Internet of Things and Data Analytics Handbook E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1316

Veröffentlichungsjahr: 2016

Beschreibung

This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view.

Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing for IoT projects. It provides readers with knowledge in planning, designing, and implementing IoT projects. The book is written by experts on the subject matter, including international experts from nine countries in the consumer and enterprise fields of IoT. The text starts with an overview and anatomy of IoT, ecosystem of IoT, communication protocols, networking, and available hardware, both present and future applications and transformations, and business models. The text also addresses big data analytics, machine learning, cloud computing, and consideration of sustainability that are essential to be both socially responsible and successful. Design and implementation processes are illustrated with best practices and case studies in action. In addition, the book:

• Examines cloud computing, data analytics, and sustainability and how they relate to IoT
• overs the scope of consumer, government, and enterprise applications
• Includes best practices, business model, and real-world case studies

Hwaiyu Geng, P.E., is a consultant with Amica Research (www.AmicaResearch.org, Palo Alto, California), promoting green planning, design, and construction projects. He has had over 40 years of manufacturing and management experience, working with Westinghouse, Applied Materials, Hewlett Packard, and Intel on multi-million high-tech projects. He has written and presented numerous technical papers at international conferences.  Mr. Geng, a patent holder, is also the editor/author of Data Center Handbook (Wiley, 2015).

