Smart Sensors at the IoT Frontier (eBook)

Hiroto Yasuura is the Director of the System LSI Research Center and Professor in the Graduate School of Information Science, at Kyushu University, in Fukuoka, Japan. Chong-Min Kyung received B.S. in EE from Seoul National University in 1975, M.S. and Ph.D. in EE from KAIST in 1977 and 1981, respectively. From 1981 to 1983, he worked at Bell Telephone Laboratories, Murray Hill. Since he joined KAIST in 1983, he has been working on CAD algorithms, 3-D graphics, and System-on-a-Chip design and verification methodology, development of various RISC/CISC microprocessors, VLIW and reconfigurable DSP cores. His current research includes system-level low-power design, electrical/thermal co-design in 3D IC, architectures of H.264 video CODEC and energy-rate-distortion-power optimization. He served as General Chair in the Korean Semiconductor Conference 2002, ISOCC 2004, A-SSCC 2007, and ASP_DAC 2008. He is founding Director of the IDEC(Integrated Circuit Design Education Center), and currently leads Center for Integrated Smart Sensors, funded as Global Frontier Project by Korean Government. He received the Most Excellent Design Award, and Special Feature Award in the University Design Contest in the ASP-DAC 1997 and 1998, respectively. He received the Best Paper Awards in the 36th DAC held in New Orleans, LA, the 10th ICSPAT, Orlando, FL, in September 1999, and the 1999 ICCD Austin, TX. In 2000, he received National Medal from Korean government for his contribution to research and education in IC design. He is a member of National Academy of Engineering Korea, and Korean Academy of Science and Technology. He is IEEE fellow and Hynix Chair Professor at KAIST. Yongpan Liu received his B.S., M.S. and Ph.D. degrees from Electronic Engineering Department, Tsinghua University in 1999, 2002, and 2007. He worked as a Research Assistant in Circuit and System Divisions in Tsinghua University from 2002 to 2004. (Referred to CV) His main research interests include: Embedded System, Nonvolatile Computing, Electronics Design Automation and Power/Thermal Aware VLSI Design and Test. Our group currently focuses on High Performance Low Power circuit device, chip architecture and system design methodology on next generation computing applications. Youn-Long Lin is a Tsing Hua Chair Professor of Computer Science of the National Tsing Hua University. He was born in Yun-Lin, Taiwan. He received his B.S. degree in electronics engineering from National Taiwan University of Science and Technology (formerly, National Taiwan Institute of Technology), Taipei, Taiwan, in 1982, and his Ph.D. in computer science from the University of Illinois, Urbana-Champaign, IL, U.S.A. in 1987. Upon his graduation, he joined National Tsing Hua University, Hsin-Chu, Taiwan, where he established the THEDA Group (Tsing Hua EDA), served as Director of the University Computer and Communication Center, Chairman of the Department of Computer Science, Secretariat General of the University, Chief Librarian of the University Library, and the Dean of Research & Development. He is also an adjunct professor of Peking University, Beijing and a guest professor of Waseda University, Japan.

Contents 5
Introduction 7
1 Part I Device Technology for IoT 8
2 Part II Sensing Technology for IoT 9
3 Part III System and Application 11
Part I Device Technology for IoT 13
Energy-Autonomous Supply-Sensing Biosensor Platform Using CMOS Electronics and Biofuel Cells 14
1 Introduction 14
2 Supply-Sensing Biosensor Platform 16
2.1 Principle of Supply-Sensing Biosensor Platform 16
2.2 Biofuel Cell 17
2.3 Supply-Controlled Ring Oscillator (SCRO) 17
2.4 Inductive-Coupling Transmitter 17
3 Test Chip Design and Measurement Setup 19
4 Measurement Results 19
