smart intelligent computing and applications

Smart Innovation, Systems and Technologies 105

Suresh Chandra Satapathy Vikrant Bhateja · Swagatam Das Editors

Smart Intelligent Computing and Applications Proceedings of the Second International Conference on SCI 2018, Volume 2

Smart Innovation, Systems and Technologies

Volume 105

Series editors

Robert James Howlett, Bournemouth University and KES International,Shoreham-by-sea, UKe-mail: [email protected]

Lakhmi C. Jain, University of Technology Sydney, Broadway, Australia;University of Canberra, Canberra, Australia; KES International, UKe-mail: [email protected]; [email protected]

The Smart Innovation, Systems and Technologies book series encompasses thetopics of knowledge, intelligence, innovation and sustainability. The aim of theseries is to make available a platform for the publication of books on all aspects ofsingle and multi-disciplinary research on these themes in order to make the latestresults available in a readily-accessible form. Volumes on interdisciplinary researchcombining two or more of these areas is particularly sought.

The series covers systems and paradigms that employ knowledge and intelligencein a broad sense. Its scope is systems having embedded knowledge and intelligence,which may be applied to the solution of world problems in industry, the environmentand the community. It also focusses on the knowledge-transfer methodologies andinnovation strategies employed to make this happen effectively. The combination ofintelligent systems tools and a broad range of applications introduces a need for asynergy of disciplines from science, technology, business and the humanities. Theseries will include conference proceedings, edited collections, monographs, hand-books, reference books, and other relevant types of book in areas of science andtechnology where smart systems and technologies can offer innovative solutions.

High quality content is an essential feature for all book proposals accepted for theseries. It is expected that editors of all accepted volumes will ensure thatcontributions are subjected to an appropriate level of reviewing process and adhereto KES quality principles.

More information about this series at http://www.springer.com/series/8767

http://www.springer.com/series/8767

Suresh Chandra Satapathy • Vikrant BhatejaSwagatam DasEditors

Smart Intelligent Computingand ApplicationsProceedings of the Second InternationalConference on SCI 2018, Volume 2

123

EditorsSuresh Chandra SatapathySchool of Computer EngineeringKIIT Deemed to be UniversityBhubaneswar, Odisha, India

Vikrant BhatejaDepartment of Electronics andCommunication Engineering

Shri Ramswaroop Memorial Groupof Professional Colleges

Lucknow, Uttar Pradesh, India

Swagatam DasElectronics and CommunicationSciences Unit

Indian Statistical InstituteKolkata, West Bengal, India

ISSN 2190-3018 ISSN 2190-3026 (electronic)Smart Innovation, Systems and TechnologiesISBN 978-981-13-1926-6 ISBN 978-981-13-1927-3 (eBook)https://doi.org/10.1007/978-981-13-1927-3

Library of Congress Control Number: 2018950782

© Springer Nature Singapore Pte Ltd. 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,Singapore

https://doi.org/10.1007/978-981-13-1927-3

Organizing Committee

Organizing Chairs

Dr. Suresh Chandra Satapathy, PVPSIT, IndiaDr. B. V. SubbaRao, PVPSIT, India

Program Chairs

Dr. M. V. Rama Krishna, PVPSIT, IndiaDr. J. Rajendra Prasad, PVPSIT, India

Program Co-chairs

Dr. S. Madhavi, PVPSIT, IndiaDr. P. V. S. Lakshmi, PVPSIT, IndiaDr. A. Sudhir Babu, PVPSIT, India

Conference Convenor

Dr. B. Janakiramaiah, PVPSIT, India

Conference Co-convenor

Dr. P. E. S. N. Krishna Prasad, PVPSIT, IndiaMr. Y. Suresh, PVPSIT, India

Publicity Chairs

Ms. A. Ramana Lakshmi, PVPSIT, IndiaMr. A. Vanamala Kumar, PVPSIT, IndiaMs. D. Kavitha, PVPSIT, India

Special Session Chairs

Dr. B. Srinivasa Rao, PVPSIT, IndiaMs. G. Reshma, PVPSIT, India

v

Workshop/Tutorial Chairs

Mr. B. N. Swamy, PVPSIT, IndiaMs. A. Haritha, PVPSIT, India

Web Master

Mr. K. Syama Sundara Rao, PVPSIT, IndiaMr. L. Ravi Kumar, PVPSIT, India

Organizing Committee Members

Computer Science and Engineering

Ms. J. Rama DeviMs. G. Lalitha KumariMs. V. Siva ParvathiMs. B. Lakshmi RamaniMr. I. M. V. KrishnaMs. D. SwapnaMr. K. Vijay KumarMs. M. SailajaMs. Y. SurekhaMs. D. Sree LakshmiMs. T. Sri LakshmiMr. N. Venkata Ramana GuptaMs. A. MadhuriMs. Ch. Ratna JyothiMr. P. Anil KumarMr. S. Phani PraveenMr. B. Vishnu VardhanMr. D. Lokesh Sai KumarMs. A. DivyaMr. A. Yuva KrishnaMr. Ch. Chandra MohanMr. K. VenkateshMr. V. RajeshMr. M. Ramgopal