Leseprobe

Table of Contents

COVER

TITLE PAGE

LIST OF CONTRIBUTORS

FOREWORD

TECHNICAL ADVISORY BOARD MEMBERS

PREFACE

ACKNOWLEDGMENTS

ABOUT THE COMPANION WEBSITE

PART I: INTERNET OF THINGS

1 INTERNET OF THINGS AND DATA ANALYTICS IN THE CLOUD WITH INNOVATION AND SUSTAINABILITY

1.1 INTRODUCTION

1.2 THE IoT AND THE FOURTH INDUSTRIAL REVOLUTION

1.3 INTERNET OF THINGS TECHNOLOGY

1.4 STANDARDS AND PROTOCOLS

1.5 IoT ECOSYSTEM

1.6 DEFINITION OF BIG DATA

1.7 IoT, DATA ANALYTICS, AND CLOUD COMPUTING

1.8 CREATIVITY, INVENTION, INNOVATION, AND DISRUPTIVE INNOVATION

1.9 POLYA’S “HOW TO SOLVE IT”

1.10 BUSINESS PLAN AND BUSINESS MODEL

1.11 CONCLUSION AND FUTURE PERSPECTIVES

REFERENCES

FURTHER READING

USEFUL WEBSITES

2 DIGITAL SERVICES AND SUSTAINABLE SOLUTIONS

2.1 INTRODUCTION

2.2 WHY IoT IS NOT JUST “NICE TO HAVE”

2.3 SERVICES IN A DIGITAL REVOLUTION

2.4 MOBILE DIGITAL SERVICES AND THE HUMAN SENSOR

2.5 NOT JUST ANOTHER APP

2.6 THE HIDDEN LIFE OF THINGS

2.7 THE UMBRELLAS ARE NOT WHAT THEY SEEM

2.8 INTERACTING WITH THE INVISIBLE

2.9 SOCIETY AS OPEN SOURCE

2.10 LEARN FROM YOUR HACKERS

2.11 ENSURING HIGH‐QUALITY SERVICES TO CITIZENS

2.12 GOVERNMENT AS A PLATFORM

2.13 CONCLUSION

REFERENCES

3 THE INDUSTRIAL INTERNET OF THINGS (IIoT)

3.1 INTRODUCTION TO THE IIoT

3.2 SOME EXAMPLES OF IIoT APPLICATIONS

3.3 TOWARD A TAXONOMY OF THE IIoT

3.4 STANDARDS AND PROTOCOLS FOR CONNECTIVITY

3.5 CONNECTIVITY ARCHITECTURE FOR THE IIoT

3.6 DATA‐CENTRICITY MAKES DDS DIFFERENT

3.7 THE FUTURE OF THE IIoT

REFERENCES

4 STRATEGIC PLANNING FOR SMARTER CITIES

4.1 INTRODUCTION

4.2 WHAT IS A SMART CITY?

4.3 SMART CITIES AND THE INTERNET OF THINGS

4.4 WHY STRATEGIC PLANNING MATTERS

4.5 BEGINNING THE JOURNEY: FIRST THINGS FIRST

4.6 FROM VISION TO OBJECTIVES TO EXECUTION

4.7 PULLING IT ALL TOGETHER

REFERENCES

5 NEXT‐GENERATION LEARNING

5.1 INTRODUCTION

5.2 LEARNING, ANALYTICS, AND INTERNET OF THINGS

5.3 IoT LEARNING DESIGN PROCESS

5.4 CONCLUSION

REFERENCES

FURTHER READING

6 THE BRAIN–COMPUTER INTERFACE IN THE INTERNET OF THINGS

6.1 INTRODUCTION

6.2 THE SCIENCE BEHIND READING THE BRAIN

6.3 THE SCIENCE OF WRITING TO THE BRAIN

6.4 THE HUMAN CONNECTOME PROJECT

6.5 CONSUMER ELECTROENCEPHALOGRAPHY DEVICES

6.6 SUMMARY

REFERENCES

7 IoT INNOVATION PULSE

7.1 THE CONVERGENCE OF EXPONENTIAL TECHNOLOGIES AS A DRIVER OF INNOVATION

7.2 SIX DIMENSIONS OF THE PLECOSYSTEM

7.3 FIVE PRINCIPLES OF THE PLECOSYSTEM

7.4 THE BIOLOGIC ORGANISM ANALOGY FOR THE IoT

7.5 COMPONENTS FOR INNOVATION WITH THE ORGANISMAL ANALOG

7.6 SPINOZAN VALUE TRADE‐OFFS

7.7 HUMAN IoT SENSOR NETWORKS

7.8 ROLE OF THE IoT IN SOCIAL NETWORKS

7.9 SECURITY AND CYBERTHREAT RESILIENCE

7.10 IoT OPTIMIZATION FOR SUSTAINABILITY OF OUR PLANET

7.11 MAINTENANCE OF COMPLEX IoT NETWORKS

7.12 THE ACCORDION MODEL OF LEARNING AS A SOURCE OF INNOVATION

7.13 SUMMARY

REFERENCES

FURTHER READING

PART II: INTERNET OF THINGS TECHNOLOGIES

8 INTERNET OF THINGS OPEN‐SOURCE SYSTEMS

8.1 INTRODUCTION

8.2 BACKGROUND OF OPEN SOURCE

8.3 DRIVERS FOR OPEN SOURCE

8.4 BENEFITS OF USING OPEN SOURCE

8.5 IoT OPEN‐SOURCE CONSORTIUMS AND PROJECTS

8.6 FINDING THE RIGHT OPEN‐SOURCE PROJECT FOR THE JOB

8.7 CONCLUSION

REFERENCES

FURTHER READING

9 MEMS: AN ENABLING TECHNOLOGY FOR THE INTERNET OF THINGS (IoT)

9.1 THE ABILITY TO SENSE, ACTUATE, AND CONTROL

9.2 WHAT ARE MEMS?

9.3 MEMS AS AN ENABLING TECHNOLOGY FOR THE IoT

9.4 MEMS MANUFACTURING TECHNIQUES

9.5 EXAMPLES OF MEMS SENSORS

9.6 EXAMPLE OF MEMS ACTUATOR

9.7 THE FUTURE OF MEMS FOR THE IoT

9.8 CONCLUSION

REFERENCES

OTHER INFORMATION

10 ELECTRO‐OPTICAL INFRARED SENSOR TECHNOLOGIES FOR THE INTERNET OF THINGS

10.1 INTRODUCTION

10.2 SENSOR ANATOMY AND TECHNOLOGIES

10.3 DESIGN CONSIDERATIONS

10.4 APPLICATIONS

10.5 CONCLUSION

REFERENCES

FURTHER READING

11 IPv6 FOR IoT AND GATEWAY

11.1 INTRODUCTION

11.2 IP: THE INTERNET PROTOCOL

11.3 IPv6: THE NEXT INTERNET PROTOCOL

11.4 6LoWPAN: IP FOR IoT

11.5 GATEWAYS: A BAD CHOICE

11.6 EXAMPLE IoT SYSTEMS

11.7 AN IoT DATA MODEL

11.8 THE PROBLEM OF DATA OWNERSHIP

11.9 MANAGING THE LIFE OF AN IoT DEVICE

11.10 CONCLUSION: LOOKING FORWARD

FURTHER READING

12 WIRELESS SENSOR NETWORKS

12.1 INTRODUCTION

12.2 CHARACTERISTICS OF WIRELESS SENSOR NETWORKS

12.3 DISTRIBUTED COMPUTING

12.4 PARALLEL COMPUTING

12.5 SELF‐ORGANIZING NETWORKS

12.6 OPERATING SYSTEMS FOR SENSOR NETWORKS

12.7 WEB OF THINGS (WoT)

12.8 WIRELESS SENSOR NETWORK ARCHITECTURE

12.9 MODULARIZING THE WIRELESS SENSOR NODES

12.10 CONCLUSION

REFERENCES

FURTHER READING

13 NETWORKING PROTOCOLS AND STANDARDS FOR INTERNET OF THINGS

13.1 INTRODUCTION

13.2 IoT DATA LINK PROTOCOLS

13.3 NETWORK LAYER ROUTING PROTOCOLS

13.4 NETWORK LAYER ENCAPSULATION PROTOCOLS

13.5 SESSION LAYER PROTOCOLS

13.6 IoT MANAGEMENT PROTOCOLS

13.7 SECURITY IN IoT PROTOCOLS

13.8 IoT CHALLENGES

13.9 SUMMARY

REFERENCES

14 IoT ARCHITECTURE

14.1 INTRODUCTION

14.2 ARCHITECTURAL APPROACHES

14.3 BUSINESS MARKITECTURE

14.4 FUNCTIONAL ARCHITECTURE

14.5 APPLICATION ARCHITECTURE

14.6 DATA AND ANALYTICS ARCHITECTURE

14.7 TECHNOLOGY ARCHITECTURE

14.8 SECURITY AND GOVERNANCE

REFERENCES

15 A DESIGNER’S GUIDE TO THE INTERNET OF WEARABLE THINGS

15.1 INTRODUCTION

15.2 INTERFACE GLANCEABILITY

15.3 THE RIGHT DATA AT THE RIGHT TIME

15.4 CONSISTENCY ACROSS CHANNELS

15.5 FROM PUBLIC TO PERSONAL

15.6 NONVISUAL UI

15.7 EMERGING PATTERNS

15.8 CONCLUSION

REFERENCES

FURTHER READING

16 BEACON TECHNOLOGY WITH IoT AND BIG DATA

16.1 INTRODUCTION TO BEACONS

16.2 WHAT IS BEACON TECHNOLOGY

16.3 BEACON AND BLE INTERACTION

16.4 WHERE BEACON TECHNOLOGY CAN BE APPLIED/USED

16.5 BIG DATA AND BEACONS

16.6 SAN FRANCISCO INTERNATIONAL AIRPORT (SFO)

16.7 FUTURE TRENDS AND CONCLUSION

REFERENCES

17 SCADA FUNDAMENTALS AND APPLICATIONS IN THE IoT

17.1 INTRODUCTION

17.2 WHAT EXACTLY IS SCADA?

17.3 WHY IS SCADA THE RIGHT FOUNDATION FOR AN IoT PLATFORM?

17.4 CASE STUDY: ALGAE LAB SYSTEMS

17.5 THE FUTURE OF SCADA AND THE POTENTIAL OF THE IoT

REFERENCES

FURTHER READING

PART III: DATA ANALYTICS TECHNOLOGIES

18 DATA ANALYSIS AND MACHINE LEARNING EFFORT IN HEALTHCARE

18.1 INTRODUCTION

18.2 DATA SCIENCE PROBLEMS IN HEALTHCARE

