July2018

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING JULY 2018 VOL- 8 NO-7

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk 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 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018

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International Journal of Innovative Technology & Creative Engineering Vol.8 No.7 July 2018

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018

From Editor's Desk Dear Researcher, Greetings!

Research article in this issue discusses about motivational factor analysis.

Let us review research around the world this month.

In the future, leaving your phone charger at home will mean only one thing: You forgot to put on pants. Just as smart phones untethered users from their desktop computers, smart clothing is poised to bring personal electronics out of our pockets and onto our sleeves. The current generation of wearable technology that includes smart glasses and watches is still more marginal than mainstream. Google Glass fizzled out, and nearly a third of the people who buy fitness trackers lose interest over time. But gadget-packed garments may have an edge when it comes to seamless integration into our lives. One conference, somebody stood up and get that wearable technology is a thing, but I just don’t think I’m going to be willing to get up every single day and remember to put something on, recalls wearable technology researcher Lucy Dunne. I looked at her and said, ‘You’re wearing clothes right now. I’m pretty sure you do that already.

Color-changing purse now you see it using yarns that change hues in response to small temperature changes, researchers have fashioned garments and bags that switch patterns with the tap of a Smartphone screen. Plus, technology-laden clothing is right next to and against your body. It has a large surface area compared to personal devices and it goes with us everywhere, says Dunne of the University of Minnesota campus in St. Paul. That kind of access is rich with opportunity. Some advanced apparel is already for sale, like gloves threaded with heat-conducting wires to warm fingers on extra cold days, or bathing suits equipped with UV sensors to alert sun tanners when they are close to over baked.

It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies 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

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 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 ShangaiJiaotong 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. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.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 Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

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Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-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 SchoolRuaItapeva, 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 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (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. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita 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.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018 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. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG 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-BangloreWesternly 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,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018 Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-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,RuaItapeva, 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.

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Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh.

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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),UniversitiSainsMalaysia,School of Mathematical Sciences,

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018 Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami 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. SeraphinChallyAbou 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.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.6 JULY 2018

Contents An analysis of the child abduction and the rescue measures implemented in Tamil Nadu C.Akila , Dr.R.Gunavathi ……………………………. [501] Comparison of Rainfall Prediction Based On Almanac Using Data Mining Techniques K.S. Mohanasundaram, Dr. G. M. Nasira ……………………………. [504] Communication and Network Architectures of Intelligent Transport System: A Review Savitha Sivalingam ……………………………. [509]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.8 NO.7 JULY 2018

An analysis of the child abduction and the rescue measures implemented in Tamil Nadu C.Akila Assistant Professor, Department of IT, Sree Saraswathi Thyagaraja College Pollachi,Tamil Nadu, India.

Dr.R.Gunavathi Head of the Department(MCA) Sree Saraswathi Thyagaraja College Pollachi, Tamil Nadu, India.

Abstract- In the recent situations, the abduction of

the children is becoming the great concern. Taking care of the children seems to be the greatest chore for the parents. Based on the reports produced, the kidnapping of the children for various reasons is reaching its hike. This paper gives the indication of the abduction and the measures to crack the problems.

Keywords- Child Abduction, Trafficking.

1. INTRODUCTION Abduction is defined as taking away a person by influence, by fraud, or by open force or violence. Child Abduction is the crime of wrongfully removing or wrongfully retaining, detaining or concealing a child or baby .

Child abuse is a state of emotional, physical, economic and sexual maltreatment meted out to a person below the age of eighteen and is a globally prevalent phenomenon which is found now a days[1].

According to World Health Organization (WHO), the term Child abuse is the term that comes with physical, emotional, sexual abuse.  Physical Abuse: It describes that the children are made agonized physically like burning, punching, hitting, beating etc.  Sexual Abuse: This means having inappropriate sexual behavior with the children.  Emotional abuse: The children are affected psychologically, mentally due to the bizarre forms of punishment given by the parents/care takers  Neglect: It is the failure to provide for the child's basic needs. Neglect can be physical, educational or emotional.

The abduction of children is of two types viz  Family abduction  Non Family abduction The most common type of abduction is family abduction, which occurs when a child is taken by a parent or family member, where the event involves intent to deprive a lawful guardian of custodial privileges .Family abductions

often occur because of dissatisfaction with custodial arrangements following a divorce, marital separation, or the breakup of a non marital relationship.

Nonfamily abduction is defined as an incident in which a stranger or non familial acquaintance takes or detains a child without lawful authority or permission from parents or legal guardian. The more dangerous type of nonfamily abduction is referred to as stereotypical abduction, or abduction perpetrated by a stranger or slight acquaintance, which occurs in conjunction with ransom, murder, or with the intent to keep the child permanently.

2. OBJECTIVE The major goal of this study is  To focus the causes for child abduction  To understand the existing method of rescuing the children from the abduction.  To apply the technology in rescuing the children from abductions.

3. THE CAUSES FOR ABDUCTION It was analyzed that the ground modality of the abduction is called Trafficking. The protocol provided the definition of trafficking as, “the recruitment, transportation, transfer, harboring or receipt of persons by means of the threat or use of force or other forms of coercion, of abduction, of fraud, of deception, of the abuse of power or of a position of vulnerability or of the giving or receiving of payments or benefits to achieve the consent of a person having control over another person, for the purpose of abusement. Exploitation shall include, at a minimum, the exploitation of the prostitution of others or other forms of sexual abusement, forced labour or service, slavery or practices similar to slavery, servitude or the removal of organs”

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As per NHRC report, it was found that at least 2 children were missing every day, which may rise to trafficking. The trafficking might be for prostitution, handed over to criminal gangs or sold for illegal adoption [4].

Fig 2: Count of kidnapping

Fig 1: Trafficking [2] International Labor Organization (ILO) defines the child trafficking as “Child trafficking is about snatching the children out of their protective environment and preying on their weakness for the purpose of abusement”.

4. CHILD ABDUCTION IN TAMILNADU The total population of the children in Tamilnadu during 2016 is 1,38,20,661.

Table 1.1: Projected children population of different age group in Tamilnadu S.no Age group Male Female Total in yrs 1

6-10 Yrs

2848615

2686363

5534978

2

11-13 Yrs

1931547

1775449

3706996

3

14-15 Yrs

1255352

1148801

2404153

4

16-17 Yrs

1132653

1041881

2174534

Among the children population in 2016, at least 2,741 children were kidnapped. In Tamil Nadu 16,183 children were reported missing between 2009-2014[3]. In the present scenario, the children in the age group of 4 to 15 are kidnapped for various reasons such as for illegal adoption, for sexual abuse, for removing the organs, for child labour and it becomes the major issue all over the world.

As per National Human Rights Commission (NHRC) report, during 2003 Tamilnadu takes 4th place in trafficking and it was about 12.3% [2]. The report also produces that intra-state trafficking was the common phenomena in Tamilnadu. For each year, the abduction rate increases [4].

5. THE EXISTING STRATEGY APPLIED TO RESCUE THE CHILDREN There are diverse strategies are prevailing to rescue the children from kidnapping. Few of them are  Getting help from an NGOs (Childline, Thozhamai)  Getting help from Police.  Through the tracking device Through the Non Government Organizations like Childline, Thozhamai the services are being provided to the society by forming the committee and monitoring the children of the village or town. They are giving awareness programme to the children and as well as to their parents as the way to safeguard the children in all circumstances. Through their helpline also, they are providing the service to help the children.