Information Technology

Ms. J. SirishaMr. K. Pavan KumarMs. G. LakshmiMr. D. RatnamMr. M. Sundara Babu

vi Organizing Committee

Mr. S. Sai KumarMs. K. Swarupa RaniMr. P. Ravi PrakashMr. T. D. Ravi KiranMs. Y. PadmaMs. K. Sri VijayaMs. D. Leela DharaniMr. G. VenugopalMs. M. SowjanyaMr. K. PrudvirajuMr. R. Vijay Kumar Reddy

Organizing Committee vii

Preface

The Second International Conference on Smart Computing and Informatics (SCI2018) was successfully organized by Prasad V. Potluri Siddhartha Institute ofTechnology, Vijayawada, Andhra Pradesh. The objective of this internationalconference was to provide a platform for academicians, researchers, scientists,professionals, and students to share their knowledge and expertise in the field ofsmart computing comprising of soft computing, evolutionary algorithms, swarmintelligence, Internet of things, machine learning, etc. and address various issues toincrease an awareness of technological innovations and to identify challenges andopportunities to promote the development of multidisciplinary problem-solvingtechniques and applications. Research submissions in various advanced technologyareas were received, and after a rigorous peer review process with the help oftechnical program committee members, elite quality papers were accepted. Theconference featured special sessions on various cutting-edge technologies whichwere chaired by eminent professors. Many distinguished researchers likeDr. Lakhmi C. Jain, Australia; Dr. Nabil Khelifi, Springer, Germany; Dr. RomanSenkerik, Tomas Bata University in Zlin, Czech Republic; Dr. B. K. Panigrahi, IITDelhi; and Dr. S. Das, Indian Statistical Institute, Kolkata, attended the conferenceand delivered the talks.

Our sincere thanks to all special session chairs, reviewers, authors for theirexcellent support. We would like to thank Siddhartha Academy for all the supportto make this event possible. Special thanks to Sri B. Sreeramulu, Convenor,PVPSIT, and Dr. K. Sivaji Babu, Principal, PVPSIT, for their continuous support.We would like to extend our special thanks to very competitive team members fromthe Department of CSE and IT, PVPSIT, for successfully organizing the event.

Editorial BoardsBhubaneswar, India Dr. Suresh Chandra SatapathyLucknow, India Dr. Vikrant BhatejaKolkata, India Dr. Swagatam Das

ix

Details of Special Sessions

1. Special Session on “Emerging Trends in BigData, IoT and Social Networks(ETBIS)”

Session Chair

Dr. L. D. Dhinesh Babu, VIT University, Vellore, TN, India

Co-session Chair(s)

Dr. S. Sumathy, VIT University, Vellore, TN, IndiaDr. J. Kamalakannan, VIT University, Vellore, TN, India

2. Special Session on “Applications of Computing in InterdisciplinaryDomains”

Session Chair

Prof. Dr. V. SumaDean, Research and Industry Incubation CentreProfessor, Department of Information Science and Engineering,Dayananda Sagar College of EngineeringBengaluru, 560078, India

3. “Recent Trends in Data Science and Security Analytics”

Session Chair(s)

Dr. Sireesha Rodda, GITAM University, Visakhapatnam, IndiaDr. Hyma Janapana, GITAM University, Visakhapatnam, India

4. Special Session on “Trends in Smart Healthcare Technologies (TSHT)”

Session Chair

Dr. K. Govinda, VIT University, Vellore, TN, India

xi

Co-session Chair(s)

Dr. R. Rajkumar, VIT University, Vellore, TN, IndiaDr. N. Sureshkumar, VIT University, Vellore, TN, India

xii Details of Special Sessions

Contents

An Adaptive ARP Approach for Fog-Based RSU Utilization . . . . . . . . . 1G. Jeya Shree and S. Padmavathi

Identification of Meme Genre and Its Social Impact . . . . . . . . . . . . . . . 11Abhyudaya Pandey, Sandipan Mukherjee, Sandal Bajajand Annapurna Jonnalagadda

Shannon’s Entropy and Watershed Algorithm Based Techniqueto Inspect Ischemic Stroke Wound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23V. Rajinikanth, K. Palani Thanaraj, Suresh Chandra Satapathy,Steven Lawrence Fernandes and Nilanjan Dey

Scheme for Unstructured Knowledge Representation in MedicalExpert System for Low Back Pain Management . . . . . . . . . . . . . . . . . . 33Debarpita Santra, Sounak Sadhukhan, S. K. Basu, Sayantika Das,Shreya Sinha and Subrata Goswami

An Unvarying Orthogonal Search with Small Triangle Patternfor Video Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43S. Immanuel Alex Pandian and J. Anitha

Imputation of Multivariate Attribute Values in Big Data . . . . . . . . . . . . 53K. Shobha and S. Nickolas

Retrofitting of Sensors in BLDC Motor Based e-Vehicle—A StepTowards Intelligent Transportation System . . . . . . . . . . . . . . . . . . . . . . 61N. Pothirasan and M. Pallikonda Rajasekaran

Statistical Analysis of EMG-Based Features for DifferentHand Movements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71C. N. Savithri and E. Priya