18.3 QUALIFICATIONS AND PERSONNEL IN DATA SCIENCE

18.4 DATA ACQUISITION AND TRANSFORMATION

18.5 BASIC PRINCIPLES OF MACHINE LEARNING

18.6 CASE STUDY: PREDICTION OF RARE EVENTS ON NONSPECIFIC DATA

18.7 FINAL REMARKS

REFERENCES

19 DATA ANALYTICS AND PREDICTIVE ANALYTICS IN THE ERA OF BIG DATA

19.1 DATA ANALYTICS AND PREDICTIVE ANALYTICS

19.2 BIG DATA AND IMPACT TO ANALYTICS

19.3 CONCLUSION

REFERENCES

20 STRATEGY DEVELOPMENT AND BIG DATA ANALYTICS

20.1 INTRODUCTION

20.2 MAXIMIZING THE INFLUENCE OF INTERNAL INPUTS FOR STRATEGY DEVELOPMENT

20.3 A HIGHER EDUCATION CASE STUDY

20.4 MAXIMIZING THE INFLUENCE OF EXTERNAL INPUTS FOR STRATEGY DEVELOPMENT

20.5 CONCLUSION

REFERENCES

FURTHER READING

21 RISK MODELING AND DATA SCIENCE

21.1 INTRODUCTION

21.2 WHAT IS RISK MODELING

21.3 THE ROLE OF DATA SCIENCE IN RISK MANAGEMENT

21.4 HOW TO PREPARE AND VALIDATE RISK MODEL

21.5 TIPS AND LESSONS LEARNED

21.6 FUTURE TRENDS AND CONCLUSION

REFERENCES

22 HADOOP TECHNOLOGY

22.1 INTRODUCTION

22.2 WHAT IS HADOOP TECHNOLOGY AND APPLICATION?

22.3 WHY HADOOP?

22.4 HADOOP ARCHITECTURE

22.5 HDFS: WHAT AND HOW TO USE IT

22.6 YARN: WHAT AND HOW TO USE IT

22.7 MapReduce: WHAT AND HOW TO USE IT

22.8 APACHE: WHAT AND HOW TO USE IT

22.9 FUTURE TREND AND CONCLUSION

REFERENCES

23 SECURITY OF IoT DATA

23.1 INTRODUCTION

23.2 IoT DATA IN HADOOP

23.3 SECURITY IN IoT PLATFORMS BUILT ON HADOOP

23.4 ARCHITECTURAL CONSIDERATIONS FOR IMPLEMENTING SECURITY IN HADOOP

23.5 BREADTH OF CONTROL

23.6 CONTEXT FOR SECURITY

23.7 SECURITY POLICIES AND RULES BASED ON PxP ARCHITECTURE

23.8 CONCLUSION

REFERENCES

PART IV: SMART EVERYTHING

24 CONNECTED VEHICLE

24.1 INTRODUCTION

24.2 CONNECTED, AUTOMATED, AND AUTONOMOUS VEHICLE TECHNOLOGIES

24.3 CONNECTED VEHICLES FROM THE DEPARTMENT OF TRANSPORTATION PERSPECTIVE

24.4 POLICY ISSUES AROUND DSRC

24.5 ALTERNATIVE FORMS OF V2X COMMUNICATIONS

24.6 DOT CONNECTED VEHICLE APPLICATIONS

24.7 OTHER CONNECTED VEHICLE APPLICATIONS

24.8 MIGRATION PATH FROM CONNECTED AND AUTOMATED TO FULLY AUTONOMOUS VEHICLES

24.9 AUTONOMOUS VEHICLE ADOPTION PREDICTIONS

24.10 MARKET GROWTH FOR CONNECTED AND AUTONOMOUS VEHICLE TECHNOLOGY

24.11 CONNECTED VEHICLES IN THE SMART CITY

24.12 ISSUES NOT DISCUSSED IN THIS CHAPTER

24.13 CONCLUSION

REFERENCES

25 IN‐VEHICLE HEALTH AND WELLNESS

25.1 INTRODUCTION

25.2 HEALTH AND WELLNESS ENABLER TECHNOLOGIES INSIDE THE CAR

25.3 HEALTH AND WELLNESS AS AUTOMOTIVE FEATURES

25.4 TOP CHALLENGES FOR HEALTH AND WELLNESS

25.5 SUMMARY AND FUTURE DIRECTIONS

REFERENCES

26 INDUSTRIAL INTERNET

26.1 INTRODUCTION (HISTORY, WHY, AND BENEFITS)

26.2 DEFINITIONS OF COMPONENTS AND FUNDAMENTALS OF INDUSTRIAL INTERNET

26.3 APPLICATION IN HEALTHCARE

26.4 APPLICATION IN ENERGY

26.5 APPLICATION IN TRANSPORT/AVIATION AND OTHERS

26.6 CONCLUSION AND FUTURE DEVELOPMENT

FURTHER READING

27 SMART CITY ARCHITECTURE AND PLANNING

27.1 INTRODUCTION

27.2 CITIES AND THE ADVENT OF OPEN DATA

27.3 BUILDINGS IN SMARTER CITIES

27.4 THE TRIFECTA OF TECHNOLOGY

27.5 EMERGING SOLUTIONS: UNDERSTANDING SYSTEMS

27.6 CONCLUSION

REFERENCES

FURTHER READING

28 NONREVENUE WATER

28.1 INTRODUCTION AND BACKGROUND

28.2 NRW ANATOMY

28.3 ECONOMY AND CONSERVATION

28.4 BEST PRACTICE STANDARD WATER BALANCE

28.5 NRW CONTROL AND AUDIT

28.6 LESSONS LEARNED

28.7 CASE STUDIES

28.8 THE FUTURE OF NONREVENUE WATER REDUCTION

28.9 CONCLUSION

REFERENCES

29 IoT AND SMART INFRASTRUCTURE

29.1 INTRODUCTION

29.2 ENGINEERING DECISIONS

29.3 CONCLUSION

REFERENCES

FURTHER READING

30 INTERNET OF THINGS AND SMART GRID STANDARDIZATION

30.1 INTRODUCTION AND BACKGROUND

30.2 DIGITAL ENERGY ACCELERATED BY THE INTERNET OF THINGS

30.3 SMART GRID POWER SYSTEMS AND STANDARDS

30.4 LEVERAGING IoTs AND SMART GRID STANDARDS

30.5 CONCLUSIONS AND RECOMMENDATIONS

REFERENCES

31 IoT REVOLUTION IN OIL AND GAS INDUSTRY

31.1 INTRODUCTION

31.2 WHAT IS IoT REVOLUTION IN OIL AND GAS INDUSTRY?

31.3 CASE STUDY

31.4 CONCLUSION

REFERENCES

32 MODERNIZING THE MINING INDUSTRY WITH THE INTERNET OF THINGS

32.1 INTRODUCTION

32.2 HOW IoT WILL IMPACT THE MINING INDUSTRY

32.3 CASE STUDY

32.4 CONCLUSION

FURTHER READING

33 INTERNET OF THINGS (IoT)‐BASED CYBER–PHYSICAL FRAMEWORKS FOR ADVANCED MANUFACTURING AND MEDICINE

33.1 INTRODUCTION

33.2 MANUFACTURING AND MEDICAL APPLICATION CONTEXTS

33.3 OVERVIEW OF IoT‐BASED CYBER–PHYSICAL FRAMEWORK

33.4 CASE STUDIES IN MANUFACTURING AND MEDICINE

33.5 CONCLUSION: CHALLENGES, ROAD MAP FOR THE FUTURE

ACKNOWLEDGMENTS

REFERENCES

PART V: IoT/DATA ANALYTICS CASE STUDIES

34 DEFRAGMENTING INTELLIGENT TRANSPORTATION

34.1 INTRODUCTION

34.2 THE TRANSPORT INDUSTRY AND SOME LESSONS FROM THE PAST

34.3 THE TRANSPORT INDUSTRY: A LONG ROAD TRAVELED

34.4 THE TRANSPORT INDUSTRY: CURRENT STATUS AND OUTLOOK

34.5 USE CASE: oneTRANSPORT—A SOLUTION TO TODAY’S TRANSPORT FRAGMENTATION

34.6 oneTRANSPORT: BUSINESS MODEL

34.7 CONCLUSION

ACKNOWLEDGMENT

REFERENCES

35 CONNECTED AND AUTONOMOUS VEHICLES

35.1 BRIEF HISTORY OF AUTOMATED AND CONNECTED DRIVING

35.2 AUTOMATED DRIVING TECHNOLOGY

35.3 CONNECTED VEHICLE TECHNOLOGY AND THE CV PILOTS

35.4 AUTOMATED TRUCK CONVOYS

35.5 ON‐DEMAND AUTOMATED SHUTTLES FOR A SMART CITY

35.6 A UNIFIED DESIGN APPROACH

35.7 ACRONYM AND DESCRIPTION

REFERENCES

36 TRANSIT HUB

36.1 INTRODUCTION

36.2 CHALLENGES

36.3 INTEGRATED SENSORS

36.4 TRANSIT HUB SYSTEM WITH MOBILE APPS AND SMART KIOSKS

36.5 CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

37 SMART HOME SERVICES USING THE INTERNET OF THINGS

37.1 INTRODUCTION

37.2 WHAT MATTERS?

37.3 IoT FOR THE MASSES

37.4 LIFESTYLE SECURITY EXAMPLES

37.5 MARKET SIZE

37.6 CHARACTERISTICS OF AN IDEAL SYSTEM

37.7 IoT TECHNOLOGY

37.8 CONCLUSION

38 EMOTIONAL INSIGHTS VIA WEARABLES

38.1 INTRODUCTION

38.2 MEASURING EMOTIONS: WHAT ARE THEY?

38.3 MEASURING EMOTIONS: HOW DOES IT WORK?

38.4 LEADERS IN EMOTIONAL UNDERSTANDING

38.5 THE PHYSIOLOGY OF EMOTION

38.6 WHY BOTHER MEASURING EMOTIONS?

38.7 USE CASE 1

38.8 USE CASE 2

38.9 USE CASE 3

38.10 CONCLUSION

FURTHER READING

39 A SINGLE PLATFORM APPROACH FOR THE MANAGEMENT OF EMERGENCY IN COMPLEX ENVIRONMENTS SUCH AS LARGE EVENTS, DIGITAL CITIES, AND NETWORKED REGIONS