One more measure which applied in rescuing the children from abduction is getting assistance from police. The Policemen are providing their service to the children community through various means. As on April 30 2015, according to the SCRB (state crime records bureau) records, 181 boy and 151 girl missing cases are now under investigation by the Chennai city police[5]. Through the CCTV footage the police officers are tracing the culprits who kidnapped the children.

Even though all the measures are taken by different NGOs and the police officers, the entire group involved in kidnapping is not getting rid of. They can safeguard only few (say approximately only 5%)children. The remaining was not yet found. This problem is becoming major issue. So, some measures are required to safeguard the children from abduction.

The Department of Police formed the Zip net network , a portal to file the details about the missing children in the year 2012. Unexpectedly it was found that the actual missing of persons were much more than the report submitted

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by NCRB. So, the Government of India has taken the steps to create the mechanism of forming Integrated Child Protection Scheme and Integrated Human Trafficking Units which comprises of the NGOs and Civil Society[6].

6. CONCLUSION Thus, the existing scenario that is prevailing in Tamilnadu regarding Children abduction and the measures taken to preserve the children is in underneath level. To evade this kind of problem some technological measures is to be taken. In future, this kind of problem can be avoided using the technology called Wireless sensor network. The sensors can be built at affordable cost.

REFERENCES [1] Loveleen Kacker, Srinivas Varadan,Pravesh Kumar, “Study on Child Abuse INDIA 2007”, Kriti, New Delhi,2007.

[2] “Child Trafficking in India”, HAQ: Centre for Child Rights, 2016.

NEWSPAPER [3] Sibi Arasu, “9 kids go missing in Tamil Nadu every day.

But rescuing them isn't enough”, catch news, February 2017.

[4] Anuradha Nagaraj, “Missing children in Tamil Nadu raise trafficking fears”, Reuters India,April 1, 2016.

[5] A Subramani, “Hundreds of child missing cases in Tamil Nadu come under Madras high court scrutiny”, The times of India, Jun 17, 2016.

[6] Shakti Vahini, “Missing Children In India Government initiatives And Court Orders”, trackthemissingchild.gov.in/.../News.../NATIONAL_LEG AL_RESEARCH_DESK.pdf.

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COMPARISON OF RAINFALL PREDICTION BASED ON ALMANAC USING DATA MINING TECHNIQUES K.S. Mohanasundaram Research Scholar, Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamil Nadu, India.

Assistant Professor in Computer Science and Application, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.

Dr. G. M. Nasira Assistant Professor & Head, Department of Computer Applications, Chikkanna Government Arts College, Tiruppur, Tamil Nadu, India.

Rainfall becomes a major factor in agricultural based country like India. Rainfall prediction has become one of the most systematically and technically taxing problems in around world. Farmers in Tamil Nadu are still following the agronomic activities based on astrological facts of Panchangam (Almanac). Yet there is very few ever attempted to see the rationality of the ancient knowledge system.

Almanac also has a mathematical base for predicting the meteorological occurrences. During the study, the rainfall prediction by one of the traditional Almanac is studied in concentration for one cycle of 60 Tamil year’s corresponding to the Gregorian Year from 1957 to 2017. We have a lot of data mining techniques to extract information. In this work, we applied various classification algorithms such as SMO, Random Forest and REPTree on the almanac rainfall dataset in WEKA tool. This paper shows REPTree is best for prediction of rainfall using data mining techniques based on almanac.

Abstract-

Keywords-Rainfall Prediction, Almanac, Prediction, Classification.

1. INTRODUCTION Data mining is the search and analysis of large data sets, in order to discover meaningful patterns and rules. The key idea is to find effective ways to combine the computer’s power to process data with the human eye’s ability to detect patterns. Data mining techniques have been broadly applied almost in all fields to analysis the data for pattern the rules, classification, prediction, decision trees, fuzzy rules and so on.

Rainfall is important for planning the activities of agriculturists, builders, water supply engineers, and all activity plans in the nature. India is an agricultural country and its economy is largely based upon crop productivity. Thus rainfall prediction becomes a significant factor in agricultural based countries like India. Rainfall Prediction is one of the most challenging tasks. Though already many algorithm have being proposed but still accurate prediction of rainfall is very

difficult. In an agricultural country like India, the success or failure of the crops and water scarcity in any year is always viewed with greatest concern.

Astronomy is an area where Data Mining has been playing a big role. Several techniques of Data mining have been used to solve tasks in Astrology. There has been increasing research interest in use of data mining techniques to scrutinize in the Astrology area.

At present the Meteorology Department is informing only short term forecasting about weather but long term forecasting is needed for planning. This can be achieved by two methods namely traditional forecasting and scientific weather forecasting. Traditional forecasting is based on observations and experience using combinations of plants, animals, insects, meteorological and astronomical indicators, and almanacs or panchangs over a period of time. The scientific weather forecasting is based on past records of climate prevailed in the area using mathematical models.

2. EXISTING APPROACH Kolluru Venkata Nagendra [1] surveys a range of classification techniques used by various researchers.

Artificial Neural Network is applied for Rainfall Forecasting on various parameters are analyzed. They found that MLP method, Naïve Bayesian classifiers and Support Vector Machines are best to predict rainfall compare to other techniques (Numerical & Statistical). They identify that for weekly, monthly and yearly rainfall forecasting Naïve Bayesian , Feed Forward Neural Network and SVM gives best performance respectively.

Dhawal Hirani [2] reports a detailed survey on rainfall prediction using different rainfall prediction methods extensively survey lasted 20 years. From the survey it has been found that most of the researchers used artificial neural networks for rainfall prediction and got significant results.

They found that MLP, BPN, RBFN, SOM & SUM are suitable for predict rainfall forecasting techniques.

Seema Mahajan [3] examined the relationship of Gujarat rainfall with significant universal parameters such as SLP, SST, U – Wind & Windstress and V- Wind & Windstress. They taken one month lagged (June – July) for 40 years (From 1960 to 1999) data from National Oceanic and Atmospheric Administration (NOAA) and perform multilinear

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regression on the generated and measured rainfall series. They found 0.8377 is the correlation coefficient between generated and measured rainfall series.

B. Kavitha Rani [4] applied ANN to predict the summary rainfall data in Thailand. And found that back propagation gives accuracy result. Valmik B Nikam [5] extract the IMD (Indian Meteorological Department) Pune, weather data comprising of 36 attributes, only 7 attributes are relevant to rainfall prediction is taken and used Bayesian approach and got accuracy result. Jyothis Joseph [6] used clustering and classification techniques for prediction of rainfall and got the accuracy 87%. M. Kannan [7] used Multiple Linear Regression Model for rainfall prediction.

They got approximate value not accurate value. Parneet Kaur [8] & M. Sivasakthi [9] found that Multi Layer Perception gives the best accuracy in EDM (Educational Data Mining) among Naive Bayes, SMO, J48 and REPTree classifications.

N. Vivekanandan [10] [22] applies ANN based MLP and RBF for AER of Joshimath and Tohana rain-gauge stations. And found MLP for Joshimath and RBF for Tohana is sutiable for AER. A. Subasini [11] explore the applicability of data mining technique to predict the breast cancer. And analyzes the performance of C5.0, ID3, APRIORI, C4.5 and Naïve Bayes algorithms. Experimental found C5.0 gives highest accuracy. Ozlem Terzi [12] used to estimate monthly rainfall values of Isparta. The monthly rainfall data of Senirkent, Uluborlu, Egirdir and Yalvac stations are taken.

