would have been impossible to find an institution of this character.

This institution is a memorable experiment in the moral and technological

regeneration of India. It stands for nothing less.

We proposed to maintain here standards of discipline and decorum, of decency,

dignity and character building are equaled by few and surpassed by none in contemporary

education systems.

With this, we are proud to conduct the 4th National Conference on ADVANCED

COMPUTING TECHNOLOGIES NCACT11 , on 2nd February 2011. We wish and thank

the Principal and faculty members who have involved them in this Conference and the

participants who have really come forward to benefit themselves to develop the academic

knowledge of confidence by this conference.

P. VENKATESH RAJA


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Dr. S. SUYAMBAZHAHAN, M.E., Ph.D.,(IITM) PRINCIPAL

MESSAGE

I appreciate the initiative taken by the heads of department and the faculty

members of computer science and engineering for conducting 4th National

Conference on ADVANCED COMPUTING TECHNOLOGIES NCACT11 , on

2nd February 2011 at our college campus.

It also gives sense accomplishment and achievements by the student and

staff of

CSE department to release the proceedings on the occasion, which focus on the

latest

advancements in the areas of computing Technology. I am sure; this Conference

highlights and brings out the best of every paper presented by the authors from

academics, R&D institutions and student community pursuing higher degree and

doctoral programmes in various institutions.

I sincerely appreciate the efforts made by the Principal, HOD, staff and

students with the great sense of belongingness and ownership and wish them to

have a great success and in the coming times as well.

Dr.S.SUYAMBAZHAHAN


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S.NO NAME OF THE AUTHOR(S) TITLE OF THE PAPER NAME OF THE COLLEGE

MAIL ID/CONTACT NO

1. VINITHRA.I

EARLY DETECTION AND

CONGSTION CONTROL IN

HETEROGENEOUG

NETWORKS

PITAM [email protected]

2. GOMATHI.V

CONTENT BASED IMAGE

RETRIEVAL ON MOBILE

DEVICES BY NAVIGATION

PATTERN BASED

RELEVANCE FEEDBACK


m

3. ANNA ARASU .A

R.NAKEERAN

REVERSE NEAREST

NEIGHBOR FOR

ANONYMOUS QUERIES

DR.PAULS

ENGINEERING COLLEGE

[email protected]

[email protected]

4. BHANUMATHI.R

AN EFFICIENT IMAGE

ENHANCEMENT BASED ON

DWT AND SVD


5. UMAMAHESWARI.P.K

ANALYSIS AND PROTECTION

OF KEY DISTRIBUTION

SCHEME FORSECURE

GROUP COMMUNICATION


6. JABALIDBIN.M

S.MADHAN KUMAR

SECURE INFORMATION

DELIVERY IN WIRELESS

SENSOR NODES

VELTECHMULTITECH

DRRRDRSRENGINEERIN

GCOLLEG

[email protected]

[email protected]

7.

GEETHANJALI

JAYACHANDRAN

N.GOMATHI

V.R. VIMAL

CONTENT AWARE PLAYOUT

FOR VIDEO STREAMING

VELTECH MULTITECH

SRS ENGG

[email protected]

[email protected]

[email protected]


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8. ANUSHA.S

B.BHUVANESWARAN

CRYPTANALYSIS OF AN

EDGE CRYPT ALGORITHM

RAJALAKSMI

ENGINEERING COLLEGE

[email protected]

[email protected]

9. GAYATHRI.U

A CONCURRENCY CONTROL

PROTOCOL USING ZR+-

TREES FOR SPATIAL JOIN

AND KNN QUERIES

RAJALAKSHMI

ENGINEERING COLLEGE [email protected]

10. LEKSHMI PRIYA.R

SECURE ENERGY EFFICIENT

DATA AGGREGATION

PROTOCOL FOR DATA

REPORTING IN WIRELESS

SENSOR NETWORKS

RAJALAKSHMI


11.

EVANGELIN HEMA

MARIYA.R

EFFICIENT ENERGY SAVING

USING DISTRIBUTED

CLUSTER HEADS IN

WIRELESS SENSOR

NETWORKS

RAJALAKSHMI


12. VADHANI.R

A NOVEL FRAMEWORK FOR

DENIAL OF PHISHING BY

COMBINING HEURISTIC &

CONTENT BASED SEARCH

ALGORITHM

RAJALAKSHMI


13. SURESHBABU.D

C.PRABHAKARAN

RISK ESTIMATION USING

OBJECT-ORIENTED METRICS

VEL TECH MULTI TECH

DR.RR & DR.SR

ENGINEERING

COLLEGE,

[email protected]

14. PIRAMANAYAGAM.M

M.YUVARAJU

SECURE ENCRYPTION AND

KEYING BASED ON VIRTUAL

ENERGY FOR WIRELESS

SENSOR NETWORKS

DEPARTMENT OF

COMPUTER SCIENCE

AND ENGINEERING,

ANNA UNIVERSITY OF

TECHNOLOGY,

COIMBATORE

[email protected]

15. SIVARANJANI.P

P.NEELAVENI

HYBRID INFRASTRUCTURE

SYSTEM FOR EXECUTING

SERVICE WORKFLOWS.

G.K.M COLLEGE OF

ENGINEERING AND

TECHNOLOGY,PERINGA

LATHUR, CHENNAI

[email protected]

16. NANDHINI.T.J

IMPROVISED SOLUTION

THROUGH MERKLE TREE

RAJALAKSHMIENGINEE

RINGCOLLEGE [email protected]


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ALGORITHM FOR SECURE

MULTIPATH ROUTING WITH

EFFICIENT COLLABORATION

OF BLACK HOLES

17. LAKSHMI PRIYA.R.V

R. TAMILARASI

SYBIL GUARD: DEFENDING

AGAINST SYBIL ATTACKS

VIA SOCIAL NETWORKS

VELAMMAL

ENGINEERING

COLLEGE, CHENNAI,

[email protected]

[email protected]

18. ANAND.V.J,

SELVAKUMAR.V.S

REDUNDANCY CHECK

ARCHITECTURE

RAJALAKSHMI

ENGINEERING COLLEGE

[email protected]

[email protected]

u.in

19. LINGESAN.J

R.KANNAMMA

MODELING BOTNET

PROPAGATION FOR

DETECTING BOTMASTERS

PRATHYUSHA

INSTITUTE OF

TECHNOLOGY AND

MANAGEMENT

[email protected],

[email protected]

m

20.

SENTHILMURUGAN.T

SENTHIL.P

MANIKANDAN.T

IMPROVING SECURITY

PERFORMANCE OF MOBILE

AD-HOC NETWORKS

AGAINST ATTACKS

VEL TECH DR.RR &

DR.SR TECHNICAL

UNIVERSITY

VEL TECH DR.RR &

DR.SR TECHNICAL

UNIVERSITY

TAGORE ENGINEERING

COLLEGE

[email protected]

Mobile No.: 9176031383

[email protected]

[email protected]

21.

POONGUZHALI.C

D.CHITHRA

IMAGE RECOGNITION FOR

DESIGNING CAPTCHAS

S.A.ENGINEERING

COLLEGE

[email protected]

[email protected]

22.

RAMNATH.M

S.ARUNA

P.PRABHU

PMG BASED HANDOFF IN

WIRELESS MESH NETWORKS

VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING COLLEGE

, AVADI, CHENNAI

[email protected]

23. ARJUNADHITYAA.K.R

D.ANANDHI

A NOVEL TECHNIQUE FOR

DETECTING DATA HIDDEN

ON DIGITAL IMAGE USING

STEGANOGRAPHY

VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING COLLEGE

[email protected]

[email protected]


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24. MAHAALAKSHMI K

NEELAKANDAN S

AUTOMATIC DATA

EXTRACTION FROM

WEBPAGES BY WEBNLP

VEL TECH MULTI TECH

DR.RR & DR.SR ENGG

COLLEGE,

[email protected]

[email protected]

[email protected]

25. MATHEW P C

M. ARUNKUMAR

IMPLEMENTATION OF

ENCRYPTED IMAGE

COMPRESSION USING

RESOLUTION PROGRESSIVE

COMPRESSION SCHEME

PSNA COLLEGE OF

ENGINEERING AND

TECHNOLOGY,

DINDIGUL, TAMIL NADU.

[email protected]

26. REVATHI.P

J. JAGADEESH

IDENTIFICATION OF

STRUCTURAL CLONES

USING ASSOCIATION RULE

AND CLUSTERING

VEL TECH MULTI TECH

DR.RANGARAGAN &

DR.SAKUNTHALA

ENGINEERING

COLLEGE.

[email protected]

[email protected]

27. ASOKKUMAR.S

DATA MINING TECHNIQUES

FOR CUSTOMER

RELATIONSHIP

MANAGEMENT

RESEARCH SCHOLAR,

ANNA UNIVERSITY OF

TECHNOLOGY

COIMBATORE

[email protected]

28.