39.1 INTRODUCTION

39.2 RESILIENT CITY: SELEX ES SAFETY AND SECURITY APPROACH

39.3 CITY OPERATING SYSTEM: PEOPLE, PLACE, AND ORGANIZATION PROTECTION

39.4 CYBER SECURITY: KNOWLEDGE PROTECTION

39.5 INTELLIGENCE

39.6 A SCALABLE SOLUTION FOR LARGE EVENTS, DIGITAL CITIES, AND NETWORKED REGIONS

39.7 SELEX ES RELEVANT EXPERIENCES IN SECURITY AND SAFETY MANAGEMENT IN COMPLEX SITUATIONS

39.8 CONCLUSION

APPENDIX 39.A HOW BUILD THE PROPOSITION

APPENDIX 39.B DETAILS ABOUT REVISION OF THE INITIATIVE

REFERENCE

40 STRUCTURAL HEALTH MONITORING

40.1 INTRODUCTION

40.2 REQUIREMENT

40.3 ENGINEERING DECISIONS

40.4 IMPLEMENTATION

40.5 CONCLUSION

REFERENCES

FURTHER READING

41 HOME HEALTHCARE AND REMOTE PATIENT MONITORING

41.1 INTRODUCTION

41.2 WHAT THE CASE STUDY IS ABOUT

41.3 WHO ARE THE PARTIES IN THE CASE STUDY

41.4 LIMITATION, BUSINESS CASE, AND TECHNOLOGY APPROACH

41.5 SETUP AND WORKFLOW PLAN

41.6 WHAT ARE THE SUCCESS STORIES IN THE CASE STUDY

41.7 WHAT LESSONS LEARNED TO BE IMPROVED

FURTHER READING

PART VI: CLOUD, LEGAL, INNOVATION, AND BUSINESS MODELS

42 INTERNET OF THINGS AND CLOUD COMPUTING

42.1 INTRODUCTION

42.2 WHAT IS CLOUD COMPUTING?

42.3 CLOUD COMPUTING AND IoT

42.4 COMMON IoT APPLICATION SCENARIOS

42.5 CLOUD SECURITY AND IoT

42.6 CLOUD COMPUTING AND MAKERS

42.7 AN EXAMPLE SCENARIO

42.8 CONCLUSION

REFERENCES

43 PRIVACY AND SECURITY LEGAL ISSUES

43.1 UNIQUE CHARACTERISTICS

43.2 PRIVACY ISSUES

43.3 DATA MINIMIZATION

43.4 DEIDENTIFICATION

43.5 DATA SECURITY

43.6 PROFILING ISSUES

43.7 RESEARCH AND ANALYTICS

43.8 IoT AND DA ABROAD

REFERENCES

44 IoT AND INNOVATION

44.1 INTRODUCTION

44.2 WHAT IS INNOVATION?

44.3 WHY IS INNOVATION IMPORTANT? DRIVERS AND BENEFITS

44.4 HOW: THE INNOVATION PROCESS

44.5 WHO DOES THE INNOVATION? GOOD INNOVATOR SKILLS

44.6 WHEN: IN A PRODUCT CYCLE WHEN DOES INNOVATION TAKES PART?

44.7 WHERE: INNOVATION AREAS IN IoT

44.8 CONCLUSION

REFERENCES

FURTHER READING

45 INTERNET OF THINGS BUSINESS MODELS

45.1 INTRODUCTION

45.2 IoT BUSINESS MODEL FRAMEWORK REVIEW

45.3 FRAMEWORK DEVELOPMENT

45.4 CASE STUDIES

45.5 DISCUSSION AND SUMMARY

45.6 LIMITATIONS AND FUTURE RESEARCH

REFERENCES

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 01

TABLE 1.1 Internet of Things Units Installed Base by Category (Millions of Units)

TABLE 1.2 Internet of Things End Point Spending by Category (Billions of Dollars)

TABLE 1.3 The Technologies Enabling the Internet of Things

TABLE 1.4 The Internet of Things Requires a Mindset Shift

Chapter 04

TABLE 4.1 Steps in an Envisioning Process

TABLE 4.2 Extracting Objectives from Goals

TABLE 4.3 Mapping Projects to Common IoT Technology

Chapter 06

TABLE 6.1 Brain Wave Frequencies and Associated Mental States

Chapter 08

TABLE 8.1 Industry Consortiums

TABLE 8.2 Protocols and Operating Systems

TABLE 8.3 APIs, Horizontal Platforms, and Middleware

TABLE 8.4 Node Flow Editors, Toolkits, Data Visualizations, and Search

TABLE 8.5 Hardware and In‐Memory Data Grids

TABLE 8.6 Home Automation, Robotics, Mesh Network, Health, Air Pollution, and Water

Chapter 10

TABLE 10.1 Summary of Important Commercially Available Photon IR Materials

TABLE 10.2 General Considerations in the Selection of an Infrared Photon Detector

TABLE 10.3 Key Criteria for Selecting a Thermal Sensor for an Application

TABLE 10.4 Comparison of Various Sensing Methods for Gas Detection

Chapter 11

TABLE 11.1 The ISO Seven‐Layer Model

TABLE 11.2 ISO Model Versus Internet Model

Chapter 12

TABLE 12.1 Difference of Architecture between WSN, WLAN, and OSI

Chapter 13

TABLE 13.1 A Comparison of IoT Session Layer Standards

Chapter 18

TABLE 18.1 Sources of General Healthcare Data

TABLE 18.2 Performance of Binary Classifier for Mortality Prediction Based on Nonspecific Clinical Data

Chapter 21

TABLE 21.1 Distinguishing “Patterns” and “Tendencies” in Fraud

Chapter 30

TABLE 30.1 Smart Grid Standards for Distribution and Customer Domains and Cyber Security

TABLE 30.2 Electric Vehicle and Battery Storage DER Value for Grid Services

TABLE 30.3 GWAC Data Models, Transport Mechanisms, and Security Applications for OpenADR

Chapter 34

TABLE 34.1 Comparison of Transport and Telecom Industries Today

Chapter 45

TABLE 45.1 Categories of IoT Strategy

TABLE 45.2 Various Layers of the IoT Value Chain [17]

TABLE 45.3 Proposed IoT Business Model Framework

List of Illustrations

Chapter 01

FIGURE 1.1 2015 was planet Earth’s warmest year since modern record keeping began in 1880, according to a new analysis by NASA’s Goddard Institute for Space Studies.

FIGURE 1.2 Selected significant climate anomalies and events in 2015.

FIGURE 1.3 Hype cycle for emerging technologies, 2015.

FIGURE 1.4 Big data reference architecture.

FIGURE 1.5 Internet of Things ecosystem.

FIGURE 1.6 Big data by the numbers from IBM big data and analytics hub (http://ibmbigdatahub.com).

FIGURE 1.7 Typical data aggregation process.

FIGURE 1.8 Foster creativity.

FIGURE 1.9 The archetypal business model.

Chapter 02

FIGURE 2.1 Data connects the city in ways that offer citizens with better services while reducing emissions and optimizing city resources.

FIGURE 2.2 Desire lines show how people use their own tactics and logic of everyday life, which is impossible to specify by the planner of both physical and virtual spaces.

Chapter 03

FIGURE 3.1 Connected medical devices will intelligently analyze patient status, create “smart alarms” by combing instrument readings, and ensure proper patient care. An intelligent, distributed IIoT system will help care teams prevent hundreds of thousands of deaths.

FIGURE 3.2 A modern hospital needs hundreds of types of devices. These must communicate to improve patient safety and outcome, to aid resource deployment and maintenance, and to optimize business processes. RTI Connext DDS adapts to handle many different types of data flows, different computing platforms, and transports.

FIGURE 3.3 An intelligent Patient Controlled Analgesia system. The supervisor combines oximeter and respirator readings to reduce false alarms and stop drug infusion to prevent overdose. The RTI DDS databus connects all the components with appropriate real‐time reliable delivery.

FIGURE 3.4 Medical devices must operate in a complex hospital environment. The system must be able to find data sources, track them as patients move, and scale to handle the load. This realistic test simulated a large hospital.

FIGURE 3.5 A Microgrid uses peer‐to‐peer data communication and edge intelligence to automate local power generation and balance against the power load. Microgrids help integrate intermittent energy sources like solar and wind.

FIGURE 3.6 NASA KSC’s launch control is a massive, reliable SCADA system. It comprises over 400,000 points, spread across the launch platform and the control room. The launch control system integrates many thousands of devices, from tiny sensors to large enterprise storage systems. It spreads over many miles.

