Smart Innovation, Systems and Technologies 107

Suresh Chandra Satapathy · Amit Joshi Editors

Information and Communication Technology for Intelligent Systems Proceedings of ICTIS 2018, Volume 2

Smart Innovation, Systems and Technologies

Volume 107

Series editors

Robert James Howlett, Bournemouth University and KES International,Shoreham-by-sea, UK

Lakhmi C. Jain, Faculty of Engineering and Information Technology,Centre for Artificial Intelligence, University of Technology Sydney,Sydney, NSW, Australia

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

Suresh Chandra Satapathy • Amit JoshiEditors

Informationand CommunicationTechnology for IntelligentSystemsProceedings of ICTIS 2018, Volume 2

123

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

Amit JoshiSabar Institute of TechnologyGujarat Technological UniversityAhmedabad, Gujarat, India

ISSN 2190-3018 ISSN 2190-3026 (electronic)Smart Innovation, Systems and TechnologiesISBN 978-981-13-1746-0 ISBN 978-981-13-1747-7 (eBook)https://doi.org/10.1007/978-981-13-1747-7

Library of Congress Control Number: 2018949057

© 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

Preface

This SIST volume contains the papers presented at the ICTIS 2018: ThirdInternational Conference on Information and Communication Technology forIntelligent Systems. The conference was held during April 6–7, 2018, inAhmedabad, India. It was organized by Global Knowledge Research Foundation,Raksha Shakti University and Computer Engineering Division Board—theInstitution of Engineers (India)—supported by Gujarat Innovation Society andGujarat Council of Science and Technology. It will target state-of-the-art as well asemerging topics pertaining to ICT and effective strategies for its implementation inengineering and intelligent applications. The objective of this international con-ference is to provide opportunities for the researchers, academicians, industrypersons, and students to interact and exchange ideas, experience, and expertise inthe current trend and strategies for information and communication technologies.Besides this, participants will also be enlightened about the vast avenues andcurrent and emerging technological developments in the field of ICT in this era andits applications will be thoroughly explored and discussed. The conference isanticipated to attract a large number of high-quality submissions and stimulate thecutting-edge research discussions among many academic pioneering researchers,scientists, industrial engineers, students from all around the world and provide aforum to researcher; propose new technologies, share their experiences, and discussfuture solutions for design infrastructure for ICT; provide a common platform foracademic pioneering researchers, scientists, engineers, and students to share theirviews and achievements; enrich technocrats and academicians by presenting theirinnovative and constructive ideas; and focus on innovative issues at the interna-tional level by bringing together the experts from different countries. Researchsubmissions in various advanced technology areas were received, and after a rig-orous peer review process with the help of the program committee members andexternal reviewer, 72 papers were accepted with an acceptance rate of 0.23. Theconference featured many distinguished personalities like Narottam Sahoo, Advisorand Member Secretary, GUJCOST, DST, Government of Gujarat, Gujarat, India;Prof. Milan Tuba, Vice-Rector, Singidunum University, Serbia; Shri Aninda Bose,Sr. Publishing Editor, Springer Nature, New Delhi, India; Dr. Nilanjan Dey, Techno

v

India College of Engineering, Kolkata, India; Dr. Shyam Akashe, Professor, ITMUniversity, Gwalior, India; Dr. Parikshit Mahalle, Professor, Singhad Group ofInstitutions, Pune, India; Mr. Bharat Patel, chairman CEDB, the Institution ofEngineers (India); Dr. Priyanka Sharma, Professor and Head, Raksha ShaktiUniversity, Ahmedabad, India. Separate invited talks were organized in industrialand academic tracks in both days. We are indebted to all organizing partners fortheir immense support to make this conference possible on such a grand scale.A total of 14 sessions were organized as a part of ICTIS 2018 including 11 tech-nical, 1 plenary, 1 keynote, and 1 inaugural sessions. A total of 52 papers werepresented in the 5 technical sessions with high discussion insights. The total numberof accepted submissions was 72 with a focal point on ICT and intelligent systems.Our sincere thanks to all sponsors, press, print and electronic media for theirexcellent coverage of this conference.

Bhubaneswar, India Suresh Chandra SatapathyAhmedabad, India Amit JoshiApril 2018

vi Preface

Contents

Smart Live Monitoring of Aquarium—An IoT Application . . . . . . . . . . 1Sharada Kori, Sudha Ayatti, Veena Lalbeg and Akshata Angadi

Automation of Process Evaluation of Saccharification of Wheat StarchFollowed by Fermentation of Glucose to Prepare Bioethanol UsingDigital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Neha Patni, Pooja Shah, Jayneel Vora and Vinit Shah

An Optimal Cryptographic Approach for Addressing SecurityBreaches to Build Resilient WSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Nischaykumar Hegde and Linganagouda Kulkarni

An Improved Intelligent Transportation System: An Approachfor Bilingual License Plate Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 29Nikita Singh and Tarun Kumar

Inductor-Based Modified Dickson Charge Pump Boost VoltageConverter with Higher Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Gyan Prabhakar, Rabindra Kumar Singh and Abhishek Vikram