The best appropriate algorithm is multilinear regression & it gives relative error is 0.7%. M Ramzan Talib [13] collected weather data for 10 years from 2007 to 2016 at Faisalabab city, Pakistan and applied K-means clustering algorithm and Decision Tree algorithm for these data. Sarah N. Kohail [14] applied knowledge discovery process to take out knowledge from Gaza city weather data for 9 years from 1977 to 1985.

Outlier analysis, prediction, classification, association and clustering data mining techniques applied. Harneet Kaur [15] studied an overview of different techniques and tools of data mining such as KNIME, Orange, RapidMiner, Tanagra and WEKA. And identified the challenges in health care domain.

An implementation was shown in Tanagra tools for Classification and Visualization methods. Godfrey C. Onwubolu [16] used enhanced Group Method of Data Handling (e-GMDH) which uses the daily pressure & temperature and monthly rainfall and gives the good experimental results. Sweta [17] suggested to improving quality of service by properly managed security concerns.

Divya Chauhan [18] reviewed the different algorithms and techniques in predicting various weather phenomenons like rainfall, thunderstorms and temperature.

Then comparison is done between the techniques and found that decision trees and k-mean clustering gives the best results. Dhananjay P. Atole [19] computed the rainfall prediction for 5 India cities (Chennai, Delhi, Mumbai, Nagpur and Pune) using 7 years data of rainfalls daily, weekly, monthly. Accuracy result is got by using Multi variable polynomial regression (MPR) technique. Siddharth S. Bhatkande [20] used meteorological data from 2012 to 2015 for various cities and Decision tree algorithm for classification of weather parameters such as minimum & maximum

temperature of the data. They proved decision tree is best for weather prediction.

Norraseth Chantasut [21] computed the rainfall prediction for monthly from historical rainfall data from 1941 to 1999 from 245 rainfall monitor stations in Thailand around Chao Phraya River using ANN in which the number of training pattern is 372 and testing pattern is 96. The Neural Network gives 99.6% and 96.9% of accuracy of training and testing data respectively. R. Sukanya [23] compared several classification algorithms CART, C4.5, ID3, Back propagation and SVM. Finally concludes that nowadays almost researches using hybrid method for getting more accuracy results.

D Angchok [24] predict rainfall by Tibetan astrological theories with meteorological predictions was accepted. They suggested as very few scientific studies have ever been conducted in ancient Astro-science and almost all of them have reported encouraging and positive outputs, there seems to have enormous scope lying in studying ancient sciences, especially Astro - disciplinary approaches. S Sivaprakasam [25] suggested the traditional methods of forecasting rainfall may be riddled with in accuracies but they cannot be ignored altogether. R. Raja [26] analysis 90 years (1909-1999) historical annual rainfall data of Coimbatore correlation with a particular Tamil year cycle with fourth coming Tamil cycle years. Pankaj S. Kulkarni [27] deals with converting ancient principles related to astrology into predictions using data mining techniques. Neelam Chaplot [28] taken total 102 records, an half of the records were of persons are doctor and other half records of are not doctor by Profession,. They compared various Supervised classification techniques such as Logistic, Naïve Bayes, Simple Cart, Decision Stump, Decision Table and DTNB algorithm. The better results were produced by simple logistic with 12 fold cross validation with an accuracy of 54.902%. Decision Stump algorithm with 14 fold classification gave results with an accuracy of 50%. S. R. Gedam [29] analyzed five data mining algorithms such as Bayesian, Decision table, Multilayer Perception, Random Decision Tree, and Random Forest. And got the accuracy result 84.7458%, 98.3051%, 94.9153,99.0202 and 100% respectively. Concluded that Random Forest Method is the best classification method. Rahul Shajan [30] analyzed the several data mining algorithms to correctly classify and predict health of a human being. They got the results 81.25 % accuracy for J48, J48 graft and Naïve Bayes algorithms and 93.75% accuracy for Random forest algorithm.

3. PROPOSED APPROACH Experimental research methodology has been adopted for this work. Through the extensive search of literature and discussion with exports, the number of attributes which influencing the rainfall has been finalized. For this work, data are collected from particular almanac or panchang.

This data is then filtered out using manual techniques. Then data is transformed into a standard format required by the WEKA tool.

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4. RESULT & DISCUSSION TABLE II. PERFORMANCE OF VARIOUS CLASSIFICATION ALGORITHMS

SMO

Kuruni Pathaku

A record of 60 years data from year 1957 to year 2017 from almanac has taken for analysis. From an panchang or almanac we have considered five influencing attributes for rainfall are King, Minister, Megathipathi, Megam (Cloud Type), Rainfall value (Marakkal) each of which has sub item sets, which as shown in Table I. TABLE I. ATTRIBUTES INFLUENCING FOR RAINFALL S. No. Attribute Description Domain Value 1 King Ruling {Sun, Moon, Planet of the Mars, Mercury, year Jupiter, Venus, Saturn} 2 Minister Minister {Sun, Moon, Planet of the Mars, Mercury, year Jupiter, Venus, Saturn} 3 Megathipathi Planet {Sun, Moon, supporting Mars, Mercury, rainfall for Jupiter, Venus, the year Saturn} 4 Megam(Clou Type/Format {Aavarta, d Type) ion of the Samvarta, Cloud Pushkara, Drona, Kaala, Neela, Varuna, Vayu, Dhamo} 5 Almanac Rainfall of { Kuruni, Rainfall the year as Pathaku, per Almanac Mukkuruni, Thooni }

Precisi on 0.875

Random Forest

REPTree

Recall

Precis ion

Reca ll

Precisio n

Recall

0.933

0.929

0.867

0.933

0.933

1

0.857

1

0.857

1

0.857

Mukkuruni

0.947

0.947

0.900

0.947

0.947

0.947

Thooni

0.947

0.947

0.950

1

0.950

1

Weighted Average

0.935

0.933

0.935

0.933

0.951

0.950

We got the results by tested and analyze with three data mining classification algorithms such as SMO, RandomForest and REPTree that shows in above Table II. The correct accuracy of all the algorithms is given below in Table III. TABLE III. ACCURACY OF CLASSIFICATION ALGORITHMS Data Mining Algorithm Accuracy (%) SMO 93.33 RandomForest 93.33 REPTree 95 Both SMO and RandomForest algorithm got accuracy 93.33%. The best accuracy is 95% performed by REPTree algorithms. The following chart shows the performance accuracy of algorithm.

Fig 2. Comparison of Accuracy

In this work various data mining techniques are used to predict rainfall. WEKA is used to apply the classification techniques and for predictions. The output has been analyzed with three classification algorithms such as SMO, RandomForest and REPTree.

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5. CONCLUSION In this paper, Rainfall predicting attitudes and data sets are taken for cycle of 60 Tamil year’s related to the Gregorian Year from 1957 to 2017 from Almanac. For this data set three data mining classification algorithms such as SMO, RandomForest and REPTree was applied using in WEKA tool. We get more accuracy result 95% for REPTree classification data mining algorithm. So, the existing REPTree algorithm is sufficient to find the similar patterns in the almanac rainfall predictions.

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M Ramzan Talib, Toseef Ullah, M Umer Sarwar, M Kashif Hanif and Nafees Ayub “Application of Data Mining Techniques in Weather Data Analysis, International Journal of Computer Science and Network Security, VOL.17 No.6, June 2017. PP- 2228.

Sarah N. Kohail, Alaa M. El-Halees “Implementation of Data Mining Techniques for Meteorological Data Analysis ( A case Study for Gaza Strip), Volume 1 No.3, July-2011, PP-96-100, ISSN-2223-4985.