FIGURE 3.7 NASA’s launch control SCADA system is massive. It captures sensor data to both recording services (for forensic use) and persistence service (for durability). The Routing Service‐to‐Routing Service bridge encrypts data between the event platform and control room.

FIGURE 3.8 The Ground Based Sense and Avoid (GBSAA) system includes many distributed radars. It will soon allow unmanned vehicles to fly in the U.S. National Airspace System (NAS). Applications of unmanned vehicles will include operator training, repositioning, search and rescue, and disaster relief.

FIGURE 3.9 Autonomous vehicles must analyze complex situations and react quickly. They merge information from multiple sensors, plan trajectories through traffic and road lanes, and control the vehicle in real time. Slower subsystems support navigation, monitoring, and route optimization.

FIGURE 3.10 Environment does not indicate architecture. Dividing animals by “land, sea, and air” environment is scientifically meaningless. The biological taxonomy instead divides by fundamental characteristics.

FIGURE 3.11 Industry does not indicate architecture. Dividing IIoT applications by “medical, power, or transportation” environment is as scientifically meaningless as dividing animals by their environments. To make progress, we need an IIoT taxonomy that instead divides by fundamental characteristics.

FIGURE 3.12 IIoT reliability‐critical applications. Hydropower dams can quickly modulate their significant power output by changing water flow rates and thus help balance the grid: even a few milliseconds of unplanned downtime can threaten stability. Air traffic control faces a similar need for continuous operation: a short failure in the system endangers hundreds of flights. A proton‐beam radiation therapy system must guarantee precise operation during treatment: operational dropouts threaten patient outcomes. Applications with severe consequences of short interruptions in service require a fully redundant architecture, including computing, sensors, networking, storage, and software.

FIGURE 3.13 Added server latency. Although the hardware transmit time is often negligible, sending data through a server “hop” requires traversing the sending machine’s transmit stack, the server’s receive stack, the server’s processing queue, the server’s transmit stack, and finally the destination’s receive stack. Each of these has threads, queues, and buffers that add uncontrolled latency. Worse, the server cannot easily prioritize traffic as easily as the end points. Thus, systems that are sensitive to maximum latency often cannot use data servers.

FIGURE 3.14 IIoT real‐time applications. To provide quality feel to surgeons, distributed control loops for medical robotics must run at rates up to 3 kHz and control the “jitter” to only tens of microseconds. Similarly, autonomous cars must react fast enough to safely control the vehicle and prevent collisions. These fundamental performance needs imply a system architecture that does not send data through intermediaries.

FIGURE 3.15 IIoT applications with many data items. IIoT systems often produce far too much data to send everything to every possible consumer. “Gust control” in a wind turbine farm, for instance, needs weather updates from the turbines immediately “upwind,” a specification that changes with time. Traffic control systems are very interested only in vehicles approaching an intersection. These applications require the architecture to provide selective data availability, so only the right information loads the network and the participants.

FIGURE 3.16 IIoT applications built by large teams. Hundreds of different types of hospital medical devices, from heart monitors to ventilators, must combine to better monitor and care for patients. Similarly, ship systems integrate dozens of complex functions like navigation, power control, and communications. When a complex “system of systems” integrates many complex interfaces, the system architecture itself must help to manage system integration and evolution.

FIGURE 3.17 IIoT device integration challenge. Large systems assembled in the field from a large variety of “devices” face a challenge in understanding and discovering interacting devices and their relationships. The most common example applications are in manufacturing. These applications benefit from a design that offers the ability for remote applications and human interfaces to “browse” the system, thus discovering data sources and relationships.

FIGURE 3.18 IIoT applications needing data distribution. Many applications must deliver the same data to many potential end points. Coordinated vehicle fleets may update a cloud server, but then that information must be delivered to many distributed vehicles. An emergency service communication system must allow many remote users access to high‐bandwidth distributed voice and video streams. Many industries use “hardware in the loop” simulation to test and verify modules during development. Across all these industries, an efficient architecture must deliver data to multiple points easily.

FIGURE 3.19 IIoT collection and monitoring applications. Collecting and analyzing field‐produced data is perhaps the first “killer app” of the IIoT. The IIC’s “track and trace” test bed, for instance, tracks tools on a factory floor so the system can automatically log use. Other applications include monitoring gas turbines for efficient operation, testing aircraft landing gear for potentially risky situations, and optimizing gas pipeline flow control. Since there is little interdevice flow, “hub and spoke” system architectures that ease collection work well for these systems.

FIGURE 3.20

n

‐Dimensional requirement space. Architectural approaches and their implementing technologies satisfy some range of each of the dimensions above and thus occupy a region in an

n

‐dimensional “requirement space.” The value of a taxonomy is to help designers decompose their problem into relevant dimensions so they can then select an appropriate approach.

FIGURE 3.21 IoT protocol road map. Devices communicate with each other (D2D) and send data to the IT infrastructure (D2S). The IT infrastructure servers use the data (S2S), communicating back to devices or to people.

FIGURE 3.22 Message Queuing Telemetry Transport (MQTT) implements a hub‐and‐spoke data collection system.

FIGURE 3.23 Extensible Messaging and Presence Protocol (XMPP) provides text communications between diverse points.

FIGURE 3.24 Data Distribution Service (DDS) connects devices at physics speeds into a single distributed application.

FIGURE 3.25 Advanced Message Queuing Protocol (AMQP) shares data reliably between servers.

FIGURE 3.26 OPC UA allows applications to browse a system’s object model and interpret the devices and their connections.

FIGURE 3.27 The IIRA connectivity architecture specifies a Quality‐of‐Service controlled, secure connectivity core standard. All other connectivity standards must only bridge to this one core standard.

FIGURE 3.28 Data‐centric communications. The databus links any language, device, or transport. It automatically discovers information sources, understands data types, and communicates them to interested participants. It scales across millions of data paths, enforces submillisecond timing, ensures reliability, supports redundancy, and selectively filters information. Each path can be unicast, multicast, open data, or fully secure. In the figure, a medical device that produces heart waveforms will send only one patient’s information to the nursing station, at a rate it can handle, if it has permission to receive the information.

FIGURE 3.29 Data‐centric middleware does for data in motion what a database does for data at rest. The database’s data‐centric storage fundamentally enables the simplified development of very complex information systems. Analogously, the databus offers data‐centric networking that fundamentally enables the simplified development of very complex distributed systems. Both move much of the complexity from the application (user code) to the infrastructure.

Chapter 04

FIGURE 4.1 Smart city focus areas.

FIGURE 4.2 Foundational IoT technologies for smart cities.

FIGURE 4.3 Steps to create and deliver a smart city.

FIGURE 4.4 Relationship between vision, goals, and objectives.

Chapter 05

FIGURE 5.1 Initial proximity learning system design.

FIGURE 5.2 Data collection in emergency response and trauma bay and visualization in debrief.

FIGURE 5.3 Detection of proximity of sensors by team member role, event (number), mannequin heart rate (triangle), and time (horizontal axis).

FIGURE 5.4 Trauma bay listener distances as measured through Radius Network Bluetooth beacons (experiment performed in May, 2015). Time of day is indicated in

x

, and distance in meters is the

y

axis.

Chapter 09

FIGURE 9.1 An illustration of the

Internet of Things

(IoT) showing a myriad of physical objects represented as devices, products, and systems (including common household and business appliances) that are all connected together and where information can be gathered by the devices and then stored, processed, and accessed from the cloud.

FIGURE 9.2 An illustration of a control loop that employs a decision‐making capability that based on the difference between the set point and the measured output directs an actuator to make a physical motion; an actuator that makes a physical motion; a system under control that based on the motion of the actuator changes its state; and a sensor that measures the output parameter of the system and converts it into a form that can be compared to the set point.