Boosted Clock Generator Using NAND Gate for Dickson ChargePump Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Gyan Prabhakar, Rabindra Kumar Singh and Abhishek Vikram

Location Based Secured Task Scheduling in Cloud . . . . . . . . . . . . . . . . . 61Srijita Basu and Abhishek Anand

A Low-Cost Air Pollution Monitoring System Using ZigBee-BasedWireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Tanima Bhowmik, Anagha Bhattacharya and Indrajit Banerjee

Indians’ Choice of Payment Mode(s): A Study Based on the Impactof Demonetization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Harvey Antony Moses Jayakumar and Deepak Gupta

vii

Enhancing of Data Retrieval by Means of Database QueryAnalyzer (DBQA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95S. B. Misal and Ashok T. Gaikwad

Review of EEG Signal Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Ashlesha R. Chakole, Praful V. Barekar, Rajeshree V. Ambulkarand Shailesh D. Kamble

How to Reverse Engineer ICS Protocols Using Pair-HMM . . . . . . . . . . 115Zewei Wu, Min Shu, Junzheng Shi, Zigang Cao, Fei Xu, Zhen Li,Gang Xiong and S. M. Yiu

Classification of Plants Using Invariant Features and a NeuralNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Manisha M. Amlekar, Mouad M. H. Ali and Ashok T. Gaikwad

CSTeller: Client or Server is a Question . . . . . . . . . . . . . . . . . . . . . . . . . 137Meng Wu, Gaopeng Gou, Zhen Li and Xiong Gang

Geographical Zone-Based Cluster Head for Routing in UrbanVehicular Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149R. Brendha and V. Sinthu Janita Prakash

Congestion Management by Static Var Compensator (SVC)Using Power World Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Archana Shirbhate, V. K. Chandrakar and R. M. Mohril

Combining User-Based and Item-Based Collaborative FilteringUsing Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Priyank Thakkar, Krunal Varma, Vijay Ukani, Sapan Mankadand Sudeep Tanwar

Predictive Monitoring System Using K-NN, QDC Classifiersof Physiological Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Madhav Vaidya, Nidhi Dahatkar and Balika Kolhe

Recommender System Based on OSN Data Analytics . . . . . . . . . . . . . . . 189Aysha Khan and Rashid Ali

Early Detection of Ransomware by Indicator Analysisand WinAPI Call Sequence Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201Harshit Sharma and Shri Kant

A Novel Integrated Framework Based on Modular Optimizationfor Efficient Analytics on Twitter Big Data . . . . . . . . . . . . . . . . . . . . . . . 213R. Merlin Packiam and V. Sinthu Janita Prakash

Study of Controller Techniques for Precision Positioning . . . . . . . . . . . . 225Prabha Niranjan, M. J. Dileep Kumar, Shashikantha Karinkaand K. V. S. S. S. S. Sairam

viii Contents

An Effective Content-Based Strategy Analysis for Large-ScaleDeduplication Using a Multi-level Pattern-Matching Algorithm . . . . . . . 235A. Sahaya Jenitha and V. Sinthu Janita Prakash

Tamil Sign Language Translator—An Assistive Systemfor Hearing- and Speech-Impaired People . . . . . . . . . . . . . . . . . . . . . . . . 249Hancy Jose and Anitha Julian

Aspect-Based Sentiment Analysis of Students’ Feedback to ImproveTeaching–Learning Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Ganpat Singh Chauhan, Preksha Agrawal and Yogesh Kumar Meena

Fair Mechanisms for Combinatorial Reverse Auction-BasedCloud Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267Dinesh Kumar, Gaurav Baranwal, Zahid Raza and Deo Prakash Vidyarthi

Timbre-Vibrato Model for Singer Identification . . . . . . . . . . . . . . . . . . . 279Deepali Y. Loni and Shaila Subbaraman

A Range-Based Adaptive and Collaborative Localizationfor Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Vijay Ukani, Priyank Thakkar and Vishal Parikh

Image Edge Detection Techniques Using Sobel, T1FLS,and IT2FLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303Rosepreet Kaur Bhogal and Aayushi Agrawal

Detection of Mimicry Attacks on Speaker Verification Systemfor Cartoon Characters’ Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Raag Anarkat, Sapan H. Mankad, Priyank Thakkar and Vijay Ukani

On the Performance of Cepstral Features for Voice-BasedGender Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327Isha Kanani, Heenal Shah and Sapan H. Mankad

Efficient Resource Allocation Using Data Offloading Mechanismin Distributed SDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335Bhumi K. Desai, Parul V. Pithadia and Sarosh K. Dastoor

Security Breach and Forensics in Intelligent Systems . . . . . . . . . . . . . . . 349M. S. Girija Devi and Manisha J. Nene

Multi-agent Approximation of User Behavior for AR SurgeryAssistant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361Anton Ivaschenko, Alexandr Kolsanov, Sergey Chaplyginand Aikush Nazaryan

Trust Framework for IAAS—A Tool Based on Security ChecksThrough Standards and Certifications . . . . . . . . . . . . . . . . . . . . . . . . . . . 369Archana B. Saxena and Meenu Dawe

Contents ix

IP Address-Based Mitigation Against Denial-of-ServiceFlooding Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377Arvind R. Bhagat Patil and Nileshsingh V. Thakur