A Secure and Hybrid Approach for Key Escrow Problemand to Enhance Authentic Mobile Wallets . . . . . . . . . . . . . . . . . . . . . . . 81D. Shibin and Jaspher W. Kathrine

xiii

Extended Lifetime and Reliable Data Transmission in WirelessSensor Networks with Multiple Sinks . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Vasavi Junapudi and Siba K. Udgata

Prediction of Software Cost Estimation Using SpikingNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101V. Venkataiah, Ramakanta Mohanty and M. Nagaratna

Modeling of PV Powered Seven-Level Inverter for PowerQuality Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113R. Arulmurugan and A. Chandramouli

Social Group Optimization and Shannon’s Function-BasedRGB Image Multi-level Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . 123R. Monisha, R. Mrinalini, M. Nithila Britto, R. Ramakrishnanand V. Rajinikanth

Effective Neural Solution for Multi-criteria Word Segmentation . . . . . . 133Han He, Lei Wu, Hua Yan, Zhimin Gao, Yi Feng and George Townsend

Detection and Classification of Trendy Topics for RecommendationBased on Twitter Data on Different Genre . . . . . . . . . . . . . . . . . . . . . . . 143D. N. V. S. L. S. Indira, R. Kiran Kumar, G. V. S. N. R. V. Prasadand R. Usha Rani

An Enhanced RGB Scan Image Encryption UsingKey-Based Partitioning Technique in Cloud . . . . . . . . . . . . . . . . . . . . . . 155Karuna Arava and L. Sumalatha

An Unbiased Privacy Sustaining Approach Based on SGOfor Distortion of Data Sets to Shield the Sensitive Patternsin Trading Alliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165B. Janakiramaiah, G. Kalyani, Suresh Chittineniand B. Narendra Kumar Rao

Contrast-Enhanced Recursive Visual Cryptography SchemeBased on Additional Basis Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Thomas Monoth

Authenticity and Integrity Enhanced Active Digital ImageForensics Based on Visual Cryptography . . . . . . . . . . . . . . . . . . . . . . . . 189T. E. Jisha and Thomas Monoth

Studying the Contribution of Machine Learning and ArtificialIntelligence in the Interface Design of E-commerce Site . . . . . . . . . . . . . 197Megharani Patil and Madhuri Rao

Discrete Wavelet Transform Based Invisible WatermarkingScheme for Digital Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207Swapnil Satapathy, Khushi Jalan and Rajkumar Soundrapandiyan

xiv Contents

Noise Removal in EEG Signals Using SWT–ICA CombinationalApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Apoorva Mishra, Vikrant Bhateja, Aparna Gupta and Ayushi Mishra

Non-local Mean Filter for Suppression of Speckle Noisein Ultrasound Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225Avantika Srivastava, Vikrant Bhateja, Ananya Gupta and Aditi Gupta

Various Image Segmentation Algorithms: A Survey . . . . . . . . . . . . . . . 233Kurumalla Suresh and Peri Srinivasa rao

Peak-to-Average Power Ratio Performance of TransformPrecoding-Based Orthogonal Frequency Division MultiplexingOffset Quadrature Amplitude Modulation . . . . . . . . . . . . . . . . . . . . . . . 241N. Renuka and M. Satya Sairam

On the Notch Band Characteristics of CPW-Fed Elliptical Slot . . . . . . . 249B. Surendra Babu, K. Nagaraju and G. Mahesh

HDFS Pipeline Reformation to Minimize the Data Loss . . . . . . . . . . . . . 259B. Purnachandra Rao and N. Nagamalleswara Rao

Short Note on the Application of Compressive Sensingin Image Restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267Chiluka Ramesh, D. Venkat Rao and K. S. N. Murthy

An Automated Big Data Processing Engine . . . . . . . . . . . . . . . . . . . . . . 275Leonid Datta, Abhishek Mukherjee, Chetan Kumar and P. Swarnalatha

Predictive Analysis of Stocks Using Data Mining . . . . . . . . . . . . . . . . . . 283G. Magesh and P. Swarnalatha

Efficient Load Balancing Algorithm for Task Preprocessingin Fog Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291A. B. Manju and S. Sumathy

An Empirical Comparison of Six Supervised Machine LearningTechniques on Spark Platform for Health Big Data . . . . . . . . . . . . . . . . 299Gayathri Nagarajan and L. D. Dhinesh Babu

Novel Text Preprocessing Framework for Sentiment Analysis . . . . . . . . 309C. S. Pavan Kumar and L. D. Dhinesh Babu

Improved Genetic Algorithm for Monitoring of Virtual Machinesin Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Sayantani Basu, G. Kannayaram, Somula Ramasubbareddyand C. Venkatasubbaiah

Technology Convergence for Smart Transportationand Emergency Services in Health care . . . . . . . . . . . . . . . . . . . . . . . . . 327G. Kannayaram and Shubham Saraff

Contents xv

A Honey Bee Inspired Cloudlet Selection for Resource Allocation . . . . . 335Ramasubbareddy Somula and R. Sasikala

A New Indexing Technique for Healthcare IOT . . . . . . . . . . . . . . . . . . . 345P. Sony and N. Sureshkumar

Monitoring Individual Medical Record by Using WearableBody Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355Jagadeesh Gopal, E. Sathiyamoorthy and R. Mohamad Ayaaz