5 Energy-Autonomous Operation 21
5.1 Performance of Organic Biofuel Cell 21
5.2 Demonstration of Energy-Autonomous Biosensing 21
6 Discussion 22
7 Conclusion 24
References 24
Smart Microfluidic Biochips: Cyberphysical Sensor Integration for Dynamic Error Recovery 27
1 Background 27
2 Automated Design Flow for Digital Microfluidic Biochips 29
3 Fluidic-Level Synthesis 31
3.1 Droplet Routing and Cross-Contamination 31
3.2 Problem Formulation of Functional and Washing Droplet Routing 33
3.3 Algorithm Overview 36
3.4 Functional Routing and Compaction 37
3.4.1 Functional Path Routing 38
3.4.2 Path Ordering 39
3.4.3 Functional Path Compaction 44
3.5 Washing Droplet Routing 45
3.5.1 Washing Duration Relaxation 46
3.5.2 Washing Order Decision and Washing Path Computation 47
3.6 Simultaneous Functional and Washing Path Compaction 52
3.7 Computational Simulation Results 54
4 Chip-Level Design 59
4.1 Electrode Addressing and Wire Routing 59
4.2 Problem Formulation of Electrode Addressing and Wire Routing 61
4.3 Algorithm Overview 62
4.4 SVM-Based Clustering 64
4.5 Escape Routing to Control Pins 69
4.6 Experimental Results 70
5 Cyberphysical Sensor Integration for Dynamic Error Recovery 72
References 73
Reducing Timing Discrepancy for Energy-Efficient On-Chip Memory Architectures at Low-Voltage Mode 76
1 Introduction 76
2 Low-Voltage Influence on an 8T Cell 78
2.1 SRAM Faults on an 8T Cell 78
2.2 Wide Delay Distribution of SRAM Cells in Low Voltage 79
2.3 Effect of the Stored Value on the Latency 79
3 Non-Capacity-Loss Fault-Tolerant Design to Reduce Timing Discrepancy in Local Memory 81
3.1 Lightweight EDC with Zero Counting 82
3.1.1 System Architecture and Execution Flow 82
3.1.2 Access-Time Failure Detection by “0” Counting 83
3.1.3 Detection Granularity Trade-Off 84
3.2 Dynamic Timing Calibration SRAM 84
3.2.1 Architecture of DTC-SRAM 84
3.2.2 Dynamic Timing Calibration by Twice Data Fetch 86
3.2.3 Details of Dynamic Timing Calibrator 87
4 Flexible Space Management Strategies for L1 Cache to Reduce Aggressive Timing Discrepancy 88
4.1 Timing-Aware LRU Policy 88
4.2 Bit-Level Failure-Mask Management Strategy 89
4.2.1 Access-Time Failure Masking via Data Mirroring 89
4.2.2 Architecture of the Cross-Matching Cache 90
4.2.3 Additional Miss Detection and Prediction 91
4.3 Data Allying Management Strategy 92
4.3.1 8T SRAM with Alliable Read Wordline 92
4.3.2 Turbo Cache Management Strategies to Reduce the Unnecessary Penalty 94
5 Evaluation 99
5.1 Experimental Environment 99
5.2 Comparison of Slow Cell Tolerance Ability 100
5.3 Performance Analysis 101
5.4 Design Complexity 104
5.4.1 Energy Overhead 104
5.4.2 Area Overhead 105
5.4.3 Consideration for Out-of-Order Processors 105
6 Related Works 105
6.1 Reliable Low-Voltage Cache Designs 105
6.2 Error Correction Code Designs 106
6.3 Robust Circuit Designs 106
6.4 Tolerating Access-Time Failure Designs 107
6.5 Timing Speculation of the Pipeline 107
7 Conclusion 108
References 108
Redesigning Software and Systems for Nonvolatile Processors on Self-Powered Devices 110
1 Introduction 110
2 Software Techniques for System Consistency 114
3 Software Design and Optimizations for Nonvolatile Processor 115
3.1 Checkpoint Locating 116
3.2 Register-Oriented Optimizations 117
3.2.1 Backup for Small Register Files 117
3.2.2 Backup for Large Register Files 118
3.3 On-Chip Memory Optimizations 118
3.3.1 Backup for Main Memory 118
3.3.2 Backup for Cache 119
3.4 Operating System-Level Optimizations 121
3.5 Prototype and Tools 121
3.6 Discussions 122
4 Conclusion 122
References 123
Part II Sensing Technology for IoT 127
OEICs for High-Speed Data Links and Tympanic Membrane Transducer of Hearing Aid Device 128
1 OEICs for Intensive Data Link 128
1.1 CMOS Photodetectors 129
1.2 Spatially Modulated Photodetectors 134