NANCY.P.N

PROF.R.PRASANNA

KUMAR

DR.T.RAVI

LOCATION DEPENDENT

PRIVACY AWARE

MONITORING FRAMEWORK

FOR SAFE REGION MOVING

OBJECTS

JAYA ENGINEERING

COLLEGE [email protected]

29. SANTHIKALA.M

ANANTHARAJ. B

PRIVACY-PRESERVING

USING TUPLE AND

THRESOLD MATCHING IN

DISTRIBUTED SYSTEMS

THIRUVALLUVAR

COLLEGE OF

ENGGINEERING AND

TECHNOLOGY,

VANDHAVASI.

[email protected].

9789074232

30.

HIMAVANTHA RAJU

VATSAVAI

MS.G.MUNEESWARI

M.E.(PH.D)

DDOS DEFENSE

MECHANISMS FOR

DETECTING, TRACING AND

MITIGATING NETWORK WIDE

ANOMALIES

R.M.K ENGINEERING

COLLEGE,KAVARAIPETT

AI

[email protected]

31. SHAHINA BEGAM.I SPATIO-TEMPORAL INDEX

STRUCTURE ANALYSIS

VELTECHHIGHTECHDR.R

R& DR.SR ENGG [email protected]


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DR.KARTHIKEYANI.V.TAJU

DIN.K.,

PARVIN BEGAM.I

COLLEGE,

32. SIVAKAMY.N

B.MURUGESWARI,

DR.C.JAYAKUMAR,

MODIFIED DELAY STRATEGY

IN GRID ENVIRONMENT

S.A. ENGINEERING

COLLEGE,CHENNAI [email protected]

33. INDUMATHY.C

AN EFFECTIVE WEB-BASED

E-LEARNING BY MANAGING

RESOURCES USING

ONTOLOGY

EASWARI ENGINEERING

COLLAGE

[email protected]

34. ESWARI.R

EFFECTIVE AND EFFICIENT

QUERY PROCESSING FOR

IDENTIFYING VIDEO

SUBSEQUENCE

EASWARI ENGINEERING


35. VEENA.K

COMPUTER AND

INFORMATION SECURITY ANNA UNIVERSITY [email protected]

36. ALEN JEFFIE PENELOPE.J

L.BHAKAYA LASKSHMI

ENHANCING THE LIFETIME

OF DATA GATHERING

WIRELESS SENSOR

NETWORK BY BALANCING

ENERGY CONSUMPTION

EASWARI ENGINEERING


37.

AMUDHA.S

ALLIRANI.P

M.KAVITHA

SECURITY ISSUES AND

PRIVACY OF CLOUD

COMPUTING

ST PETERS UNIVERSITY

SRIRAM ENGINEERING

COLLEGE

SRIRAM ENGINEERING

COLLEGE

[email protected],

9994554412

38. FATHIMA.K

MS. KOUSALYA

COST EFFECTIVE WIRELESS

HEALTH MONITORING

SYSTEM FOR INDUCTION

MOTORS

VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING

COLLEGE,AVADI,

[email protected]

39. SANTHOSHKUMAR.S.P

M.YUVARAJU

RANDOM CHECKPOINTING

ARRANGEMENT IN

DECENTRALIZED MOBILE

ANNA UNIVERSITY OF

TECHNOLOGY,

COIMBATORE, INDIA.

[email protected]

n


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GRID COMPUTING

40. AARTHI.S

NOVEL METHOD FOR

SEQUENCE NUMBER

COLLECTOR

PROBLEM IN BLACK HOLE

ATTACK DETECTION-AODV

BASED MANET

RAJALAKSHMIENGINEE

RINGCOLLEGE,

CHENNAI

[email protected]

41. JESHIFA.G.IMMANUEL

A.FIDAL CASTRO

PROF.E.BABU RAJ

ADVANCED CONGESTION

CONTROL TECHNIQUE FOR

HEALTH CARE MONITORING

IN WIRELESS BIOMEDICAL

SENSOR NETWORKS

JAYA ENGINEERING

COLLEGE,

THIRUNINDRAVUR-

602024

[email protected]

42. NITHYA KUMARI.K

BHAGYALAKSHMI.L

A GAME THEORETIC

FRAMEWORK FOR

POWER CONTROL IN

WIRELESS AD HOC

NETWORKS

EASWARI ENGINEERING


43. UMA.R

L. PAUL

JASMINE RANI

SMABS: SECURE MULTICAST

AUTHENTICATIONBASED ON

BATCH SIGNATURE

S.A.ENGINEERING


44. SANJAIKUMAR.K

G.UMARANISRIKANTH,

M.E.(PHD)

AFFINE SYMMETRIC

IMAGEMODEL

S.A.ENGINEERING

COLLEGE

[email protected]

45. MADHAVI.S

S. KALPANA DEVI

COMBINING TPE SCHEME

AND SDEC FOR SECURE

DISTRIBUTED NETWORKED

STORAGE

S.A.ENGINEERING

COLLEGE

[email protected]

46. LAVANYA.R

E.SUJATHA**

PERFORMANCE EVALUATION

OF FLOOD SEQUENCING

PROTOCOLS IN SENSOR

NETWORKS

S.A.ENGINEERING

COLLEGE

[email protected]

47. PARVIN BEGUM.I

DR.KARTHIKEYANI.V

KNOWLEDGE DISCOVERY

PROCESS THROUGH

TEXTMINING

SOKA IKEDA COLLEGE

OF ARTS AND SCIENCE [email protected]


20

TAJUDIN.K

SHAHINA BEGAM.I.,

48. SUNIL.P

DATA LEAKAGE DETECTION

USING

ROBUST AUDIO HIDING

TECHNIQUES

PRATHYUSHA

INSTITUTE OF

TECHNOLOGY AND

MANAGEMENT

ARANVAYALKUPPAM

[email protected]

49. KIRUTHIKA DEVI .S

A.BHAGYALAKSHMI

A SECURED CLOUD

COMPUTING FOR LIFE CARE

INTEGRATED WITH WSN

S.A. ENGINEERING

COLLEGE, CHENNAI. [email protected]

50. RENUKADEVI.M

G.UMARANI SRIKANTH

TRAFFIC ANALYSIS AGAINST

FLOW CORRELATION

ATTACKS

S.A .ENGINEERING

COLLEGE, CHENNAI-77 [email protected],

51.

SAMSUL ADAM.M

U.SYED ABUDHAGIR M.

DEIVAMANI

A FAULT TOLERANT BASED

RESOURCE ALLOCATION

FOR THE GRID

ENVIRONMENT

COLLEGE OF

ENGINEERING , GUINDY [email protected]

52.

SIVAPERUMAL.V

P. MAHALAKSHMI

OPTIMIZED ROUTING

ALGORITHM FOR WIRELESS

MESH NETWORKS

JERUSALEM COLLEGE

OF ENGINEERING,

ANNA UNIVERSITY

CHENNAI

[email protected]

53. SIVARANJANI.G

M.RAJALAKSHMI

AUTOMATIC MULTILEVEL

THRESHOLDING OF

DIGTAL IMAGES

ADIPARASAKTHI

ENGINEERIMG

COLLEGE,

MELMARUVATHUR.

[email protected]

54. BALAJI.V

PARTHEEBAN.N

AN EFFICIENT CROSS LAYER

INTRUSION DETECTION

TECHNIQUE FOR MANET

S.A.ENGINEERING



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55. HANSON THAYA.S

WIRELESS SENSOR

NETWORK SECURITY USING

VIRTUAL ENERGY BASED

ENCRYPTION

S.A.ENGINEERING


56. SANGEETHA.J

QOS-AWARE CHECKPOINTING

ARRANGEMENT IN MOBILE

GRID ENVIRONMENT

S.A.ENGINEERING


57. SIREESHA.P

S.A.ENGINEERING


58. SIVASAKTHI.K

EXTENDED QUERY

ORIENTED, CONCEPT-BASED

USER PROFILES FROM

SEARCH ENGINE LOGS

S.A.ENGINEERING


59. ALGUMANI.S

ENSEMBLE REGISTRATION

OF MULTI SENSOR IMAGES

S.A.ENGINEERING


60. UMA.S

G.SHOBA

EMBEDDING

CRYPTOGRAPHY IN VIDEO

STEGANOGRAPHY

DR.PAULS


61. KRISHNA KUMAR.N

MADHU SUDHANAN.S

RULE CLASSIFICATION FOR

MEDICAL DATASET

VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING COLLEGE

[email protected]

om

62. NIROSHA.N

ENHANCED VEHICLE

DETECTION BY EARLY

OBJECT IDENTIFICATION


63. BHARATHIRAJA.S

S.SUMATHI

EFFICIENT ROUTING BASED

ON LOAD BALANCING IN


VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING COLLEGE

[email protected]

64. SWAPNA .P

K.SAILAKSHMI

S.V.V.S.N. ENGINEERING


65. USHA.M ROUTING BASED ON LOAD VELLAMMAL [email protected]


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BALANCING IN MULTI-HOP


ENGINEERING COLLEGE

66. DURAI MURUGAN.J

FLEXIBLE LOAD BALANCING

IN MULTI_SERVER GRID

ENVIRONMENT

ADIPARASAKTHI

ENGINEERIMG

COLLEGE,

MELMARUVATHUR.