CLOUD COMPUTING-TMACS: A Robust and Verifiable ThresholdMulti-authority Access Control System in Public Cloud Storage . . . . . . 365K. Selvakumar, L. SaiRamesh, S. Sabena and G. Kannayaram

Analysis of CPU Scheduling Algorithms for Cloud Computing . . . . . . . 375Ramasubbareddy Somula, Sravani Nalluri, M. K. NallaKaruppan,S. Ashok and G. Kannayaram

Sentiment Analysis of Restaurant Reviews . . . . . . . . . . . . . . . . . . . . . . . 383R. Rajasekaran, Uma Kanumuri, M. Siddhardha Kumar,Somula Ramasubbareddy and S. Ashok

Content-Based Movie Recommendation System Using GenreCorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391SRS Reddy, Sravani Nalluri, Subramanyam Kunisetti, S. Ashokand B. Venkatesh

Iterative Approach for Frequent Set Mining Using HadoopOver Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399S. Prasanna, Subashini Narayan, M. K. NallaKaruppan,Chunduru Anilkumar and Somula Ramasubbareddy

A Case Study on Recommendation Systems Based on Big Data . . . . . . . 407M. Sandeep Kumar and J. Prabhu

Design of EHR in Cloud with Security . . . . . . . . . . . . . . . . . . . . . . . . . . 419R. Prathap, R. Mohanasundaram and P. Ashok Kumar

Comparative Analysis of a Systematic Coherent EncryptionScheme for Large-Scale Data Management Using CryptographicEncryption Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427A. Stephen Dass and J. Prabhu

Attacks in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Vyshnavi Nagireddy and Pritee Parwekar

A Predictive Approach to Estimate Software Defects DensityUsing Weighted Artificial Neural Networks for the GivenSoftware Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449T. Ravi Kumar, T. Srinivasa Rao and Sandhya Bathini

xvi Contents

Heterogeneous Data Distortion for Privacy-Preserving SVMClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459J. Hyma, P. Sanjay Varma, S. V. S. Nitish Kumar Gupta and R. Salini

Microarray Analysis Using Multiple Feature Data ClusteringAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469B. SivaLakshmi and N. Nagamalleswara Rao

Privacy Preserving Data Clustering Using a HeterogeneousData Distortion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477Padala Preethi, Kintali Pavan Kumar, Mohammed Reezwan Ullhaq,Anantapalli Naveen and Hyma Janapana

Dimensionality Reduction Using Subset Selection Methodin Framework for Hyperspectral Image Segmentation . . . . . . . . . . . . . . 487B. Ravi Teja, J. Hari Kiran and B. Sai Chandana

A Survey on Business Intelligence Tools for Marketing, Financial,and Transportation Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495Chavva Subba Reddy, Ravi Sankar Sangam and B. Srinivasa Rao

Performance and Cost Evolution of Dynamic Increase HadoopWorkloads of Various Datacenters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505N. Deshai, S. Venkataramana and G. Pardha Saradhi Varma

Selection of Commercially Viable Areas for Taxi Drivers UsingBig Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517Kavya Devabhakthuni, Bhavya Munukurthi and Sireesha Rodda

Relevance Feedback Mechanism for Resolving TranscriptionAmbiguity in SMS Based Literature Information System . . . . . . . . . . . 527Varsha M. Pathak and Manish R. Joshi

Integration of Vector Autoregression and Artificial NeuralNetworks: A Robust Model for Prediction of Nonstationary Data . . . . . 543Akhter Mohiuddin Rather

Clustering with Polar Coordinates System: Exploring Possibilities . . . . 553Yogita S. Patil and Manish R. Joshi

Dynamic Modelling of Runoff in a Watershed UsingArtificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561Sandeep Samantaray and Dillip K. Ghose

Estimation of Aquifer Potential Using BPNN, RBFN, RNN,and ANFIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Umesh Kumar Das, Sandeep Samantaray, Dillip K. Ghoseand Parthajit Roy

Contents xvii

Design and Implementation of a Scenario-Based CommunicationModel for VANETs in EXata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577V. Ravi Ram, B. G. Premasudha and R. Suma

Predicting the Severity of Closed Source Bug Reports UsingEnsemble Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589M. N. Pushpalatha and M. Mrunalini

Survey on Sentiment Analysis from Affective MultimodalContent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599Sujay Angadi and R. Venkata Siva Reddy

An Effective Technique for Controlling the Speed of SensorlessInduction Motor Through Modified EKF . . . . . . . . . . . . . . . . . . . . . . . . 609C. Kamal Basha and S. Mohan Krishna

A Microservices-Based Smart IoT Gateway System . . . . . . . . . . . . . . . . 619Pradyumna Sridhara, Narsimh Kamath and Sreeharsha Srinivas

Privacy Preserving in Data Stream Mining Using StatisticalLearning Methods for Building Ensemble Classifier . . . . . . . . . . . . . . . 631P. Chandrakanth and M. S. Anbarasi

A Novel Public Key Crypto System Based on BernsteinPolynomial on Galois Fields 2m to Secure Data on CFDP . . . . . . . . . . . 639Smitha Sasi and L. Swarna Jyothi