Extraction of Vegetation Using Modified K-Means Clustering . . . . . . . . 391Sujata R. Kadu, Balaji G. Hogade, Imdad Rizvi and Sarika Yadav

Web Page Classification on News Feeds Using Hybrid Techniquefor Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Ankit Dilip Patel and Yogesh Kumar Sharma

Smart Helmet for Safety Driving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407N. Rajathi, N. Suganthi and Sourabh Modi

A BFS-Based Pruning Algorithm for Disease-Symptom KnowledgeGraph Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417Safikureshi Mondal and Nandini Mukherjee

Analyzing Political Sentiment Using Twitter Data . . . . . . . . . . . . . . . . . . 427Rajesh Bose, Raktim Kumar Dey, Sandip Roy and Debabrata Sarddar

High Gain and Highly Directive Microstrip Patch Antennafor Radar and Satellite Communication . . . . . . . . . . . . . . . . . . . . . . . . . . 437Divesh Mittal, Amarveer Singh Dhillon, Aman Nagand Rajkiran Bargota

Digital Watermarking—An Overview and a Possible Solution . . . . . . . . 447Kartik U. Sharma, Pooja P. Talan, Pratiksha P. Nawade, Mir Sadique Aliand Akshay U. Sharma

A New K-means-Based Algorithm for Automatic Clusteringand Outlier Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457Trushali Jambudi and Savita Gandhi

Big Data Analytics in Retail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469Shubham Lekhwar, Shweta Yadav and Archana Singh

Application of LSTM, GRU and ICA for Stock Price Prediction . . . . . . 479Akhil Sethia and Purva Raut

Bench Automation Computer Using Raspberry Pi . . . . . . . . . . . . . . . . . 489Utkarsh Shukla, Atul Kumar Verma and Srishti Verma

Performance Evaluation of Image Segmentation Processfor Recognition of Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499M. V. Rege, B. W. Gawali and Santosh Gaikwad

Energy Aware Computing Resource Allocation Using PSOin Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511Vanita Chaudhrani, Pranjalee Acharya and Vipul Chudasama

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Performance Improvement in Preprocessing Phaseof Fingerprint Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521Meghna B. Patel, Satyen M. Parikh and Ashok R. Patel

Significance of Hetero-Junction in Charge Plasma Gate All AroundTFET: An Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531Alemienla Lemtur, Priyanka Suman, Jyoti Patel and Dheeraj Sharma

Bearing Fault Diagnosis Using Frequency Domain Featuresand Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539Amandeep Sharma, Rajvardhan Jigyasu, Lini Mathewand Shantanu Chatterji

A Phase-Wise Fault Prediction Using Soft Computing . . . . . . . . . . . . . . 549Sushant Kumar and Prabhat Ranjan

Temporal TF-IDF-Based Twitter Event Summarization IncorporatingKeyword Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559Amrah Maryam and Rashid Ali

Performance Booster Electrical Drain SiGe Nanowire TFET(EDD-SiGe-NW-TFET) with DC Analysis and Optimization . . . . . . . . . 567Jyoti Patel, Priyanka Suman, Alemienla Lemtur and Dheeraj Sharma

Navigation Through Eye-Tracking for Human–ComputerInterface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575M. Lakshmi Pavani, A. V. Bhanu Prakash, M. S. Shwetha Koushik,J. Amudha and C. Jyotsna

Selection of Optimum Sensors for Cooperative Sensingin Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587Ashwini Kumar Varma and Dishani Lahiri

Smart Water Hardness Monitoring System . . . . . . . . . . . . . . . . . . . . . . . 595Sanket Suthar, Neel Carpenter and Milan Chhatralia

Designing of Radix-2 Butterfly for Digital Signal Processorfor FFT Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603Prasad Kulkarni, B. G. Hogade and Vidula Kulkarni

Swine Flu Predication Using Machine Learning . . . . . . . . . . . . . . . . . . . 611Dvijesh Bhatt, Daiwat Vyas, Malaram Kumhar and Ajay Patel

Parallel Image Forgery Detection Using FREAK Descriptor . . . . . . . . . 619M. Sridevi, S. Aishwarya, Amedapu Nidheesha and Divyansh Bokadia

Combining Hyperlink Structure and Content of Webpagefor Personalization of Search Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631I. T. Anjusha and M. Abdul Nizar

Contents xi

k-Factor-Based Cosine Similarity Measurement . . . . . . . . . . . . . . . . . . . 643Nadia Siddiqui and Saiful Islam

Dynamic Modeling of the Complete Hormonal NetworkUsing Flow Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651Ramsha Fatima and Rashid Ali

Combined Elephant Herding Optimization Algorithm with K-meansfor Data Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665Eva Tuba, Diana Dolicanin-Djekic, Raka Jovanovic, Dana Simianand Milan Tuba

Anomaly Detection by Using CFS Subset and Neural Networkwith WEKA Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675J. Jabez, S. Gowri, S. Vigneshwari, J. Albert Mayanand Senduru Srinivasulu

Efficient Service Broker Policy for Intra DatacenterLoad Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683Ritesh Patel and Sandip Patel