[email protected]

67. JEGADEESAN.R

EFFICIENT LOAD

BALANCING IN

VIDEO SERVERS

FOR VOD SYSTEM

VEL HIGH TECH [email protected]

68. RAMALINGAM.D

A TOOL FOR FINDING BUGS

IN WEB APPLICATIONS

ADIPARASAKTHI

ENGINEERIMG

COLLEGE,

MELMARUVATHUR.

[email protected]

69. JENIFA SUBHA PRIYA.S

DESIGN OF DETERMINISTIC

KEY DISTRIBUTION FOR WSN

JERUSALAM


70.

SURENDRAN.M

M.SARANYA

S.SUBRAMANIAN

VIRTUAL MOUSE USING HCI

SRIRAM ENGINEERING


71. PANNER SELVI.R

EFFICIENTLY IDENTIFYING

DDOS ATTACKS BY GROUP

BASED THEORY

VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING COLLEGE

[email protected]

72. TAMILARASI.P

DEDUCING THE SCHEMA FOR

WEBSITES USING PAGE-

LEVEL WEB DATA

EXTRACTION

VEL TECH MULTI TECH

DR.RANGARAJAN

DR.SAKUNTHALA

ENGINEERING COLLEGE

[email protected]


23

73. SUGANYA.N

QOS METRICS IN PARTICLE

SWARM TECHNIQUE FOR

SELECTION, RANKING AND

UPDATION OF WEB SERVICE

RAJALAKSMI

ENGINEERING COLLEGE

[email protected]

74. BINU JOHN

ANALYSIS ON THE

PERFORMANCE OF VARIOUS

DATA MINING ALGORITHMS

FOR CARDIOVASCULAR RISK

FACTORS

RAJALAKSMI


75. VICTORIYA

MINIMIZATION OF HANDOFF

FAILURE PROBABILITY IN

NGWS USING CHMP

S.K.P.ENGINEERING

COLLEGE,

TIRUVANNAMALAI,

[email protected]

76. SUDHA RAJESH

LEARNING

DISCRIMINATIVE

CANONICALCORRELATI

ONS FOR OBJECT

RECOGNITION WITH

IMAGE

SRR ENGINEERING

COLLEGE --

77. BHUVANESWARI

DISTRIBUTED DATA BACKUP

AND RELIABLE

RECOVERY FROM MOBILE

GRID ENVIRONMENT

ADHIPARASAKTHI

ENGINEERING

COLLEGE,

MELMARUVATHUR

[email protected]


24

EARLY DETECTION AND CONGSTION CONTROL IN HETEROGENEOUG NETWORKS

* Vinithra.I

PG SCHOLAR, M.E. CSE, Prathyusha Institute of Technology and Management,

Email id:[email protected], 9003983630 Abstract The heterogeneous congestion control protocols that react to different pricing signals share the same network, the current theory based on utility maximization fails to predict the network behavior. Unlike in a homogeneous network, the bandwidth allocation now depends on router and flow arrival patterns. This paper studies the nature of the network like fairness and uniqueness in the network and the withstanding capability of the network is analyzed along with optimality and stability properties it extends the study with two objectives: analyzingthe optimality and stability of such networks and designing controlschemes to improve those properties. First, we demonstrate theintricate behavior of a heterogeneous network through simulationsand present a framework to help understand its equilibriumproperties. Second, we propose a simple source-based algorithmto decouple bandwidth allocation from router parameters and flow arrival patterns by only updating a linear parameter in the sources algorithms on a slow timescale. It steers a network to the unique optimal equilibrium. The scheme can be deployed incrementally as the existing protocol needs no change and only new protocols need to adopt the slow timescale adaptation. Index TermsCongestion control, heterogeneous protocols, optimal allocation, stability. I. INTRODUCTION CONGESTION control in Transmission Control Protocol(TCP), first introduced in [1], has enabled the explosive growth of the Internet. The currently predominant implementation, referred to as TCP Reno in this paper, uses packet loss as the congestion signal to dynamically adapt its transmission rate, or more precisely, its window size.1 It has

worked remarkably well in the past, but its limitations in wireless networks and in networks with large bandwidth-delay product have motivated various proposals, some of which use different congestion signals. For example, in addition to loss based protocols such as HighSpeed TCP[3] , STCP[4] and BIC TCP[6] , schemes that use queuing delay include the earlier proposals CARD [9],DUAL [10] and Vegas [10], and the recent proposal FAST [10]. Schemes that use one-bit congestion signal include ECN [15],and those that use multibit feedback include XCP [9], MaxNet [16], and RCP [16]. Indeed, the Linux operating system already allows users to choose from a variety of congestion control algorithms since the kernel version 2.6.13, including TCP-Illinois that uses both packet loss and delay as congestion signals. Recently, compound TCP [12] which also uses multiple congestion signals is deployed in Windows Vista and Window Server 2008 TCP stack [13]. Furthermore, if explicit feedback is deployed, it will become possible to feed back different signals to different users to implement new applications and services. Note that in this case, the heterogeneous signals can all be loss-based different users receiving different explicit values based on the same actual link loss rate or all delay-based, or a mix. Clearly, going forward, our network will become more heterogeneous in which protocols that react to different congestion signals interact. Yet, our understanding of such a heterogeneous network is rudimentary. For example, a heterogeneous network, as shown in an early companion paper [11], may have multiple equilibrium points, and they cannot all be stable unless the equilibrium is globally unique.


25

In a homogeneous network, even though the

sources may control their rates using different algorithms, they all adapt to the same congestion signal, e.g., all react to packet loss rate, as in the various variants of Reno and TFRC [8], or all to queuing delay, as in Vegas and FAST. For homogeneous networks, besides various detailed studies there is already a well-developed theory, based on network utility maximization, that can help understand and engineer network behaviors. In particular, it is known that a homogeneous network of general topology always has a

unique equilibrium (operating point). It maximizes aggregate utility, and the fairness associated with it can be well predicted and controlled. More importantly, the bandwidth allocation depends only on the congestion control algorithms (equivalently ,its underlying utility functions) but not on network

parameters (e.g., buffer sizes) or flow arrival patterns, and hence can be designed through the choice of end-to-end TCP algorithms. It means that in general we cannot predict, nor control, the bandwidth allocation purely through the design of end-to-end congestion control algorithms for heterogeneous

networks. This implies, for example, the standard TCP friendly concept is not well defined anymore given equilibrium point, we propose a general scheme to steer an arbitrary heterogeneous network to the unique equilibrium that maximizes the standard weighted aggregate utility by updating a linear scaler in the sources algorithms on a slow timescale . The scheme requires only local end-to-end information but does assume all flows have access to a common price, which is generally true in practice since the common price can be what the incumbent dominate protocol uses. It can be deployed incrementally as theexisting protocol needs no change and only the new protocolsneed to adopt the slow timescale adaption. Packet-level (ns-2)simulation results using TCP Reno and FAST are presented in and Linux experiments on a realistic testbed .We summarize here the main results that we have derived about heterogeneous congestion control in [11] and this paper. Existence of equilibrium: Theorem 2 in [11];

Uniqueness of equilibrium. Local uniqueness: Theorem 3 in [11]; Global uniqueness: Theorems 7 and 12 in [11]. Optimality of equilibrium Efficiency: Theorems 1 and Corollary 3 in this paper Fairness: Theorems 2 and 3 in this paper. Stability of equilibrium: Local stability: Theorem 4 in this paper; II. TWO MOTIVATING EXAMPLES

In this section, we describe two simulations to illustrate some particular throughput behavior in heterogenous networks. All simulations use TCP Reno, which uses packet loss as congestion signal, and FAST TCP, which uses queueing delay as congestion signal. The first

experiment (Example 1a) shows that when a Reno flow shares a single bottleneck link with a FAST flow, the relative bandwidth allocation depends critically on the link parameter (buffer size): the Reno flow achieves higher bandwidth than FAST when the buffer size is large and smaller bandwidth when it is small.

