Table Of ContentHiroto Yasuura · Chong-Min Kyung
Yongpan Liu · Youn-Long Lin Editors
Smart
Sensors
at the IoT
Frontier
Smart Sensors at the IoT Frontier
Hiroto Yasuura • Chong-Min Kyung
Yongpan Liu (cid:129) Youn-Long Lin
Editors
Smart Sensors at the IoT
Frontier
123
Editors
HirotoYasuura Chong-MinKyung
KyushuUniversity DepartmentofElectricalEngineering
Fukuoka,Japan KoreaAdvancedInstituteofScience
andTechnology(KAIST)
YongpanLiu Daejeon,SouthKorea
CircuitsandSystemsDivision
TsinghuaUniversity,Beijing Youn-LongLin
Beijing,China NationalTsingHuaUniversity
Hsinchu,Taiwan,Taiwan
ISBN978-3-319-55344-3 ISBN978-3-319-55345-0 (eBook)
DOI10.1007/978-3-319-55345-0
LibraryofCongressControlNumber:2017939972
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Contents
Introduction ...................................................................... 1
HirotoYasuura
PartI DeviceTechnologyforIoT
Energy-AutonomousSupply-Sensing BiosensorPlatformUsing
CMOSElectronicsandBiofuelCells .......................................... 9
KiichiNiitsu
SmartMicrofluidicBiochips:CyberphysicalSensorIntegration
forDynamicErrorRecovery ................................................... 23
HailongYao,QinWang,andTsung-YiHo
Reducing Timing Discrepancy for Energy-Efficient On-Chip
MemoryArchitecturesatLow-VoltageMode................................. 73
Po-HaoWangandTien-FuChen
Redesigning SoftwareandSystems forNonvolatileProcessors
onSelf-PoweredDevices......................................................... 107
ChunJasonXue
PartII SensingTechnologyforIoT
OEICs for High-SpeedData Links and Tympanic Membrane
TransducerofHearingAidDevice............................................. 127
Wei-ZenChen,Shih-HaoHuang,andJhong-TingJian
DepthEstimationUsingSingleCamerawithDualApertures.............. 167
Hyun Sang Park, Young-GyuKim, YeongminLee, Woojin Yun,
Jinyeon Lim, Dong Hun Kang, MuhammadUmar Karim Khan,
Asim Khan, Jang-SeonPark,Won-Seok Choi,YoungbaeHwang,
andChong-MinKyung
v
vi Contents
Scintillator-BasedElectronicPersonalDosimeter
forMobileApplication........................................................... 191
Gyuseong Cho, Hyunjun Yoo, Daehee Lee, Jonghwan Park,
andHyundukKim
PartIII SystemandApplication
LEDSpectrophotometryandItsPerformanceEnhancementBased
onPseudo-BJT ................................................................... 221
SeongwookChoiandYoungJunePark
AnAirQualityandEventDetectionSystemwithLife Logging
forMonitoringHouseholdEnvironments..................................... 251
HyuntaeCho
MobileCrowdsensingtoCollectRoadConditionsandEvents ............. 271
Kenro Aihara, Hajime Imura, Bin Piao, Atsuhiro Takasu,
andYuzuruTanaka
SensingandVisualizationinAgriculturewithAffordableSmart
Devices............................................................................. 299
Takashi Okayasu, Andri Prima Nugroho, Daisaku Arita,
TakashiYoshinaga,YoshikiHashimoto,andRin-ichiroTachiguchi
Learning Analytics for E-Book-Based Educational Big Data
inHigherEducation ............................................................. 327
Hiroaki Ogata, Misato Oi, Kousuke Mohri, Fumiya Okubo,
Atsushi Shimada, Masanori Yamada, Jingyun Wang,
andSachioHirokawa
SecurityandPrivacyinIoTEra................................................ 351
OrlandoArias,KelvinLy,andYierJin
Introduction
HirotoYasuura
Internet of Things (IoT) has become a big trend in the ICT (information and
communicationtechnologies)field.Inadditiontosmartphones,tablets,andpersonal
computers,a wide rangeof itemsincludingdailynecessities suchas refrigerators,
bathrooms,andairconditionersaredirectlyconnectedtotheInternet.Manyofthe
newICT-basedservicesthatcreatepotentiallylargemarketsareexpectedtobecome
availablebasedonIoT.
One of the large and well-known examples of IoT activities is “Industrie 4.0”
jointly developed by the German government/industry/academia. The goal is to
connectallmachinesinthefactoryviathenetworktodigitizethewholeprocessin
factoryactivities.Itcompletelychangesthestyleoftheproductionprocess.Inthe
normalmanufacturingprocess,thestructureoftheprocessiscarefullydesigned,but
onceitisbuilt,itwillbefixedforacertainperiodoftime.Bycontrast,inIndustrie
4.0,theprocessincludingthephysicalplacementofthefactorymachineischanged
dynamicallyreferringtothedataobtainedbyobservingtheactivitiesoftheprocess
viathesensornetwork.Dataincludesnotonlythestatusofallthemachineryinthe
factory but also the activities of workers in the factory, demand for products, and
requestsfromcustomers.Theyarethefourth“industrialrevolution,”andproduction
costswillbedrasticallyreduced.
