Wireless Networks: Characteristics and Applications

232 Pages·2019·28.993 MB·English

Journal of
Sensor and
Actuator Networks
Wireless Networks
Characteristics and
Applications
Selected articles published by MDPI
www.mdpi.com/journal/jsan
Wireless Networks

Wireless Networks
Characteristics and Applications
SelectedArticlesPublishedbyMDPI
MDPI•Basel•Beijing•Wuhan•Barcelona•Belgrade
This is a reprint of articles published online by the open access publisher MDPI from 2017 to 2018.
The responsibility for the book’s title and preface lies with Dharma P. Agrawal, who compiled this
selection.
Forcitationpurposes,citeeacharticleindependentlyasindicatedonthearticlepageonlineandas
indicatedbelow:
LastName,A.A.; LastName,B.B.; LastName,C.C.ArticleTitle. JournalNameYear,ArticleNumber,
PageRange.
ISBN978-3-03921-684-0(Pbk)
ISBN978-3-03921-685-7(PDF)
(cid:2)c 2019bytheauthors. ArticlesinthisbookareOpenAccessanddistributedundertheCreative
Commons Attribution (CC BY) license, which allows users to download, copy and build upon
published articles, as long as the author and publisher are properly credited, which ensures
maximumdisseminationandawiderimpactofourpublications.
Contents
Prefaceto”WirelessNetworks” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
ChiaraBedon,EnricoBergamo,MatteoIzziandSalvatoreNoe`
PrototypingandValidationofMEMSAccelerometersforStructuralHealthMonitoring—The
CaseStudyofthePietratagliataCable-StayedBridge
Reprintedfrom:J.Sens.ActuatorNetw.2018,7,30,doi:10.3390/jsan7030030 . . . . . . . . . . . . 1
MarcoCattani,CarloAlbertoBoano,andKayRo¨mer
An Experimental Evaluation of the Reliability of LoRa Long-Range Low-Power Wireless
Communication
Reprintedfrom:J.Sens.ActuatorNetw.2017,6,7,doi:10.3390/jsan6020007 . . . . . . . . . . . . . 19
AnupKumarPaulandTakuroSato
LocalizationinWirelessSensorNetworks:ASurveyonAlgorithms,MeasurementTechniques,
ApplicationsandChallenges
Reprintedfrom:J.Sens.ActuatorNetw.2017,6,24,doi:10.3390/jsan6040024 . . . . . . . . . . . . 38
SimoneGrimaldi,AamirMahmoodandMikaelGidlund
An SVM-Based Method for Classification of External Interference in Industrial Wireless
SensorandActuatorNetworks
Reprintedfrom:J.Sens.ActuatorNetw.2017,6,9,doi:10.3390/jsan6020009 . . . . . . . . . . . . . 61
HindBangui,MouzhiGe,BarboraBuhnova,SaidRakrak,SaidRaghayandTomasPitner
Multi-CriteriaDecisionAnalysisMethodsintheMobileCloudOffloadingParadigm
Reprintedfrom:J.Sens.ActuatorNetw.2017,6,25,doi:10.3390/jsan6040025 . . . . . . . . . . . . 86
AntonioJose´Caldero´nGodoyandIsa´ıasGonza´lezPe´rez
IntegrationofSensorandActuatorNetworksandtheSCADASystemtoPromotetheMigration
oftheLegacyFlexibleManufacturingSystemtowardstheIndustry4.0Concept
Reprintedfrom:J.Sens.ActuatorNetw.2018,7,23,doi:10.3390/jsan7020023 . . . . . . . . . . . . 105
KhandakarAhmed,JanO.Blech,MarkA.GregoryandHeinzW.Schmidt
SoftwareDefinedNetworksinIndustrialAutomation
Reprintedfrom:J.Sens.ActuatorNetw.2018,7,33,doi:10.3390/jsan7030033 . . . . . . . . . . . . 126
HamzaDjelouat,AbbesAmiraandFaycalBensaali
CompressiveSensing-BasedIoTApplications:AReview
Reprintedfrom:J.Sens.ActuatorNetw.2018,7,45,doi:10.3390/jsan7040045 . . . . . . . . . . . . 147
Beiyu Lin, Yibo Huangfu, Nathan Lima, Bertram Jobson, Max Kirk, Patrick O’Keeffe,
Shelley N. Pressley, Von Walden, Brian Lamb and Diane J. Cook
Analyzing the Relationship between Human Behavior and Indoor Air Quality
Reprintedfrom:J.Sens.ActuatorNetw.2017,6,13,doi:10.3390/jsan6030013 . . . . . . . . . . . . 178
AlexAdimObinikpoandBurakKantarci
BigSensedDataMeetsDeepLearningforSmarterHealthCareinSmartCities
Reprintedfrom:J.Sens.ActuatorNetw.2017,6,26,doi:10.3390/jsan6040026 . . . . . . . . . . . . 196
v

Preface to ”Wireless Networks”
Wirelesstechnologyhasbecomeextremelyimportantforhumanlifeandnearlyeveryonecarries
atleastonecell/mobilephone. Voicecommunicationaffectsourdailylivesandweareinfluenced
byday-to-dayroutine. Wirelesssystemsarebeingexploredfornumerousapplicationsinaddition
totheircurrentcommunicationfunction. Onecanonlyimaginethepossibleinnovationsfroman
area is expanding at an unprecedented rate and offers significant future potentials. This volume
isacarefullyselectedcollectionofpapersthatcharacterizesthetechnologyandestablishesitsuse.