This implies that one cannot control the fairness between Reno and FAST through just the design of end-to-end congestion control algorithms, since fairness is now linked to network parameters, unlike in the case of homogeneous networks. The second experiment (Example 2a) shows that even on a(multilink) network with fixed parameters, one cannot control the fairness between Reno and FAST because the relative allocation can change dramatically depending on which flow starts first! FAST [16] is a high speed TCP variant that uses delay as its main control signal. Periodically, a FAST flow adjusts its congestion window according to (1) In equilibrium, each FAST flow achieves a throughput, where is the equilibrium queueing delay observed by flow . Hence, is the number of packets that each FAST flow maintains in the bottleneck links along its path. In this example, one FAST flow and one Reno flow share a single bottleneck link with capacity of 8.3 packets per ms (equivalent to 100 Mbps with m


26

The second experiment (Example 2a) shows that even on a(multilink) network with fixed parameters, one cannot control the fairness between Reno and FAST because the relative allocation can change dramatically depending on which flow starts maximum packet size) and roundtrip propagation delay 50 ms. The topology is shown in Fig. 1.The FAST flow fixes its parameter at 50 packets. In all of the ns-2 simulations in this paper, heavy-tail noise traffic is introduced at each link at an average rate of 10% of the link capacity.2 Fig. 2 shows

the result with a bottleneck buffer size packets. In this case, FAST gets an average of 2.1 packets per ms while Reno gets 5.4 packets per ms. III. OPTIMALITY

As we have shown in [36], for heterogeneous congestion control networks, equilibrium cannot be characterized anymore. In this section, we further investigate the deviation of optimality in terms of both efficiency and fairness. This analysis provides insights on networks with heterogeneous congestion

signals, for example, how to define interprotocol fairness. It also motivates the algorithm design .

A. Example 1a: Dependence of Bandwidth Allocation on Network Buffer Size In this section, we further investigate the deviation of optimality in terms of both efficiency and fairness. This analysis provides insights on networks with heterogeneous congestion signals, for example, how to define interprotocol fairness. A. Efficiency We first make the following key observation,

which motivates other results on optimality and algorithm development. Theorem 1: Given an equilibrium p *, there exists a positive vector (p) , such that the equilibrium rate vector x*(p) is the unique solution of following problem: Maxx>=0 i,j riui

jxij

subject to Rx=0 . Suppose the optimal aggregate utility is U* and U^ is the achieved aggregate utility at an equilibrium (x^ ) of a network with heterogeneous protocols. Then (U^/U*)>=(min /max) B. Fairness In this subsection, we study fairness in networks shared by heterogeneous congestion control protocols. Two questions we address are: how the flows within each protocol share among themselves (intraprotocol fairness) and how these protocols share bandwidth in equilibrium (interprotocol fairness). The results here generalize the corresponding theorems in [35].


27

1) Intraprotocol Fairness: when a network is shared only by flows using the same congestion signal, the utility functions describe how the flows share bandwidth among themselves. When flows using different congestion signals share the same network, this feature is still preserved locally within each protocol. Theorem 2: Given an equilibrium(x,p) , let j :=R j x j be the total bandwidth consumed by flows using protocol at each link. The corresponding flow rates x j are the unique solution of

Maxxj>=0 i=1

Nj uij (xi

j)

subject to Rj x

j=0 such that xs() IV. STABILITY For general dynamical systems, a globally unique equilibrium point may not even be locally stable . In this section, we focus on the stability of heterogeneous congestion control protocols, which dictates whether an equilibrium can manifest itself experimentally or not. For general networks, it is shown that once the degree of heterogeneity is properly bounded, the equilibrium is not only unique but also locally stable. We now state the general result on local stability. It essentially says that if the similarity condition on price

mapping functions that guarantees uniqueness

is satisfied, the unique equilibrium is also locally stable. In particular, if for any l all mjl are the same, the equilibrium is locally stable. This certainly agrees with our knowledge on the homogeneous case. Theorem 4: If for any vector j{1,..j}Land any permutations (,k,n) in{1,.L}L l=1

L ml[k(j)]t + l=1

L ml[n(j)]t>= l=1

L ml[(j)]t

then the equilibrium of a regular network is locally stable V. SLOW TIMESCALE UPDATE A. Motivation As pointed out in Corollary 2, all equilibria are Pareto efficient. However, based on analysis , large efficiency loss may occur and no guarantee on fairness can be provided. This motivates us to turn from analysis to design, and develop a readily implementable control

mechanism that drives any network with heterogeneous congestion control protocols to a target operating point with a fair and efficient bandwidth allocation. Our target equilibrium is the maximizer of some weighted aggregate utility. The first step is to set up the existence and uniqueness of such a solution. Theorem 5: For any given network (c,m,R,U), for any positive vector , there exists a unique positive vector such that, if every source scales their own prices byji , i.e., Xji=(U

ji)

i-1((1/ji)mjl(pl))

Algorithm 1:Two Time Scale Adaptation

1. Every source chooses its rate byXji(t)=(U

)-1(qji(t)/

ji(t) ).

2. Every source updates its ji by

ji(t+T)=


28

ji(t)+ kji((( lL(j,i) m

jl(l (t+T))/ lL(j,i)l

(t+T))- ji(t)).Where as, kji =stepsize of the

flow(j,i).T is large enough so that the fast timescale dynamics among X and pcan reach steady state. VII . SIMULATION RESULTS :RENO AND FAST In this section, we apply Algorithm 1 to the case of Reno and FAST coexisting in the same network to resolve the issues illustrated in Section II. It demonstrates how the algorithm can be deployed incrementally where the existing protocol (Reno in this case) needs no

change and only the new protocols (FAST in this case) need to adopt slow timescale adaptation for the whole network to converge to the unique equilibrium that maximizes (weighted) aggregate utility. Experiments in this section were conducted in ns-2.

We take Renos loss probability as the link price, i.e.,m1i(pi)=pi for Reno. Algorithm 1 then reduces to an adaptation scheme for FAST that uses only end-to-end local information that is available to each flow. This algorithm, displayed as Algorithm 2, tunes the value of according to the signals of queue delay and

loss on a large timescale. The basic idea is that FAST should adjust its aggressiveness (parameter ) to the proper level by looking at the ratio of end-to-end queueing delay and end-to-end loss. Therefore FAST also reacts to loss in a slow timescale.

Algorithm 2: - Adaptation Algorithm

1. Every update interval (2 min by default), calculate: *=(q/lw)0 0is the initial value; q and l are average queueing delay and average packet loss rate over the interval; w is a parameter with the same unit of q/l . It determines the relative fairness between delay-based and loss-based protocols. Then if

= { min{(1+), *)}, if < * { max{(1-), *)}, if >* if where determines the responsiveness and is 0.1 by default. 2. Every window update interval (20 ms by default), run.

Fig. 3. FAST versus Reno with buffer size 400 packets(a) a sample and (b) an average behaviour

Fig. 4. FAST versus Reno with buffer size 80 packets(a) a sample and (b) an average behaviour

A. Example 1b: Independence of Bandwidth Allocation onBuffer Size We repeat the simulations in Example 1a with Algorithm 2, with set to 125 s6..With Algorithm 2, FAST achieves 3.4 packets per


29

ms with buffer size of 400 and 3.2 packets per

ms with buffer size of 80, while Reno gets 4.2 and 4.1 packets per ms, respectively. The fairness is greatly improved and essentially independent of buffer size now. This is summarized in Table I by listing the ratio of Renos bandwidth to FASTs.We also note that the utilization of the link for increases significantly from 53.6% to 97.7%. The trajectories of with different buffer sizes are presented in Fig. 4. It is clear that although FAST starts with in both cases, it finally ends up with a much larger in the scenario where , as it experiences much higher equilibrium

queueing delay with the large buffer. B. Example 2b: Independence of Bandwidth Allocation on Flow Arrival Pattern We repeat the simulations in Example 2a with

Algorithm 2,with set to 1,820 s. Figs. 5 and 6 show the effect of adaptation in the multiple-bottleneck . Theorem 5 guarantees a unique equilibrium when we adapt according to Algorithm 2. In this particular case, this single equilibrium is around the point where each Reno flow gets a throughput of 0.6 packets

per ms and each FAST flow gets 1.5 packets per ms. At this single equilibrium, link 1 and link 3 are the bottleneck links. In Fig. 5, FAST flowsstart at time zero and link 2 becomes the bottleneck. When Reno flows join at the 100th second, the ratio of queue delay and loss at link 2 is much higher than the target value. The FAST flows hence reduce their values gradually and the set of bottleneck links switches from link 2 to links 1 and 3 around the 2000th second. After that, FAST flows and Reno flows converge to the unique equilibrium.

Fig.5.FAST starts first (a) a sample and (b) an

average behaviour

Fig.5.Reno starts first (a) a sample and (b) an average behaviour VII. CONCLUSION Congestion control has been extensively studied for networks running a single protocol. However, when sources sharing the same network react to different congestion signals, the existing duality model no longer explains the behavior of bandwidth allocation. The existence and uniqueness properties of equilibrium in heterogeneous protocol case are examined in [11]. In this paper, we study the nature of the network like fairness and uniqueness in the network and the withstanding capability of the network is analyzed along with optimality and stability properties. In particular, it is shown that equilibrium is still Pareto efficient, but

there is efficiency loss. On fairness, intraprotocol fairness is still determined by utility maximization problem, while interprotocol fairness is the part which we do not have control on. However, we can achieve any desired interprotocol fairness by properly choosing protocol parameters. Motivated by

the analytical results, we further propose a distributed scheme to steer the whole network to the unique optimal equilibrium. The scheme only needs to update a linear scalar in the source algorithm on a slow timescale. It can be deployed incrementally as

the existing protocol needs no change and only the new protocols need to adapt on the slow timescale. There are several interesting directions in this relatively open area. For example, more efforts are still needed to fully


30

clarify the global dynamics of the two

timescale system. The main technical difficulty here is that the fast timescale system may have multiple equilibria and therefore the usual two timescale argument (e.g., singular perturbation) is not applicable. Our current model assumes each protocol only reacts to one particular price on the fast timescale, even when they have access to multiple types of prices. Finally, the current results should be extended from static to dynamic setting where flows come and go .

