Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions

339 Pages·2023·8.573 MB·English

by  Sarvesh Pandey, Udai Shanker, Vijayalakshmi Saravanan, Rajinikumar Ramalingam

EAI/Springer Innovations in Communication and Computing
Sarvesh Pandey
Udai Shanker
Vijayalakshmi Saravanan
Rajinikumar Ramalingam   Editors
Role
of Data-Intensive
Distributed
Computing Systems
in Designing Data
Solutions
EAI/Springer Innovations in Communication
and Computing
SeriesEditor
ImrichChlamtac,EuropeanAllianceforInnovation,Ghent,Belgium
The impact of information technologies is creating a new world yet not fully
understood. The extent and speed of economic, life style and social changes
already perceived in everyday life is hard to estimate without understanding the
technological driving forces behind it. This series presents contributed volumes
featuring the latest research and development in the various information engi-
neering technologies that play a key role in this process. The range of topics,
focusing primarily on communications and computing engineering include, but
arenotlimitedto,wirelessnetworks;mobilecommunication;designandlearning;
gaming;interaction;e-healthandpervasivehealthcare;energymanagement;smart
grids;internetofthings;cognitiveradionetworks;computation;cloudcomputing;
ubiquitousconnectivity,andinmodegeneralsmartliving,smartcities,Internetof
Thingsandmore.Theseriespublishesacombinationofexpandedpapersselected
from hosted and sponsored European Alliance for Innovation (EAI) conferences
that present cutting edge, global research as well as provide new perspectives on
traditional related engineering fields. This content, complemented with open calls
forcontributionofbooktitlesandindividualchapters,togethermaintainSpringer’s
and EAI’s high standards of academic excellence. The audience for the books
consists of researchers, industry professionals, advanced level students as well as
practitioners in related fields of activity include information and communication
specialists, security experts, economists, urban planners, doctors, and in general
representativesinallthosewalksoflifeaffectedadcontributingtotheinformation
revolution.
Indexing:ThisseriesisindexedinScopus,EiCompendex,andzbMATH.
About EAI - EAI is a grassroots member organization initiated through coopera-
tion between businesses, public, private and government organizations to address
the global challenges of Europe’s future competitiveness and link the European
Research community with its counterparts around the globe. EAI reaches out to
hundreds of thousands of individual subscribers on all continents and collaborates
with an institutional member base including Fortune 500 companies, government
organizations, and educational institutions, provide a free research and innovation
platform. Through its open free membership model EAI promotes a new research
and innovation culture based on collaboration, connectivity and recognition of
excellencebycommunity.
Sarvesh Pandey • Udai Shanker •
Vijayalakshmi Saravanan •
Rajinikumar Ramalingam
Editors
Role of Data-Intensive
Distributed Computing
Systems in Designing Data
Solutions
Editors
SarveshPandey UdaiShanker
ComputerScience MadanMohanMalaviyaUniversityof
BanarasHinduUniversity Technology
Varanasi,India Gorakhpur,UttarPradesh,India
VijayalakshmiSaravanan RajinikumarRamalingam
UniversityofSouthDakota DeutschesElektronen-SynchrotronDESY
SouthDakota,SD,USA Hamburg,Germany
ISSN2522-8595 ISSN2522-8609 (electronic)
EAI/SpringerInnovationsinCommunicationandComputing
ISBN978-3-031-15541-3 ISBN978-3-031-15542-0 (eBook)
https://doi.org/10.1007/978-3-031-15542-0
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerland
AG2023
Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether
thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse
ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and
transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar
ordissimilarmethodologynowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Foreword
Data analytics and machine learning technologies, particularly in a decentralized
scenario, are offering cost-effective solutions for many real-life problems. Recent
developments in computer technology have led to increased research interests in
thefieldofmoderndata-intensivedistributedcomputingsystems.Today,whenuser
requirements are becoming exponentially complex, it is not possible to meet the
expectations ofsocietybyapplyingcoreknowledgeofanysingleresearchareaof
computerscience;rather,thereisaneedforintegratedeffortswiththeumbrellaof
researchtopics.Thispromptedtheresearcherstothinkaboutthemulti-disciplinary
natureofworktoprovideasolutionforthechallengessetforthduetovariousfuture
requirements.Inthisdirection,datasystemsserveasastrongcomponentthatweare
eitherusingorwouldbeusinginnearfuture.
Advancement in the field of modern computing will continue to be critical
for computer science researchers and a matter of concern for the end users.
Therefore,theobjectiveofthebookRoleofData-IntensiveDistributedComputing
Systems in Designing Data Solutions, edited by Sarvesh Pandey, Udai Shanker,
VijayalakshmiSaravanan,andRajinikumarRamalingam,istointroducethereader
torecentresearchactivitiesinthefieldofmodern-daydata-drivendecision-making
processes. It is an excellent example of a collection of advanced works applied to
relevant problems. It covers areas like real-time systems, machine learning, data
analytics, medical imaging, and applications of all these areas considering ever-
growing user demands. Some of the chapters of this book provide interesting
information on the integration of this wonderful and disruptive technology with