A Framework for Dynamic Decision Making by Multi-agentCooperative Fault Pair Algorithm (MCFPA) in Retail ShopApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693Deepak A. Vidhate and Parag Kulkarni

A Framework for Integrating Cloud Computing and Big DataAnalytics into E-Governance Using Openstack Sahara . . . . . . . . . . . . . . 705Bhushan Jadhav and Archana B. Patankar

Implementation Strategy for Code Analyser Componentof Rule-Based Self-Learning Model for Automatic Evaluationof C++ Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715Maxwell Christian

A Novel Website Fingerprinting Method for Malicious WebsitesDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723Xudong Zeng, Cuicui Kang, Junzheng Shi, Zhen Li and Gang Xiong

Autoregressive Modeling-Based Feature Extractionof EEG/EOG Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731Aparna Gupta, Vikrant Bhateja, Apoorva Mishra and Ayushi Mishra

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741

xii Contents

About the Editors

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 Jawaharlal NehruTechnological University (JNTU), Hyderabad, India, and M.Tech. in CSE fromNIT Rourkela, Odisha, India. He has 27 years of teaching experience. His researchinterests are data mining, machine intelligence, and swarm intelligence. He hasacted as program chair of many international conferences and edited six volumes ofproceedings from Springer LNCS and AISC series. He is currently guiding eightscholars for Ph.D. He is also Senior Member of IEEE.

Amit Joshi is a young entrepreneur and researcher who holds an M.Tech. incomputer science and engineering and is currently pursuing research in the areas ofcloud computing and cryptography. He has 6 years of academic and industrialexperience at the prestigious organizations in Udaipur and Ahmedabad. Currently,he is working as Assistant Professor in the Department of Information Technology,Sabar Institute of Technology, Gujarat, India. He is an active member of ACM,CSI, AMIE, IACSIT-Singapore, IDES, ACEEE, NPA, and many other professionalsocieties. He also holds the post of Honorary Secretary of the CSI’s UdaipurChapter and Secretary of the ACM’s Udaipur Chapter. He has presented andpublished more than 30 papers in national and international journals/conferences ofIEEE and ACM. He has edited three books, namely Advances in Open SourceMobile Technologies, ICT for Integrated Rural Development, and ICT forCompetitive Strategies. He has also organized more than 15 national and interna-tional conferences and workshops, including the international conference on ICTCS2014 at Udaipur through ACM’s ICPS. In recognition of his contributions, hereceived the Appreciation Award from the Institution of Engineers (India) in 2014and an award from the SIG-WNs Computer Society of India in 2012.

xiii

Smart Live Monitoringof Aquarium—An IoT Application

Sharada Kori, Sudha Ayatti, Veena Lalbeg and Akshata Angadi

Abstract Fondness for fish aquarium is a trendy affair. Thus,many of themwelcomeit in their home just as a hobby or an eye-pleasing tool. It looks beautiful and brings acharm to the house, but it’s not easy to maintain. It requires time-to-time care whereits responsibility resides in freshwater, brackish, and marine. Between these three,freshwater holds a popular place of keeping fish because it is effortless to handlewith freshwater fish and aquariums. Taking care of fish and aquariums is an intricatetask like feeding fish on-time, maintaining temperature, and also managing the light,heater, and filter of the aquarium. Till now it has been a manual process, thus itdemands a well turned-out technology. Thus in this paper, smart aquarium has beenplanned by keeping in mind, the problems of those who cannot take care of theiraquarium on daily basis. The feeder is powered by servomotor which on trigger of abutton feeds the fish, heater keeps the temperature of the aquarium under control, andwe can also be in charge of the fluorescent lamp which helps in the growth of fish andplants. Proposed technology uses Raspberry pi Webcam server to capture live videostream of aquarium, feed fishes through servomotor using pulse-width modulation,relays are used to ON/OFF light, temperature control, filter control, and android appto allow the user to access various functionalities.

Keywords Smart aquarium · IoT · Raspberry pi 3 · Android studio

S. Kori (B) · S. Ayatti · V. Lalbeg · A. AngadiKLS Gogte Institute of Technology, Belagavi, Karnataka, Indiae-mail: smkori@git.edu

S. Ayattie-mail: shayatti@git.edu

V. Lalbege-mail: vklalbeg@git.edu

A. Angadie-mail: abangadi@git.edu

© Springer Nature Singapore Pte Ltd. 2019S. C. Satapathy and A. Joshi (eds.), Information and Communication Technologyfor Intelligent Systems, Smart Innovation, Systems and Technologies 107,https://doi.org/10.1007/978-981-13-1747-7_1

1

2 S. Kori et al.

1 Introduction

The Internet is a breathing body, always varying and sprouting [1, 2]. New appli-cations and businesses are shaped incessantly. In addition to an evolving Internet,technology is also changing the landscape. Broadband connectivity is becoming con-temptible and ubiquitous; devices are becoming more influential and smaller with adiversity of on-board sensors. The proliferation of more devices becoming connectedis primary to a newparadigm—the Internet of Things. The Internet of Things is drivenby opening out of the Internet through the addition of physical objects pooled with anability to provide smarter services to the environment as more data become accessi-ble. Various applications ranging from green-IT and energy efficiency to logistics arealready initiating to advantage from Internet of Things concepts. There are challengescoupled with the Internet of Things, most explicitly in areas of reliance and safetymeasures, standardization and governance required to ensure a fair and trustworthyopen Internet of Things which provides worth to all of society. Internet of Thingsis soaring on the research agenda of several multinationals as well as the EuropeanCommission and countries such as China. The research conducted is driving thecreation of a functional and powerful Internet of Things. The benefits of Internet ofThings to the mounting and promising economies are considerable, and strategies toapprehend these needs to be found.

