Introduction to internet of things in management science and operations research : implemented studies PDF
Preview Introduction to internet of things in management science and operations research : implemented studies
International Series in Operations Research & Management Science Fausto Pedro García Márquez Benjamin Lev Editors Introduction to Internet of Things in Management Science and Operations Research Implemented Studies International Series in Operations Research & Management Science Volume 311 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 á (cid:129) Fausto Pedro García M rquez Benjamin Lev Editors Introduction to Internet of Things in Management Science and Operations Research Implemented Studies Editors FaustoPedroGarcíaMárquez BenjaminLev IngeniumResearchGroup LeBowCollegeofBusiness UniversityofCastile-LaMancha DrexelUniversity,DecisionSciencesandMIS CiudadReal,Spain Philadelphia,PA,USA ISSN0884-8289 ISSN2214-7934 (electronic) InternationalSeriesinOperationsResearch&ManagementScience ISBN978-3-030-74643-8 ISBN978-3-030-74644-5 (eBook) https://doi.org/10.1007/978-3-030-74644-5 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerland AG2021 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseof illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similarordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors, and the editorsare safeto assume that the adviceand informationin this bookarebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface This edited volume uses Operations Research and Management Science (OR/MS) disciplinestogetherwiththenewadvancesininformationsystemsandtechnologies to follow the recent market needs. Most of the services in OR/MS will be done employing Internet of Things (IoT). The main functionality of IoT is to connect devicesinlargenetworks,favoringinformationtransfer,e.g.,images,data,videos, andfiles.Itisappliedtoalldisciplinessuchasbusiness,energy,transport,financial, andhealth.Advancedanalyticsisneededtocoverthetreatmentofthisinformation, forexample,deeplearning,machinelearning,anddatamining. This volume provides an overview of thematic analysis, an exhaustive state-of- the-art analysis, and the main different novel techniques applied to many case studies, where the results are discussed and validated. We present 12 chapters where different authors demonstrate their models, cases, implemented studies, or theory of Internet of Things using Management Science and Operations Research toolsandtechniques. CiudadReal,Spain FaustoPedroGarcíaMárquez Philadelphia,PA,USA BenjaminLev v Introduction TheemergingfieldofInternetofThings(IoT)willutilizeOperationsResearchand Management Science (OR/MS) disciplines together with recent advances in infor- mationsystemsandtechnologiestofollowtherecentmarketneeds.Mostprojectsin OR/MS will be done employing IoT. The main functionality of IoT is to connect devicesinlargenetworks,favoringinformationtransfer,e.g.,images,data,videos, andfiles.Itisappliedtodisciplinessuchasbusiness,energy,transport,financial,and health.Advancedanalyticsisneededtocoverthetreatmentofthisinformation,for example,deeplearning,machinelearning,anddatamining. This volume provides an overview of thematic analysis, an exhaustive state-of- the-art analysis, and the main different novel techniques applied to many case studies,wheretheresultsarediscussedandvalidated. This book is intended for professionals in the field of engineering, information science,mathematics,economists,andresearcherswhowishtodevelopnewskillsin IoT or who employ the IoT discipline as part of their work. The authors who contributedtothisbookdescribetheiroriginalworkintheareaorprovidematerial for cases and studies successfully applying the IoT discipline in real life cases and theoreticalapproaches. Internet and Internet of Things (IoT)-based applications have brought a global changeinbridgingthevirtualworldwiththerealworld.Chapter“InternetofThings: TechnologiesandApplications”aimsatprovidinganoverviewofIoT,itscontribu- tioninimprovingthequalityoflife,thetechnologiesinvolvedinitsimplementation, andIoTapplicationsthatbroughtcomfortinthelivesofmany.Anoverviewofthe technologies included in the implementation of IoT, such as Radio frequency Identification(RFID),NearFieldCommunication(NFC),andMachine-to-Machine Communication(M2M),hasbeenprovided.TheemergenceofIoTanditsincorpo- ration in various applications of healthcare, education, and rehabilitation has made IoT an important part of Assistive Technology (AT). Assistive technologies have greatly impacted the lives of disabled people in performing their daily activities. While IoT is still in the development stage, its integration into AT services is challenging while the benefits are unlimited. Chapter “Internet of Things: vii viii Introduction Technologies and Applications” includes the introduction, state-of-the-art technol- ogies