FIGURE 9.3 Illustration of some of the variety of sensors being deployed on vehicles. Automakers originally only employed MEMS‐based manifold air pressure sensors and Hall effect sensors early on, but the number and diversity of MEMS sensors have radically increased over the past decade and will continue to increase as consumers demand increased safety, drivability, and reliability. The dramatic growth of MEMS sensors on vehicles is expected to significantly increase with the future introduction of autonomous vehicle technology.

FIGURE 9.4 Illustration of shape of the etch profiles of a (100) oriented silicon substrate after immersion in an anisotropic wet etchant solution.

FIGURE 9.5 SEM of the cross section of a silicon wafer demonstrating high aspect ratio and deep trenches that can be fabricated using DRIE technology.

FIGURE 9.6 Illustration of a surface micromachining process.

FIGURE 9.7 Polysilicon resonator structure fabricated using a surface micromachining process.

FIGURE 9.8 An overhead optical photograph of the MEMS integrated pressure sensor device that employs a piezoresistor configuration.

FIGURE 9.9 Cross‐sectional illustration of the Freescale Pressure Sensor. The materials used in the fabrication of this device are given in the legend shown earlier.

FIGURE 9.10 Cross‐sectional diagram of the Knowles, Inc. microphone sensor structure.

FIGURE 9.11 Top‐down optical micrograph of the Knowles, Inc. microphone sensor.

FIGURE 9.12 Photographs of products employing STMicroelectronics’ inertial MEMS sensor technology that provide the customer with increasing capabilities in consumer products, in this case for properly orientating the screen to the user.

FIGURE 9.13 Cross‐sectional illustration of MEMS device structure on substrate made using the STMicroelectronics’ THELMA process technology that is used to manufacture their line of inertial sensors.

FIGURE 9.14 Magnified SEM image of Texas Instrument’s DMD device with center pixel in actuated (i.e., rotated) state.

FIGURE 9.15 Cross section of one pixel of the Texas Instruments DMD.

Chapter 10

FIGURE 10.1 Digital detector schematic.

FIGURE 10.2 Relative spectral response curve of a photon detector.

FIGURE 10.3 Graph showing a typical performance parameter (

D

*) for a number of detector classes offered by Teledyne Judson Technologies (TJT), operating in the 1–20 µm spectral range [2]. See Teledyne Judson website for more information and detailed specification sheets.

FIGURE 10.4 Various detector formats: (a) discrete detectors of various sizes, (b) Quad detector, (c) position sensor, (d) linear array, and (e) two‐dimensional focal plane array (FPA).

FIGURE 10.5 Key system aspects to be considered when designing an optical sensing system.

FIGURE 10.6 Choice of sensors in the four different bands—near, short wave, mid, and long wave infrared—for typical infrared sensor applications.

FIGURE 10.7 Demonstration model of a typical gas sensing device using an infrared source and detector to measure absorption in specific bands determined by a filter.

Chapter 11

FIGURE 11.1 IP version 4 and IP version 6.

Chapter 12

FIGURE 12.1 Thread and memory hierarchy in a GPU.

FIGURE 12.2 MIC microarchitecture.

FIGURE 12.3 WSN architecture.

Chapter 13

FIGURE 13.1 IoT ecosystem.

FIGURE 13.2 Protocols for IoT.

FIGURE 13.3 WirelessHART architecture.

FIGURE 13.4 LTE‐A architecture.

FIGURE 13.5 MQTT architecture.

FIGURE 13.6 AMQP architecture.

FIGURE 13.7 CoAP messages.

FIGURE 13.8 IEEE 1905.1 protocol structure.

Chapter 14

FIGURE 14.1 Reference architecture of data warehouse.

FIGURE 14.2 The state of IoT‐related consortiums for standardization.

FIGURE 14.3 Hierarchy of IoT architecture.

FIGURE 14.4 Conceptual view of IoT Cloud.

FIGURE 14.5 Functional view for healthcare. https://etrij.etri.re.kr/etrij/images/2014/v36n5/ETRI_J001_2014_v36n5_730_f002.jpg.

FIGURE 14.6 IoT application architecture.

FIGURE 14.7 Industrial Internet Consortium’s Industrial Internet Reference Architecture (IIRA). http://www.iiconsortium.org/IIRA‐1‐7‐ajs.pdf.

FIGURE 14.8 Predix—technology view of the architecture.

FIGURE 14.9 Survey on IoT‐related concerns. https://adtmag.com/articles/2015/07/29/~/media/ECG/adtmag/Images/2015/07/evans_iot.png.

Chapter 15

FIGURE 15.1 (a) Are wearables for you? or (b) choosing a pedometer. (c) Method‐controlling charge within a portable device.

Chapter 16

FIGURE 16.1 The Hitchhikers guide to iBeacon hardware. [3].

FIGURE 16.2 Interaction between beacons, mobile devices, and a server.

FIGURE 16.3 Screenshot of indoo.rs prototype at SFO—user preferences.

FIGURE 16.4 Screenshot of indoo.rs prototype at SFO—points of interest.

FIGURE 16.5 Screenshot of indoo.rs prototype at SFO—navigation on map.

FIGURE 16.6 Showing how the SFO app works for visually impaired passengers.

FIGURE 16.7 iBeacon technology poised to dominate retail. [14].

Chapter 17

FIGURE 17.1 The IoT is composed of various contemporary technologies working together.

FIGURE 17.2 Industry has evolved through a series of revolutionary leaps.

FIGURE 17.3 Major technological advances have triggered revolutionary changes in industry.

FIGURE 17.4 With the help of B‐Scada, Algae lab systems was able to enhance their product portfolio and appeal to a new class of customer.

Chapter 18

FIGURE 18.1 Venn diagram of data science in healthcare.

FIGURE 18.2 Venn diagram of analysis interests.

FIGURE 18.3 Medium‐scale data science team: sample organizational chart.

FIGURE 18.4 Types of healthcare data, volume versus reliability per year.

FIGURE 18.5 Building blocks of machine learning.

FIGURE 18.6 Building blocks of machine learning: continued.

FIGURE 18.7 Schematic representation of predictive workflow.

Chapter 19

FIGURE 19.1 Screenshots of DataSpark’s Mobile App that allows commuters to plan their route based on the average travel time to the destination and crowdedness of the boarding station in near real time.

FIGURE 19.2 Hypothesis‐driven experimentations are the smart way to start a big data initiative instead of the traditional obsolete approach of starting from your data or buying a big data solution.

FIGURE 19.3 Multidisciplinary team is required to build big data analytics capabilities.

FIGURE 19.4 Hybrid strategy in building big data ecosystems.

FIGURE 19.5 A framework to unlock the value of big data by protecting individual privacy and maintaining utility of the data.

Chapter 20

FIGURE 20.1 Transformational thinking technique.

FIGURE 20.2 Round table versus square table strategic thinking.

FIGURE 20.3 Organizational power flows in a University.

FIGURE 20.4 High‐level planning process.

FIGURE 20.5 Textual analytic process.

FIGURE 20.6 Social mobile data space.

FIGURE 20.7 Social, local, and mobile emotional models.

Chapter 21

FIGURE 21.1 Using snapshot dates to predict fraud events.

FIGURE 21.2 Simplifying feature space in submodels.

FIGURE 21.3 Choosing metrics for model accuracy.

FIGURE 21.4 Fraud ecosystem model example.

FIGURE 21.5 Triangulating on the right features.

Chapter 22

FIGURE 22.1 SAN architecture.

FIGURE 22.2 Microsoft’s Analytic Platform System.

FIGURE 22.3 High‐level view from the client into a typical Hadoop cluster.