REFERENCES [1] T. Bonald and L. Massouli, Impact of fairness on Internet performance,in Proc. ACM Sigmetrics, Jun. 2001, pp. 8291. [2] L. Brakmo and L. Peterson, TCP Vegas: End-to-end congestion avoidance on a global

Internet, IEEE J. Sel. Areas Commun., vol. 13, no. 6,pp. 146580, Oct. 1995. [3] S. Deb and R. Srikant, Rate-based versus queue-based models of congestion control, IEEE Trans. Autom. Control, vol. 51, no. 4, pp. 606618, Apr. 2006. [4] S. Floyd and V. Jacobson, Random early detection gateways for congestion avoidance, IEEE/ACM Trans. Netw., vol. 1, no. 4, pp. 397413, Aug. 1993. [5] R. Jain, A delay-based approach for congestion avoidance in interconnected heterogeneous computer networks, ACM Comput.Commun.Rev., vol. 19, no. 5, pp. 5671, Oct. 1989. [6] S. Kunniyur and R. Srikant, End-to-end congestion control: Utility functions, random losses and ECN marks, IEEE/ACM Trans. Netw., vol. 11, no. 5, pp. 689702, Oct. 2003.[22] S. Liu, T. Basar, and R. Srikant, TCP-Illinois: A loss and delay-based congestion control algorithm for high-speed

networks, in Proc. 1stVALUETOOLS, 2006, Article no. 55. [7] S. Low, A duality model of TCP and queue management algorithms, IEEE/ACM Trans. Netw., vol. 11, no. 4, pp. 525536, Aug. 2003. [8] J. Mo and J.Walrand, Fair end-to-end window-based congestion control,IEEE/ACM Trans. Netw., vol. 8, no. 5, pp. 556567, Oct. 2000. [9] K. Ramakrishnan, S. Floyd, and D. Black, The addition of explicit congestion notification (ECN) to IP, Internet Engineering Task Force, RFC 3168, 2001. [10] A. Tang, J. Wang, S. Hedge, and S. Low,

Equilibrium and fairness of networks shared by TCP Reno and Vegas/FAST, Telecommun. Syst., vol. 30, no. 4, pp. 417439, Dec. 2005. [11] A. Tang, J. Wang, S. Low, and M. Chiang, Equilibrium of heterogeneous congestion control: Existence and uniqueness, IEEE/ACMTrans. Netw., vol. 15, no. 4, pp. 824837, Aug. 2007. [12] V. Jacobson, Congestion avoidance and control, in Proc. ACM SIGCOMM,1988, pp. 314329. [13] WAN-in-Lab, [Online]. Available: http://wil.cs.caltech.edu [14] Z. Wang and J. Crowcroft, Eliminating periodic packet losses in the 4.3-Tahoe BSD TCP congestion control algorithm, ACM Comput.Commun. Rev., vol. 22, no. 2, pp. 916, Apr. 1992. [15] D. Wei, C. Jin, S. Low, and S. Hegde, FAST TCP: Motivation, architecture, algorithms, performance, IEEE/ACM Trans. Netw., vol. 14, no. 6, pp. 12461259, Dec. 2006. [16] B.Wydrowski, L. H. Andrew, and M. Zukerman, MaxNet: A congestion control architecture for scalable networks, IEEE Commun. Lett., vol. 7, no. 10, pp. 511513, 2003. [17] L. Xu, K. Harfoush, and I. Rhee, Binary increase congestion control for fast long-distance networks, in Proc. IEEE INFOCOM, 2004, vol. 4, pp. 25142524. [18] Highspeed Networks : William Stallings. Edition-4.Publication : pearson.


31

CONTENT BASED IMAGE RETRIEVAL ON MOBILE DEVICES BY NAVIGATION PATTERN

BASED RELEVANCE FEEDBACK

*V.Gomathi

PG.SCHOLAR, M.E .CSE, Prathyusha Institute of Technology and Management

Email:[email protected], 9941446176

Abstract Content-based image retrieval (CBIR) is the mainstay of image retrieval systems. Image retrieval over mobile devices is a challenging research problem. This paper presents client-server architecture whereas the client (Mobile) sends content-based query request to the server (PC) and the server performs an interactive content-based query and sends the query results to the client.The implementation of an advanced retrieval scheme is presented. The interactive query (IQ) is presented in mobile platforms can avoid un-wanted progressing query results and thus reduce the server query time and memory. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking users feedbacks into account. This proposed NPRF search (Navigation Pattern-based Relevance Feedback) algorithm, to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. This search algorithm makes use of the discovered navigation patterns and three kinds of query refinement strategies, QPM (Query-Point-Movement), QR (Query-Reweighting) and QEX (Query Expansion), to converge the search space towards the users intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. Keywords content; relevance feedback, Navigation pattern; Mobile; retrieval. I. INTRODUCTION The mobile phone industry is going through a phenomenal change over the past few years

with significant advances in the areas of communications and multimedia. Currently state-of-the-art multimedia compliant mobile phones equipped with digital cameras and camcorders have inherent support for network connection and thus, enable access to large amount of digital media. Nowadays, mobile platforms support Java [5] that provides rich programming APIs (Application Programming Interface). With the generation of digital media by capturing and storing facility in smart phones there is a need for content management and system to provide rapid retrieval of digital media items from large media archives. Therefore, it has become vital to retrieve desired information expeditiously and efficiently using these devices. Content-based image retrieval (CBIR) addresses the problem of accessing the images that bears some certain content and usually relies on the characterization of low-level features such as color, shape and texture, all of which can be extracted from the images. CBIR area possesses a tremendous potential for exploration and utilization equally for researchers and people in industry due to its promising results. It has been an active area of research for the past decade. The content based retrieval of a desired multimedia item is currently based upon indexing of the content by the extraction of low-level visual features based on shape, color and texture. Systems such as Multimedia Video Indexing and Retrieval System (MUVIS), [14], VisualSEEk [13], Photobook [12] and Virage have a framework designed for indexing and retrieving images and/or audio-video files. The contemporary MUVIS has been developed as a system for content-based multimedia retrieval on a PC-based environment. It provides a


32

unified and global framework that consists of

robust set of applications for capturing, recording, indexing and retrieval combined with browsing and various other audiovisual and semantic capabilities. On this purpose our research work targets to bring the MUVIS framework beyond the desktop environment into the realm of wireless devices such as mobile phones, Personal Digital Assistants (PDAs), communicators etc., where the user can perform query operations in large multimedia databases and query results can be retrieved within a reasonable time. Therefore, our main

goal is to design and develop a CBIR system that enables any (mobile) client supporting Java platform to retrieve images similar to the query image from an image database, which is accompanied by a dedicated server application. In general, the purpose of CBIR is to present

an image conceptually, with a set of low-level visual features such as colour, texture and shape. These conventional approaches for image retrieval are based on the computation of the similarity between the users query and images via a query-by-example system (QBE). System, the user can pick up some preferred

images to refine the image explorations iteratively. The feedback procedure, called Relevance Feedback (RF), repeats until the user is satisfied with the retrieval results. Although a number of RF studies have been made on interactive CBIR, they still incur some common problems, namely redundant browsing and exploration convergence. First, in terms of redundant browsing, most existing RF methods focus on how to earn the users satisfaction in one query process. That is, existing methods refine the query again and again by analysing the specific relevant images picked up by the users. Especially for the compound and complex images, the users might go through a long series of feedbacks to obtain the desired images using current RF approaches. The proposed approach NPRF integrates the discovered navigation patterns and three RF techniques to achieve efficient and effective images. The major difference between our proposed approach and other contemporary approaches is that it has approximated an optimal solution to resolve the problems existing in current RF, such as redundant browsing and exploration

convergence. This paper is organized as

follows: Section 2 gives a brief overview of the CBIR; Section 3 describes the basic architecture and several functionalities of M-MUVIS. Section 4 describes query techniques, Section 5 describes RF method basis on the NPRF search. Section 6 describes the Experimental results. Finally, the conclusion is section 7. II. CONTENT BASED IMAGE RETRIEVAL (CBIR) CONTENT-BASED IMAGE RETRIEVAL, KNOWN AS CBIR, EXTRACTS SEVERAL FEATURES THAT DESCRIBE

