IJITCE September 2012

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.2 NO.9 SEPTEMBER 2012


UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: [email protected] Phone: +44-773-043-0249

USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626

India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 17/14 Ganapathy Nagar 2nd Street Ekkattuthangal Chennai -600032 Mobile: 91-7598208700

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IJITCE PUBLICATION

INTERNATIONAL JOURNAL OF INNOVATIVE

TECHNOLOGY & CREATIVE ENGINEERING

Vol.2 No.9

September 2012

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From Editor's Desk

Dear Researcher, Greetings! Research article in this issue discusses about Data Mining, Inverter, and Optimal path. Let us review research around the world this month; Portable solar panel for your back pack is a technological innovation. The Fuse 10W made by Voltaic Systems is a three panel 3.4 watt light-weight solar charger; it is water proof and adaptable. You are now able to charge your electronic devices while on the go, including your laptop, simply by buckling it around your back pack or anything that it can fit. Spending one hour in the sunlight powers up a 30 minute run time for your laptop and with its 60 Watt hours. You want to eat healthy and live in an environment that is going to do you no harm. Yet, how do you really know if the food on your plate is truly organic, or what types of toxicities are in the air around you? One of electronic company has made appcessories for your phone With a stainless steel medical grade probe, you are able to test the levels of nitrates in raw foods and drinking water to locate if there are any synthetic fertilizers which will ensure that what is going in your mouth is the organic food you chose to eat without being tricked. Mercedes-Benz invisible to the environment LED car. In 2014, you could have the opportunity to rev up the engines of a Mercedes-Benz car that will have a 386-kilometre range and zero emissions, making it an invisible car to the environment. Using the idea they decided to market their vehicle by adding LED light’s onto the car and putting a camera inside the car to pick up the environment around it to display what a zero emission car would do to the environment, it would act like the LED car looks, invisible, as if it blends in with the environment instead of hurting it. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technology-related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE


Editorial Members

Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at Shangai Jiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin, Faculty of Agriculture and Horticulture, Asternplatz 2a, D-12203 Berlin, Germany Dr. Marco L. Bianchini Ph.D Italian National Research Council; IBAF-CNR, Via Salaria km 29.300, 00015 Monterotondo Scalo (RM), Italy Dr. Nijad Kabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh, Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University, No. 303, University Road, Puli Town, Nantou County 54561, Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Mr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP Project Manager - Software, Applied Materials, 1a park lane, cranford, UK Dr. Bulent Acma Ph.D Anadolu University, Department of Economics, Unit of Southeastern Anatolia Project(GAP), 26470 Eskisehir, TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602, USA.

Review Board Members

Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. Mutamed Turki Nayef Khatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), Tul Karm, PALESTINE.


Dr.P.Uma Maheswari Prof & Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India.

Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar) ,01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar), 01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Javad Robati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran Vinesh Sukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. Rostislav Choteborský, Ph.D. Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg.,Hampton University,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A., M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. Rostislav Chot•borský,ph.d, Katedra materiálu a strojírenské technologie, Technická fakulta,•eská zem•d•lská univerzita v Praze,Kamýcká 129, Praha 6, 165 21

Dr. Amala VijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE


Naik Nitin Ashokrao B.sc,M.Sc Lecturer in Yeshwant Mahavidyalaya Nanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MEN GG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed . Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,Mechanical Engineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY Seraphin Chally Abou Professor,Mechanical & Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 Ordean Court,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar) 01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462


Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India.

Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India Prema Selvaraj Bsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., P GDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),Universiti Sains Malaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, Prannath Parnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India


Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. Seraphin Chally Abou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol "Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,Giani Zail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Juki• Vice Dean for education,Virovitica College,Matije Gupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677


Contents Predictive Data Mining in KPP by Anupam Bhatia, Dr. R.K. Chauha ……………………......…………...…………[1]

IMPLEMENTATION OF A LOW COST PWM SINGLE PHASE INVER TER USING AN IRF3205 HEXFET POWER MOSFET By Omokere E. S and Nwokoye A. O. C …………………… ......……….............................…...…………[6] A Novel Approach to Find an Optimal Path in MANET U sing Reactive Routing Protocol by Bhabani Sankar Gouda, Chandan Kumar Behera ……………………......………...... ..................................................…...…………[12]


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Predictive Data Mining in KPP

Anupam Bhatia #1, Dr. R.K. Chauhan *2 # Assistant Professor, Kurukshetra University Post Graduate Regional Centre, Jind (India)

1 [email protected] * Professor, Department of Computer Science and Applications, Kurukshetra University, Kurukshetra (India)

2 [email protected] Abstract— In this paper, we have provided the Genetic Algorithm (GA) used for prediction process in Knowledge Penetration Process (KPP). The said GA is implemented and its efficiency is analyzed.

Keywords: Knowledge Penetration Process, Predictive Data Mining, Prediction, Genetic Algorithm

I. INTRODUCTION It is the step of Mining phase of KPP that performs

inference on the current data in order to make predictions. In our research, this step is referred as Prediction.

II. GENETIC ALGORITHM FOR PREDICTION There are more reasons for preference using genetic programming and genetic algorithms in general in contrast to other techniques. One of them is their robustness and ability to work on large and “noisy” datasets, they perform global search of the solution space in comparison to most other algorithms that use greedy search, coping well with attribute interaction. Owing to all possible modifications and parallel approaches to genetic algorithms, the scalability of these algorithms can be achieved. Beside robustness, this characteristic is of great importance in data mining. Moreover, these algorithms have high degree of autonomy that enables discovery of knowledge previously unknown by the user. The problem of comprehensibility of discovered rules can be addressed by properly adjusting the fitness function.