Harneet Kaur “A comprehensive study of Data Mining techniques in prediction of Cancer”, Journal of Advanced Computing and Communication Technologies, Volume No. 5 Issue No. 3, June 2017, PP-81-88, ISSN : 2347-2804.

Godfrey C. Onwubolu, Petr Buryan, Sitaram Garimella, Visagaperuman Ramachandran, Viti Buadromo and Ajith Abraham “Self-Organizing Data Mining For Weather Forecasting”, IADIS European Conference Data Ming 2007, PP-81-88.

Sweta “Need and Applications of Data Mining”, International Journal of Novel Research in Computer Science and Software Engineering, Vol. 2, Issue 2, PP:39-45, Month: May-August 2015, ISSN 23947314.

Divya Chauhan, Jawahar Thakur “Data Mining Techniques for Weather Prediction: A Review”, International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 2, Issue : 8, PP-2184-2189, ISSN : 2321-8169.

Dhananjay P. Atole, Deepak Kapgate, Rushi Longadge “Statistical Modeling for Rainfall Prediction using Data Mining Technique”, ABHIYANTRIKI An International Journal of Engineering & Technology, Vol.2, No. 1, January 2015, PP-35-39, eISSN : 2394-627X. Siddharth S. Bhatkande, Roopa G. Hubballi “Weather Prediction Based on Decision Tree Algorithm Using Data Mining Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 5, May 2016, PP-483-487, ISSN (Online) 2278-1021, ISSN (Print) 2319 5940.

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D Angchok & V K Dubey “Traditional method of rainfall prediction through Almanacs in Ladakh”, Indian Journal of Traditional Knowledge, Vol. 5(1), January 2006, pp. 145-150 S Sivaprakasam & V Kanakasabai “Traditional almanac predicted rainfall – A case study”, Indian Journal of Traditional Knowledge, Vol. 8(4), October 2009, pp. 621-625.

R. Raja, T. N. Balasubramanian and R. Karthikeyan “Almanac study on forecasting annual rainfall”, The Madras Agricultural Journal 90(10-12) : 596-600 October-December 2003.

Mr. Pankaj S. Kulkarni, Dr. S. S. Sane, Prof. N. L. Bhale “Use of Neural Networks in Horoscope prediction”, ResearchGate, Conference Paper March 2012.

Neelam Chaplot, Praveen Dhyani, O. P. Rishi “Astrological prediction for Profession Doctor using Classification techniques of Artificial Intelligence” International Journal of Computer Applications, Volume 122 – No.15, July 2015 (0975-8887).

S. R. Gedam, Dr. R. A. Ingolikar “ Application of Mining Techniques to Classification of Star”, International Journal of Advance Research in Computer Science and Management Studies, Volume 3, Issue 9, September 2015, PP-153-157, ISSN : 2321-7782 (Online).

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Communication and Network Architectures of Intelligent Transport System: A Review Savitha Sivalingam Research Scholar, Computer Science Engineering and Technology, CMR University, India E-mail: Savithas.15phd@cmr.edu.in Abstract- Intelligent Transport Systems (ITS) are

required to support the economy of the country and to address the issue of congestion management in the country. ITS is an umbrella term that covers the latest technologies and operational methods adopted for highways and transit. The use of ITS technologies has the potential to yield a new wave of road safety and other potential benefits for India.

ITS is expected to play an important role in delivering safer transportation environment by introducing autonomous vehicles. The present paper addresses the various networking technologies and communication developments introduced in the field of ITS and their impact on providing transportation solutions for the future.

The available literature on ITS technologies deployed for managing and assisting transportation were reviewed. Versatile systems and technologies that enhance the safety and efficiency of transportation with the help of communication platforms and sensing capabilities were reviewed.

Intelligent Transport Systems, Traffic Management, Communication, Vehicular networks, Road safety, Inter-vehicle, Public road transport, Autonomous vehicles, Driving, Traffic information systems, Traffic control.

Keywords-

1. INTRODUCTION The increasing number of vehicles over the decade has resulted in a saturation of the infrastructure architecture (Jalali, El-Khatib & McGregor, 2015). The current situation has an adverse impact on the lives of the people who live in urban areas. Traffic congestion, delay in transportation, and vehicular pollution are some of the major issues owing to increase in the number of vehicles. Several solutions such as wearing safety belts, constructing better roads have been introduced to address these issues. However, these solutions have a considerable amount of impact on the environment and face the limitation of space in urban areas (Figueiredo et al., 2001). Nonetheless, improving the transport infrastructure of the country is critical for the economic development of the country. Therefore, a critical need for implementing a compromise solution has been felt in the recent years. The ITS proponents foresee a substantial future for new ITS technologies in transportation applications that can

revolutionize the manner in which transport systems are designed and built.

The deployment of intelligent transportation systems technologies is expected to provide safety, and efficiency of the road-vehicle systems (Figueiredo et al., 2001). ITS applications for traffic management aids in intersection control, incident detection, vehicle classification, monitoring, revenue collection, and historical traffic data. Similarly, ITS also assists the commuters on-road with the following: congestion maps and travel time estimates, public transport and information about its arrival, individual vehicle management, and emergency accident handling.

Research in the field of ITS has been carried out in different domains such as signal processing, communication technologies, robotics, electronics, control systems, and information systems (Papadimitratos et al., 2009). The multidisciplinary nature of ITS enhances the complexity of the problem under study as it requires knowledge transfer from different research areas. The present review records the various architectures deployed for ITS. For this purpose, the paper is organized as follows: Section 2 describes the various network and communication architectures deployed in vehicular communication systems; and Section 3 discusses the possible directions for future research in the ITS field in the light of observations made during the review.

2. METHODOLOGY In this review, the main focus of the researcher was on vehicular networks and how they impact on ITS. Accordingly, studies that discuss vehicular network architecture in the field of ITS have been reviewed. The identified articles were screened with the help of inclusion and exclusion criteria and the required information was retrieved from the chosen studies. Published research articles that discuss vehicular networks and empirical studies that capture all the empirical evidence were considered. Studies that clearly define the vehicular communication architecture and the various nodes in the network were considered for the review. Further, publications between 2008 and 2018 by relevant scientific sources (IEEE, Springer, ACM, and Elsevier) were included. However, systematic reviews, surveys and meta-analyses were omitted. Studies with incomplete data and poorly defined architecture were also excluded. Also, studies that did not have full text availability were not used in the review.

After the initial screening with the help of inclusion and exclusion criteria, the abstract and the concepts discussed in the paper were read, based on which the selected papers

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were clustered to form categories for the mapping of studies.

The categories were updated periodically if the papers revealed a new architecture. The categories identified by the researcher are as follows: Hybrid, WSN, Software-Defined Networking (SDN), Vehicular cloud, Heterogeneous network architecture, and Cooperative network architecture. The research methodology is shown in Figure 1.

Fig.1.1 Study methodology 3. REVIEW OF VEHICULAR NETWORK AND COMMUNICATION ARCHITECTURES FOR ITS ITS is a technology that fuses vehicles and the communication among the vehicles; thereby, assist the drivers and the passengers a comfortable and safe traveling environment. Inter-Vehicle communication (I-VC) and RoadVehicle Communication (R-VC) are the two important communication systems that play an important role in for assisting safe driving and supporting automatic driving (Fujise et al., 2002). In the inter-vehicle communication, the communication between the vehicles is not dependent on the roadside infrastructure, unlike the road-vehicle communication. The present section describes the various architectures adopted for vehicular communication in ITS by different studies.