Compression of Medical Images Based on Devils CurveCoordinate System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649B. Santhosh and Viswanath Kapinaiah

Technology Adoption in the SME Sector for PromotingAgile Manufacturing Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659Jawahar J. Rao and Vasantha Kumar

Leaf Recognition and Classification Using Chebyshev Moments . . . . . . 667K. Pankaja and V. Suma

Predictive Analytics Techniques Using Big Data for HealthcareDatabases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679Manjula Sanjay Koti and B. H. Alamma

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687

xviii Contents

About the Editors

Dr. Suresh Chandra Satapathy is currently working as Professor, School ofComputer Engineering, KIIT Deemed to be University, Bhubaneswar, India. Heobtained his Ph.D. in computer science and engineering from JNTU, Hyderabad,and M.Tech. in CSE from NIT Rourkela, Odisha, India. He has 27 years of teachingexperience. His research interests are data mining, machine intelligence, and swarmintelligence. He has acted as program chair of many international conferences andedited six volumes of proceedings from Springer LNCS and AISC series. He iscurrently guiding eight scholars for Ph.D. He is also Senior Member of IEEE.

Dr. Vikrant Bhateja is Professor, Department of Electronics and CommunicationEngineering, Shri Ramswaroop Memorial Group of Professional Colleges(SRMGPC), Lucknow, and also Head (Academics and Quality Control) in the samecollege. His areas of research include digital image and video processing, computervision, medical imaging, machine learning, pattern analysis and recognition, neuralnetworks, soft computing, and bio-inspired computing techniques. He has morethan 90 quality publications in various international journals and conference pro-ceedings. He has been on TPC and chaired various sessions from the above domainin international conferences of IEEE and Springer. He has been Track Chair andserved in the core-technical/editorial teams for international conferences: FICTA2014, CSI 2014, and INDIA 2015 under Springer ASIC series; INDIACom-2015and ICACCI 2015 under IEEE. He is Associate Editor in International Journal ofConvergence Computing (IJConvC) and also serving in the editorial board ofInternational Journal of Image Mining (IJIM) under Inderscience Publishers. Atpresent, he is Guest Editor for two special issues floated in International Journal ofRough Sets and Data Analysis (IJRSDA) and International Journal of SystemDynamics Applications (IJSDA) under IGI Global publications.

xix

Dr. Swagatam Das received B.E. Tel.E., M.E. Tel.E. (Control Engineering), andPh.D., all from Jadavpur University, India, in 2003, 2005, and 2009, respectively.He is currently serving as Assistant Professor in the Department of Electronics andCommunication Sciences at the Indian Statistical Institute, Kolkata, India. Hisresearch interests include evolutionary computing, pattern recognition, multiagentsystems, and wireless communication. He has published one research monograph,one edited volume, and more than 200 research articles in peer-reviewed journalsand international conferences.

xx About the Editors

An Adaptive ARP Approachfor Fog-Based RSU Utilization

G. Jeya Shree and S. Padmavathi

Abstract The rapid growth in the development of IoT leads to the increased demandfor real-time and low-latency services, which is challenging for the traditional cloudcomputing model. One best solution emerged, is the Fog Computing, which providesservices and resources at the network’s edge. In this paper, we consider one of theIoT applications in smart cities, Vehicular Monitorization. Every vehicle in the smartcities communicates their information through the Road Side Units (RSU). The RSUmust be utilized optimally and minimally while covering up a maximum range ofvehicles. This paper proposes an infrastructure for vehicles and RSUs to use the Fogcomputing layer for its enumeration process and proposes an efficient AdvancedResource Provisioning (ARP) algorithm to minimize the number of RSUs utilizedin the vehicular monitorization. Results show that the cost and power consumptionof RSUs has also been reduced considerably with the minimization of RSU’s.

1 Introduction

The emerging trends in the Smart Vehicular monitorization is to use the Fog com-puting over cloud for its processing of IoT data such as traffic data, vehicle data, etc.,Already Cloud computing has been deployed to serve as a solution to vehicles whichrequire computational, storage and network resources. But the requirement such asmobility, location awareness, low latency, etc., demand fog computing which bringscomputations to the edge. The Road Side Unit (RSU) has an important role to playin the communication and transformation of messages in vehicular monitorization.Therefore, it is necessary to find an optimal location for placing the RSU [1].

G. Jeya Shree (B) · S. PadmavathiDepartment of Computer Science and Engineering, Thiagarajar Collegeof Engineering, Madurai 625015, Indiae-mail: [email protected]

S. Padmavathie-mail: [email protected]

© Springer Nature Singapore Pte Ltd. 2019S. C. Satapathy et al. (eds.), Smart Intelligent Computing and Applications,Smart Innovation, Systems and Technologies 105,https://doi.org/10.1007/978-981-13-1927-3_1

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2 G. Jeya Shree and S. Padmavathi