1. Start 2. Initialize the Population 3. Initialize the program size 4. Define the fitness fi of an individual

program corresponds to the number of hits and is evaluated by the formula:

zhat = predict(z, se.fit=TRUE) zupper =zhat$fit + 1.96 * zhat$se.fit zlower = zhat$fit - 1.96 * zhat$se.fit yupper = exp(zupper)/(1 + exp(zupper)) ylower = exp(zlower)/(1 + exp(zlower)) 5. Run a tournament to compare four programs

randomly out of the population of programs

6. Compare them and pick two winners and two losers based on fitness

7. a) Copy the two winners and replace the losers b) With Crossover frequency, crossover the

copies of the winners c) With Mutation frequency, mutate the one of the programs resulting from performing step 7(a) d) With Mutation frequency, mutate the other of the programs resulting from performing step 7(a)

Repeat through step 5 till termination criteria are matched.

III. IMPLEMTATION

All paragraphs must be indented. All paragraphs must be justified, i.e. both left-justified and right-justified.

A. Data Set

The National Stock Exchange is a stock exchange

located at Mumbai, Maharashtra, India. It is the 16th largest stock exchange in the world by market capitalization and largest in India by daily turnover and number of trades, for both equities and derivative trading. The subset of data set of NSE for the financial year 2010-11 is used for implementation.

B. Data Mining Tools Used

For the preprocessing steps Notitia Data Preparation Software is used. For the processing of Data Mining steps Discipulus Software is used.


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C. Implementation Original data set is available in Microsoft Excel Workbook. Each worksheet of the workbook contains 50 days data of NIFTY. Each worksheet is imported as a distinct data set for the Data Mining processing purpose. First the data is imported from selected worksheet. In the Raw Data tab Cleaning Process is performed by filling up the missing values or excluding the missing value columns and handling the outliers. Columns not required for specific mining process are also excluded. As soon as the data is cleaned the included columns are checked for transformation purpose. In our research, we have used two different columns as output. One is named “out” for prediction purpose and other is “out1” for classification purpose. In each processing step output column is selected and transformed accordingly

In prediction, it is predicted on the basis of 50 days data whether on the 51st day the value of share will increase or decrease. If it will increase, it is labeled “1” otherwise it is labeled “0”. Data Split It is essential to split the data set for training and test purpose. We have split our data set in three parts

1. Training Data Set:- Training Data Set is subset of data used for training purpose.

2. Validation Data Set:- Validation Data Set is another subset of data used for validation purpose of training. It is simply a test set which is mutually exclusive to training data set.

3. Applied Data Set:- Applied Data Set is mutually exclusive to training and validation data set. It is either subset of data set used for training and validation or new data set with same attributes

For the prediction, we have implemented Genetic Algorithm as stated in Section 2. The crieteria of termination is; Generation Without Improvement = 700 Project Termination after number of runs = 100 To check the quality of Prediction output, ROC charts are generated and interpreted.

In ROC charts, Area Under Curve is taken as quality measure.

Data Set 1

In data set 1 data is split in three equal size disjoint data sets. First part is used as Training Data Set. It is one third part of Data Set 1. In rest of all the computations, same data set and its results are used as training data set as intermediate results, auxiliary data, auxiliary statistics and auxiliary tuple. Second part is used as Validation Data Set. In rest of all the computations, same data set and its results are used as validation data set as intermediate results, auxiliary data, auxiliary statistics and auxiliary tuple. Third part is used as Applied Data Set. It is one third part of data set 1 All splitted data sets i.e. training, validation and applied are disjoint to each other.

ROC Curve

Random Guessing Line

Fig 1: ROC Chart for Training Data Set The ROC Chart as shown in Fig. 1 is generated with Training Data Set. Area Under Curve is found 1.


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It is a well known fact that in ROC Chart, the closer the area is to 0.5, the less accurate the corresponding model is. A model with perfect accuracy have an area under curve is 1.0. From the data of highly volatile market of National Stock Exchange, India; candidate data set with perfect training accuracy is significant, motivational compatible and outstanding.

ROC Curve


Fig 2: ROC Chart for Validation Data Set The ROC Chart as shown in Fig.2 is generated with Validation Data Set.

Area Under Curve is found to be 1. It is a well known fact that in ROC Chart, the closer the area is to 0.5, the less accurate the corresponding model is. A model with perfect accuracy have an area under curve is 1.0. From the data of highly volatile market of National Stock Exchange, India; validation of candidate data set with perfect accuracy is significant, motivational compatible and outstanding.

ROC Curve


Fig 3: ROC Chart for Applied Data Set1 The ROC Chart as shown in Fig.3 is generated with Applied Data Set1. Area Under Curve is found 0.9743 which is very close to 1. It is a well known fact that in ROC Chart, the closer the area is to 0.5, the less accurate the corresponding model is. A model with perfect accuracy have an area under curve is 1.0. From the data of highly volatile market of National Stock Exchange, India; candidate data set with almost perfect applied accuracy is significant, motivational compatible, outstanding and satisfy user interestingness. Data Set 2 In data set 2, training and validation data from data set 1 is used. Whole data set 3 is used as applied data set.


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ROC Curve


Fig 4: ROC Chart for Applied Data Set 2 The ROC Chart as shown in Fig. 4 is generated with Applied Data Set 2. Area Under Curve is found 0.9109 which is close to 1. It is a well known fact that in ROC Chart, the closer the area is to 0.5, the less accurate the corresponding model is. A model with perfect accuracy have an area under curve is 1.0. From the data of highly volatile market of National Stock Exchange, India; candidate data set with almost perfect applied accuracy is significant, motivational compatible, outstanding and satisfy user interestingness.

Data Set 3

In data set 3, training and validation data from data set 1 is used. Whole data set 3 is used as applied data set.

ROC Curve


Fig 5: ROC Chart for Applied Data Set3

The ROC Chart as shown in Fig. 5 is generated with Applied Data Set3. Area Under Curve is found 0.95 which is very close to 1. It is a well known fact that in ROC Chart, the closer the area is to 0.5, the less accurate the corresponding model is. A model with perfect accuracy have an area under curve is 1.0.