Pet ownership has been escalating at a steady pace in the last 20 years. After catsand dogs, the most well-liked pet is now the freshwater fish. The maintenance offish aquariums is a very complex task. In the current system, all equipments such aslight, heater, and filter are to be controlled manually using electrical switches whichrequire human intervention to turn on/off the equipments. The fishes need to be fedtwice a day even this requires the owner to walk up to fish tank and feed the fishmanually which makes the task of maintaining an aquarium much more difficult [3].At times when the owner is on vacation, he has no control over the aquarium andalso can’t feed the fish.

2 System Requirements

2.1 Hardware Requirements

The smart aquarium’s most components were taken from a normal aquarium suchas glass box, filter, heater, and aquarium lights. These components were responsibleto maintain clean water, specified temperature of water, and provide lighting to theaquarium. The components required to automate this process are Raspberry pi 3,relays, servomotor, jumper wires, and Webcam. Relays act as switches to controllights, filter, and heater. The Raspberry pi acts as server and receives commandsfrom the client and controls all equipments of the aquarium which are connectedto its Gpio pins. A light-emitting diode (LED) is responsible to provide lighting in

Smart Live Monitoring of Aquarium—An IoT Application 3

aquarium [4, 5]. The feeder is powered by servomotor and controlled using PWMsignals. On receiving signal, the servomotor spins and drops the fish feed. The filterkeeps the water clean by continuously pumping it through a sponge. The heatermaintains the specified temperature of water in aquarium. The Webcam captures thevideo of the aquarium and provides live video streaming in android app.

2.2 Software Requirements

Operating systemRaspbian (kernel version-4.4),Windows 10, Android, Pi4j,Motion,Android studio, Web browser, Putty, and Winscp were required to develop smartaquarium. Raspbian is an official operating system of Raspberry pi and is used topower Raspberry pi. Windows is used on laptop which is used to control the Rasp-berry pi. The Pi4j project is intended to provide a friendly object-oriented I/O APIand implementation libraries for java programmers to access the full I/O capabilitiesof the Raspberry pi platform. Android studio is the official integrated developmentenvironment (IDE) for the android platform. Putty is used to communicate with piusing ssh. winscp to transfer files to and from Raspberry pi.

3 System Architecture

3.1 System Design

1 Relays2 Raspberry pi 3 model 3 b3 Light4 Feeder5 Filter6 Heater7 Webcam8 Android smart phone (Fig. 1).

TheWebcam (iball c 12.0) is connected toRaspberry pi 3model b throughUSB2.0which provides a high-quality video stream. The video stream captured by Webcamis transferred to Raspberry pi. The Raspberry pi uses a motion freeware to connectand stream video feed received from the Webcam [6]. The motion configuration fileis fully customizable and also allows for changing video quality.

The fish feeder is powered by a servomotor. The servomotor is connected toRaspberry pi 3 model b using three pins Vcc, GND, and signal. The Vcc pin isconnected to 5 V Vcc and GND to ground. The signal pin is connected to GPIO01pin which uses pulse-width modulation to drive the motor to desired angle and back.

4 S. Kori et al.

Fig. 1 Proposed systemarchitecture

The feeder rotates a total of three times and drops appropriate food required for fish.Relays are connected using three pins i.e., Vcc, GND, and logic pin GPIO0. TheGPIO0 is used to turn relay on or off. A logical low turns it on and a logical highturns relay off. This property enables the user to control high voltage devices. Theheater is controlled using relays which are connected to Raspberry pi. Relays areconnected using three pins i.e., Vcc, GND, and logic pin GPIO2. The GPIO2 is usedto turn relay on or off. A logical low turns it on and a logical high turns relay off.The filter keeps the water clean. Relays are connected using three pins i.e., Vcc,GND, and logic pin GPIO3. The GPIO3 is used to turn relay on or off. A logical lowturns it on and a logical high turns relay off. This property enables the relay to turnon/off filter. The android app has six pages each designed for a different task. Theapp starts with a splash screen displaying the menu. The menu has four options suchas live stream, controls, help, and about. The live stream option navigates to a newpage where video stream of the aquarium is displayed. The control option directs tocontrol activity page which has four buttons to control light, feeder, heater, and filterof the aquarium. The help option has information to use the app. The about sectionhas details on the version, motivation, and developers.

4 Implementation

4.1 Live Stream

The live stream is implemented using a Webcam, Raspberry pi, and motion. TheWebcam is connected to Raspberry pi using USB 2.0. The motion software is [7,8] used to capture footage from the Webcam and stream it to Web browser on port8081. The motion software has a configuration file which can used to set width andheight of the footage [9, 10]. It also provides range of settings to modify the footage.