Thefirstpaperexplorestheuseofmicroelectro–mechanicalsystems(MEMSs)forstructuralhealth
monitoring (SHM) and considers the design and validation of an accelerometer that can be used
in monitoring the health of structures. This paper presents an original self-made MEMS sensor
prototypeandit’svalidationusinglaboratorytesting. Possibleapplicationsinstructuralassembly
arediscussed. Afull-scaleexperimentalvalidationoftheMEMSaccelerometerwasperformed,and
thedynamicresultsaresummarizedinthepaper.
Some have begun to doubt the transmission and reliability of wireless communications.
The second paper touches on this topic via an experimental reliability evaluation of low-power
communications. Byextendingthecommunicationrangeofthelinks,thenetworkdiametercanbe
reducedandcansimplifycommunicationandremovetheneedforrouting. However, long-range
low-power (LoRa) wireless technology is still at its infancy. It is, as yet, unclear whether it is
sufficiently reliable to complement existing short-range and cellular technologies, or which radio
settingscansustainthehighdeliveryrate.Thispaperpresentsanexperimentalstudyofthereliability
ofLoRabyfocusingontheimpactofphysicallayersettingsontheeffectivedatarateandenergy
efficiency.Theresultsshowthatthedatarateneednotbetunedinordertomaximizetheprobability
ofsuccessfulreception.
Wirelesssensornetworks(WSNs)areformedbymanylow-costsensorsthatcommunicatewith
eachother,sensedata,andpassitontoacentralstationcommonlyknownasthebasestation(BS).All
decisionsaremadewithintheBS.Thetaskofdeterminingthephysicalcoordinatesofsensornodes
inWSNsisknownaslocalizationorpositioning. InaWSN,itisimportanttoestimatetheplaceof
originofeventssensedbysensorsasdecisionsaremadewithinaBS.Aspositioningaccuracyvaries
fromapplicationtoapplication,differentlocalizationmethodsareadoptedindifferentapplications
andthereareseverechallengesinscenariossuchaswildfires.
Thethirdpapersurveysdifferentmeasurementtechniquesforlocalization. Further, different
localization-basedapplicationsarediscussed,andacomprehensivediscussionofthechallenges,such
as accuracy, cost, complexity, and scalability, are detailed. As the adoption of industrial wireless
sensord and actuator networks (IWSANs) has greatly increased, the time-critical performance is
affected considerably by external sources of interference. When an IEEE 802.11 network exists in
thesameenvironment,adropincommunicationreliabilityisobserved.Thiscanbeminimizedwith
long-termsampling.
Asupportvectormachine(SVM)wasusedinfourthreasearchprojecttominimizesboththe
sensing time and the memory footprint of the collected samples. A mechanism was proposed
to enable the classification of interference, while ensuring high classification accuracy. The fast
classificationwasobservedtobesuitableforTSCH-basedIWSAN.Mobilecloudcomputing(MCC)is
becomingapopularmobiletechnologythataimstoaugmentthelocalresourcesofmobiledevicesby
offloadingmobiledataandcomputation-intensiveoperationstocloudplatforms.Severaltechniques
vii
havebeenproposedtoimprovetheeffectivenessoftheoffloadingprocess. Multi-criteriadecision
analysis (MCDA) is a well-known concept that selects the best solution from several alternatives.