From the data of highly volatile market of National Stock Exchange, India; candidate data set with almost perfect applied accuracy is significant, motivational compatible, outstanding and satisfy user interestingness]

IV. CONCLUSION

In our research, we have worked on the improvement of existing KDD process. For the purpose a specific model named as Knowledge Penetration Process (KPP) is designed and explained. KPP is a five step model consists of phases Problem Analysis, Preprocessing, Pre Mining, Mining and Evaluation. To improve the quality of knowledge, Genetic Algorithms are used in the core mining process of KPP. To


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improve the runtime, intermediate results, auxiliary data, auxiliary tuples and auxiliary statistics are used. To check the efficiency of the model, various processes are implemented by using the data set of National Stock Exchange of India. KPP model is implemented three times on different data sets. In the Evaluation phase of KPP, the quality of results is measured. The quality of Prediction is measured with ROC Curve. Training and Validation are repeated until 100% accuracy is achieved in Prediction. From the data of highly volatile market of National Stock Exchange, India; candidate data set with almost perfect applied accuracy is significant, motivational compatible, outstanding and satisfy user interestingness.

REFERENCES

[1] Lin, Li. Cao, Longbing. et. al, The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation, Capital Market CRC, Sydney NSW, Australia , 2000

[2] Chauhan R.K., Bhatia Anupam, KPP : A Step Ahead to KDD, RIMT Journal of Strategic Management and Information Technology , pp. 174-177, 2008

[3] Weber, Ben G. Mateas, Michael (2009). “A Data Mining Approach to Strategy Prediction” 978-1-4244-4815 2009 IEEE.

[4] Bhatia Anupam, Chauhan R.K., Knowledge Penetration Process, A Splitted KDD, Global Journal of Computer Science and Technology, USA, 2011

[5] Olaniyi, S Abdulsalam. Kayode, S.,(2011). “Stock Trend Prediction Using Regression Analysis – A Data Mining Approach”. ARPN Journal of Systems and Software, Volume 1 No. 4.

[6] http://www.nseindia.com/content/indices/ind_niftylist.csv

[7] www.rmltech.com


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IMPLEMENTATION OF A LOW COST PWM SINGLE PHASE INVERTER USING AN

IRF3205 HEXFET POWER MOSFET Omokere E. S1 and Nwokoye A. O. C2

Department of Physics and Industrial Physics Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. 1 [email protected]

2 [email protected]

Abstract— This work presents the development of a low cost but effective inverter using simple components . DC to AC inversion is successfully achieved without th e use of complex circuitry for the design which is essent ially focused upon low power electrical appliances such a s personal computers, disc players and television set s. It has a feedback loop which regulates its output volt age irrespective of connected loads. The control circui t consists of a dedicated pulse width modulation (PWM ) IC for triggering the MOSFETs arranged in a bridge configuration. The inverter output is regulated fro m 220Volts to 240Volts at 50Hz for a variation of tes ted load rated between 40W to 365W. Keywords: Inverter, Pulse Width Modulation (PWM), Metal Oxide Semiconductor Field Effect Transistor (MOSFET), Control Feedback, Bridge Circuit.

I. INTRODUCTION With the increasing popularity of alternative power sources, such as solar and wind, the need for inverters which is usually a necessary interface to convert low dc to conventional high ac form has increased substantially. This conversion can be achieved by power transistors. There is a growing interest in development of cheaper reliable inverter systems. Pulse width modulation (PWM) is a technique that is gradually taking over the inverter market of control application. The technique combines both voltage and frequency control [1],[2]. The PWM circuit outputs a chain of constant amplitude pulses in which the pulse duration is modulated to obtain necessary specific waveform on constant output periods. [3]. Here, the controlled output voltage is easily obtained by switching the switches on and off within a cycle to generate output which is usually low in harmonic contents [1],[3]. Nowadays, there are dedicated PWM ICs that can perform pulse control of inverter switches thereby simplifying hardware with reduced components and improves the performance of inverter system

implementation. Flexibility in control and cost effectiveness is a major advantage of a dedicated PWM IC system.

II. GENERAL DESCRIPTION OF THE DESIGN

The design is unique because it is implemented through a low cost PWM IC (SG3525A) as its controller and engages MOSFETs as the preferred power switches. The operating frequency supported by the device is approximately 50hz at its output. PWM signals can be generated using this controller. voltage regulation is an important features of the inverter that requires a feedback loop for monitoring the output voltage, and MOSFET switches is most preferred for such low voltage application [3],[4]. Our designed system parameters are as given:

Power rating 500W max

Output voltage ranges from 220V – 240V

Peak output current 2.27A

Inverter switching frequency is approximately 50Hz

Maximum load powered (tested) 365W

III. THEORETICAL BACKGROUND

PWM

In conventional pulse width modulation (PWM) control strategy a sinusoidal modulating signal is compared with a repetitive switching frequency triangular or saw tooth waveform as a carrier to generate the switching pulses [2],[3],[5]. The result of this comparison is used to operate inverter switches ON and OFF. A generalised sinusoidal wave through pulse width modulation (PWM) of inverter switching is as shown in figure 1 [9].


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As is seen in figure 1, transistor device pair Q1Q3 is turned ON when the modulating voltage exceeds the carrier wave amplitude, whereas Q2Q4 is ON when the carrier amplitude is higher. The resulting pulse width modulation output voltage and its fundamental component are shown in the lower part of figure 1.

Dedicated PWM ICs are available that could convert DC level into pulses used to control inverter switches through pulse width modulation. The SG3525A is one of

such.

Figure 1: Production of sinusoidal wave through pulse width

modulation of Inverter switching

IV. INVERTER APPROACH

O S C IL L A TO R

C O N TR O L L E R

IN V E R TE R

S W ITC H E S

TR A N S F O R M E R

D C

D C

L O W

A C

E L E C TR IC A L

L O A D

H IG H

V O L TA G E

A C

IN P U T

O U TP U T

Figure 2: Inverter Block diagram

The block diagram of the inverter circuit is shown in figure 2. The operation starts by taking the 12V DC from the input supply. The 12V DC is given directly to the oscillator controller and the inverter switches which then produces low voltage AC signals. This AC signal is then step up to high voltage AC capable of powering electrical loads by the transformer. Emphasis is given only to the controller and inverter switches sections in our design consideration.