Similaractivitiesareunderwayinseveralcountries.TheIndustrialInternetCon-
sortium(IIC)intheUS,whichwasestablishedbymajorUSICTcompanies,AT&T,
Cisco,GE, IBM,andIntel,aimsatdigitalizationofnotonlyproductionprocesses
butothersocialservicessuchasmedicalservices,energyservices,etc.TheChinese
governmenthasalsopresentedtheplan“MadeinChina2025(MiC2025),”whichis
theroadmapofmanufacturingindustriesinChina.ItaimstoaugmenttheChinese
industry in many aspects, and the key ideas include enhancement of innovation,
H.Yasuura((cid:2))
KyushuUniversity,Fukuoka,Japan
e-mail:[email protected]
©SpringerInternationalPublishingAG2017 1
H.Yasuuraetal.(eds.),SmartSensorsattheIoTFrontier,
DOI10.1007/978-3-319-55345-0_1
2 H.Yasuura
quality/brand-power, environmental protection, etc. in the manufacturing. In the
fifth Science and Technology Basic Plan, the Japanese government has proposed
the concept of “Society 5.0,” where advanced ICT improves every aspect of
our life including industry, economics, health, transportation, education, etc. The
plan emphasizes the fifth social paradigm change, which follows the “hunting
and gathering society,” “agrarian society,” “industrial society,” and “information
society.” Our society is becoming truly a “cyber-physical system,” which is the
mixtureoftherealworldandthecyberworldconnectedbyIoTtechnology.
ThebackgroundofthisIoTtrendisexplainedinseveralengineeringcontexts:
1. Huge and complicated networks, which cover thoroughly our world using a
combinationofhugebandwidthwirednetworkandubiquitouswirelessnetwork,
have been realized with relatively low cost and with high throughput. This
enablesustoconnectquitealargenumberofdevicestotheInternet.
2. Thanks to the recent progress of device technology, which realizes highly
integrated, energy-efficient, and low-cost devices, a wide variety of sensors
and apparatuses with network connection capability have been developed and
availableinthemarket.
3. A huge amount of data acquired by various sensors in smartphones and ICT
devices, i.e., “big data,” are being gathered and analyzed to extract valuable
informationusingcloudservices.
4. Since a huge number of smartphones and other display devices are popularly
available, people require various types of information, especially personalized
one,viathesmartphones.Atypicalexampleisfine-grainedpredictionofsudden
andheavyrainfalls,whichpeoplewanttoavoid,anditcanbeproducedbyinte-
gratingvariouskindsof sensoryinformationofclimate conditionandlocation.
Othersincludetransportationcongestion,real-timeavailabilityofbus/taxi,etc.
The above points indicate that many progresses in ICT fields support recent
development of the IoT. To understand what is IoT and what is coming in quite
the near future, we should know what is currently going on in the field of IoT.
This book has been designed to provide such current engineering aspects on IoT,
especiallyfromtheviewpointofsmartsensing.Inthisbook,wehavecoveredwide
areasofsmartsensortechnologiesdividedintothreepartsincludingsmartdevices,
sensing methodology,and systems and applications. The topics described in each
partaresummarizedasfollows:
1 Part I Device Technology for IoT
In “Energy-AutonomousSupply-Sensing Biosensor Platform Using CMOS Elec-
tronics and Biofuel Cells (Niitsu),” the author presents a new method to build
energy-autonomous semiconductor devices, which can solve the battery issue of
electronicappliancesandwhichareinevitabletofine-grainedIoTsystems.Hehas
developedanenergy-autonomoussupply-sensingbiosensorplatformusingCMOS
Introduction 3
electronicsandbiofuelcells, which is used in humanhealth conditionsensing for
big data-based healthcare. The device enables low-voltage operation and a small
footprint,eveninacost-competitivelegacyCMOStechnology.Thisworkrealizes
converter-lessenergy-autonomousoperationusingabiofuelcell,whichisidealfor
disposablehealthcareapplications.
In“SmartMicrofluidicBiochips:Cyber-PhysicalSensorIntegrationforDynamic
Error Recovery (Yao et al.),” the authors describe the recent progress of digital
microfluidicbiochips,whicharegainingincreasingattentionwithpromisingappli-
cations for automating and miniaturizing laboratory procedures in biochemistry.
Automateddesignofdigitalmicrofluidicbiochipsincludestwomajorparts:fluidic-
level synthesis and chip-level design. They describe how a digital microfluidic
biochipisdesigned.Automaticcontrollogicisalsodescribed,wherecyber-physical
sensors can be integrated for dynamic error recovery in real-life biochemical
applications.