Smart Live Monitoring of Aquarium—An IoT Application 5

4.1.1 Raspberry Pi Server

A sever controlling lights, feeder, heater, and filter is implemented in this stage. TheRaspberry pi is programmed usingPi4j to control theGPIOpinswhich in turn controlthe various equipments of the aquarium. The server uses java socket connection toread from the client (android app). The server is started on the port 6000 using thecommands.

a. Pi4j—compile fServer.javab. Pi4j—run fServer.java

On successful run of server, message is printed: server started on port 6000.

4.1.2 Android App

The android was developed using android studio. It was divided into six modulesthe splash screen, menu, main activity, about, help, and live stream. The androidapp uses java socket connection to write to the buffer on Raspberry pi server. Thesestrings are read, and corresponding functions are called.

5 Program Code

6 S. Kori et al.

Smart Live Monitoring of Aquarium—An IoT Application 7

6 Results

Results are explained in the following snapshots:

8 S. Kori et al.

Snapshot 1: Live video stream of aquarium, setting up raspberry pi Webcam server.Live video streaming in android app.Snapshot 2: Developing fish food feeder powered by a servomotor using pulse-widthmodulation. Feeder dropping food for fishes.Snapshot 3: Control light using relay. Lights OFF.Snapshot 4: Control light using relay. Lights ON.Snapshot 5: Control heater to maintain specified temperature. Heater ON.Snapshot 6: Control filter using relay. Filter ON.Snapshot 7, 8 and 9: To design and develop android app for splash screen, menu,and control menu

Smart Live Monitoring of Aquarium—An IoT Application 9

7 Conclusion and Future Work

Considering the manual process of maintaining the fish is a tedious task. We startedwith this approach of developing a technology which may change this scenarioand helps to maintain the fishes with just a touch from anywhere in the world.The fundamentals proposed in this paper work well and can be implemented onany aquarium. Having a smart aquarium will bank our time and we need not to bebothered for our fish and their aquariums for long time.

In the future, we hope to expand our work by obtainingmultiple views from differ-ent camera angles. Use sensors to determine pH level in the water and maintain rightpH level. Brightness to be synchronized with sunrise and sunset. Enable notificationson android app to remind user for unknown activity. Artificial intelligence and imagerecognition are used to determine fish and plant’s health, and also to reduce the sizeof the entire system using nanoelectronics.

References

1. Ramli,M.I.,Wahab,A., Helmy,M., Ahmad,N.: Towards smart home: control electrical devicesonline. In: International Conference on Science and Technology: Application in Industry andEducation, 2 Aug 2006

2. Robles, T., Alcarria, R., Mart´ın, D., Morales, A.: An Internet of Things-based model forsmart water management. In: Proceedings of the 8th International Conference on AdvancedInformation Networking and Applications Workshops (WAINA’14), Victoria, Canada. IEEE,May 2014, pp. 821–826

3. Uddin, M.N., Rashid, M.M., Mostafa, M.G., Belayet, H., Salam, S.M., Nithe, N.A., Rahman,M.W., Aziz, A.: International Islamic University Malaysia Development of Automatic FishFeeder

4. Maslekar,A.,Aparna,K.,Mamatha,K., Shivakumara, T.: Smart lighting systemusing raspberryPI. Int. J. Innov.Res. Sci. Eng. Technol. (An ISO3297: 2007CertifiedOrganization) 4(7) (2015)

5. Lagu, S.S., Deshmukh, S.B.: Raspberry Pi for automation of water treatment plant. In: 2015International Conference on Computing Communication Control and Automation

6. Kanzariya, S., Vora, V.: Real Time Video Monitoring System Using Raspberry Pi7. Dickey, N., Banks, D., Sukittanon, S.: Home automation using cloud and mobile devices. In:

Proceedings of IEEE Southeastcon (2012)8. Ren, D.,Wong,W., Chan, S.-H.G.: Toward continuous push-based P2P live streaming. In: 2012

IEEE Global Communications Conference (GLOBECOM)9. Sirsath, N.S., Dhole, P.S.,Mohire, N.P., Naik, S.C., Ratnaparkhi, N.S.: HomeAutomation using

Cloud Network and Mobile Devices10. Sripanidkulchai, K., Maggs, B., Zhang, H.: An Analysis of Live Streaming Workloads on the

Internet

Automation of Process Evaluationof Saccharification of Wheat StarchFollowed by Fermentation of Glucoseto Prepare Bioethanol Using DigitalImage Processing

Neha Patni, Pooja Shah, Jayneel Vora and Vinit Shah

Abstract During the last few decades, an increase in the pollution rate has beennoticed due to extreme consumption of fossil fuels, mainly in big residential areas.All petroleum-based conventional fuels can be substituted by renewable bio-basedfuels such as biodiesel, hydrogen, bioethanol. Bioethanol is generally produced fromstarchymaterials (such as corn, wheat, cereals), lignocellulosic biomass and sucrose-containing feedstocks. Saccharification of starch is done using enzymes, followed bya fermentation process using baker’s yeast Saccharomyces cerevisiae. Experimentsare carried out to see the day-wise decrease in concentration of glucose and increasein the concentration of ethanol. This may induce manual error as regular monitoringof the process with skilled supervision is required. In this paper, we propose “AutoMonitor” to introduce the digital image processing to automatemonitoring of the saidchemical process. This automation will help us get timely and accurate monitoringof various parameters like percentage of the chemical formation.