D. Control Circuit

The control circuit of the design consists of the SG3525 PWM IC as the chosen controller IC. The control circuit is used to produce the PWM pulses and the pulses that obtained from the controller is provided to the inverter switching circuit such that the inverter MOSFETs gates can be triggered ON and OFF [10].

U

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+ ve

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VOLTAGE

Output B

Output A

Figure 3: Integrated Circuit (IC) Pin connections of IC 3525A

With a built in totem pole drivers at pin 11 and 14 outputs, the PWM IC can be configured with the MOSFET switches directly, thereby driving the MOSFET gate to turn ON and OFF [6]. The low source impedance of the output drivers provides rapid charging of the MOSFETs, this minimizes the need for external components (MOSFET drivers) as is found in other transistor switch implementations [7].


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1.) Control Feedback: The PWM IC can be connected with a feedback as shown in figure 4 from the inverter AC outputs in order to regulate the output AC voltage to the desired value. The inverter output at the transformer is tapped from 2 points and connected to a bridge rectifier setup which rectifies the AC voltage to a voltage that is proportional to the inverter output and ripples are removed by using capacitor. This voltage signal is supplied to pin 1 of the IC (inverting input of the internal error amplifier inside the PWM IC) through R1, R2, VR1 and this voltage is compared with the internal reference voltage regulator. The regulator uses reference voltage of 5.1V [6]. The error amplifier output provides input for the PWM comparator whose output determines the pulse width. This error voltage is proportional to the variation of the output voltage from the desired value and the IC adjusts the duty cycle of the drive signals at pin 14 and pin 11 in order to bring the output voltage to the desired voltage value. VR1 is used for adjusting the inverter output voltage to 240V as it directly control the amount of voltage fed back from the transformer output to the error amplifier section inside the IC. If the output voltage decreases, the DC voltage also decreases and is fed back to the controller. The feedback control system is applied so that whenever DC voltage decreases or increases, duty cycle of PWM varies to regulate the output voltage.

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Figure 4: Control feedback circuit

E. Inverter Switch Circuit The Inverter switch circuit consists of MOSFETs

arranged in a bridge circuit as shown in figure 5. The circuit contains IRF3205 advanced MOSFET as a power switches. MOSFET is used as a switch due to its low

voltage applications [3],[4],[9]. Depending on the voltage of the output required at the output, the gates of the MOSFETs are triggered with proper pulse sequence. The gate pulses are obtained from the control circuit.

V

Q3Q4

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Figure 5: Single phase full bridge inverter

Inverters are supposed to perform the DC to AC conversion very efficiently so that very little energy is wasted in the form of heat as it is being drawn from the battery to be converted into AC mains voltage. We employed full bridge configuration of our inverter switches which generates the highest output power amongst all converter types. The DC from the battery is converted into AC by using a pair of power MOSFETs Q1,Q3 and Q2Q4 which are acting as very efficient electronic switches. By switching the pair Q1,Q3, the battery current is made to flow through the primary and to ground through pair Q1,Q3. Equally by switching on pair Q2Q4 instead, the current is made to flow the opposite way through the primary and to ground. Therefore switching the two pairs of MOSFETs ON alternatively, the current is made to flow first in one direction of the primary and then in the other direction, thus producing an alternating magnetic flux in the transformer’s core. As a result a corresponding AC voltage is induced in the transformer’s secondary winding [10].

V. V. SELECTING COMPONENTS

In selecting our components, a number of considerations were made with respect to controller frequency and power MOSFET. Also the peak current and power dissipation are taken into consideration. The major components used are given:

SG3525A PWM IC, MOSFET IRF3205, Voltage regulator IC7812 and Output transformer 500W.


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A. Controller Frequency

Timing components RT AND CT, determine the inverter

frequency and maximum duty cycle of the switches. The selection of these component values is a critical consideration. The values of RT

AND CT helps to

determine the output frequency for the inverter which is represented by [6]:

FOSC ----1

RT is a timing resistor connected to pin 6 of the controller

CT is a timing capacitor connected to pin 5 of the controller.

B. MOSFET

The MOSFET utilized in this work is an IRF3205 HEXFET power MOSFET which has an ultra low ON resistance with fast switching speed. It has a rugged device design and is suitable for low profile application. With a number of characteristics [8]:

Gate threshold VGS(th) = 2V min and 4Vmax

Drain to Source ON resistance RDS(on) = 8mΩ

Drain current ID = 75A

VDSS = 55V.

The RDS(on) for the MOSFET is an important parameter in choosing it because the lower this resistance of a transistor switch is, the less power they are going to dissipate when switching [4]. The peak current and power dissipation are other important parameters when selecting switching transistors for practical applications

For an inverter, the maximum average current will occur at minimum input voltage

I= ---- 2

The power dissipated by the MOSFETs during conduction is as given by [4] and expressed as in equation (3)

P= d RDS(on) ----3

VI. TESTING AND DISCUSSION

Testing was carried out on our inverter using various loads of varying power ratings their operation and various voltage output values of the device were observed. Our testing loads included commonly available electrical appliances such as television set, ceiling fan, audio amplifier, personal computer and light bulbs of different wattage. These devices were operated individually from inverter output and also combined in turn. They were observed to work optimally. A summary of our test loads and results are shown in Tables I and II.

TABLE I: TEST LOADS AND THEIR POWER CONSUMPTION USED FOR TESTING THE INVERTER

Test Appliance Power (Watt, W)

14”TV set 43

Audio amps + 2 speakers 175

Personal Computer 65

Light Bulbs 100 200

A comparison of the output voltage and connected

load rating as gotten from our test results in Table II is shown graphically in figure 6.