1.3 Nested-Feedback TIA 140
1.4 A Multichannel OEIC with CMOS Photodetector 144
1.5 A 20-Gb/s OEIC with CMOS PD 146
1.6 Comparator-Based Optical Receiver 149
2 OEICs for Tympanic Membrane Transducer 153
2.1 Tympanic Membrane Transducer with Optical Signal and Power Transmission 153
2.2 Light-Driven Transducer 155
2.3 Circuit Design 157
2.3.1 Audio Driver 157
2.3.2 Transimpedance Amplifier 158
2.3.3 Bias Generator 158
2.3.4 Operational Amplifier 159
2.3.5 Hysteresis Comparator 159
2.3.6 Output Stage 160
3 Experimental Results 161
4 Conclusions 163
References 165
Depth Estimation Using Single Camera with Dual Apertures 168
1 Introduction 168
2 Dual-Aperture Camera 169
2.1 Color Filter Array 170
2.2 Camera Module Architecture 170
2.3 Spectral Characteristic 171
2.4 Depth Estimation Principle 171
2.5 Depth Estimation Algorithm 174
3 Proposed Depth Estimation Procedure 176
3.1 Demosaicking for Inter-color Edge Alignment 177
3.2 Multi-scale Space Edge Extraction 178
3.3 Adaptive Blur Channel Selection 180
3.4 Two-Dimensional Jittered Matching 182
3.5 Compensation for Specular Reflection 183
3.6 Hierarchical Selective Blurred Image Interpolation 184
3.7 Depth Noise Removal 185
4 Experimental Results 187
5 Concluding Remarks and Future Work 187
References 190
Scintillator-Based Electronic Personal Dosimeterfor Mobile Application 191
1 Introduction 191
2 System Design 193
2.1 Design of a Compact Scintillation Detector 193
2.2 Design of Front-End ASIC 195
2.3 System Design 202
2.3.1 Design of EPD System for Mobile Phones 202
2.3.2 Power Harvesting Through Audio Jack 203
2.3.3 Data Communication Through Audio Jack 204
2.4 Dose Conversion Algorithm 206
2.5 Application Program for Android Phone 210
3 Test Results and Discussion 211
3.1 Measurement of Gamma Energy Spectrum 211
3.2 Measurement of Personal Dose 212
3.3 Measurement of Angular Response 216
4 Conclusion 217
References 217
Part III System and Application 219
LED Spectrophotometry and Its Performance Enhancement Based on Pseudo-BJT 220
1 Spectroscopy for a Smart Sensor 220
2 Optoelectronic Devices for the LED Spectrophotometry 222
2.1 Technology of LED an Its Usage 222
2.2 Technology and Usage of Photodiode 223
2.3 Si Photodiode as a Near-Infrared Detector 224
2.3.1 FKE in Zener Diode Structure 225
2.3.2 FKE in MOSFET GIDL Range 226
3 Performance Enhancement Based on Pseudo-BJT Optical System 227
3.1 Concept of PBOS 229
3.2 Mathematical Model of PBOS 230
3.2.1 A Basic Pseudo-BJT Model 230
3.2.2 An Amplified Pseudo-BJT 232
3.3 Sensitivity of the PBOS 235
3.3.1 Sample Transmittance (Tf) 236
3.3.2 GE and Go 236
3.4 Variation of PBOS Circuit 240
4 Application of Smart LED Spectrophotometer 242
4.1 Glucose Sensing 242
4.2 Water Quality Sensor 244
References 246
An Air Quality and Event Detection System with Life Logging for Monitoring Household Environments 249
1 Introduction 249
2 Air Quality and Event Detection System for Household Environments 250
2.1 Full-Function Device (FFD) 251
2.1.1 Gas Sensors 253
2.1.2 PM Sensor 255
2.2 Operation 255
2.3 Reduced-Function Device 259
3 Networking Among Devices 259
4 Performance Evaluation 262
4.1 Evaluation of Gas Sensors 262
4.2 Power Consumption of the FFD 264
4.3 Network Performance Analysis 264
5 Conclusion 264
References 267
Mobile Crowdsensing to Collect Road Conditions and Events 269
1 Introduction 269
2 Background 270
2.1 Driving Problems: The Situation in Sapporo 270
2.2 Goal-Directed Sensing with Active Participants: Crowdsourcing 272
2.3 Diversified Sensing: Exploiting Probe Car Data 274
3 Crowdsourced Mobile Sensing and Its Applications 274
3.1 Overview 274
3.2 Service Platform 275
3.3 Mobile Applications for End Users 276
3.4 Applications for Civil Administration 279
4 ``Drive Around-the-Corner.'': A Drive Recorder Application 280