A mobile ad hoc network built upon IPv6 proxybased architecture that selects the optimal mode of communication and provides dynamic switching between vehicle-to-vehicle and Vehicle-to-Roadside communication mode was proposed by Baldessari et al. (2006). The architecture used position-based routing for communication in the vehicular ad hoc networks and includes the following distinct domains: in-vehicle, ad hoc and infrastructure domain.

The in-vehicle domain consists of the On-Board Units (OBUs) and other Application Units (AUs); whereas the ad hoc domain consists of vehicles with OBUs and Road Side Units (RSUs). The architecture was developed to support three modes of communication, namely, Direct In-Vehicle, Vehicleto-Vehicle, and Vehicle-to-Roadside communication. AlSultan et al. (2014), in their comprehensive review on VANETs, described the network architecture in detail.

Vehicle-Vehicle and Vehicle-RSU communication is established through a wireless medium known as WAVE. The service application is hosted by the RSU and the OBU is a peer device that utilizes the services. The OBU is a wave device that is mounted on-board a vehicle. It has a network device for short range wireless communication. The AU helps the vehicle to avail the services offered by the service provider by means of the communication capabilities of OBU. The AU connects with the network only through the OBU, which is responsible for the networking operations and mobility of devices. RSUs are fixed along the sides of the roads and are equipped with a network device based on IEEE 802.11p.

The important security aspects of the vehicular communication system have been described with different views such as functional view, component view, reference model view and information centric view by Gerlach et al.

(2007) while implementing a vehicular communication system that integrates the security concepts into its stack of protocols.

According to the functional view of the system, as shown in Figure 2, the lowest layer is concerned with the registration of nodes (OBUs and RSUs), the test and certification layer is concerned with the assessment of the operation of nodes, the pseudonym layer is concerned with providing anonymity to the nodes, the revocation layer is concerned with removing the nodes from the system, and the top data assessment and intrusion handling layer is concerned with assessing, auditing, detecting and handling any misbehavior of the nodes. The organizational view of the system depicts the following components of the system that are part of its security infrastructure: vehicle manufacturer and registration authority, inspection site, escrow entity, and the communication security infrastructure. The distribution of functions over different authorities is explained using the organizational view of the system. The reference model view of the system is enhanced by the following components: core security application, confidence filter, and network security components.

Fig1.2 Functional layer of security architecture Security architecture for vehicular communication that provides identity and cryptographic key management, and integrates various privacy enhancing technologies was proposed by Papadimitratos et al. (2007). The main components of the architecture include Certification Authorities (CAs) that issue certificates to all nodes upon registration and expiration of the certificate, Privacy Enhancing Mechanism known as Pseudonym provider that provides each node with a distinct certified public key without any additional information, pseudonym authority that infers

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the mapping of a vehicle’s long-term identity with its pseudonym, Secure communication patterns such as beaconing, restricted flooding, and position-based routing, and Trusted Components (TCs) such as event data recorders that store cryptographic information and perform cryptographic operations. In the simplest architecture, a CA was considered to be the Pseudonym provider as well as the resolution authority.

In order to make a Vehicular Communication (VC) system adaptable, extensible and flexible for incorporating security requirements in the future, component-based security architecture, where the components can be added or deleted, has been proposed by the SeVeCom project. According to Kargl et al. (2008), the baseline architecture for VC system includes various modules that address the security issues and contains components that implement a part of the system functionality, to achieve the required flexibility. Some of the modules include Identity Management, Cryptographic Support Module and Secure Communication Module. The Secure Communication Module ensures secure communication by implementing protocols, and consists of several components that implement a single protocol each. The Security Manager is considered to be at the center of the architecture which configures the security modules and establishes connection with the Cryptographic Support Module. With the help of hooking architecture at the interface between every layer of VC system, event-callback mechanism has been implemented to introduce new security measures without modifying the entire communication system. The communication system has been implemented as a layered stack in which Inter Layer Proxies (ILP) have been inserted at several points. The Hardware Security Module (HSM) secures the private keys and the execution of cryptographic operations. Tamper resistance of HSM was provided by implementing HSM as an Application-Specific Integrated Circuit. In the absence of an appropriate HSM hardware, the same can also be implemented in the form of a software library. Finally, the invehicle security module protects the interface between the incar networks and wireless communication systems. It consists of two components, namely, firewall that controls the flow of data from other applications to the vehicle and vice versa, and an Intrusion Detection System (IDS) that monitors the in-car systems and detects real-time attacks.

A. Hybrid architecture A hybrid architecture that combines the architectures of vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) known as vehicle-to-vehicle-to-infrastructure (V2V2I) architecture was proposed by Miller (2008). The hybrid architecture combines the benefits of fast queries and responses from the V2I architecture and a distributed architecture without a single point of failure from the V2V architecture. The new hybrid architecture (shown in Figure 3) splits the transportation network into zones and assigns a vehicle as a ‘Super Vehicle’. Super Vehicles have the

privilege of communicating with the central architecture and with other super vehicles. Also, all the other vehicles can only communicate with the Super Vehicle that is responsible for the particular zone in which the vehicle is traversing currently.

A ‘Super Vehicle Detection’ algorithm has been designed and implemented for assigning the Super Vehicle and describing how the other vehicles can identify a Super Vehicle. All the vehicles traversing in a zone will send their location and speed information to the Super Vehicle of the zone over a wireless link. The Super Vehicle is responsible for aggregating the vehicle information and sending it to the central server.

Fig.1.3 V2V2I Architecture Santa, Gómez-Skarmeta and Sánchez-Artigas (2008) realizing the VC to be the cornerstone of future vehicle equipment, studied the unified architecture of V2V and V2I communication systems based on cellular networks. Recent developments in cellular networks have enabled the cellular networks to deal with not only V2I solutions, but also with the V2V communications. A novel communication paradigm that unifies both V2V and V2I communication systems has been developed. The communication infrastructure has been created with the help of peer to peer network technology that enables the vehicles to exchange information among them and with the roadside units. A software platform for implementing on-board services has been extended with a middleware service to provide a high level communication interface for exchanging messages in both V2V and V2I systems. In the present architecture, the traffic network is split into coverage zones, each consisting of a specific communication group.

Geometry information about each area is stored in the Group Server. Local events within the communication group are handled by the Environment Server. The vehicles move from one coverage area to the other through a roaming process. The information about the coverage area is stored in the group server, which transmits the area geometry to the vehicle, and the vehicle connects to the Group Server when it has been detected to be out of the coverage area; thereby, saving communication resources. In case of events such as repairs or traffic jam, the vehicles use a special safety device to notify the event to the environment server, which in turn broadcasts the event; thereby, enhancing the warning mechanism.

Most of the existing studies on V2V communication and Device to Device (D2D) fail to discuss the issue of ‘hole to next hop’ and the resultant ‘dead-ends’ issue. Abd-

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Elrahman et al. (2015) proposed a hybrid model that extends the V2V inter-VC model with D2D architecture. The unified architecture improves the ‘dead-ends’ failure recovery delays with the help of LTE-based D2D mechanisms. In case of a dead-end, the ITS protocol stack of the last V2V transmitting node performs a channel sensing followed by a handover to the D2D mechanism. In a D2D communication network, the devices are within a particular range and connected to the same base station (eNB); thereby, enabling the D2D devices to identify each other easily. The coverage area of the V2V communication system is thus enhanced with the help of LTEenabled D2D mechanism. The chances of a message packet to reach the next hop (belonging to the next set of connected vehicles) in the direction of final destination have been improved.