TABLE II: CONNECTED LOAD INVERTER OUTPUT READINGS

Test Appliance Load rating

(Watts)

Inverter Voltage output (Volts)

14” TV 43 230

PC 65 230

Electric bulb 100 233

14”TV + 100W bulb 143 231

Electric bulb + PC 165 231

Electric bulb 200 236

14”+200W bulb 243 236

100W bulb + PC 265 235

300W bulbs (100+200)W 300 234

300W bulbs + 14”TV 343 225

300W bulbs + PC 365 225


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Figure 6: Voltage against Load observation of the inverter

During performance evaluation carried out on the device, testing was made using a 12V DC battery as DC input source to power various loads. Figure 6 shows a plot of our output voltage and the tested load ratings. It can be seen from figure 6 that at different connected load ratings, the inverter is able to sustain an AC output voltage within the range of 220V – 240V which is equivalent to AC mains voltage value.

VII. CONCLUSION

The design and implementation of a low cost single-phase inverter that produces an ac output voltage of desired magnitude and frequency was achieved. Due to the use of the H-bridge connection of the IRF3205 MOSFETs the inverter showed good performance when tested with common loads. Moreover the use of PWM IC allowed us to achieve simple control of the MOSFET to implement the DC-AC stage and to provide an AC output. We utilized a DC battery as its input voltage source during testing. The inverter output is regulated from 220Volts to 240Volts at 50Hz for a variation of tested load rated between 40W to 365W. Finally, the circuit during testing performed optimally and in the manner desired.


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REFERENCES

[1] Ismail B., Taib S., Isa M., Daut I., Mohd Saad A. R., Fauzy F., Microcontroller Implementation of Single Phase Inverter Switching Strategy, International Conference on Control, Instrumentation and Mechatronics Engineering (CIM ‘07), pp 104 – 107, 2007.

[2] Qazalbash A. A, Amin A., Manan A., and Mahveen K., Design and Implementation of Microcontroller based PWM Technique for Sine wave Inverter, International Conference on Power Engineering, Energy and Electrical Drives, pp. 163-167, 2009.

[3] Vodovozov V., Introduction to Power Electronics, Ventus Publishing, London, UK, 2010.

[4] Mohan N. First Course on Power Electronics and Drives, Mnpere, Minnaepolis, 2003.

[5] Aloquili O. A., Ghaeb J. A. and Khawaldeh A., Modulation Technique Using Boundary Pulse-Width for a Single-Phase Power inverter, Research Journal of Applied Sciences, Engineering and Technology, 2(6), pp. 532-542, 2010.

[6] STMicroelectronics http://www.st.com/internet/com/TECHNICAL_RESOURCES/TECHNICAL_LITERATURE/DATASHEET/CD00000958.PDF accessed March 2, 2012.

[7] Islam S. M. M. and Sharif G. M, Microcontroller based Sinusoidal PWM Inverter for Photovoltaic Application, 1st International Conference on Development in Renewable Energy Technology (ICDRET), pp 1-4, 2009.

[8] International Rectifier, http://www.irf.com/product-info/datasheet/data/irf3205.pdf, accessed May 7, 2012.

[9] Bose B. K., Power Electronics and Motor Drives, Academic Press, Burlington, MA, USA, 2006.

[10] Omokere E. S. and Nwokoye A. O. C., Evaluating the Performance of a Single Phase PWM Inverter using 3525A PWM IC, International Journal of Engineering Research and Technology, 1(4), pp 1-4, 2012.


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A Novel Approach to Find an Optimal Path in MANET

Using Reactive Routing Protocol Bhabani Sankar Gouda1, Chandan Kumar Behera2

Department of Computer Science & engineering National Institute of Science and Technology, Berhampur, Odisha, India

[email protected] [email protected]

Abstract- If there is no communication between the nodes in MANET, reactive protocols don’t preserve routing information in the network node level. Reactive pro tocol determines a route to a specific destination when a particular packet intends to send. We propose a rev erse reactive routing based Route discovery approach, wh ich is used to find an optimal route to the destination with lower overhead, than the flooding based reverse rou te discovery. We also showed that the process of the r everse reactive protocol performance for finding an optima l path to the destination. The discussion is based on the optimal path, which is obtained through three steps; those are reverse route calculation in route request (RREQ), reverse route calculation in route reply (RREP) and reverse route calculation in route error (RERR). Experiments have been carried out using NS2 as network simulator and resu lts show that performs better than reactive routing pro tocol (AODV).

Keywords: Mobile Ad-Hoc Networks, Reactive Routing Protocol, Route Discovery, Ns2, RAODV

I. INTRODUCTION

1.1 Mobile ad-hoc network

In the next generation of wireless communication systems, there will be a drastic need for the rapid deployment of independent mobile users for rescue operations, disaster relief, and military operations. Such type of network scenarios can not rely on centralized connectivity, and can be conceived as applications on Mobile Ad Hoc Networks. The design of network protocols for these networks is really a complex issue. Regardless of the application, MANETs need efficient distributed algorithms to determine network organization, link scheduling and routing. However, determining variable routing paths and delivering messages in a decentralized environment where network topology fluctuates is not a well-defined

problem. While the shortest path (based on a given cost function) from a source to destination in a static network is usually the optimal route. But this idea can not be easily extended to MANETs.

Mobile ad-hoc networks are self-organizing and self configuration of multi-hop wireless networks, where to interpret the network changes dynamically due to mobility of nodes [1]. The reactive routing protocol algorithm creates routes between nodes on request of source nodes with network flexibility, to allow the nodes to enter and leave the network at any point of time. The newly created routes remain active only as long as data packets are travelling along the paths from the source to the destination. A routing procedure always needs to find an optimal path to send the packets between the source and the destination [2]. Therefore the requirements of the protocol for mobile ad hoc networks are path (source, destination), hop count and sequence number, to make sure the freshness of the routes. 1.2 Reactive Routing Protocol

Reactive routing protocol is an on-demand routing protocol for mobile ad-hoc networks, which uses routing tables to store routing information. During routing, all route information maintain in tables, for unicast routes as well as for multicast routes. These routing tables hold information like destination address, next-hop address, hop-count, destination sequence number and life time. Instead of keeping static route information from one node to every other node, any reactive routing protocol can discover the route as and when required and these routes are maintained as long as necessary. The protocol comprises of three main functions like route discovery, route establishment and route maintenance.