4.1 Map with Event Information 280
4.2 Posting Event 281
4.3 Settings 282
4.4 Sensing Functions 282
4.4.1 User Data 282
4.4.2 Onboard Location and Motion Sensors 282
4.4.3 Movies 283
4.5 Website 284
4.6 Dry Run 284
4.7 Survey 287
4.8 Data Analysis 288
4.8.1 Feature Extraction and Selection 290
4.8.2 Classification 292
4.8.3 Experimental Results and Discussion 292
5 Conclusion 293
References 294
Sensing and Visualization in Agriculture with Affordable SmartDevices 296
1 Introduction 296
2 Field Environmental Monitoring and Control Framework 297
2.1 Local Management Subsystem 299
2.2 Global Management Subsystem 299
2.3 Application of Framework to Field Environmental Monitoring and Irrigation Control 300
3 Plant Growth and Motion Measurement 304
3.1 Plant Growth Measurement 304
3.2 Plant Motion Measurement 306
4 Farm Work Information Recording 311
4.1 Manual Farm Work Information Recording System 311
4.2 Automatic Farm Work Information Recording System 313
4.2.1 Farmer Position Information 313
4.2.2 Farmer Action Information 314
4.2.3 Farm Work Information and Its Application 315
5 Analysis of Agricultural Information 316
5.1 Singular Spectrum Transformation 317
5.2 Change Point Analyses for Field Environmental Data 318
6 Conclusion 320
References 320
Learning Analytics for E-Book-Based Educational Big Data in Higher Education 323
1 Introduction 323
2 The M2B System 324
3 The Integration and Visualization of Learning Logs for Learning Analytics 327
4 Visualizing Preview and Review Patterns by Analyzing e-Book Logs 330
4.1 Visualization of Preview and Review Patterns 330
4.2 The Relationship Between e-Book Logs and Academic Achievement 331
4.2.1 Correlation Between the Frequency of Previews and Academic Achievement 331
4.2.2 Preview and Review Patterns and Academic Achievement 331
5 The Visualization and Prediction of Learning Activities 333
5.1 Background 333
5.2 The Visualization of Learning Activities by Using Discrete Graphs 334
5.3 The Prediction of Learning Activities 335
6 Learning Analytics with Psychometric Data 336
6.1 Background 336
6.2 Participants and Class 336
6.3 Data Collection and Analysis 337
6.4 Results 337
6.5 Implementation in This Section 338
7 An Ontology-Based Visualization Support Systemfor E-Book Users 339
7.1 Background 339
7.2 A Semiautomatically Built Course-Centered Ontology 339
7.3 A Visualization Learning Support System Providing a Knowledge Comparison Environment 340
8 Visualization and Analysis for Improving Learning Materials 342
9 Conclusion 344
References 344
Security and Privacy in IoT Era 347
1 Introduction 347
2 Design Practices and Taxonomy of Vulnerabilities 348
2.1 Common Design Patterns 348
2.2 Security Threat Taxonomy 350
3 Case Study 1: Smart Thermostat 352
4 Case Study 2: Nike+ Fuelband 354
4.1 High-Level Overview 354
4.2 Device Security 355
4.3 Device Descriptive Overview 355
4.4 The STM32L151QCH6: A Closer Look 355
4.5 Boot Process and Device Initialization 356
4.6 Attack Vector on the Nike+ Fuelband 357
5 Case Study 3: Haier SmartCare 358
5.1 High-Level Overview 358
5.2 Hardware Analysis 359
5.3 Into the Shell 360
5.4 SmartCare Network Analysis 361
5.5 SmartCare Binary Analysis 363
6 Case Study 4: Itron Centron CL200 Meter 363
6.1 High-Level Overview 364
6.2 Hardware Analysis 364
6.3 Device ID Modification 365
6.4 Demonstration 365
7 Discussions 366
7.1 Security Impact to Network 366
7.2 Safety Concerns 367
7.3 Privacy Concerns 367
8 Related Work 368
8.1 IoT Secure Protocols and Network Protection 368
8.2 Hardware-Based Protection 369
9 Device Security Enhancement 370
9.1 Security Solutions Common to IoT and Wearable Devices 370
9.2 Specific Solutions for IoT and Wearable Devices 370
9.3 Overhead of Security Solutions 371
10 Conclusion 371
References 372