THE CONTENT OF THE IMAGE, MAPPING THE VISUAL CONTENT OF THE IMAGES INTO A NEW SPACE CALLED

THE FEATURE SPACE. THE FEATURE SPACE VALUES FOR A GIVEN IMAGE ARE STORED IN A DESCRIPTOR THAT

CAN BE USED FOR RETRIEVING SIMILAR IMAGES. TO ACHIEVE THESE GOALS, CBIR SYSTEMS USE THREE BASIC TYPES OF FEATURES: COLOUR

FEATURES, TEXTURE FEATURES AND SHAPE FEATURES. HIGH RETRIEVAL SCORES IN CONTENT-BASED IMAGE RETRIEVAL SYSTEMS CAN BE ATTAINED BY

ADOPTING RELEVANCE FEEDBACK MECHANISMS. THESE MECHANISMS REQUIRE THE USER TO GRADE THE

QUALITY OF THE QUERY RESULTS BY MARKING THE RETRIEVED IMAGES AS BEING EITHER RELEVANT OR

NOT. THEN, THE SEARCH ENGINE USES THIS GRADING INFORMATION IN SUBSEQUENT QUERIES TO BETTER

SATISFY USERS NEEDS. IT IS NOTED THAT WHILE RELEVANCE FEEDBACK MECHANISMS WERE FIRST

INTRODUCED IN THE INFORMATION RETRIEVAL FIELD, CURRENTLY RECEIVE CONSIDERABLE ATTENTION IN

THE CBIR FIELD. THIS PROJECT MAINLY FOCUSED ON EFFICIENT CONTENT BASED IMAGE RETRIEVAL ON

MOBILE DEVICE USING NAVIGATION PATTERN BASED

RELEVANCE FEEDBACK. THIS SECTION CONTAINS BASIC INFORMATION ON CBIR, AND DISCUSSES THE TECHNIQUES USED.

FOR THE DESIGN OF CONTENT-BASED RETRIEVAL SYSTEMS, A DESIGNER NEEDS TO CONSIDER FOUR

Fig 1: content based image retrieval


33

ASPECTS: FEATURE EXTRACTION AND

REPRESENTATION, DIMENSION REDUCTION OF FEATURE, INDEXING, AND QUERY SPECIFICATIONS, WHICH WILL BE SHOWN IN THE FIGURE 1. A. FEATURE EXTRACTION AND REPRESENTATION REPRESENTATION OF MEDIA NEEDS TO CONSIDER WHICH FEATURES ARE MOST USEFUL FOR

REPRESENTING THE CONTENTS OF MEDIA AND WHICH

APPROACHES CAN EFFECTIVELY CODE THE ATTRIBUTES

OF THE MEDIA. THE FEATURES ARE TYPICALLY EXTRACTED OFF-LINE SO THAT EFFICIENT

COMPUTATION IS NOT A SIGNIFICANT ISSUE, BUT LARGE COLLECTIONS STILL NEED A LONGER TIME TO

COMPUTE THE FEATURES. FEATURES OF MEDIA CONTENT CAN BE CLASSIFIED INTO LOW-LEVEL AND HIGH-LEVEL FEATURES. B. LOW-LEVEL FEATURES LOW-LEVEL FEATURES SUCH AS OBJECT MOTION, COLOR, SHAPE, TEXTURE, LOUDNESS, POWER SPECTRUM, BANDWIDTH, AND PITCH ARE EXTRACTED DIRECTLY FROM MEDIA IN THE DATABASE. FEATURES AT THIS LEVEL ARE OBJECTIVELY DERIVED FROM THE

MEDIA RATHER THAN REFERRING TO ANY EXTERNAL

SEMANTICS. FEATURES EXTRACTED AT THIS LEVEL CAN ANSWER QUERIES SUCH AS FINDING IMAGES WITH MORE THAN 20% DISTRIBUTION IN BLUE AND GREEN COLOR, WHICH MIGHT RETRIEVE SEVERAL IMAGES WITH BLUE SKY AND GREEN GRASS MANY EFFECTIVE

APPROACHES TO LOW-LEVEL FEATURE EXTRACTION HAVE BEEN DEVELOPED FOR VARIOUS PURPOSES. C. HIGH-LEVEL FEATURES HIGH-LEVEL FEATURES ARE ALSO CALLED SEMANTIC FEATURES. FEATURES SUCH AS TIMBRE, RHYTHM, INSTRUMENTS, AND EVENTS INVOLVE DIFFERENT DEGREES OF SEMANTICS CONTAINED IN THE MEDIA. HIGH-LEVEL FEATURES ARE SUPPOSED TO DEAL WITH SEMANTIC QUERIES (E.G., FINDING A PICTURE OF WATER OR SEARCHING FOR MONA LISA SMILE). THE LATTER QUERY CONTAINS HIGHER-DEGREE SEMANTICS THAN THE FORMER. AS WATER IN IMAGES DISPLAYS THE HOMOGENEOUS TEXTURE REPRESENTED

IN LOW-LEVEL FEATURES, SUCH A QUERY IS EASIER TO PROCESS. TO RETRIEVE THE LATTER QUERY, THE RETRIEVAL SYSTEM REQUIRES PRIOR KNOWLEDGE

THAT CAN IDENTIFY THAT MONA LISA IS A WOMAN, WHO IS A SPECIFIC CHARACTER RATHER THAN ANY

OTHER WOMAN IN A PAINTING. THE DIFFICULTY IN

PROCESSING HIGH-LEVEL QUERIES ARISES FROM

EXTERNAL KNOWLEDGE WITH THE DESCRIPTION OF

LOW-LEVEL FEATURES, KNOWN AS THE SEMANTIC GAP. THE RETRIEVAL PROCESS REQUIRES A TRANSLATION MECHANISM THAT CAN CONVERT THE QUERY OF

MONA LISA SMILE INTO LOW-LEVEL FEATURES. TWO POSSIBLE SOLUTIONS HAVE BEEN PROPOSED TO

MINIMIZE THE SEMANTIC GAP. THE FIRST IS AUTOMATIC METADATA GENERATION TO THE MEDIA. AUTOMATIC ANNOTATION STILL INVOLVES THE SEMANTIC CONCEPT AND REQUIRES DIFFERENT

SCHEMES FOR VARIOUS MEDIA. THE SECOND USES RELEVANCE FEEDBACK TO ALLOW THE RETRIEVAL

SYSTEM TO LEARN AND UNDERSTAND THE SEMANTIC

CONTEXT OF A QUERY OPERATION. D. DIMENSION REDUCTION OF FEATURE VECTOR

MANY MULTIMEDIA DATABASES CONTAIN LARGE NUMBERS OF FEATURES THAT ARE USED TO ANALYZE

AND QUERY THE DATABASE. SUCH A FEATURE-VECTOR SET IS CONSIDERED AS HIGH DIMENSIONALITY. HIGH DIMENSIONALITY CAUSES THE CURSE OF DIMENSION PROBLEM, WHERE THE COMPLEXITY AND COMPUTATIONAL COST OF THE QUERY INCREASES

EXPONENTIALLY WITH THE NUMBER OF DIMENSIONS. DIMENSION REDUCTION IS A POPULAR TECHNIQUE TO OVERCOME THIS PROBLEM AND SUPPORT EFFICIENT

RETRIEVAL IN LARGE-SCALE DATABASES. HOWEVER, THERE IS A TRADEOFF BETWEEN THE EFFICIENCY

OBTAINED THROUGH DIMENSION REDUCTION AND THE

COMPLETENESS OBTAINED THROUGH THE

INFORMATION EXTRACTED. IF EACH DATA IS REPRESENTED BY A SMALLER NUMBER OF DIMENSIONS, THE SPEED OF RETRIEVAL IS INCREASED. HOWEVER, SOME INFORMATION MAY BE LOST. ONE OF THE MOST WIDELY USED TECHNIQUES IN MULTIMEDIA RETRIEVAL

IS PRINCIPAL COMPONENT ANALYSIS (PCA). PCA IS USED TO TRANSFORM THE ORIGINAL DATA OF HIGH

DIMENSIONALITY INTO A NEW COORDINATE SYSTEM

WITH LOW DIMENSIONALITY BY FINDING DATA WITH

HIGH DISCRIMINATING POWER. THE NEW COORDINATE SYSTEM REMOVES THE REDUNDANT DATA AND THE

NEW SET OF DATA MAY BETTER REPRESENT THE

ESSENTIAL INFORMATION. E. INDEXING THE RETRIEVAL SYSTEM TYPICALLY CONTAINS TWO MECHANISMS: SIMILARITY MEASUREMENT AND MULTI-DIMENSIONAL INDEXING. SIMILARITY MEASUREMENT IS


34

USED TO FIND THE MOST SIMILAR OBJECTS. MULTI-

DIMENSIONAL INDEXING IS USED TO ACCELERATE THE

QUERY PERFORMANCE IN THE SEARCH PROCESS. F. SIMILARITY MEASUREMENT TO MEASURE THE SIMILARITY, THE GENERAL APPROACH IS TO REPRESENT THE DATA FEATURES AS