In routing protocol, on request of source node, route discovery function is responsible for the discovery of new routes. Route establishment function is responsible for detection of the link of discovered routes by route


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establishment function. Finally route maintenance function is responsible for detection of the link failures and repair of an existing route. Reactive routing protocols, such as the AODV [3] nodes have four types of message to communicate between each other. These are Route Request, Route Reply, Route Error and Hello messages with a key feature that doesn’t require any distribution routing information and then, keep the routing information about the failure links [4]. During packets transmission, every intermediate node in the discovery route create routing table to store the information regarding neighbour node and the destination node information. The routing table information updated for every packet transmission during the message transmission. When communication between two nodes completes, the nodes discard all these routing and neighbour information.

II. RELATED WORK

Numerous frameworks have been proposed in mobile Ad-hoc network for performance-based routing protocol. Few of them are frameworks are simulated. This framework uses the concept of reverse reactive routing to find an optimal path between source and destination. khan et al. [5] conclude that when the MANET setup, for a small amount of time, then AODV is better because of low initial packet loss. DSR is not preferable because of its packet loss. On the other hand if we have to use the MANET for a longer duration so we can use both protocols, because after sometimes both have the same behavior. AODV have very good packet receiving ratio in comparison to DSR. At the end, they concluded that the combined performance of both AODV and DSR routing protocol could be the best solution for routing in MANET. In [6], OPNET 14.5 was used for simulation. The simulation study for MANET network under five routing protocols AODV, DSR, OLSR, TORA and GRP were deployed using FTP traffic analyzing. These protocols were tested with three QoS parameters. From their analysis, the OLSR outperforms others in both delay and throughput. In [7], Barakovic et al. compared performances of three routing protocols: DSDV, AODV and DSR. They analyzed these routings with different load and mobility scenarios with Network Simulator version 2 (NS-2). They concluded that in low mobility and low load scenarios, all three protocols react in a similar way, but when mobility or load is increasing, DSR outperforms AODV and DSDV. In [8], Bindra et al. evaluate the performance of AODV and DSR routing protocol for a scenario of Group Mobility Model such as military battlefield. They used Reference Point Group Mobility (RPGM) Model for their scenario. They concluded that in Group mobility model with CBR traffic sources, AODV is better than DSR but when TCP traffic used, DSR perform better in stressful situation like high load or high mobility. DSR routing load is always less

than AODV in all type of traffic. Average end-to-end delay of AODV is less than DSR in both type of traffic. Over all the performance of AODV is better than DSR in CBR traffic and real time delivery of data. But DSR perform better in TCP traffic under limitation of bandwidth. In [9], Kaushik et al. compared three routing protocols DSDV, AODV and DSR. They concluded that AODV performs predictably because it delivers the data at node with low mobility virtually, and it has problem when node mobility increases. But DSR was very good in situation that node has mobility and DSDV performs almost as well as DSR, but it needs many routing overhead packets. As far as packet delay and dropped packets ratio are concerned, DSR/AODV performs better than DSDV with large number of nodes. So for real time traffic AODV is preferred over DSR and DSDV. For less number of nodes and less mobility, DSDV’s performance is better. In [11] Performance of AODV, TORA and DSDV protocols is evaluated under both CBR and TCP traffic pattern. Extensive Simulation is done using NS-2. Simulation results show that Reactive protocols perform better in terms of packet delivery ratio and average end-to-end delay.

III. ROUTING PROTOCOL Mobile ad-hoc networks, also well-known as short-term networks, are autonomous systems of mobile nodes forming network in the absence of centralized access point. Absence of fixed infrastructure poses several types of challenges for this type of networking. Among these challenges routing is one of them. Routing protocols of mobile ad-hoc network lean to need different approaches from existing protocols, since most of the existing Internet protocols were proposed to support routing in a network with fixed structure. The proposed routing protocol for find an optimal path in MANET using the following route discovery approaches.

3.1 Random way point mobility model

In mobility management, the random waypoint model is a random model for the movement of mobile users, and how their location, velocity and acceleration change over time. Mobility models are used for simulation purposes when new network protocols are evaluated. It is one of the most popular mobility models and the "benchmark" mobility model to evaluate other Mobile ad hoc network (MANET) routing protocols, because of its simplicity and wide availability. In random-based mobility models, the mobile nodes move randomly and freely without restrictions. To be more specific, the destination, speed and direction are all chosen randomly and independently of other nodes. We have taken this model to model a real life simulation.


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3.2 Optimal Path Finding Approach:

We study the problem of selecting an optimal route in terms of transition probability and link available time. Finally we calculate optimal path between source and destination node by three steps, which execute and forwarding RREQ (route request) packets, RREP (route reply) packet and RRER (route error) packets. Experiments have been carried out using NS2 as network simulator ware and results encouraging.

3.3 Computation of reverse route in RREQ

In mobile ad-hoc network each node will create a reverse route table when it receives a RREQ (route request), the RREQ is discards if it has already been processed. It records and indicates the route to the source node; otherwise the source address and the broadcast ID from RREQ resolve is there buffered to prevent it from being processed again. Furthermore, each node will calculate the distance every time, and most importantly, this distance is the key reason to choose the shortest path from the source node. Initially, when a node receives RREQ, it will create a reverse route entry which indicates the next hop (forwarding the RREQ) of the source node and calculate the distance between the next hop node and the source node. Second, each node will also make the similar decision when it receives RREQ and update reverse route table or discard RREQ [12].

In this case, we have use two variables (Exit and new) to indicate how to make reverse route calculation in RREQ. The Exit is distance the node calculates at the first time when it receives RREQ or the distance at current time. The new is distance the node calculates when it receives RREQ again. Once an intermediate node receives a RREQ, the node sets up a reverse route entry for the source node in its reverse route table. Reverse route entry consists of <Source IP address, Source seq. number, number of hops to source node, Destination IP address, Destination seq. Number>.