Fog computing is an emerging paradigm that extends cloud computing and ser-vices at the network edge. In contrast to cloud, fog computing is aimed at deployingservices in a widely distributed manner whereas it is centralized in cloud [2]. Fogprovides storage and computation resources as well as application services to theusers, like cloud [3]. Fog computing is closely related to IoT. IoT is generating anenormous amount of data day by day. By the time the data reaches the cloud for itsanalysis, the actual need for that data might be lost [2]. Arkian et al. [4] proposesa MIST-Fog based analytics scheme for cost-efficient resource provisioning for IoTapplication. The data consumer association, task distribution and virtual machineplacement problem is also considered in minimizing the overall cost while the QoSrequirement is still satisfied [4]. A fog node can be any device with storage, comput-ing and network connectivity. The mobile vehicles and infrastructures in roads arealso fog nodes [5]. Fog layer acts on IoT data in a fraction of seconds and analysesthe most time sensitive data at the edge close to the end-users where it is generated.This reduces the cost, time and effort in outsourcing them to the cloud. Fog has beenemerged as a powerful platform to deploy various applications like energy, healthcare, traffic and so on [6]. Thus, fog computing outperforms cloud by creating a dis-tributed framework for IoT to cope up with needs of sensors and embedded systemssuch as data processing and storage.

Considering the ITS applications in smart cities, there are various solutions avail-able which aims at improving the overall infrastructure by improving RSU function-alities. One such solution is proposed by Sankaranarayanan andMala [7] which usesgenetic algorithm to model an Optimized RSU based Travel Time system to estimatethe travel time for vehicular users. However, it is challenging to identify the optimallocation for placing the RSUs as well as to minimize its cost of installation on roads.Nawaz et al. [8] proposed a modified CLB approach which estimates the number ofRSUs a vehicle cross while heading its destination, thus the connectivity problem invehicles had overcome. By this the overload of a RSU can be redistributed amongother RSUs and the performance of the system can be maximized. By placing theRSUs using scalable Task Duplication Based (TDB) technique, the coverage can bemaximized as well as the efficiency can be increased [9]. Simple geometric rules thatdepend on sending node’s position is applied to extend the coverage area of RSU.The sending nodes are responsible for the propagation of data in different directions[10]. TheCooperative LoadBalancing transfer (CLB)mechanism is available amongRSUs, that distributes requests of a heavily loaded RSU to a lesser loaded neighbourRSU [11].

First, we consider Efficient ARP algorithm for minimizing the number of RSU’sutilized in the smart vehicular monitorization while minimizing the cost and powerconsumption of RSU. Then, we propose fog computing layer for computation andenumeration of traffic data coming from the RSU’s and connected vehicles. Finally,we consider the simulation of essential nodes (fog nodes, sensors, actuators) in thefog environment. Then we perform a comparative performance analysis before andafter ARP.

The paper is organized as follows. In Sect. 2, we present the preliminaries andbackground of fog computing. The proposed scheme and its methodology are dis-

An Adaptive ARP Approach for Fog-Based RSU Utilization 3

cussed in Sect. 3 and in its subsections. In Sect. 4, we discuss the results and perfor-mance analysis. Finally, Sect. 5 concludes the paper.

2 Preliminaries

2.1 Need for Fog Computing

Nowadays, the Internet of Things (IoT) has been adopted by a large number oforganizations and enterprises. So, the demand for quick access of such enormousamounts of data is keep on increasing. The characterization of Big Data is alongthree dimensions such as volume, velocity and variety. But, IoT use cases like smarttransportation, smart cities and smart grid are generally distributed in nature. Hencethis needs a fourth dimension to the characterization of Big Data, Geo-distribution.Outsourcing all the operations to cloud, causes delay and network congestion thathinder the performance of cloud. Hence, a distributed intelligent platform for man-aging the distributed computing, networking and storage resources is needed at theedge. This is where the concept of ‘Fog Computing’ comes to play, which extendsfrom the edge to the cloud, in a geographically distributed manner. So, instead of anincreasingly backed up centralized data model (cloud), we will start to use a decen-tralization of data (fog). Fog computing is generally associated with cloud comput-ing thus forming a three-layer model ‘Things-Fog-Cloud’. The ideal use cases offog computing are Agriculture, Wind Energy, Surveillances and Smart cities. In thispaper, we consider the use case of smart city application only. Figure 1 shows thethree-layer architectural model.

2.2 Intelligent Transportation System (ITS)

As per the definition given by Intelligent Transportation Society of America in theyear 1998, the aim of Intelligent Transportation System (ITS) is to use technologyin transportation to save lives, money and to improve safety as well as travel timeson the transportation system. The major goals of an ITS system is to provide drivingcomfort and to reduce any kind of fatal and financial losses due to the road accidents.Some examples of systems concerned by ITS such as traffic management, Advancevehicle control and safety, Emergency management, Rail Road crossing safety andElectronic payment.


Fig. 1 ‘Things-Fog-Cloud’ architecture

2.3 RSU—Road Side Units

In vehicular network, Vehicle-to-Vehicle communication (V2V) and Vehicle-to-Infrastructure communication (V2I) are the two main communication architectures.Here the vehicles are On-Board Units (OBU) and the infrastructures on the roadsare Road Side Units (RSU). Road Side Unit is the Computing device located onthe roadside that provides connectivity support to passing vehicles. The routing invehicular networks can be improved by the capabilities of RSU, the range and thereliability of the V2I communications is increased due to the higher antenna heights.Considering the case of emergency messages, these characteristics are important inavoiding network congestion by load balancing the traffic.

