Table Of ContentInternational Series in
Operations Research & Management Science
Fausto Pedro García Márquez
Benjamin Lev Editors
Internet
of Things
Cases and Studies
International Series in Operations Research
& Management Science
Volume 305
SeriesEditor
CamilleC.Price
DepartmentofComputerScience,StephenF.AustinStateUniversity,
Nacogdoches,TX,USA
AssociateEditor
JoeZhu
FoisieBusinessSchool,WorcesterPolytechnicInstitute,Worcester,MA,USA
FoundingEditor
FrederickS.Hillier
StanfordUniversity,Stanford,CA,USA
Moreinformationaboutthisseriesathttp://www.springer.com/series/6161
Fausto Pedro García Márquez (cid:129) Benjamin Lev
Editors
Internet of Things
Cases and Studies
Editors
FaustoPedroGarcíaMárquez BenjaminLev
IngeniumResearchGroup,ETSI LeBowCollegeofBusiness
IndustrialesdeCiudadReal DrexelUniversity,DecisionSciencesand
UniversityofCastilla-LaMancha MIS
CiudadReal,Spain Philadelphia,PA,USA
ISSN0884-8289 ISSN2214-7934 (electronic)
InternationalSeriesinOperationsResearch&ManagementScience
ISBN978-3-030-70477-3 ISBN978-3-030-70478-0 (eBook)
https://doi.org/10.1007/978-3-030-70478-0
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Preface
Internet of Things (IoT) is a relatively new discipline, an emerging research field
whichutilizestoolsandtechniquestakenfromoperationsresearchandmanagement
science(OR/MS).IoTisanetworkofdevicesconnectedandcapableoftransferring
information over the network. Devices on the network can include machine,
equipment, human, animal, parking lot, or anything. It can transfer text, signals,
media, and software. This book introduces the topic and its origin. It starts with
basicconceptsandpresentcases,applications,theory,andpotentialbenefitsasseen
fromOR/MSpointofview,forexample,advancedanalyticsandmachinelearning.
Examples are from smart industry, city, transportation, home, and smart devices.
The future applications, trends, and potential benefits of this new discipline are
discussed.ThebookprovidesaninterfaceamongOR/MS,engineering/technology,
organization and administration, and IoT. It is yet to be seen how OR/MS will
influencethedevelopment,direction,andshapeofIoT.ThefutureofIoTisbright
and strong. It will stay with us for decades, will be part of our daily life, will be
almosteverywhere,andwillbeextremelyuseful.
CiudadReal,Spain FaustoPedroGarcíaMárquez
Philadelphia,PA,USA BenjaminLev
v
Introduction
Internet of Things (IoT) is a relatively new discipline, an emerging research field
thatrequirestheuseandapplicationofmanagementscience.Thisbookintroduces
thetopicanditsorigin.Itstartswithbasicconceptsandoverviewandthenpresents
cases,applications,theories,andpotentials.Thechaptersinthisbookcoverawide
array of topics as space permits. Examples are smart industry, city, transportation,
home,andsmartdevices.Italsodiscussesfutureapplications,trends,andpotential
ofthisnewdiscipline.
IoT is a network of devices connected and capable of transferring information
over the network. The network can be a group of machines, equipment, human,
animal,parking lot,andsoon.Itcould transfertext,signals,media, and software.
To build and manage IoT, new disciplines in management science have emerged
such as advanced analytics and machine learning. Specifically, this book provides
an interface between the main disciplines of engineering/technology and the
organizational,administrative,andplanningcapabilitiesofmanagingIoT.
This book provides relevant theoretical frameworks and the latest empirical
research findings in IoT. It has been written for professionals to show their
understanding of the strategic role of IoT at various levels of the information and
knowledge organization, that is, IoT at the global economy level, at networks and
organizations,atteamsandworkgroupsofinformationsystems,and,finally,atthe
levelofindividualsasplayersinthenetworkedenvironments.
This book is intended for professionals in the field of engineering, information
science,mathematics,economists,andresearcherswhowishtodevelopnewskills
in IoT, or who employ the IoT discipline as part of their work. The authors who
contributedtothisbookdescribetheiroriginalworkintheareaorprovidematerial
forcasesandstudiessuccessfullyapplyingtheIoTdisciplineinreal-lifecasesand
theoreticalapproaches.
Blockchain is a new technology resulting from a continuous research on
consensus mechanisms to ensure the integrity of a distributed shared replica. It
represents a data structure built on a hash function and distributed among the
various participants according to previously agreed consensus rules. Chapter 1
aims to carry out a comprehensive survey of the consensus mechanism that forms
vii
viii Introduction
the heart of blockchain technology and its suitability for the IoT. It begins by
explaining blockchain technology from a historical and technical point of view
before approaching the different philosophical approaches within the consensus
mechanism,theirdisadvantage,andtheirsuitabilityfortheIoTsector.