Keywords Bioethanol · Fermentation · Image processing · AutomationSaccharification

N. Patni · P. Shah · J. Vora (B) · V. ShahNirma University, S.G. Highway, Ahmedabad, Indiae-mail: 15bce048@nirmauni.ac.in

N. Patnie-mail: neha.patni@nirmauni.ac.in

P. Shahe-mail: pooja.shah@nirmauni.ac.in

V. Shahe-mail: 12bit034@nirmauni.ac.in

© Springer Nature Singapore Pte Ltd. 2019S. C. Satapathy and A. Joshi (eds.), Information and Communication Technologyfor Intelligent Systems, Smart Innovation, Systems and Technologies 107,https://doi.org/10.1007/978-981-13-1747-7_2

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12 N. Patni et al.

1 Introduction

1.1 Production of Bioethanol

A liquid biofuel, which is generally produced from several different biomass feed-stocks and conversion technologies is termed as bioethanol, known as grain alcohol.It is a striking oxygenated alternative fuel, as it is a renewable bio-based reserve,thereby offering the possibility to reduce particulate emissions in compression-ignition engines [1–3].

Presently the major portion of ethanol worldwide is produced by the fermentationof sugar obtained from molasses, cereals and fruits. Bioethanol feedstocks can besuitably categorized into three types:

• feedstocks containing sucrose, for example, sugar cane and sugar beet• materials containing a lot of starch, e.g. barley, corn and wheat and• lignocellulosic biomass, e.g. grasses, straw, and wood [4–6].

Industrially, the manufacture of bioethanol from feedstocks (containing starch) takesplace in five phases:

• Themachine-driven grating of the cereal grains to discharge the starch constituent,that is milling.

• Saccharification in which heating along with addition of water and enzymes forconversion into fermentable sugar is done.

• Sugar conversion into bioethanol and carbon dioxide due to fermentation of themash using yeast.

• Distillation and rectification in which concentration and washing the ethanol pro-duced during distillation is done by removing side products.

• Drying (dehydration) of the bioethanol [7].

After preparing bioethanol, preliminary test is done and then the sample is sub-jected to UV–Vis analysis by absorbing the blue-green (560 nm) colour of the light,formed in the reaction of ethanol with potassium dichromate and sulphuric acid. Acalibration curve is produced, in order to decide the actual amount of alcohol present.Absorbance of the light and concentration of a chemical in a sample are related toeach other as shown by the curve. Standard graphs are drawn to estimate glucoseconcentration in the broth. Similarly, day-wise confirmation of the ethanol formedis also done by checking for the carbon dioxide gas evolved.

But the process is not economically viable, for that this shortcoming is to beresolved. Daily analysis of percentage of glucose fermented and amount of ethanolformed is very important. It needs a careful and skilled supervision and is not easy tocalculate only with the help of these calibration curves. So if we have an automationpossible for the process, that will give authenticate results without the wastage oftime and chemicals.

To analyse the chemical process, the following features are supposed to be iden-tified under a microscope. The analysis and monitoring of the process are done by

Automation of Process Evaluation … 13

Fig. 1 Environmental image of cornstarch granules—bimodal structure [8]

taking some amount of sample and checking it with addition of chemical every day.Daily analysis is done to check whether the product is forming or whether the reac-tant is disappearing. So that progress can be shown daily, and analysis done at theend of reaction will give the yield of product, e.g. 90 chemicals needed to makereactions, involvement of manual intervention (thereby inducing errors) and find outthe product formation progress (Fig. 1).

1.2 Microscopic Image Processing

Microscope image processing is the usage of various digital image processing tech-nologies for the processing, analysis and presentation of images obtained from amicroscope with the help of various computer algorithms. The kind of processingdiffers from image to image. It permits ample broader range of algorithms applicableto the input image. For example, for an SEM image shown below, if it is possibleto measure the quantity, say density of the molecules, i.e. the product percentageformed, then it can be useful to infer the relation between the costs incurred for dif-ferent amount of product obtained. This can be useful to deliver the product as earlyas possible, if there is not going to be much change in its quantity even after waitingfor some extra time to let the reaction complete. This has been implemented on thefollowing image on the platform of MATLAB. The edges are first detected usingCanny edge detector. This image is then subjected to two spatial domain filters—Sobel and Prewitt. By the combination of the outputs obtained from filters, the imageis converted to a binary image. For the data analysis section, the number of pixelsconstituting molecules is found, and hence, the percentage is calculated. All theintermediate outputs of image processing are shown in Fig. 2.

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Fig. 2 Microscopic image processing

There is a lot of research and development seen in the medical field when itcomes to microscopic image processing, but not much is done in chemical processmonitoring which is prime focus of this paper. Thus, there is a scope of research andimplementation in this avenue.