and applications of IoT, case studies, followed by conclusion and future recommendations. BroadutilizationofIoTindifferentindustriesandmanufacturingsegments,from healthcare to transportation and supply chain, smart house, smart city, and smart medicine,generatedahugeamountofdatathroughsuchsmartdevices.Prescriptive analytical power of firms enables them to gain a competitive advantage from generateddata.Prescriptiveanalyticscanbeenabledbydeeplearningtoovercome the complexity of pattern recognition and data analysis of censored data. Deep learningwithlearning(un)structureddatahasgreatcapabilityinidentifyinghidden knowledge.Therefore,deeplearningplaysanenablerroleforprescriptiveanalytics. The goal of prescriptive analytics is to create business value by enhancing firms’ performance by offering optimal solutions. In chapter “Prescriptive Analytics in Internet of Things with Concentration on Deep Learning,” absorptive capacity theoryisdeployedandexplainsthattotheextentthatfirmscanempowerprescrip- tiveanalyticswithdeeplearningtechnologyandoptimizationalgorithmstoidentify and leverageexternalknowledge, firms can gain advantage of exploitativeinnova- tioncapabilityandenhancetheirperformancesignificantly. Chapter “IoT Security and New Trends of Solutions” focuses on the security issues of the IoT. It explores the development direction of IoT security through discussionoftechnology.TheIoTcanmakeindividualsmorecloselyconnected,but it may spread individual or local insecurity factors further apart. Specifically, the chapteraddressesIoTsecurityindetailinthefollowingsixsections.Thefirstsection discusses the foundation of IoT security. Then, it presents the security issues in perception layer. This layer is the core of IoT, and its security is a prerequisite for informationsecurityandprivacyprotectionofIoT.ThethirdsectionisIoTsecurity inthenetworklayerandthenitanalysesthesecurityinIoTapplications.Intermsof securityinapplicationlayer,itconsiderssecuritytechnologiesandsecuritystrategies forIoTapplications.Asmoreandmoreapplicationsarerunningwithcloudservice, thischapterfocusesonthesecurityissuesofcloudcomputingapplications.Section5 discusses some case studies of IoT security,and thelast section discusses possible futureresearchdirectionsforIoTsecurity. In recent years, there has been an intense debate surrounding two modalities of learninginhighereducation,thetraditionalandtheinnovative.Themodelpresented inchapter“AHybridModelofLearningMethodologyAnalyzedThroughtheUseof Machine Learning Techniques” aims to combine the best of both methodologies, thereby creating and developing a hybrid learning model. The model includes multimedia tools, traditional tools, and techniques derived from the field of neuro- science.UsingIoTthroughtheCanvasdigitallearningplatform,whichmonitorsthe student during the course, a large amount of data can be obtained. These data are analyzed and employed to evaluate the hybrid model using machine learning techniques to support the decision-making in the learning methodology. The obtained results show: (1) a change in the traditional structure of a class; (2) a positiveeffectonperformance,especiallythroughvideolessons;(3)boththehybrid Introduction ix modelandtheCanvasdigitallearningplatformgeneratedpositiveeffectswithinthe learningenvironment. When Industrial Internet of Things (IIoT) appears in our enterprise network, a newsecuritybreacharises,withnewpossibilities,andatleastwitharequestofone speciallayerofcybersecurity(CS)whichshouldbeappliedtoit.Oneofthemethods whichisgrowinginpopularityduetoitssimplicityandinnerstrengthistheuseof Blockchain(BC)asafirstlayertosecurethesensors,thedata,theactuators,andthe actors, persons who access and monitor IIoT information from smart devices, existing inthisspecialpart ofourenterprisenetwork. The aimofchapter“Internet of Things Cybersecurity: Blockchain as First Securitisation Layer of an IoT Net- work”istosetsomeofthebasesofthismixofIIoTandBlockchainforenterprise cybersecurity. Forasmartcitywithstochasticdemand,adata-drivensignalcontrolisconsidered for trafficresponsive andtime-varying roadtrafficnetworks.Forperiod-dependent stochastic demand, a flow-based scenario design for traffic assignment can be formulated as a complementarity. A new solution using stochastic gradients is proposed in chapter “A Data-Driven Traffic-Responsive Signal Control for a Smart City Road Network Under Uncertainty.” To demonstrate feasibility of the proposedapproach,numericalexperimentsusingreal-worldexampleroadnetworks are performed. To investigate computational tractability of the proposed approach, numerical comparisons are made with the state-of-the-art existing traffic signal control strategies. These results reported indicate that the proposed data-driven traffic responsive signal control can achieve reliably better results for a smart city road network than did those