MULTI-DIMENSIONAL POINTS AND THEN TO CALCULATE THE DISTANCES BETWEEN THE CORRESPONDING

MULTI-DIMENSIONAL POINTS. SELECTION OF METRICS HAS A DIRECT IMPACT ON THE PERFORMANCE OF A

RETRIEVAL SYSTEM. EUCLIDEAN DISTANCE IS THE MOST COMMON METRIC USED TO MEASURE THE

DISTANCE BETWEEN TWO POINTS IN MULTI-DIMENSIONAL SPACE. HOWEVER, FOR SOME APPLICATIONS, EUCLIDEAN DISTANCE IS NOT COMPATIBLE WITH THE HUMAN PERCEIVED SIMILARITY. A NUMBER OF METRICS (E.G., MINKOWSKI-FORM DISTANCE, EARTH MOVERS DISTANCE, AND PROPORTIONAL TRANSPORTATION DISTANCE) HAVE

BEEN PROPOSED FOR SPECIFIC PURPOSES. G. MULTI-DIMENSIONAL INDEXING RETRIEVAL OF THE MEDIA IS USUALLY BASED NOT ONLY ON THE VALUE OF CERTAIN ATTRIBUTES, BUT ALSO ON THE LOCATION OF A FEATURE VECTOR IN THE

FEATURE SPACE. IN ADDITION, A RETRIEVAL QUERY ON A DATABASE OF MULTIMEDIA WITH MULTI-DIMENSIONAL FEATURE VECTORS USUALLY REQUIRES

FAST EXECUTION OF SEARCH OPERATIONS. TO SUPPORT SUCH SEARCH OPERATIONS, AN APPROPRIATE MULTI-DIMENSIONAL ACCESS METHOD HAS TO BE USED FOR INDEXING THE REDUCED BUT STILL HIGH

DIMENSIONAL FEATURE VECTORS. POPULAR MULTI-DIMENSIONAL INDEXING METHODS INCLUDE R-TREE AND R*-TREE. THESE MULTI-DIMENSIONAL INDEXING METHODS PERFORM WELL WITH A LIMIT OF

UP TO 20 DIMENSIONS. AN APPROACH TO TRANSFORM MUSIC INTO NUMERIC FORMS AND DEVELOPED AN

INDEX STRUCTURE BASED ON R-TREE FOR EFFECTIVE RETRIEVAL. H. QUERY SPECIFICATIONS QUERYING IS USED TO SEARCH FOR A SET OF RESULTS WITH SIMILAR CONTENT TO THE SPECIFIED

EXAMPLES. BASED ON THE TYPE OF MEDIA, QUERIES IN CONTENT-BASED RETRIEVAL SYSTEMS CAN BE DESIGNED FOR SEVERAL MODES (E.G., QUERY BY SKETCH, QUERY BY PAINTING [FOR VIDEO AND IMAGE], QUERY BY SINGING [FOR AUDIO], AND QUERY BY EXAMPLE).QUERIES IN MULTIMEDIA RETRIEVAL

SYSTEMS ARE TRADITIONALLY PERFORMED BY USING

AN EXAMPLE OR SERIES OF EXAMPLES. THE TASK OF THE SYSTEM IS TO DETERMINE WHICH CANDIDATES

ARE THE MOST SIMILAR TO THE GIVEN EXAMPLE. THIS DESIGN IS GENERALLY TERMED QUERY BY EXAMPLE (QBE) MODE. THE SUCCESS OF THE QUERY IN THIS APPROACH HEAVILY DEPENDS ON THE INITIAL SET OF

CANDIDATES. I. Relevance Feedback HIGH RETRIEVAL SCORES IN CONTENT-BASED IMAGE RETRIEVAL SYSTEMS CAN BE ATTAINED BY

ADOPTING RELEVANCE FEEDBACK MECHANISMS. THESE

MECHANISMS REQUIRE THE USER TO GRADE THE

QUALITY OF THE QUERY RESULTS BY MARKING THE RETRIEVED IMAGES AS BEING EITHER RELEVANT OR

NOT. THEN, THE SEARCH ENGINE USES THIS GRADING INFORMATION IN SUBSEQUENT QUERIES TO BETTER

SATISFY USERS' NEEDS. IT IS NOTED THAT WHILE RELEVANCE FEEDBACK MECHANISMS WERE FIRST

INTRODUCED IN THE INFORMATION RETRIEVAL FIELD, THEY CURRENTLY RECEIVE CONSIDERABLE ATTENTION

IN THE CBIR FIELD. III. M-MUVIS FRAME WORK our research work targets to bring the MUVIS framework beyond the desktop environment

into the realm of wireless devices such as mobile phones, Personal Digital Assistants (PDAs), communicators etc., where the user can perform query operations in large multimedia databases and query results can be retrieved within a reasonable time. Therefore, our main goal is to design and develop a CBIR system that enables any (mobile) client supporting Java platform to retrieve images similar to the query image from an image database, which is accompanied by a dedicated server application. The developed system, so called Mobile MUVIS (M-MUVIS), shown in Figure 2 is structured upon contemporary MUVIS framework and has client-server architecture. The M-MUVIS server basically comprises of two Java servlets [5] running inside a Tomcat [8] web server, which in effect transforms standalone MUVIS into a web application. The MUVIS Query Server (MQS) has native libraries for efficient image query related operations. The second servlet so called MUVIS Media Retrieval Server (MMRS) is used for the media retrieval. In order to take the advantage of flexibility and portability of Java, a M-MUVIS


35

client application has been developed by using

Java 2, Micro Edition (J2ME) [5]. Such a system can find its application in sharing or reuse of digital media, content management, networked photo album, shopping and travel.

IV. QUERY TECHNIQUES With growing image content, an efficient image retrieval technique is deemed required. Specially, for a mobile device user, performing the query can be annoying experience due to the large query processing time [2], [6]. It is therefore, vital to devise a method which not only reduces the query processing time but also performs the query operation without requiring a system equipped with high performance hardware such as fast processors and large memory. In this paper present an Interactive Query (IQ) [6] for a mobile device which achieves retrieval performance that may not require a superior performing system on the server side and reduce network bandwidth and processing power on the client side. Before IQ, M-MUVIS supported Normal Query (NQ) and Progressive Query (PQ) [2]. In NQ the query results were based on comparing similarity distances of all the images primitives present in the entire database and performing ranking operation afterwards. NQ is costly in terms of processing power and in case of abrupt stopping during the query processes the retrieved query information is lost. PQ generates the query results after a fix time interval. In large image database with small time interval PQ generates many results that consume lot of memory and the server processing power. The server sends the desired intermediate result (as selected by

client) to the client. Sending the intermediate

results to the client consume extra network bandwidth, RAM, processing power and battery power of the device. Whereas IQ provides an efficient retrieval without generating many intermediate query results in larger image database. V. NPRF SEARCH Despite the power of the search strategies, it is very difficult to optimize the retrieval quality of CBIR within only one query process. The hidden problem is that the extracted visual features are too diverse to capture the concept of the users query. To solve such problems, in the QBE system, the user can pick up some preferred images to refine the image explorations iteratively. The feedback procedure, called Relevance Feedback (RF), repeats until the user is satisfied with the retrieval results. Although a number of RF studies have been made on interactive CBIR,

they still incur some common problems, namely redundant browsing and exploration convergence. To resolve the aforementioned problems, we propose a novel method named NPRF (Navigation Pattern-Based Relevance Feedback) to achieve the high retrieval quality of CBIR with RF by using the discovered

navigation patterns. The proposed approach NPRF integrates the discovered navigation patterns and three RF techniques to achieve efficient and effective images. Query-Reweighting (QR): Some previous work keeps an eye on investigating

what visual features are important for those images (positive examples) picked up by the users at each feedback (also called iteration).For this kind of approach, no matter how the weighted or generalized distance function is adapted, the diverse visual features extremely limit the effort of image retrieval.

Figure 3 illustrates this limitation that, although the search area is continuously updated by re-weighting the features, some targets could be lost.