1.3 Characteristics of Microscopic Images

The distinguishing feature of microscopic image is that it offers the best probabledemonstration of structures on the real matter. Accurate production of colours andcomparative visual outcome are of slight worry. The optical techniques, such asphase contrast and the similar, might have intensely changed the image. For imagestaken with phase systems, finest to use is just the green image, in case of conventionalphotography, use a green light source and monochrome film. Images of slides taintedwith a single-colour dye usually disclose the most features when the image is notedmonochrome near the wavelength of maximum absorption of the dye. Images takenin blue light are markedly sharper than those taken in red [9].

Automation of Process Evaluation … 15

Fig. 3 Context of Auto Monitor

2 Proposal

This paper suggests “Auto Monitor” (Fig. 2), an automation of the time-consumingchemical formation monitoring and analysis process by various image processingtechniques. Working of the Auto Monitor can be briefly described as: The SEMimages can be sent to the computer, and the computer can analyse and generateanalysis reports. This computer-based analysis outcome will be more accurate thanthe manual analysis. The images of the sample sent by SEM can be analysed usingimage processing techniques. Here, in automation, we need to differentiate ethanol,wheat starch and glucose using their shape. It is easy to differentiate as the shape ofeach substance is different from other. We have different algorithms in image pro-cessing that can be used to differentiate them by detecting shape of the substance.Hough transform [10, 11] is a popular algorithm to detect different shape. Variousversions of Hough transform have increased accuracy in detection, i.e. generalizedHough transform and randomized Hough transform [11]. After differentiating, wecan easily find the percentage of each substance in the sample image. These percent-ages give detail about how much ethanol is fermented from wheat starch. But from asingle image, the percentages are approximate. After analysing multiple images, wecan easily find out the more reliable percentage of ethanol and starch, and we canuse this data in automation (Fig. 3).

2.1 Implementation Details

The implementation of the project is done inMATLAB.Asmentioned in themethod-ology part, the first step is image acquisition. The SEM images of the chemical pro-cess are obtained from either themanufacturing unit or the chemical lab. Here, for theproject, the data is taken from an online repository available for research purpose. Inthe image preprocessing stage, contrast enhancement is done using histogram equal-ization. Histogram equalization is an effective modelling method used for the taskof contrast stretching. It uses the image’s histogram for this process. This method isgenerally used when the data in use is represented by very close contrast values. HEcontracts the areas with local contrast to attain higher level of contrast. HE plays avital role by spreading the most used intensity values as effectively as possible. Here,this method is used to differentiate the foreground from the background. The darkerportion of the image shows the backgroundwhile the lighter portion is the foreground

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Fig. 4 Contrast stretching

or the chemical substance under observation. The equation for the process is givenbelow:

Sk = T (rk) =k∑

j=0

n j/N =k∑

j=0

pr (r j ) (1)

Figure4 shows the image after contrast stretching. This helps in further processingthat is done on the image.

2.2 Methods for Parameter Calculation for Automation

2.2.1 Length of Edges

The first method tried is length of edges. After the image preprocessing, the imageis either converted into a binary image by thresholding or left as it is and then edgedetection algorithm is applied to the image. The edge detection technique used isCanny edge detection, since it is a multistage edge detection, and it helps in detectingthe smallest of the edges and that too in all the directions. Since the nature of theimage is such that other edge detection algorithms like Sobel and Prewitt are notused. The different steps involved in Canny edge detection are:

Firstly, Gaussian filter is applied to reduce the noise, intensity gradients of theimage are found, non-maxima suppression is performed, double thresholding is doneto detect edges, and finally, edge detection is done by connecting the weaker edgesand the stronger ones. After the edge is detected, the length of the edges is found. Theassumption for choosing this method was that since the initial state has a lot of smallgranular structures, it would have a longer edge, and as the process goes forward,the grains become sparser and the fibres are formed and the length of edge woulddecrease. But this method does not work efficiently as the assumption of the nature

Automation of Process Evaluation … 17

Fig. 5 Centroid detection

of image is wrong. If the image is captured from a farther distance, there would be apossibility of the length of edge of both the initial and final stage be the same (Fig. 5).

2.2.2 Centroid

The other methods tried in the experiments were focused on finding the centroid ofthe different substances formed and to take the distance from the centroid to the edge.Centroid is basically the point in the closed structure where all the mass of the objectis concentrated. This method was chosen for the fact that the granular structureswould have almost the same distance from the centre to the edge in all the fourdirections, while this would not hold true in case of fibrous structures. The methodfailed for the reason that the structures formed are not exactly closed structures, andas a result, the centroids detected are not the ones desired (Fig. 6).

2.2.3 Nearest Neighbourhood

Looking at the images it was found that in spite of fibres or granules belonging tothe same one, there was a difference in intensity values, and after binarizing theimage and applying edge detection, the fibres saw cuts and breaks. The conceptof 8-neighbourhood was implemented whereby the values of neighbouring pixelswere taken into consideration, and the pixel intensity with the highest frequencywas replaced as the centre pixel of that neighbourhood. The implementation of thisprovided very bad results, and as a result, the method was discarded.