B. Wireless sensor network architecture In order to provide support for both accident prevention and post-accident investigation, Bohli et al. (2008) proposed WSN Roadside architecture. The architecture was purely based on software security solutions and hence does not need RSUs and tamper resistant modules for the sensor nodes. The communication between the vehicle and roadside units are based on wireless sensor networks. In order to prevent road accidents, the sensor units along the road measure the road conditions at several positions and communicate the aggregated information to the passing vehicle, which generates and distributes the warning message using geocast. The cost of WSN islands can be reduced by equipping each vehicle with an OBU with two RFs (IEEE 802.11p and IEEE 802.15.4), which in turn eliminates the need for RSUs other than the WSNs. Further, post-accident investigation is also supported by the sensor nodes that continuously store the information about the road condition.

Though this information may be of little interest for the forensic team, it can be of interest for a specified group of people (like the insurance companies). Authorized queries to the roadside WSN units can be passed with the help of an IEEE 802.15.4 enabled reader device.

Parallel control and management system has been proposed as a new mechanism for intelligent transportation systems as it is a data-driven approach that considers both the engineering and social complexities for making decisions.

Wang (2010) described the architecture of Parallel Transport Management System (PtMS) that was observed to be effective to be implemented in a complex networked traffic system. The Artificial societies, Computational experiments and Parallel execution (ACP) approach for ITS embeds both cyber physical systems (CPS) and cloud computing along with cyber physical-social systems (CPSS). The parallelism offered by this approach supports a wide-range of new application scenarios. The architecture of ACP-based PtMS has five major components, namely, the actual transportation system, ATS (Artificial Transportation System), OTSt (traffic operator and administrator training systems), DynaCAS (dynamic

network assignment based on Complex Adaptive Systems), and aDAPTS (agent-based Distributed and Adaptive Platforms for Transportation Systems). OTSt is responsible for the learning and training of administrators and traffic operators. DynaCAS is responsible for designing and evaluating transportation experiments, traffic patterns and support the adoption of advanced traveler information systems. aDAPTS provides the supporting environment for developing, managing and managing the agent functions for various traffic tasks, with the help of a Global Traffic Operating Center and other Regional Traffic Operating Centers that can be either virtual or real.

Wireless sensor-based hybrid architecture was proposed by Qureshi, Abdullah and Anwar (2014). The vehicles communicate with the help of OBUs, and various routing applications, through wireless networks. There are two types of sensors deployed, sink and source nodes. While the source nodes measure the traffic conditions, the sink nodes have a processing capability. Both the sensors can communicate with both the vehicle nodes and base station.

The base station gathers information from the sensors and vehicles, which is used by the data center for traffic management and forecast.

Safety and comfort of the passengers can be enabled by quicker distribution of data. Ratnani, Vaghela and Shah (2015) presented a novel architecture for controlling vehicular traffic and enabling quicker distribution of data. The architecture is based on Vehicular Sensor Networks and utilizes the maximum available bandwidth for message transmission and realizes low latency levels for the distribution of messages. In the present architecture, vehicles can automatically detect their transmission range; thereby, effectively broadcast the message with minimum hops. The messages generated by the applications are broadcasted several times. Vehicles fitted with OBUs process the message packets at different layers (MAC, network and transport layer). A priority scheme was adopted to select the next hop forwarder and the message is rebroadcasted based on the distance from the previous sender. The transmission range is calculated dynamically and is used for transmitting the message with as few hops as possible.

C. Software-defined network architecture The VANET architectures were not found to be flexible enough to deploy large scale services and protocols.

Therefore, research scholars propose SDN as an emerging network paradigm for addressing the issue of scalability in VANETs. Ku et al. (2014) demonstrated how the concept of SDN can be implemented to improve the features and services of VANETs. A Software-Defined VANET improves its resource utilization, selects the best routes, and facilitates network programmability by implementing the concepts and functionalities of SDN. The main components include: SDN controller (the central intelligence of the VANET system), SDN wireless node (data plane elements, vehicles), and the

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SDN RSU (data plane elements, RSUs). The architecture deploys different wireless technologies to control and forward the planes in the future (flexibility). Long range wireless connections (LTE/ WiMAX) are used for the control plane and high bandwidth wireless connections (Wi-Fi) were used for the data plane. SDN also has an open-flow enabled switch that allows for different modes of operation to be carried out in VANET environment. Depending on the configuration of the services to be supported, the number of Wi-Fi interfaces used as data channels may vary. An SDN module consists of an interface that accepts data from a separated control plane, and packet processing units. The SDN wireless nodes have a local SDN agent which acts as a backup controller in the absence of SDN controller communication. These agents support traditional ad hoc routing protocols that provide fallback mechanisms for the SDN network to resume ad hoc operations in the absence of communication with SDN controller. As any information from any wireless node passes through its own SDN module, the SDN controller can evaluate user-traffic access into the network. The SDN wireless node internals are shown in Figure 4.

Fig.1.4 SDN Wireless node internals SDN-based vehicular Adhoc with fog computing called FSDN VANET was proposed by Truong, Lee and Ghamri-Doudane (2015). Fog computing was proposed as it delivers delay sensitive and location-aware services. The components of the architecture include SDN controller: global intelligence and manages resources for the fog, SDN wireless nodes: data plane elements, SDN RSU: a fog device controlled by SDN controller, SDN RSU Controller: stores local road system information and provides emergency services, and Cellular Base Station: offers fog services. The work of SDN controller is shared with BSs and RSUCs in the hybrid control mode. The RSUCs, BSs and SDN controller offer virtualization to provide cloud services. The proposed architecture was found to optimize resource utilization, reduce latency and augment V2V, V2I, vehicle-base station communication, and SDN centralized control.

Salahuddin, Al-Fuqaha and Guizani (2015) proposed a novel SDN-based RSU cloud for IoV. In order to

dynamically instantiate, replicate, and migrate the services, the architecture uses traditional and specialized RSUs that employ SDN. The deep programmability of SDN is supported by the decoupling of control and splitting of communication planes into the physical data plane and abstract control plane.

The RSU microdatacenters have additional hardware and software components to provide SDN-enabled communication services and virtualization. The hardware includes a small form factor computing device and open-flow switch. The software components include the host OS and a hypervisor for providing virtualization. The datacentres also have OpenFlow controllers, cloud controllers, and RSU CRMs. While the RSU CRM communicates service hosting/ migration information to the OpenFlow and cloud controllers, in the data plane, the cloud controllers govern the service migration and hyervizers to instantiate new VMs to host services. The switch flow rules are updated by the OpenFlow controllers through the control plane.

D. Vehicular cloud architecture A new shift in the technology that utilizes the advantages of cloud computing to serve the drivers of VANETs was observed. The resultant technology was known as Vehicular Cloud Computing (VCC). The evolution of VANETS with two emerging paradigms, namely, cloud computing and information centric networking was studied by Lee et al. (2014). The resultant system was known as the Vehicular Cloud Networking (VCN). The vehicle cloud is created by interconnecting the resources that are available in the vehicles and roadside units; thereby, maximizing the collaboration efficiency. Each vehicle in the cloud has three resources: data storage, computing and sensors. The data storage stores the vehicle information, the sensors detect and self-actuate the events in the physical environment, and the computing resource is a collection of mobile resources, thereby merging mobile cloud model with vehicular networks to handle network service provisioning. The resources are connected via P2P connections.