modern applications. Also, one chapter introduces the readers to a system model
for detecting the original camera that clicked a particular image – this would help
insolvingmanyreal-lifeissuesinthenearfuture.Researchaddressingperformance
issuesofthesesystemsisarelativelynovelarea,andthecontentsinthechapterare
goodenoughtoevincetheinterestfordevelopinginnovativesolutionstotheopen
technicalchallenges.
This book will be very helpful to students, researchers, scientists, and industry
professionals working in the field of computing. A genuine attempt is made to
increasetheunderstandingofhowdataisgoingtoplayacentralroleinmanyofthe
v
vi Foreword
emergingresearchdomains.Itwouldempowerthereaderstoworkonnewresearch
domains,whichwouldbeusefulforsociety.Atlast,thisbookindeedprovidesfuture
insightsontheperformanceissueswithmoderndata-intensivesystems.
DirectoratIIIT,Pune,Maharashtra,India AnupamShukla
Preface
Thisbook,titledRoleofData-IntensiveDistributedComputingSystemsinDesign-
ing Data Solutions, is centered on discussing various new opportunities created
by the fast-computing power and big data collectively. There were more than 40
submissions; out of these, 16 submissions have been finally included in this book
proceedingafterrigorousreview.Weappreciateeveryonewhoconsideredthisvenue
for the possible publication of their research articles; congratulations to all the
authorswhosebookchaptersareincluded.
To better organize the contents, this book is divided into three sections. Part
I, which consists of four chapters, is mainly on integration of data systems and
traditional computing research. Part II, which consists of seven chapters, is about
how data-driven decision-making is now a reality. Finally, Part III, which consists
of five chapters, discusses the critical role of data management in healthcare
functioning.Thethemesoftheacceptedbookchaptersarediscussedbelowinbrief
sothataudiencecanunderstandwhatthisbookhastocater.
PartI:On IntegrationofDataSystemsandTraditional
ComputingResearch
Chapter 1 talks about energy-conscious scheduling of resources for fault-tolerant
distributed computing systems. This chapter emphasizes the point that reliability
shouldbegivenequalweightageasthattodeadlineaspectofsuchsystemdesign.
Chapter 2 discusses how advanced morphological component analysis and
steganographycouldbeutilizedforthepurposeofsecretdatatransmission.
Chapter 3 puts light on cyber-security aspects of data management in wireless
sensor networks. Chapter 4 proposes a dynamic privacy protection scheme for
trajectorydata.
vii
viii Preface
PartII:Data-DrivenDecision-MakingSystems
Chapter 5 proposes an idea of how integration of mobile agent systems with e-
governancecanleadtobetter/transparentanddynamicinfrastructurewithnolossto
reliabilityandfaulttolerance.
When we are living in a world where a countless number of websites are on
the Internet, it is important that we should design a system to make sure that end
users do not fall into the trap of phishing websites. Chapter 6 not only discusses
this problem but also attempts to resolve this issue by using some of the existing
machinelearningtechniques.
Source camera identification method, which can be used to identify the source
cameraoftheimages/photos,playsaveryimportantroleintoday’sera,especiallyin
thedomainofdigitalimageforensics.InChap.7,usingmachinelearningclassifiers,
authorsattemptedtopredictdevice-specificinformationfrompicturedata.
Dependence on vehicles has increased manifold in the twentieth century. Now,
withadventoftheInternet,researchersstartedworkingontheideaof“Internetof
Vehicles (IoV).” After that, since 2015, a cross-injection of IoV and blockchain
technologyhascontinuedtobearesearchareawithlotsofpotential.Chapter8puts
lightonalltheseaspects.
Traditionalbiddingsystemcanalsobenefitfromblockchaintechnology.Chapter
9discussesthis.Withintegrationofblockchain,withoutanydoubt,transparencyof
biddingprocesswouldincrease.
Chapter 10 talks about vehicular ad hoc networks (VANETs). Various security
challenges one may face with VANET-based systems are nicely discussed in this
chapter.Thisexploratorystudyalsolistsfuturepromisingsolutions.
Chapter11isallaboutprovidingauser-friendlyGUItothelearners.Intherecent
past,wefacedanunprecedentedthreatofCOVID-19.Thishasprovenyetagainthat
onlinelearningsystemsareourfriendsandcanco-existwithtraditionalclassroom
teachingmethods,andbyutilizingboth,wecouldimprovetheoutcomestoagreater
extent.
PartIII:Data-IntensiveSystemsin Healthcare
AftertheCOVID-19outbreak,thefirstthingwestruggledwithwastheneedforan
efficient medical kit to test whether someone is COVID-19 positive or not. In the
fight against COVID-19, it has been an accepted practice that CT scans could be
reliedonfortesting.Chapter 12details ontheaspectofanalyzing high-resolution
CTimagesforCOVIDtesting.
Chapter13proposestheuseofanattention-baseddeeplearningapproachforthe
analysisofX-rayimages.
Theefficacyofswarm-basedmethodsinprocessingmedicalimagesisdiscussed
indetailinChapter14.
Preface ix
Chapter15talksaboutanalyzingcardiacMRIimagesusingconvolutionneural
networkstodetectcardiovasculardiseases.
Along the line of Chap. 15, Chap. 16 focuses on analyzing brain images using
deep learning to detect brain tumors. In constrained circumstances, where people
with medical expertise may get overwhelmed, the techniques presented in Chaps.
15and16couldbeofgreatassistivehelp.
To summarize, we are of the view that this book has perfectly covered various
applicationareaswithcentralfocusonbigdata.
Varanasi,India SarveshPandey
UttarPradesh,India UdaiShanker
SD,USA VijayalakshmiSaravanan
Hamburg,Germany RajinikumarRamalingam