Fig. 1 Structure of Mobile Ad hoc Network

By using the reverse route a next node can send a RREP to the source node. Reverse route entry also has

life time field. RREQ reaches to the destination, In order to respond to RREQ a next node should have in its route table unexpired entry for the destination and sequence number of destination at least as great as in RREQ (for loop prevention). If both conditions are meet & the IP address of the destination matches with that in RREQ the node responds to RREQ by sending a RREP. If conditions are not satisfied, then node increments the hop count in RREQ and broadcasts to its neighbors. Ultimately the RREQ will make to the destination.

Fig2: Node RREQ Broadcasting

In Fig. 2, when node A broadcasts RREQ to node B and E, node B and E will create a reverse route entry which indicates the next hop to the source node when packet arrives at node B and E. Besides, node B and E would calculate the distance between forwarding node and source node. In this situation, the next hop to source node for node B and E is node A and the first for node B and E is 0, because node A is both the forwarding node and source node. And then, when node B forwards the RREQ to node E, node E will calculate the new which is the distance between forwarding node (node B) and the source node ( A). Then, node E will compare the new with Exit (the first distance when node E receives RREQ from node A).Since new > Exit, the node discard this RREQ.This reverse route entry table Update route table in RREQ.

Fig.3 (a) Update Reverse route Table in RREQ. Fig.3 (b) The Result of Updating Reverse route tables in RREQ

As shown in Fig. 3(a), node F create reverse route entry when it receives RREQ from node G and select node G as the next hop to the source node (A). The same process happens when node F receives the same RREQ again from node C. Node F calculates the new between the forwarding node (node C) and the source node (A). Since new < Exit, node F updates the route table and select node C as the next hop to the source node. Fig.3 (b) is the finally route after node C broadcasts RREQ.


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3.4 Computation of reverse route in RREP We have used the similar calculation mechanism to

find the optimal path in forwarding RREP. The simply difference is that the distance we calculate in RREP is from the node forwarding RREP to the destination node

Fig 4: Reverse route entry and Calculates distance in RREP

As shown in Fig. 4, the destination node (node D) receives the RREQ from node F and then creates the RREP and unicasts it to node F. Node F forwards this RREP to node C according to the reverse route table created by forwarding the RREQ.

Fig 5(a): Update reverse route table in RREP. 5(b) and 5(c) Optimal Path between source and destination

When node C receives the RREP from node F, it creates the reverse route entry and calculates the Exit, which indicates the next hop is node F when the message whose destination node is node D arrives at node C. And then, when node C receives RREP from node D, it will calculates the new and finds that new<Exit, as shown in Fig. (a), (b)And (b), node C updates the route table, and then finally optimal path is found.

3.5 Computation of reverse route in RERR

We have used the similar calculation mechanism to find the optimal path in forwarding RERR. The simply difference is that When a node detects a link break (for example, receives a link layer feedback signal from the MAC protocol, does not receive passive acknowledgments, does not receive hello packets for a certain period of time, etc.), it performs a one hop data broadcast to its immediate neighbours.

Fig 6: Reverse Route Entry and Calculates distance in RERR

Fig 7(a): Update reverse route table in RERR. 7(b) Reverse Route Error Entry and Calculates all distances in RERR

As shown in Fig. 7(a), the destination node (node D) moves out of a range and does not receives the RREQ from node F and then creates the RERR and unicasts it to node F. Node F forwards this RERR to node C according to the reverse route table created by forwarding the RREQ. As shown in Fig 7 (b), the destination node (node D) specifies in the data header that the link is disconnected from F and thus the packet is candidate for alternate routing. Upon receiving this packet, neighbor nodes that have an entry for the destination in their alternate route table, unicast the packet to their next hop node. Node D receives the RREQ from node J and then creates the RERR and unicasts it to node J. Node J forwards this RERR to node C according to the reverse route table created by forwarding the RREQ.

Fig 8(a) Route Discovery and (b) Update Reverse Route tables in RERR

As shown in Fig 8, the destination node (node D) specifies in the data header that the link is disconnected


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from F and J. Therefore the packet is candidate for alternate routing. Upon receiving this packet, neighbor nodes that have an entry for the destination in their alternate route table, unicast the packet to their next hop node. Node D broadcasts ROUTE REQUEST (RREQ) through Route Discovery and then creates the RREP and unicasts it to node C. Node C forwards this RREP to node B according to the reverse route table created by forwarding the RREQ. Each intermediate node (node C and B) updates the route.

Fig 9: Optimal path communication between A to D

As shown in Fig 9, the destination node (node D) receives RREQ and count hops, when node C receives the RREP from node D; it creates the reverse route entry and calculates the exit, which indicates the next hop is node B when the message whose destination node is node C arrives at node B. And then, when node C receives RREP from node D, it will calculates the new and finds that new < exit and calculate the minimum length of hops, shows that Fig. 9, node C updates the route table, and then finally choose lower hop count get the optimal path between A to D. Therefore data packets can be delivered through one or more alternate routes and are not dropped when route breaks occur.

IV. PERFORMANCE EVALUATION

We have performed simulations to evaluate several performance metrics of our schemes. First, we would like to see how obtained optimal path of route discovered by reverse route calculation reduced. Then we compare our schemes with DSR in terms of packet delivery ratio, routing overhead and end-to-end delay. Simulation environment

To evaluate and compare the effectiveness of these routing protocols with existing proposed models [13], we performed extensive simulations in NS2. Each simulation is carried out under a constant mobility. The simulation parameters are listed in table1.

Table 1: Simulation Parameters

4.1 Simulation Results and Analysis

• No of nodes Vs Bandwidth:

The number of nodes was varied each time and the throughput was calculated at destination node during entire simulation period whose amount was as in fig. 2.

Figure 10: Bandwidth variation

RAODV shows higher throughput compare to DSR and AODV. The RAODV has much more routing packets than DSR because the RAODV avoids loop and freshness of routes while DSR uses stale routes. Its throughput is higher than other two routing protocols at high mobility.

• No. of nodes Vs Packet Drop:

A packet is dropped in two cases: the buffer is full when the packet needs to be buffered and the time that the packet has been buffered exceeds the limit. Packet dropping was observed for several nodes and varied the nodes each time and the dropped was counted at destination node during entire simulation period.