In“ReducingTimingDiscrepancyforEnergy-EfficientOn-ChipMemoryArchi-
tecturesatLow-VoltageMode(Chen),”theauthordescribesatechniquetoreduce
power consumption in processor systems, especially in SRAM cache memory,
which is commonly used in modern processor systems. To reduce the power
consumption, voltage scaling is an effective technique, but timing discrepancies
between on-chip memory and CPU cores occur with the voltage scaling down,
which significantly harms the system performance. These discrepancies are pri-
marily caused by severe processvariationsof a few slow SRAM cells. This work
addressestheissueofan8Tr.SRAMcacheandproposessomesolutionstotolerate
thoseslowcellstoeliminatetimingdiscrepancies.
In “Redesigning Software and Systems for Nonvolatile Processors on Self-
Powered Devices (Xue),” the author presents how energy harvesting in circuits
shouldbehandled.Theenergyharvestingisquiteanimportantaspectofwearable
devices and other very small-scaled systems. The author develops a method to
utilize nonvolatile processors (NVP), which can back up the volatile state before
the battery energyis used up and which can resume the program execution when
enoughenergyissupplied.TheNVPisrequiredinsystemswithenergyharvesting,
wherethepowersupplytendstobeunstable.Duetobackupandresumptionproce-
duresresultedfrompowerfailures,the nonvolatileprocessorexhibitssignificantly
differentcharacteristicsfromtraditionalprocessors,necessitatinga setof adaptive
designandoptimizationstrategies.Theauthorprovidesanoverviewofthestate-of-
the-artNVPresearchincludingthesoftwareandsystemlevel.
2 Part IISensingTechnologyfor IoT
In “OEICs for High-Speed Data Links and Tympanic Membrane Transducer of
Hearing Aid Device (Chen et al.),” the authors describe the design of photonics
integrated with electronics (OEICs) for the applications in data-intensive optical
links and tympanic membrane transducer of hearing aid devices. OEICs are
4 H.Yasuura
expected to be one of the key enablers for emerging applications, covering from
short distance sensing and data links to the backbones for the next-generation
telecommunications network. In their chapter, very high-speed, fully integrated
CMOS optical receivers incorporating on-chip photodetectors are presented first.
Then, the authors present a novel architecture for signal and power transfer in a
tympanicmembranetransducerusingOEIC,showingthefeasibilitytomechanically
stimulatethetympanicmembrane(TM)toimprovesoundquality.
In “DepthEstimation Using Single Camera with Dual Apertures(Park et al.),”
theauthorspresentedanewsensing,orimaging,methodtoacquiredepthinforma-
tionorthedistancetoobjectsfromthecamera.Thedepthinformationisveryuseful
todetectandtoanalyzeeventsintherealworld,andtherearemanydepthsensors
available, such as Microsoft Kinect. The uniquenessof the authors’ method is its
simplicity:onlya one-shotimage is capturedwith dualapertures.In theirsystem,
IR (infrared) light is captured through a small aperture, and only visible light is
capturedthrougha largerRGB-pass aperture.The differenceof the aperturesizes
causestheblursizedifferencebetweenthesharpIRandblurrycolorcomponents,
whichisthecluetoestimatethedepth.
In “Scintillator-Based Electronic Personal Dosimeter for Mobile Application
(Cho et al.),” an electronic personal dosimeter (EPD) which measures the energy
spectrumandthepersonaldoserateinradiationexposureenvironmentispresented.
This device is composed of a compact radiation sensor to detect gamma ray; an
integrated circuit of preamplifier, peak holder, etc.; and a software to calculate
the personal dose from the measured spectrum. The CsI(Tl)-coupled pin-diode is
used as a compactspectroscopic radiation sensor to measure the energyspectrum
for the radioisotopeidentification or the activity analysis. To optimallydesign the
sizeofthecompactradiationsensortobeusedasanaccessaryofmobilepersonal
devices,the authorshave determineda guidelinesuchthat the sensor mustsatisfy
the internationalcriteria of angular response, as well as have the maximum value
ofafigureofmeritwhichisaproductofthegeometricdetectionefficiencyandthe
energyresolution.
In“LEDSpectrophotometryandItsPerformanceEnhancementBasedonPseudo
BJT (Choi et al.),” the authors present a LED-based spectrophotometry, which
can be implemented in a small feature size with relatively small cost and can
provide a suitable way to integrate the optical spectrometer into the smart and
mobile sensor systems. In addition, recent advances in LED technology extend
a wavelength selection window of LED from a deep ultraviolet region to an
infrared region. In this work, a guide to set up the LED-PD system is provided
forLEDspectrophotometrycoveringadeviceselection,drivingcircuitcomposition
and applications. As applications of LED spectrophotometry for the bio- and
chemicalsensor, some examplesincludingthe water pollutionand glucosesensor
arediscussed.