Fig 2: M-MUVIS framework


36

Query-Point-Movement (QPM):Another solution for enhancing the accuracy of image retrieval is moving the query point towards the contour of the users preference in feature space. QPM regards multiple positive examples as a new query point at each feedback. After several forceful changes of location and contour, the query point should be close to a convex region of the users interest. Query Expansion (QEX): If QR and QPM cannot completely cover the users interest spreading in the broad feature space, As a result, diverse results for the same concept are difficult to obtain. For this reason, the modified version MARS groups the similar relevant points into several clusters, and selects good representative points from these clusters to construct the multipoint query. Overview of NPRF (Navigation Pattern based Relevance Feedback) The task of the proposed approach shows various operations .As depicted in Figure 4, each operational phase contains some critical components for completing the specific process. The first query process is called initial feedback. Next the good examples picked up by the user deliver the valuable information to the image search phase, including new feature weights, new query-point and the users intention. Then, by using the navigation patterns, three search strategies, with respect

to QPM, QR and QEX are hybridized to find the desired images. Overall, at each feedback, the results are presented to the user and the related browsing information is stored in the log database. After accumulating long-term Users browsing behaviours, off-line operation for knowledge discovery is triggered to perform navigation pattern mining and pattern indexing. The frame work of the proposed approach is briefly described as follows:

Initial query processing Phase:

Without considering the feature-weight, this phase extracts the visual features from the original query image to find the similar images. Afterward, the good examples picked up by the user are further analyzed at the first feedback. Image search phase: Behind the

search phase, our intent is to extend the one search point to multiple search points by integrating the navigation patterns and propose algorithm NPRF search. In this phase, a new query point at each feedback is generated by the preceding positive examples,

and then the K nearest images to the new query point can be found by expanding the weighted query. The search procedure does not stop unless the user is satisfied with the retrieval results. Knowledge Discovery Phase: Learning from users behaviours in image retrieval can be viewed as one type of knowledge discovery. The navigation patterns from users behaviour support to predict optimal image browsing paths. Data Storage phase: The databases in this phase can be regarded as the knowledge marts of a knowledge warehouse, which store integrated, time variant and non-volatile collection of useful data including images, navigation patterns, log files, and image features, The knowledge warehouse in very helpful to improve the quality of image retrieval.

Fig 3: Query Refinement techniques.

Fig 4: Workflow of NPRF Search


37

Algorithm NPRF Search The NPRF Search algorithm In brief, the iterative search procedure can be decomposed into several steps: 1)Generate a new query point by averaging the visual features of positive examples,2) Find the top s relevant visual query points from the set of the nearest leaf nodes and 3) Finally, the top k relevant images are returned to the user. By collecting a large number of query transactions, most queries can be well answered for matching users interests by NPRF Search. The details of the NPRF Search algorithm are described as follows. The simplest algorithm for identifying a sample from the test set is called the Nearest Neighbor method. The object of interest is compared to every sample in the training set, using a distance measure, a similarity measure, or a combination of measures. This

process is computationally intensive and not very robust.We can make the Nearest Neighbor method more robust by selecting not just the closest sample in the training set,but by consideration of a group of close feature vectors. This is called the K-Nearest Neighbor method, where, for example K = 5. Then we

assign the unknown feature vector to the class that occurs most often in the set of K-Neighbors. This is still very computationally intensive, since we have to compare each unknown sample to every sample in the training set, and we want the training set as large as possible to maximize success. We can reduce this computational burden by using a method called Nearest Centroid. Here, we find the centroids for each class from the samples in the training set, and then we compare the unknown samples to the representative centroids only. The centroids are calculated by finding the average value for each vector component in the training set. K means Algorithm Step 1: Enter How Many Clusters (Let k). Step 2: Randomly Guess K Cluster center Locations. Step 3: Each Data point finds out which center its closest to. Step 4: Thus Each Center Owns Set of Points. Step 5: Each Center Finds the Centroid of its Own Points.

Step 6: Center now moves to the New

Centroid. Step 7: Repeat Step 3 to Step 6 Until Terminated. VI. EXPERIMENTAL RESULT Experimental Data The experimental data came from the collection of the corel image database and the web images. We prepared different kinds of datasets .Each category contains 200 images. All the experiments were implemented in JAVA, running on mobile (Java Enabled like Nokia N95, Nokia5800) and personal computer

with 3.5 GHz processor and 1G RAM. Retrieval Efficiency To analyze the effectiveness of our proposed approach, two major criterions, namely precision and coverage, are used to measure the experimental evaluations. They

are defined as: Precision = No. of relevant images *100 Total number of images retrieved Coverage = No. of correct images *100 Total number of images relevant Experimental Result

Fig 5: The query result shown on Nokia 5800


38

Fig 6: The resulting example for NPRF VII. CONCLUSION The dramatic rise in the sizes of images databases has stirred the development of effective and efficient retrieval systems. The development of these systems started with retrieving images using textual annotations but later introduced image retrieval based on content. This came to be known as CBIR or Content Based Image Retrieval. Systems using CBIR retrieve images based on visual features such as colour, texture and shape, as opposed to depending on image descriptions or textual indexing. In this paper researched various modes of representing and retrieving the image properties of colour, texture and shape.This system aims mainly at content based efficient image retrieval on mobile devices. That is client-server architecture where a server is running on a personal computer and a client on the device. The client sends content-based query request to the server and the server performs an interactive content-based query and sends the query results to the client. To be more profitable, NPRF search techniques were incorporated into CBIR such that more precise results can be obtained by taking users feedbacks into account. NPRF search can bring out more accurate results. REFERENCES [1] I. Ahmad, S. Kiranyaz and M. Gabbouj, An Efficient Image Retrieval Scheme on Java Enabled Mobile Devices, MMSP 05, International Workshop on Multimedia Signal Processing, Shanghai, China, November, 2005.

[2] I. Ahmad, S. Abdullah, S.Kiranyaz,

M.Gabbouj, Progressive query technique for image retrieval on mobile devices, CBMI, June 21-23, 2005, Riga, Latvia. [3] V. Chopra, Amit Bakore, Jon Eaves, Ben Galbraith, Sing Li, Chanoch Wiggers, Professional Apache Tomcat 5, published by Wrox, May, 2004, ISBN 0764559028. [4] H. M. Deitel, P. J. Deitel, Harvey M. Deitel, Paul J. Deitel, Java How to Program, 5th Edition, published by Prentice Hall, December 1999 . [5] J. Keogh, The Complete Reference J2ME, published by McGraw-Hill OSBORNE Edition. Feb 27, 2003. ISBN: 0072227109. [6] S. Kiranyaz, Moncef Gabbouj, "Hierarchical Cellular Tree: An Efficient Indexing Scheme for Content-based Retrieval on Multimedia Databases", IEEE Transactions on Multimedia, vol. 9, no. 1, January 2007, pp. 102-119 .

[7] Ahmad, S. Kiranyaz and M. Gabbouj, An Efficient Image Retrieval Scheme on Java Enabled Mobile Devices, MMSP 05, International Workshop on Multimedia Signal Processing, Shanghai, China, 2005.

[8] Moncef Gabbouj, Esin Guldogan, Mari PartioBirinci, Ahmad Iftikhar-An Extended Framework Structure in MUVIS for Content-based Multimedia Indexing and Retrieval IEEE 2007. [9] Moncef Gabbouj, Iftikhar Ahmad, Malik Yasir Amin and Serkan, Content-based Image Retrieval for Connected Mobile Devices IEEE2003. [10] Venkat N Gudivada Relevance Feedback in Content-Based Image RetrievalMarshall University, Huntington, IEEE 2000. [11] Sagarmay Deb Yanchun Zhang An Overview of Content-based Image Retrieval Techniques School of Computer Science and Mathematics, Australia. [12] Facebook, http://www.facebook.com/ [13] Flickr, http://www.flickr.com/ [14] MUVIS, http://muvis.cs.tut.fi/ [15] Nokia, http://www.nokia.com/


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REVERSE NEAREST NEIGHBOR FOR ANONYMOUS QUERIES

*A.Anna Arasu **R.Nakeeran

*PG Student in Department of Computer Science and Engineering,

Dr.Pauls Engineering College, Anna University,

Vanur, Tamilnadu, India

[email protected]

**Professor in Department of Computer Science and Engineering,

Dr.Pauls Engineering College, Anna University,

Vanur, Tamilnadu, India

[email protected]

Abstract In this paper we propose an algorithm for answering reverse nearest neighbor (RNN) for anonymous queries. The class of queries is strongly related to that of nearest neighbor queries, although the two are not necessarily complementary. The increasing availability of location-aware mobile devices has given rise to a flurry of location-based services (LBSs). On the other hand, revealing exact user locations to (potentially untrusted) LBS may pinpoint their identities and breach their privacy. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. We design location obfuscation techniques that: 1) provide anonymous LBS access to the users and 2) allow efficient query processing at the LBS side. Our methods are experimentally evaluated with real and synthetic data.

Keywords Location based service, Reverse nearest neighbor, Anonymous query. INTRODUCTION

The past decade has seen the assimilation of sensor networks and location-based systems in real world applications such as enhanced 911 services, army strategic planning, retail services, and mixed-reality games. The continuous1 movement of data objects within these applications

calls for new query processing techniques that scale up with the high rates of location updates. While numerous works have addressed continuous range queries (e.g., see [1], [2], [4],) and continuous nearest neighbor queries there is still a lack of research in addressing the

continuous reverse nearest neighbor (RNN) queries. We are currently experiencing rapid developments in key technology areas that combine to promise widespread use of mobile, personal information appliances, most of which will be on-line, i.e., on the Internet. Industry analysts uniformly predict that wireless, mobile

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