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Fig. 11 Packet Lost variation

Efficient protocols can wisely find out routing direction thus packets dropping rate reduces for them. The packet dropped for DSR is less than that of AODV and RAODV as it outperforms with fewer nodes and no periodic update is maintained in DSR.

• Packet Received Vs Propagation Delay:

Packet receiving statistic were performed for several propagation delays in case of all MANET protocols, whose nature of packet variation becomes as in fig 4. DSR perform better when the propagation delay of nodes increases because nodes become more stationary will lead to more stable path from source to destination. DSR is superior to AODV as well as RAODV especially when the node’s propagation delay begins to rise.

For RAODV, it shows significant dependence on route stability, thus its packet received rate is lower. Although, the amount of packet received is inversely proportional to propagation delay, DSR has the best performance than AODV and RAODV.

Fig. 12(a) Packet delay variation

Fig. 12 (b) Packet received variation

• Throughput Vs Simulation Time:

Throughput was gained at destination node against various dimension of networks and varied the simulation time uniformly for each protocol whose measure was as in fig 5.Throughput is the average rate of successful message delivery over a communication channel. This data may be delivered over a physical or logical link, or pass through a certain network node. The throughput is usually measured in bits per second (byte/sec), and sometimes in data packets per second or data packets per time slot. This is the measure of how soon an end user is able to receive data. It is determined as the ratio of the total data received to required propagation time. A higher throughput will directly impact the user’s perception of the quality of service (QoS).

Fig 13: Throughput variation

Based on the fig 13, it is shown that AODV perform better when the time increases because nodes become more stationary will lead to more stable path from source to destination. AODV has higher throughput than RAODV and DSR because of avoiding the formation of loops and it uses stale routes in case of broken links. The rate of packet received for RAODV is better than the AODV because this periodic broadcast also add a large overhead into the network. For RAODV, the routing overhead is not likely affected as generated in AODV. For RAODV, it shows significant dependence on


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route stability, thus its throughput is lower when the time decreased.

Path Optimality:

Fig.14 Optimal Path

The above displayed graph clarifies the fact that the proposed protocol is superior to the basic AODV protocol in both best cases as well as in worst case. But in worst case the performance of the proposed protocol is very indebted. From the calculation it was found that the newly proposed protocol is almost 85% fast as compare to the other reactive routing protocols. The ratio between the numbers of hops of the shortest path is to the number of hops in the actual path taken by the packets.

V. CONCLUSION

This study was conducted to propose a reactive routing protocol, consists of three steps to find the optimal path. Initially, we calculated the shortest path to the source node and created reverse route table. Then, we filtered these paths to obtain optimal path for communication in the mobile ad-hoc network by calculating distance to the destination node. Then in third step, a comparative analysis conducted in between three different protocols in terms of packet delivery ratio, routing overhead, throughput and average end to end delay, by using NS2 simulator. Finally, according to the average end to end delay, we have shown that DSR is lower than AODV, where the number of nodes we have used in our experiment is 10. We anticipate that our simulated results can be helpful for the future work for finding the optimal path in MANETs.

REFERENCES

[1] Jie Li, Member, IEEE, Hisao Kameda, and Keqin Li, Senior Member, IEEE “Optimal Dynamic Mobility Management for PCS Networks “IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 8, NO. 3, JUNE 2000.

[2] Giovanna Carofiglio , Carla-Fabiana Chiasserini,Michele Garetto,Emilio Leonardi “ Route stability in MANETs under the Random Direction Mobility Model “ September 2009 IEEE Transactions on Mobile Computing , Volume 8 Issue 9.

[3] C.Perkins, E.Belding-Royer, S.Das “Ad hoc On-Demand Distance Vector (AODV) Routing” Feb.2003.http://www.ietf.org/internet-drafts/draftietf-manet-aodv-13.txt

[4] S.Basagni, I. Chlamtac, V. R. Syrotiuk, and B. A. Woodward, “A distance routing effect algorithm for mobility (dream),” in Proceedings of the IEEE/ACM international Conference on Mobile Computing and Networking (MOBICOM’98), 1998, pp. 76–84.

[5] J. khan, s. I. Hyder and S. M. Fakar “Modeling and simulation of

dynamic intermediate nodes nnd performance analysis in MANETS reactive routing protocols”, International Journal of Grid and Distributed Computing Vol. 4, No. 1, March 2011.

[6] R. Al-Ani, “Simulation and performance analysis evaluation for variant MANET routing protocols”, International Journal of Advancements in Computing Technology, Volume 3, Number 1, February 2011

[7] S. Barakovic, S. Kasapovic, and J. Barakovic, “Comparison of MANET routing protocols in different traffic and mobility models”, Telfor Journal, Vol. 2, No. 1, 2010.

[8] H. S. Bindra1, S. K. Maakar, A. L. Sangal, “Performance evaluation of two reactive routing protocols of MANET using group mobility model”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 10, May 2010 ISSN (Online): 1694-0784.

[9] S. Kaushik, P.R.Deshmukh, “Comparison of effectiveness of AODV, DSDV AND DSR routing protocols in mobile ad hoc networks”, International Journal of Information Technology and Knowledge Management July-December 2009, Volume 2, No. 2, pp.499-502.

[10] V. Singla and P. Kakkar, “Traffic pattern based performance comparison of reactive and proactive protocols of mobile ad-hoc networks”, International Journal of Computer Applications (0975 – 8887) Volume 5– No.10, August 2010.

[11] Akshatha. P. S, Namita Khurana, Anju Rathi,” OPTIMAL PATH FOR MOBILE AD-HOC NETWORKS USING REACTIVE ROUTING PROTOCOL” International Journal of Advances in Engineering & Technology, Sept 2011., ISSN: 2231-1963, ©IJAET.

[12] Satveer Kaur,” Performance Comparison of DSR and AODV Routing Protocols with Efficient Mobility Model in Mobile Ad-Hoc Network”, IJCST Vol. 2, Issue 2, June 2011.


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