However,itisstillchallengingtoachieveasatisfactoryqualityofserviceinoffloading. Inthefifth
paper,areviewoftheliteraturewasconductedtopromoteabetterunderstandingoftheusabilityin
theoffloadingoperation.Challengesandopportunitiesformobilecloudcomputingarediscussed.
Networksofsensorsandactuatorsarebeingimplementedusingindustrialfieldbuses, where
automationunitsandsupervisorysystemsalsoexchangeoperationalinformation. Thesixthpaper
presents a solution to enhance the connectivity of a flexible manufacturing system. This system
includesafieldbusthatinterconnectsthesensors,actuators,andcontrollers. Toestablisheffective
communicationbetweenthesensorandactuatornetworks,ahardwareandsoftwareapproachwas
implemented.Theexperimentalresultsshowedproperoperationofsuchasystem.
TheInternetofThingshasincreasedtheuseofinnovativenetworktechnologiesinindustrial
automation, leading to efficient manufacturing and process automation with minimal human
intervention. Duetoongoingevolution,anewopportunityforsoftwaredefinednetworking(SDN)
hasemerged. Intheseventhpaper,abriefoverviewofSDNisprovidedandanetworkarchitecture
calledthesoftwaredefinedindustrialautomationnetwork(SDIAN)isproposed.Twonewsolutions
forflowcreationwereproposed,andtheanalyticalsolutionsarequantified. Theanalyticalmodel
wasverifiedusingMonteCarlosimulations. TheproposedSDIANarchitecturewasevaluatedand
analyzedusingtheMininetemulator. AnexperimentalfoodprocessingplantfeaturedRaspberryPi
asasoftware-definedcontrollerthatdemonstratedcharacteristicsofSDIAN.
TheIoTholdsgreatpromiseforprovidingcutting-edgetechnologythatwillenablenumerous
innovative services related to healthcare, manufacturing, smart cities, and various other human
activities. Many self-powered smart devices collect real-world data and communicate with each
otherandwiththecloudthroughawirelesslink. However, highenergyconsumptioninwireless
transmissionlimitstheperformanceofthesedevices.Thus,differentapproachessuchascooperative
transmission,multi-hopnetworkarchitectures,andsophisticatedcompressiontechniqueshavebeen
explored. Compressivesensing(CS)isaveryattractiveparadigminthedesignofIoTplatforms.
TheeighthpaperassessestheextantliteraturethathasaimedtoincorporateCSinIoTapplications.
Moreover,emergingtrendsarehighlightedforfutureCS-basedIoTresearch.
In the coming decades, global population growth and global aging issues are expected, and
thereareincreasingconcernsaboutthequalityoftheairbothinsideandoutsideofbuildings. The
ninth paper examines the relationship between home occupant behavior and indoor air quality.
Both sensor-based behavior data and chemical indoor air quality measurements in smart home
environmentswerecollected.Anovelmachinelearning-basedapproachwasintroducedtoquantify
the correlation between smart home features and chemical measurements of air quality. This
informationcouldbeusefulinplanningforthefutureasanintegralpartofsmartcities.
TheIoTconceptanditsintegrationwithsmartconnectedhealthsystemsappearedasanintegral
componentofsmartcityservices.Hardsensing-baseddataacquisitionthroughwearableprobes,and
softsensingsuchascrowd-sensing,couldresultinhiddenpatterns. Recentresearchaddressedthis
challengethroughdeeplearning.Inthislastarticle,deeplearningtechniquesthatcanbeusedtosense
data,improvepredictionandmakesmartdecisionswerereviewed.Acomparisonandtaxonomyof
thesemethodologiesarepresentedbasedontypesofsensorsandsenseddata.Thoroughdiscussions
oftheopenissuesandresearchchallengesineachcategoryarealsoprovided.
Thecollectedarticlesprovideasummaryofvariouscharacteristicsofwirelessnetworks,their
viii
limitations, and ways to overcome these limitaitons effectively and quickly. There are numerous
applications where wireless technology, such as the IoT, sensor networks, and other networks, is
beingexploredandmanynovelapplicationsarecoveredhereindetail.
DharmaP.Agrawal
ix