This paper presents an algorithm for minimizing the number of RSU’s that areprovisioned in Intelligent Transportation Systems (ITS).

3 Proposed Methodology

The problem is formulated as follows. Given a set of ‘m’ lanes, L �{l1, l2, l3…., lm}with ‘μ’ RSU’s placed sporadically in some li’s, where 0≤ i≤1 and μ ≤ l with eachμ’s storing ‘λ’ units of power and costing an amount ‘k’. The task is to minimize μ,λ, and k such that,


l⋃

i�0

Sμi ≤ L (1)

Equation (1) shows the objective of ARP algorithm, that isminimizing the numberof RSUs utilized. The equation ensures that every lane is mapped with some RSUs.

3.1 ARP Algorithm for Minimizing RSU Utilization

For the optimal utilization of RSU over a given set of lanes several parameters areconsidered such as lane number, length of the lane, cost of installation of RSU andpower consumed by the RSU. Several other parameters such as driving patterns,frequency of accidents, vehicle distribution over time are also considered to decideon where to place the RSU. i.e., On lanes where traffic and vehicle movement areless, the RSU in that lane can be set off and the nearby lane’s RSU can take care of thislane, thus saving one RSU. Similarly, in this way we consider all the parameters andoptimally utilize the minimum number of RSU’s from the complete set of availableRSU.ARP Algorithm:

Input: ‘m’ number of lanes and ‘m’ binary codes representing the presence ofRSU in the respective lanes.Output: Minimization in the number of RSUs utilized.Steps:

(1) Input the number of lanes ‘m’(2) Input ‘m’ binary codes for m lanes with ‘0’ representing no RSUs in that lane

and ‘1’ representing the presence of RSU in that lane(3) Considering the parameters, the less and heavy traffic lane is identified. Thus,

the right locations for placing the RSU will be known(4) If the less traffic lanes contain RSU in it, it can be either be set OFF to save its

utilization or can be used to cover some additional adjacent neighbour lanes(5) In heavy traffic lanes, RSU is set ON to its maximum coverage(6) Similarly, every other parameter is considered for the optimal number of RSUs

utilization

3.2 Proposed Infrastructure for Vehicle Monitorization

The proposed infrastructure for RSU and vehicles uses fog computing for its trans-formation of data where the enumeration and processing of data can be carried outeasily and more effectively. With more amount of data transferring to and fromthe cloud causes network traffic and the network traffic will lead to congestion andincrease in latency. The benefit of fog computing is that it reduces network traffic and


provides platform for filtering and data analytics. Fog computing provides resourcesand services at the edge of the network or maybe even at end devices. This improvesthe Quality of Service and reduces service latency, therefore resulting in enhancedoverall user-experience. The distinctive characteristics of fog computing are Loca-tion awareness and low latency, Geo-distributed, Mobility support, Real-time dataanalytics, Interoperability and Heterogeneity.

The importance of fog computing is that it processes data locally before trans-mitting to the cloud. IoT data are processed in smart devices or a data hub closer tothe device which generating it. In cloud, accessing data always requires bandwidthallocation and we had to depend upon cloud repository, whereas in Fog computing,we can access data locally between devices independent on the cloud repository.Hence accessibility will be improved.

Fog computing allows the edgenodedevices to carry out theLocal data processing,Cache data management, Dense geographical distribution, Local resource pooling,Load balancing, local device management, latency reduction for better QoS and edgenode analytics. The critical aspect considered in the working of fog computing isTime. The most time-critical data are locally and it results in reduced latency andprevents from occurrence of major damages (E.g. Alarm status, Device status, Faultwarnings). The less time-critical data are sent to the central mainframe which resultsin persistent, periodical storage that can be retrieved as and when required (E.g.Files, Reports for historical analysis, Device logs). The key advantage of using Fogcomputing in vehicular monitorization is that it offers many advantages over cloudcomputing, while the core technology is as such in cloud.

Figure 2 shows the infrastructure proposed in this paper. The proposed infrastruc-ture presents a way for RSU to transform travel related information to fog insteadof cloud where it can utilize the whole advantage of fog so far discussed. Generally,the transformation of information from RSU to centralized server may require someenumerations or preprocessing of data, which can be easily and optimally carried outin Fog layer. Fog will overcome the limitations of cloud, not as a replacement of it.By this way, instead of sending anything and everything to cloud, only data neededfor long term analysis can be sent.

4 Results and Discussions

4.1 RSU Utilization After ARP

The ARP algorithm is proposed in a generic way for provisioning the resources,which in this paper is applied to the vehicular monitorization problem treating theRSUs as resources. Therefore, after applying ARP to the vehicular monitorization,the optimal utilization of RSUs is shown in the table. We have chosen iFogSimfor simulating the proposed algorithm [12]. RSUs are created as fog nodes in the


Fig. 2 Proposed infrastructure for vehicle monitorization

Table 1 RSU utilization using ARP (for m �10 lanes)

Lane number 1 2 3 4 5 6 7 8 9 10

Binary code 0 1 0 1 0 0 1 0 1 0

Solution after ARP −1 1 −1 0 −1 −1 2 −1 −1 0

iFogSim simulation environment and the number of lanes is also predefined in thisapproach.