IoT implementation depends, to a large extent, on human and technological
resourcesavailabilityandculturalaspectsandprioritiesoforganizations.According
to their state, the different factors can be enablers or inhibitors of IoT imple-
mentation. Since IoT implementation is a case of digitalization, main elements of
digitalization and most typical enablers and inhibitors are presented in Chap. 2.
Based on scientific and professional literature, the specific enablers and inhibitors
ofIoTarepresented.Theyinvolvetechnologicalenablers(abilitytoprovidepower
to the devices, connectivity, communication capability between elements, and
data handling capacity), strategic enablers (IoT adoption as a strategic element,
formulation of a global architecture of IoT), organizational enablers (digital talent
and skills and digital culture), organizational barriers (need for standardization,
securityrisks,privacyrestrictions,cost,andregulatoryissues),andculturalbarriers
(perceptionofcomplexityandlackoftrust)
The use of the IoT in the healthcare sector has shown to be a promising
solution to reduce the workload of doctors and provide better service to patients.
However,shareddatamaybesubjecttotheftormisuseduetothesecurityissueson
various devices. Moreover, transparency among stakeholders, confidentiality, and
micropayments need to be addressed. The objective of Chap. 3 is to use federated
learning over blockchain data generated from IoT devices with the usage of zero-
knowledgeprooforconfidentialtransactions.Theproposedarchitectureensuresthe
user a level of privacy set by them while making sure of sharing relevant insights
withtheconcernedparties.
IoT has been undergoing a rapid development and has obtained increasing
visibility. Many scientific research achievements have been published. Chapter 4
aims to provide a bibliometric review for highly cited papers in the field of IoT
usingtheEssentialScienceIndicators,awidelyuseddatabasetoevaluatescientific
outputs. Through the retrieval process, 388 papers were identified as highly cited
papers. Based on these 388 papers, it analyzes their characteristics from four
perspectives: annual and discipline distributions; productive players in terms of
journals, countries, institutes, and authors; top-15 most-cited papers; and author
keyword analysis. Interesting results are given after the analyses. Through author
keyword analysis, the chapter also provides research trends of the IoT for future
study.
Inmacroeconomics,“information”isacopiableproductwithnoadditionalcost.
DespiteservicesonIoTconsidertheinformation,theirscalabilitiesaresmallerthan
other information products. Chapter 5 models IoT services as a composition of
information, devices, and electricity, and points out that the existence of devices
andelectricitypreventsreducingmarginalcostsoftheservices.Itismentionedthat
the informatization of the computer industry in the past was the replacement of
accounting subjects. The chapter describes that design to scale IoT services is to
developincentiveslettingothersshoulderthecostsofdevicesandelectricity
Introduction ix
In Chap. 6, COVID-19 data are analyzed using the biclustering approach to
gain insights such as which group of countries have similar epidemic trajectory
patterns over the subset of COVID-19 pandemic outburst days (called bicluster).
Countries within these groups (biclusters) are all in the same phase but with a
slightlydifferenttrajectory.AnapproachbasedontheGreedyTwo-WayK-Means
biclustering algorithm is proposed to analyze COVID-19 epidemiological data,
which identifies subgroups of countries that show a similar epidemic trajectory
patternsoveraspecificperiod.Itisthefirsttimethatthebiclusteringapproachhas
beenappliedtoanalyzeCOVID-19data.Infact,theseCOVID-19epidemiological
dataarenotarealcountbecausenotalldatacanbetrackedproperly,andthereare
otherpracticaldifficultiesincollectingthedata.Evenindevelopedcountries,ithas
hugepracticalproblems.Therefore,iftheIoT-basedCOVID-19monitoringsystem
can be used todetect the originof theCOVID-19outbreak, then therealsituation
can be identified in each country. Results confirm that the proposed approach can
alertandhelpthegovernmentauthoritiesandhealthcareprofessionalstoknowwhat
toanticipateandwhichmeasurestoimplementtodeceleratethespreadofCOVID-
19.