The potential of cloud computing technologies to improve road safety and the travel experience in ITS has been realized by various researchers. A multi-layered data cloud architecture that combines the technologies of cloud computing and IoT has been proposed by He, Yan and Da Xu (2014). The proposed vehicular data cloud platform integrates the following devices to support V2V and V2I communication mechanisms: controllers, actuators, sensors, mobile phones, internet access devices, road side infrastructure (street lights, smart metering), communication technology (satellite and wireless networks, internet), middleware, and cloud computing IoT. Such a multi-layered platform can provide secure and on-demand services using the associated clouds (conventional and temporary cloud). The IoT based vehicular cloud architecture is shown in Figure 5. While the conventional cloud which comprises of virtual computers provide cloud services for the client, the temporary cloud is

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formed on demand and comprises of storage and networking facilities and under-utilized computers. The bottom layer of the architecture provides the required functional support for the layers above. Heterogeneous web services, applications, middleware systems that provide information and communication services and connect in-vehicle and outvehicle devices in the vehicular data cloud are integrated by applying Service-Oriented Architecture (SOA).

in an overall higher efficiency of the network. IC consists of static RSUs and mobile entities, and the communication between different ICs is carried out with the help of local servers. BEC consists of extensive resources that can be used by the vehicles for data processing and storage and serves high bandwidth requirements.

Table 1.1 Communication networking of VCN (Ahmad et al., 2015) Communication Tier Cloud Scope network Vehicular Cloud (VC)

Vehicle ↔ Vehicle Vehicle ↔ VC

Local

2

Infrastructure Cloud (IC)

Vehicle ↔ RSU Vehicle ↔ IC Base Station ↔ RSU Base Station ↔ IC Base Station ↔ Local Server

Local to small geographical areas

3

Back-End Cloud (BEC)

Local Server ↔ BEC

Large geographical areas

1

Fig.1.5 IoT based Vehicular Cloud architecture A similar cloud computing model known as VANET Cloud was proposed by Bitam, Mellouk and Zeadally (2015).

The permanent sub-model consists of virtual machines, storage and processing units, and bandwidth that are available to the VANET entities (vehicles and RSUs). The temporary sub-model consists of computing resources and passenger devices and is added to the permanent model. All the components are organized into layers: client layer, communication layer and the cloud layer. The client layer consists of the end users that use Service Access Points (SAPs) for sending service requests and receiving service responses. The communication layer establishes connections between the clients at the lower layer and the VANET cloud server. The communication technologies deployed at this layer include VANETs, wireless sensors, 3G/ 4G networks, RSUs, cellular base stations, private networks and so on. The cloud layer consists of the data centers that provide data services.

The permanent and temporary sub-models are connected via a network that comprises of all the data centers from both the layers.

Ahmad et al. (2015) presented a Vehicular Cloud Networking (VCN) technology by integrating vehicles and the adjacent infrastructure with the traditional internet clouds. The architecture consists of three types of clouds (three-tier architecture), namely, Vehicular Cloud, infrastructure cloud (IC) and the traditional back-end (IT) cloud (BEC). The communication network and the scope of each cloud are shown in Table 1. VC consists of the physical resources of the vehicles that are shared among the vehicles; thereby, resulting

Clustering techniques have been proposed to solve the problem of resource limitation in vehicular cloud networks. Arkian et al. (2015) proposed a vehicular cloud architecture that consists of a flexible cluster. The cluster head is selected by means of a fuzzy logic and Q-learning techniques (to select a service provider) and queuing strategies were used to solve the resource allocation problem. As vehicles form dynamic clusters, the most suitable ones are assigned as the cluster heads (CH) which functions as the cloud controller and is held responsible for creating, maintaining and removing the vehicular cloud. The virtualized physical resources of the vehicles are registered with the CH, which in turn schedules the resources for the vehicles in the cloud. Vehicles are grouped into clusters based on their location, speed and direction of travel. This information is broadcasted to the neighboring vehicle within the communication range. The CH is elected for the cluster based on the Fit Factor of the nodes, which is calculated by a Fuzzy Logic Controller based on a set of fuzzy logic rules embedded in the fuzzy inference engine.

More recently, Hagenauer et al. (2017) presented a virtual network infrastructure that deployed clusters of parked cars to form a virtual network. The basic idea behind this infrastructure is that cars driving by connect to a parked car and accesses all the services or applications within the cluster

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through this parked car. In order to maintain an uninterrupted connection, the connection will be handed over to another parked vehicle after the lapse of a certain amount of time. The parked cars must be equipped with a short range networking technology such as IEEE 802.11p, a basic GPS server, an address-based routing protocol, and distributed storage functionality such as Distributed Hash Table (DHT). In order to minimize the load on the channel, a subset of the parked cars has been selected as the active gateways to the cluster.

E. Heterogeneous network architecture Sadek et al. (2015) proposed a heterogeneous LTE/ Wi-Fi vehicular system to meet the different application requirements simultaneously. The proposed system supports both infotainment and ITS traffic control data. The heterogeneous vehicular system is a combination of long range (LTE) and short range services (Wi-Fi). While the LTE offers a high coverage area, Wi-Fi offers a relatively higher capacity that is cost-effective. The overall performance of the system is enhanced by coupling high capacity with long range communication. The architecture addresses the following VC networks: backhaul connection, Infrastructure-to-Vehicle (I2V) communication, and On-board Vehicle Communication (OVC). The I2V network connects vehicles with the LTE eNodeB (fixed along the roads) and provides access to the LTE network; whereas, the OVC network consists of OBUs fitted to the vehicles, Wi-Fi access points (APs), and passenger devices. Internet access and connectivity is provided using the LTE as a backhaul link, to the vehicles that use Wi-Fi to connect to the last mile link. The ITS data are sent to the vehicle’s OBU through the Ethernet and the infotainment data is sent to the passenger’s devices through Wi-Fi. Similarly, Ucar, Ergen and Ozkasap (2016) developed a hybrid architecture known as VMaSC-LTE by combining IEEE 802.11p based VANETs with the LTE cellular technology (4G cellular system) to achieve high data packet delivery ratio and low delay.

Heterogeneous vehicular networks (HetVNETs) have been believed to meet the various Quality of Sevices (QoS) requirements for ITS. However, on the face of ever changing network landscape, HetVNETs cannot effectively address the QoS requirements. Zheng et al. (2016) proposed the use of cloud RAN (Radio Access Network) architecture to support the dynamic nature of HetVNETs. The new architecture integrates various wireless access schemes. It consists of a Remote Radio Head (RRH) which converts the digital signals to amplified analog signals before air transmission. The base band processing cloud is responsible for implementing the functionalities of RAN. The RRH and the base band processing cloud are deployed separately and are connected by optical fiber cables; thereby, supporting soft-defined HetVNETs (SERVICE) and enabling dynamic shared resource allocation via open platform and real-time virtualization technology. The flexibility to customize the computing environments is also provided with the help of

SERVICE. The SERVICE consists of a number of base stations with wireless access techniques, and its infrastructure has various processing and storage abilities. The multi-layered cloud architecture is deployed on the SERVICE, where each cloudlet consists of a cluster of communication resources and computation devices. The SERVICE cloudlet is integrated with the local RAN to provide physical proximity.

A heterogeneous wireless network for seamless VANET connectivity was proposed by Agrawal, Tyagi and Misra (2016). The hierarchical architecture comprises of WLAN (802.11p) that covers 150-300 m and has a high bandwidth, at the lowest level, followed by cellular technology that covers several kms at the middle level, and WiMAX technology that has a much wider coverage area (50 kms) and higher bandwidth is present at the top layer.