Table 1 shows the optimized number of RSU utilized by using ARP algorithmfor 10 lanes. The binary code ‘1’ indicates the presence of RSU in that lane and ‘0’indicates no RSU for that lane. The ‘−1’ in the solution indicates that we do not needRSU for that lane. ‘0’ indicates that the RSU will cover that lane alone, ‘1’ indicatesthat the RSU in that lane cover up one adjacent lane on both sides and ‘2’ indicatesthat RSU in that lane cover up two adjacent lanes on both sides.


4.2 Performance Analysis

In our proposed methodology all the process is carried out on the fog environment.All the mentioned parameters are analysed and obtained in the iFogSim Simulator.For analysing the performance results, we run the approach for multiple times and itis visualized. The necessary information for the vehicle travellers are transmitted tothem without any interruption. Since RSU is the essential part in transformation ofvehicular information, the optimal location for placing the RSU is an important factorto be found. Different values for multiple scenarios are generated using simulationfor a given lanes. The proposed system is analysed by adopting ARP algorithm iniFogSim simulator.

In the analysis, parameters such as μ, λ, k is treated equally. Figure 3 shows thatnumber ofRSU’s utilized is lowwhen optimized usingARPalgorithm. It also ensuresan effective communication and transformations of data. The values for RSUwithoutARP are obtained by considering the binary code for all lanes as 1.When the numberof lanes is less (say 10 or 20), the ARP does not show any improvements, whereaswhen the number of lanes increases, the ARP shows optimal results by utilizing veryless RSUs. This proves the solution is good when covering more number of lanes.Figure 4 shows that the power consumed by the RSU is less for optimized numberof RSU using ARP when compared with deployment of all RSU’s. Similarly, Fig. 5shows that cost of installation of RSU is lower when weminimized the number usingARP. Figures 3, 4 and 5 show the effectiveness of ARP algorithm in providing anoptimal number of RSU when the number of lanes is increasing rapidly. It is alsoclearly seen that the proposed ARP algorithm achieves a considerable efficiency inutilizing the RSU such that it has a maximum coverage while significantly reducingthe installation cost and power consumed by RSU’s.

Fig. 3 Efficiency of ARP in RSU utilization


Fig. 4 Efficiency of ARP with respect to Power consumption

Fig. 5 Efficiency of ARP with respect to cost

5 Conclusion

This paper optimally minimizes the number of RSUs utilized in the vehicular moni-torization application by using an efficient ARP approach, ensuring that, it covers allthe lanes. This approach also finds the right locations to place the RSUs to cover allthe lanes. And for the processing of the traffic and vehicular data, a superimposed fogcomputing layer is suggested and an infrastructure is also proposed. RSU are createdas fog nodes in the simulation environment and the simulation results shows that thenumber of RSUs has been minimized considerably after applying ARP algorithmand the installation cost as well as the power consumption of RSU is minimized. Theusage of fog computing layer for processing of data ensures low-latency services.The proposed approach can be expanded by using intelligent techniques such as PSOand other search techniques for handling different type of IoT data and applicationsthat are deployed in Fog computing layer.


References

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7. Sankaranarayanan,M.,Mala, C.:Genetic algorithmbased efficientRSUdistribution to estimatetravel time for vehicular users. In: IEEE International Conference On Soft Computing andMachine Intelligence. IEEE (2015)

8. Ali, G.M.N., Mollah, A.S., Samantha, S.K., Mahmud, S.: An efficient cooperative load bal-ancing approach in RSU based vehicular ad hoc networks (VANETs). IEEE InternationalConference on Control System. Computing and Engineering, pp. 28–30. Penang, Malaysia(2014)

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Identification of Meme Genre and ItsSocial Impact

Abhyudaya Pandey, Sandipan Mukherjee, Sandal Bajajand Annapurna Jonnalagadda

Abstract Memes have taken the center stage as far as present day social medianetwork is concerned. Every second, about a million memes are shared on varioussocial media. In this paper we analyze a dataset which consist of data related tomeme sharing and corresponding phrases used in the process. Popularity of memesis tracked followed by timestamps to identify the most shared memes across theinternet. Meme sharing or propagation is also based on some hidden patterns likea social incident, a soccer game, a war or speeches by a public figures. The paperaims to produce a multivariate graph with the source of memes as nodes which areconnected to other sources sharing the same meme using edges. The graph is thenanalyzed to identify patterns and genre of popular memes.

1 Introduction

In present-day socialmedia,memes have become a very important attribute of expres-sion of thoughts. Every second, on an average, about a million new memes enter thedomain of social media. Users of social media share these memes with other usersin their posts or via pages or blogs which are present abundantly on the Internet. Thesharing of memes help in analyzing their lifecycle and the popularity they gainedthrough it. Sustenance of a given meme depends on the impact it makes on a com-munity that it dwells in.

A. Pandey · S. Mukherjee (B) · S. Bajaj · A. JonnalagaddaSchool of Computer Science and Engineering, VIT, Vellore, Tamil Nadu 632014, Indiae-mail: [email protected]

A. Pandeye-mail: [email protected]

S. Bajaje-mail: [email protected]

A. Jonnalagaddae-mail: [email protected]

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