Chapter 7 aims to review IoT applications in the healthcare domain that are
representative and active in practice and research. The chapter introduces the
existing IoT products in the healthcare market; reviews the studies on developing,
using, and improving IoT healthcare applications; and presents and discusses the
recent trend and focus of IoT healthcare applications. First, the chapter describes
a general picture of IoT healthcare applications. Then, the chapter studies IoT
healthcareapplicationsinfourscenarios:(1)acutediseasecare–threeapplications
areintroducedtoshowhowIoTbenefitsacutecare:vitalsignmonitoring,acutecare
telemedicine,andIoT-baseddetectionandcontrolofinfectiousdiseases;(2)chronic
disease care – the chapter focuses on remote health monitoring used for patients
with chronic diseases, especially patients with Alzheimer’s disease, diabetes, and
heart failure; (3) self-health management – the chapter pays attention to the most
common representative device for self-health management, smartwatches, and
analyzesthetwomainfunctionsofsmartwatchesonself-healthmanagement,sleep
monitoring, and exercise monitoring; (4) hospital operations management – the
chapteralsodiscussesIoTapplicationforhospitaloperationmanagementincluding
assetandautomatedhospitalworkflowmanagement,sinceitcanfinallyimprovethe
efficiencyandeffectofhealthcaredeliveryandthenbenefitpatientsanddoctors.
Chapter 8 presents a use-case based on the development of an interactive,
integrated, and adaptable visiting system for complex buildings and surrounding
grounds (smart places). The system features a mobile application that allows the
usertoaccessinformationfromseveralsmartplacesinasingleapplication,andan
indoor location and tracking system that infers the user location during the smart
placevisit.Thesystemcalculatesthetrackingandlocationoftheuserbasedonthe
positioningofneighboringBLEdevicessensedviaBluetoothontheuser’smobile
device. The approximate location, behavior, and interests, and hence the visiting
profileofeachuser,canbeinferredbythesignalsfrommultiplebeaconsinstalledon
thebuildingatspecificpre-definedpositions.Thesystemalsointegratesabackend
x Introduction
contentmanagementsystemtoallowthecreationandmanagementofsmartplaces
informationandsupportsinformationimportfromBIMtools.
Chapter9analyzescontemporarymachinelearningtechniquesforthecomputa-
tion of market and asset liquidity risk for multiple-assets portfolios. Furthermore,
this chapter focuses on the theoretical aspects of asset liquidity risk and presents
two critically robust machine learning processes to measuring the market liq-
uidity risk for trading securities as well as for asset management objectives. To
that end, this chapter extends research literature related to the computation of
market and asset liquidity risks by providing generalized theoretical modeling
algorithms that can assess both market and liquidity risks and integrate both risks
into multiple-assets portfolios settings. The robust modeling algorithms can have
practical applications for multiple-securities portfolios, and can have many uses
and application in financial markets, particularly in light of the 2007–2009 global
financial meltdown in issues related to machine learning for the policymaking
processandmachinelearningtechniquesfortheIoTdataanalytics.Inaddition,risk
assessment algorithms can aid in advancing risk management practices and have
importantapplicationsforfinancialtechnology(FinTech),artificialintelligence,and
machinelearninginbigdataenvironments.
The current IoT development involves ambient intelligence, which ensures that
IoT applications provide services that are sensitive, adaptive, autonomous, and
personalized to the needs of the users. A key issue of this adaptivity is context
modeling and reasoning. Multiple proposals in the literature have tackled this
problem according to various techniques and perspectives. Chapter 10 provides a
reviewofcontextmodelingapproaches,withafocusonservicesofferedinambient
assisted living (AAL) systems for persons in need of care. The chapter presents
thecharacteristics ofcontextual information,services offered byAAL systems,as
well as context and reasoning models that have been used to implement them. A
discussionhighlightsthetrendsemergingfromthescientificliteraturetoselectthe
mostappropriatemodeltoimplementAALsystemsaccordingtothecollecteddata
andtheservicesprovided.
IoT is a platform governed by information and communication technologies
that facilitates affordable data communication among heterogeneous devices in
large scale. However, computation rich applications running on IoT need specific
considerations as user response plays key role in critical situations particularly in
transportation,healthcareandsmartcities.Inthisview,Chap.11explainsindetails
characteristics ofvarious softwarecomponents thatareinuseinIoTover existing
communicationnetworks,andpresentsvariousproblemssolvingtechniquesforIoT
applications in intelligent transport systems. Further, the role of algorithms and
computational structures for development of efficient IoT application is illustrated
indetailwiththreerealtimecasestudiesintransportationdomain.
Chapter 12 considers the “Mobile Kukan Toukei™” (mobile spatial statistics)
to examine characteristics of the and spatial movement patterns in specific tourist
destinations in Nagoya City (Japan). This chapter also attempts to estimate visitor
volumeandflowusingmovementdataacquiredbyWi-Fitrackingsensorsinstalled
widely in tourism destinations. A Wi-Fi tracking sensor is a device that acquires
