Smart Sensors at the IoT Frontier PDF
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Hiroto 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 ©SpringerInternationalPublishingAG2017 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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.
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