recently proposed against a high consequence of stochasticdemands. Chapter “Utilization of Consumer Appliances in Smart Grid Services for Coor- dinationwithRenewableEnergySources”presentsaninnovativemodelforincreas- ingconsumerparticipationinsmartgridelectricitymarkets.Thegoaloftheresearch istwofold.Firstly,weaimtodevelopbusinessmodelsthatofferapotentialsolution to the unpredictable output of solar and wind power plants. The model assumes consumers’positiveattitudestowardgreenenergyandallowsthemtotakeanactive role in lessening the unpredictability of renewables by offering their household devices in smart grid services. Secondly, we aim to design an IoT infrastructure for the realization of the proposed model based on smart plugs working within a Blockchainenvironment.Thesoftwarecomponentisextendedwithacollaborative Blockchain-basedloyaltyschemewiththegoalofallowingfurtherconsumerincen- tives. The proposed model has been evaluated through a pilot study of consumer’ attitudesandthemodelisexpectedtobeacceptedandimplementedinthesmartgrid inSerbia. Asmartgridisanewconceptthatprovidestwo-wayinformationandelectricity connectionthathaspreparedanopportunitytoinvolvecustomersfor awareness of load profile, the participation of distributed generation, demand management, cost optimization, and variety in customer services. With the emergence of newer technologies in the fifth generation (5G) and beyond, like the IoT, devices tend to be always online. IoT provides the necessary platform for the exchange of x Introduction information.Runningdemand-sidemanagement(DSM)needstechnologiessuchas advancedmeteringinfrastructure(AMI)ontheconsumptionside.Ontheotherhand, new devices can connect to the Internet. By sending a signal from the utility and receivingitbytheAMI,ashiftablehomeappliancecanworktolowpricewithout human activity. Chapter “Application of Internet of Things (IoT) to Demand-Side Management in Smart Grids” focuses on providing an overview of the smart grid and the application of IoT in smart grids by focusing on DSM. Accordingly, well- known IoT algorithms based on DSM used in smart homes are reviewed and investigated in this chapter. Accordingly, the basics of IoT and DSM and their applicationtothesmartgridsareinvestigatedandreportedinthisstudy. In chapter “A Hybrid Model of Learning Methodology Analyzed Through the UseofMachineLearningTechniques,”thekeydeterminantsorfactorsinfluencing consumers’behavioralintentiontoacceptautonomousvehicles(AVs)areexplored andtested.Althoughthereisaplethoraofstudiesintheliteratureevaluatingvarious technologies,mostofthemodelsoftechnologyacceptanceandtheoriesselectedare appliedonbarrierstosuccessfulITimplementationinorganizationsorgenerallyon technologiesthathavelongbeendeployed.Thereareveryfewstudiesconductedon disruptivetechnologiessuchasAV.Inchapter“UnderstandingtheFactorsInfluenc- ingConsumers’BehaviourTowardsAutonomousVehiclesAdoption,”thisissueis addressedwith theidentificationofcontext-related constructs partly retrievedfrom existing literature that describe a modeling process for a technology acceptance attempting to explain the main factors influencing people behavioral intention to adopt autonomous vehicle technology. Constructs such as trust, self-efficacy, anx- iety,perceivedsafety,andlegalregulationareconsidered.Themaincontributionof the study is the detailed exploration on the users’ acceptance of AV. It proposes a theoretical model called Autonomous Vehicle Technology Acceptance Model (AVTAM) that gives us a better understanding of what will eventually influence future AV customers. The main findings of this work suggest that users’ safety on theroad,thecostassociatedwiththetechnology,andthetrustoncarmanufacturers willplayaveryimportantroleforlarge-scaleAVadoption. IoT in Wuxi city has grown energetically and boomingly with a series of remarkabledevelopmentachievements.Inchapter“DevelopmentPath, Experience and Implications of the Internet of Things Industry in Wuxi, Jiangsu Province, China,” development path of the IoT industry in Wuxi is analyzed. Then, the development experience and implications of the IoT industry are summarized. At thesametime,existingproblemsinthedevelopmentprocessoftheIoTindustryare also discussed. At last, countermeasures are proposed to promote the sustainable development of the IoT industry in Wuxi city. The special Wuxi intelligence and WuxiexperienceintheIoTindustrycouldprovidereferencefordevelopingtheIoT industryintheothercities. The automotive industry is composed of multiple manufacturing processes of primarypartsforassemblyofassemblies.Thesepartsaregenerallybuiltonmassive manufacturingprocesses,basedonstampingandsimpleassemblies,throughnailing, welding, grinding, adding components, etc. Defects in elements that affect the process can lead to the generation of whole batches of defective parts. The
The list of books you might like