Continuous availability of the network and reliable communication of messages has been made possible by the integration of these three wireless radio access technologies.

F. Cooperative network architecture A cooperative vehicular networking architecture was proposed by Zhou et al. (2015). In the proposed architecture, multiple (UAVs) Unmanned Aerial Vehicles form an aerial subnetwork. The vehicular subnetwork on the ground is aided by the UAV through A2A (Air-to-Air) and A2G (Air-toGround) communications. As the UAVs are flexible in terms of mobility, they can be used as intermediate relays during network partitions in the round vehicular subnetwork. Further, disaster rescue and polluted area investigation can be carried out with the help of such cooperative networks. A multipleUAV-aided vehicular network includes the following components: UAVs, Ground vehicles, and Control centers (Ground stations). The UAVs are equipped with sensors (imaging and position), communication modules and embedded processors. The collected image data are passed to the ground subnetwork. The ground vehicles carry communication and processing modules that facilitate cooperation between the UAVs and ground vehicles. The information from ground vehicles are transmitted through a multi-hop ground vehicle route. In order to maintain coordination within the two-layered network, it is inevitable to set up control centers that are mainly responsible for data processing, vehicle and UAVs scheduling, and connecting aerial and ground sub-networks. The present architecture houses three types of networks: aerial networks (A2A), ground networks (V2V), and air-ground networks (A2V). The A2A network uses heterogeneous radio interfaces (IEEE 802.15.4/ IEEE 802.11), V2V uses intermitted V2V links (IEEE 802.11p), the links in A2V network must facilitate component scheduling, subnetwork coordination, and communication relay.

During the following year, Kitazato et al. (2016) proposed a new system known as Proxy CAM (Cooperative Awareness Message) to assist V2V messaging. CAMs are the messages exchanged by vehicles in Cooperative Intelligent

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Transportation System (CITS). The Proxy CAM generates V2V messages on behalf of sender vehicles in the roadside unit. The system design was based on the ITS Station architecture and compliant with CAM. The system consists of Roadside Sensors that detect vehicles and sends the vehicle information to the server in the infrastructure, Sensor Fusion Database stores the vehicle information in the database, Proxy CAM Generator generates the CAMs by inserting data into the CAM field in the database, and Proxy CAM Transmitter that broadcasts the generated proxy CAMs with the help of IEEE 802.11p at the network layer, GeoNetworking at the network layer and Basic Transport Protocol (BTP) at the transport layer.

In order to utilize the resources and infrastructure of intelligent transport system (ITS) effectively, Sharma, Moon and Park (2017) proposed a novel vehicular architecture known as Block-VN that is based on Blockchain in smart city which allows for the development of a distributed network of large scale vehicles effectively. The present architecture consists of the controller nodes, minor nodes, and other ordinary nodes. The controller and minor nodes are connected in a distributed manner to achieve scalability and availability of the network. Every time a new vehicle is registered, its manufacturers provide all the details about the vehicle to the revocation authority which decides on the minor nodes outside the nodes of the controller. The revocation node provides all the vehicle information to the ordinary and minor nodes in the distribute network. The controller node is equipped with a hash, timestamp, nonce, and a Merkle root that stores all the service information; whereas, the minor nodes have the devices for sensing, storing and computing. The controller node operates at the individual level to process the data and transmits the data securely in a distributed manner using public-private key encryption.

4. KEY FINDINGS OF THE STUDY A summary of the review is presented in Table 2.

Table 1.2 Significant findings Catego ry

Hybrid

WSN

Author and year

Key findings

Miller (2008)

Offers the advantages of both V2V (fast queries and responses) and V2I architecture (distributed architecture).

Santa, GรณmezSkarmeta and SรกnchezArtigas (2008) AbdElrahman et al. (2015) Bohli et al.

(2008)

Catego ry

Author and year

Qureshi, Abdullah and Anwar (2014)

ACP-based PtMS integrates engineering with social complexities for decision-making in complex networked traffic system.

Implementation of WSN in vehicular networks reduces the investment and enhances the traffic efficiency.

Ratnani, Vaghela and Shah (2015)

Enables quicker distribution of data by utilizing the maximum available bandwidth.

Ku et al.

(2014)

Software-defined VANETs enhances resource utilization, network routing, and scalability fo the network.

Wang (2010)

SDN

Truong, Lee and GhamriDoudane (2015) Salahuddin, Al-Fuqaha and Guizani (2015) Lee et al.

(2014)

He, Yan and Da Xu (2014)

Vehicul ar cloud

Bitam, Mellouk and Zeadally (2015) Ahmad et al.

(2015) Arkian et al.

(2015) Hagenauer et al. (2017)

Cellular networks can be adopted for dealing with V2I and V2V communications.

Sadek et al.

(2015) Heteroge Ucar, Ergen neous and Ozkasap network (2016) architectu Agrawal, re Tyagi and Misra (2016) Zheng et al.

(2016)

Integration of V2V with D2D architecture improves the dead-ends failure recovery delays.

Software solutionss that are independent of tamper resistant modules for sensor nodes to aid in accident prevention and investigation.

516

Key findings

By integrating Fog framework with SDN-based VANET delay sensitive and location-aware services can be offered to meet the future needs of VANETs. The deep programmability of SDN can be used to reconfigure the services hosted by the network to meet the demands of the vehicles.

VCN combines the benefits of cloud computing and information networking, and enhances collaboration efficiency.

Multi-layered cloud architecture combines cloud computing and IoT to provide on-demand services with the help of temporary and conventional clouds.

Cloud computing technologies provide flexible solutions for improving road safety and travel experience.

The VCN connects the vehicles with adjacent infrastructure to improve the overall efficiency of the network.

Flexible clusters are formed to solve the issues of resource limitation.

Clusters of parked cars can be used to form a virtual network to minimize the channel load.

Combines long range LTE with short range services (Wi-Fi) to meet various application requirements simultaneously.

Cellular technologies integration with VANETs increase the packet data delivery ratio.

Continuous availability of network can be achieved by integrating radio access technologies at various levels.

Cloud RAN architecture can be proposed for HetVNETs to meet

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Catego ry

Author and year

[3]

Key findings various QoS requirements.

Zhou et al.

(2015)

Cooperati ve network architectu re Kitazato et al.

(2016)

Cooperative networks that combines aerial subnetwork and vehicular subnetwork, can be utilized effectively for disaster recovery and investigation of polluted areas.

The V2V messages are generated by proxy CAMs on behalf of sender vehicles to solve the issues of obstacle interference and mixed environment.

[4]

[5]

5. CONCLUSION AND KEY CHALLENGES This paper presented a review of the existing network architectures with their relative advantages and disadvantages.

Routing protocols are the kernels for any VC. While adopting position-based routing protocols for vehicular networks, enhancing the position accuracy and availability of vehicles in all environments, addressing the compatibility of routing protocols in a complex vehicular network environment, minimizing delay under various constraints, security support, and data protection are some of the key challenges to be addressed. High mobility and constant topological changes have always remained the key constraints for developing efficient routing techniques for communication network architecture.

In case of vehicular cloud networks, lifetime periods and forwarding zones for the data must be defined appropriately to avoid network congestion. Standardization of technologies to overcome the problem of technologies incompatibility and communication, and data aggregation techniques for optimizing resource utilization must be adopted to address the issues of technologies coexistence.

Architecture scalability challenges with respect to the organization, technology, and road topology must be addressed while defining a vehicular network. Further, information selection and dissemination policies must be selected carefully to optimize the resources and avoid network congestion by selecting suitable paths.

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