FIGURE 22.4 Hadoop as an ELT solution.

FIGURE 22.5 Hadoop architecture stack.

FIGURE 22.6 Accessing HDFS from the Command Line.

FIGURE 22.7 Ambari HDFS Files view.

FIGURE 22.8 YARN framework.

Chapter 23

FIGURE 23.1 IoT data management built on Hadoop on‐premises and in the cloud.

FIGURE 23.2 Hadoop and its relation to other platforms.

Chapter 24

FIGURE 24.1 Example onboard components supporting vehicle connectivity and automation. www.ni.com.

FIGURE 24.2 Example of sensors required to support autonomous vehicle operations.

FIGURE 24.3 Examples of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communications.

FIGURE 24.4 Pikalert Vehicle Data Translator—an example of Connected Vehicles Application.

FIGURE 24.5 List of Connected Vehicle Applications from USDOT.

FIGURE 24.6 Onboard Diagnostics (OBD) Port and Dongle.

FIGURE 24.7 Estimates of Autonomous Vehicle Market Penetration.

FIGURE 24.8 Autonomous Vehicle Sales, Fleet, and Travel Projections.

FIGURE 24.9 Estimated Market for Connected Car Technologies, 2016–2021.

FIGURE 24.10 Smart Cities, Connected Vehicles, and Intelligent Transportation Systems.

Chapter 25

FIGURE 25.1 Ford Vehicle Consumer Services Interface.

FIGURE 25.2 Heart rate monitoring seat.

FIGURE 25.3 Allergy monitor system.

FIGURE 25.4 Mobile Connected Health Experience.

FIGURE 25.5 Driver distraction.

FIGURE 25.6 An architecture overview of the unified vehicle and wearable data integration framework.

Chapter 26

FIGURE 26.1 GE’s Predix Cloud captures and analyzes the unique volume, velocity, and variety of machine data.

FIGURE 26.2 PowerUp performance curve.

Chapter 27

FIGURE 27.1 Courtesy of PlanIT Impact.

Chapter 28

FIGURE 28.1 Replacement of aging infrastructure that caused water losses in Australia.

FIGURE 28.2 International Water Association best practices standard water balance [2]. Note: All data in volume for the period of reference, typically 1 year.

FIGURE 28.3 Four‐pillar approach to control apparent losses [2].

FIGURE 28.4 Four‐pillar approach to control apparent losses [2].

FIGURE 28.5 Replacement of aged pipes.

FIGURE 28.6 Identification of background leakage. The arrow indicates the flow increase trend detected by TaKaDu, which was not detected manually given the moderate change and background fluctuations. The statistical nature of the technology allows TaKaDu to identify small, invisible leaks.

FIGURE 28.7 Sites of leaks detected with TaKaDu’s support.

Chapter 30

FIGURE 30.1 Smart Grid standards interoperability at different levels for AutoDR signals.

FIGURE 30.2 Framework for the Internet of Things and applications for Smart Grid.

FIGURE 30.3 Conceptual Smart Grid architecture and interoperability standards interfaces.

FIGURE 30.4 Example framework for interconnected grid, DER integration, and the IoT role.

FIGURE 30.5 Five layers of integration for Smart Grid domain interfaces.

FIGURE 30.6 Open standards and interfaces for DER at transmission and distribution domains.

Chapter 31

FIGURE 31.1 Schematic IoT technical architecture.

FIGURE 31.2 An oil and gas life cycle at a high level. Throughout the life cycle IoT and Big Data analytics can be used.

FIGURE 31.3 Several critical areas for IoT Deployment in Oil and Gas Industry.

FIGURE 31.4 A simple framework for IoT‐based Health, Safety and Environment at Oil and Gas.

Chapter 32

FIGURE 32.1 Economic impact of the 12 most significant disruptive technologies by 2025, in US$ trillions per year and countries’ current GDP in 2014, in US$ trillions.

FIGURE 32.2 Historical and forecasted number of connected devices in the world.

FIGURE 32.3 Cash cost breakdown in BHP Billiton’s Western Australian iron ore assets, FY 2014.

FIGURE 32.4 Mining industry facts.

FIGURE 32.5 Chinese GDP growth and Chinese GDP by sector.

FIGURE 32.6 Investments in infrastructure, 1992–2011, weighted average, % of country’s GDP.

FIGURE 32.7 Iron ore sales per company, in Mt.

FIGURE 32.8 Mining workforce productivity in Australia.

FIGURE 32.9 Platts IODEX 62% iron ore CFR China, in US$/dmt.

FIGURE 32.10 How could IoT transform mining operations?

FIGURE 32.11 Innovation versus R&D ranks.

FIGURE 32.12 Open innovation: expanding the universe of problem solvers.

FIGURE 32.13 Discipline management versus innovation.

FIGURE 32.14 Types of innovation.

FIGURE 32.15 Organizational structures: traditional mining versus innovation.

FIGURE 32.16 Smelter shutdown demographics and mobility PoC statistics.

FIGURE 32.17 Summary of Rio Tinto’s automation efforts.

Chapter 33

FIGURE 33.1 The IoT‐based cyber–physical test bed.

FIGURE 33.2 Overview of the main cyber–physical tasks in the collaborative framework.

FIGURE 33.3 The virtual assembly environment.

FIGURE 33.4 Assembly tasks.

FIGURE 33.5 A physical cell.

FIGURE 33.6 Architecture of virtual environment for orthopedic surgery (VEOS).

FIGURE 33.7 Flowchart for position training.

FIGURE 33.8 View of the virtual surgical environment.

Chapter 34

FIGURE 34.1 Evolution of transport applications and transport data assets.

FIGURE 34.2 oneM2M service enablement layer international standard.

FIGURE 34.3 oneTRANSPORT platform vision.

FIGURE 34.4 oneTRANSPORT opens data assets once to many users.

FIGURE 34.5 Business model evolution of oneTRANSPORT.

Chapter 35

FIGURE 35.1 Categories of automated driving as defined in SAE J3016 are illustrated noting that level 5 full automation is the final goal.

FIGURE 35.2 An interesting example of a level 2 partially automated driving vehicle from 2011 is illustrated here. The vehicle automation hardware is completely add‐on with no additional holes drilled in the car. All the add‐on hardware was easily removed completely in about 2 hours by two people. This platform was extended in 2012 to a level 3 conditional automation of the same brand of vehicle and demonstrated in autonomous driving.

FIGURE 35.3 A level 4 highly automated driving vehicle under construction is displayed here. The actuators, sensors, and electronic control units used for automation have a very low footprint in this type of automation where driving is taken care of by the autonomous system with the driver taking control only if there is a need.

FIGURE 35.4 An example of connected driving is seen here with two platoons of connected vehicles following a speed profile communicated to them by the lead vehicle. The vehicles use CACC in the longitudinal direction. Traffic light SPAT information and speed limits are communicated by roadside units to the vehicles.

FIGURE 35.5 Predictive cruise control of a platoon of trucks is shown. Predictive cruise control systems for trucks are currently available from manufacturers. Current research on predictive cruise control focuses on incorporating traffic speed and traffic light information to the cruise speed profile optimization. The highest fuel economy benefits will be obtained if the trucks are also equipped with regenerative braking.

FIGURE 35.6 The V‐diagram of a unified approach for developing connected and automated driving systems starting with model‐in‐the‐loop simulations and ending in road testing.

Chapter 36

FIGURE 36.1 Trip planner, route comparison view, and details view.

FIGURE 36.2 Real‐time view.

FIGURE 36.3 Transit Hub design.

FIGURE 36.4 Transit Hub analytics dashboard.

FIGURE 36.5 Example of a Kiosk System—Siemens smart city hub.

FIGURE 36.6 Decision support by a Kiosk System..

Chapter 37

FIGURE 37.1 This screenshot is an example from the app Presence. It depicts the screen of an end user coordinating connected devices to work together for their benefit.

FIGURE 37.2 We are increasingly utilizing mobile devices to keep up with our increasingly mobile lifestyles.

FIGURE 37.3 This is an example of a security pack with a clean, elegant design and a vast array of sensors so that users don’t need to go out of their way to purchase their own sensors.

FIGURE 37.4 This screenshot shows an example of an app that is customizable to the end user’s needs. The user can add or delete devices at the touch of a button.

FIGURE 37.5 This figure displays a friend’s list on the first screen and on the second screen, an example of how you can have your friend David Moon receive alerts about your “expensive whiskey” for you.

FIGURE 37.6 IoT software architecture.

FIGURE 37.7 Harmony social engagement layer.

Chapter 38

FIGURE 38.1 Use of FACS within a PR campaign.

FIGURE 38.2 System 1 and System 2.

FIGURE 38.3 Image from “Unsound” screened in SXSW 2011.

FIGURE 38.4 Sensum platform for measuring emotional insights.

FIGURE 38.5 Conscious (dotted line) and nonconscious (solid line) reactions to images of life after work.

FIGURE 38.6 Retirement DNA.

FIGURE 38.7 Measuring the excitement of driving a Jaguar.

FIGURE 38.8 Captured and visualized due to an exciting driving experience.

Chapter 39

FIGURE 39.1 ARUP categories and indicators synthesis.

FIGURE 39.2 Resilience framework categories and Selex ES‐integrated security solutions.

FIGURE 39.3 Selex ES City Operating System.

FIGURE 39.4 Selex ES Cyber security services.

FIGURE 39.5 Selex ES solution from City Operating System to Resilient City.

FIGURE 39.6 Operation control rooms for Italian civil protection department.

FIGURE 39.7 Living lab engagement model.

FIGURE 39.8 Living lab in Genoa’s district Marassi.

Chapter 40

FIGURE 40.1 BMU mounted on a pilot site in Taiwan.

FIGURE 40.2 Yuan Shan Bridge in Taipei where two BMUs have been operating since 2014.

FIGURE 40.3 Data from a dynamic inclinometer processed to show clearly identifiable peak on the frequency spectrum. Shifts of frequency peaks are important indicators in SHM.

FIGURE 40.4 Spectrum of one of the dynamic inclinometers over time. The darker bands indicate the vibration nodes of the structure.

Chapter 41

FIGURE 41.1 Application of IoT in healthcare.

FIGURE 41.2 IoT patient tracking system diagram.

FIGURE 41.3 Patient tracking system using IoT sensor devices.

FIGURE 41.4 Healthcare IoT devices and classification.

FIGURE 41.5 Digital health ecosystem with IoT components.

Chapter 42

FIGURE 42.1 Disruptive forces in IoT.

FIGURE 42.2 Example IoT device and cloud pattern.

FIGURE 42.3 Remote monitoring.

FIGURE 42.4 Asset management.

FIGURE 42.5 Predictive maintenance.

FIGURE 42.6 Example cloud‐enabled IoT scenario.

Chapter 44

FIGURE 44.1 Relation between innovation and creativity.

FIGURE 44.2 Areas of innovation.

FIGURE 44.3 The innovation matrix.

FIGURE 44.4 Types of innovation.

FIGURE 44.5 A systematic approach to do innovation.

FIGURE 44.6 A nine‐step innovation journey.

Chapter 45

FIGURE 45.1 “The archetypal business model.”

FIGURE 45.2 Architecture for Internet of Things.

FIGURE 45.3 Framework design for a business model framework in the IoT context.

FIGURE 45.4 Information‐driven value chain for IoT.

FIGURE 45.5 The position of the motion sensor (PIR) and a door sensor installed in the testing premise.

FIGURE 45.6 The duration against the start time at the main bedroom for over 30 days.

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INTERNET OF THINGS AND DATA ANALYTICS HANDBOOK

Edited by

HWAIYU GENG

Amica ResearchPalo Alto, CA, USA

Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New JerseyPublished simultaneously in Canada

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 750‐4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748‐6011, fax (201) 748‐6008, or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Library of Congress Cataloging‐in‐Publication Data

Names: Geng, Hwaiyu, editor.Title: Internet of things and data analytics handbook / edited by Hwaiyu Geng.Description: Hoboken, New Jersey : John Wiley & Sons, 2017. | Includes bibliographical references and index.Identifiers: LCCN 2016039867| ISBN 9781119173649 (cloth) | ISBN 9781119173625 (epub) | ISBN 9781119173632 (Adobe PDF)Subjects: LCSH: Internet of things. | Data mining. | Cooperating objects (Computer systems) | Big data.Classification: LCC TK5105.8857 .I58 2017 | DDC 004.67/8–dc23LC record available at https://lccn.loc.gov/2016039867

Cover image: Pitju/gettyimages; Oleksiy Mark/gettyimages; Maxiphoto/gettyimage

LIST OF CONTRIBUTORS

Scott Amyx, Amyx McKinsey, San Francisco, CA, USA

Arun Aryasomajula, Division of Analytics Research and Clinical Informatics, Department of Data Science, Geisinger Health System, Danville, PA, USA

Brenda Bannan, Ph.D., George Mason University, Fairfax, VA, USA

David Bartlett, General Electric, San Ramon, CA, USA

Peter Burnham, FJORD, San Francisco, CA, USA

Alan Carlton, InterDigital Europe Ltd, London, UK

J. Cecil, Ph.D., Co‐Director, Computer Science Department, Center for Cyber Physical Systems, Oklahoma State University, Stillwater, OK, USA

Rafael Cepeda, Ph.D., InterDigital Europe Ltd, London, UK

Hubert C.Y. Chan, DBA, The Hong Kong Polytechnic University, Hong Kong, China

Yifan Chen, Ph.D., Ford Research and Advanced Engineering, Dearborn, MI, USA

Dominique Davison, AIA, DRAW Architecture + Urban Design, Kansas City, MO, USA

Abhishek Dubey, Ph.D., Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA

Mumin Tolga Emirler, Ph.D., Ohio State University, Columbus, OH, USA

David Y. Fong, Ph.D., CITS Group, San Jose, CA, USA

Joshua Frank, Ph.D., Intuit Inc., Woodland Hills, CA, USA

Neil Fraser, Ph.D., Macquarie University, Sydney, New South Wales, Australia

Dan Freudberg, Nashville Metropolitan Transport Authority, Nashville, TN, USA

Shane Gallagher, Ph.D., Advanced Distributed Learning, Alexandria, VA, USA

Tim Gammons, ARUP, London, UK

Hwaiyu Geng, P.E., Amica Research, Palo Alto, CA, USA

Girish Ghatikar, Greenlots, San Francisco, CA, USA; Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Francoise Gilbert, J.D., Greenberg Traurig LLP, Silicon Valley, East Palo Alto, CA, USA

Aniruddha Gokhale, Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA

Rikke Gram‐Hansen, Copenhagen Solutions Lab, City of Copenhagen, Copenhagen, Denmark

Bilin Aksun Guvenc, Ph.D., Ohio State University, Columbus, OH, USA

Levent Guvenc, Ph.D., Ohio State University, Columbus, OH, USA

Ashley Z. Hand, AIA, CityFi, Los Angeles, CA, USA

David R. Hardoon, Ph.D., Azendian, Singapore, Singapore

David Hindman, FJORD, San Francisco, CA, USA

Michael A. Huff, Ph.D., MEMS and Nanotechnology Exchange (MNX), Corporation for National Research Initiatives, Reston, VA, USA

Rich Hunzinger, B‐Scada, Inc., Crystal River, FL, USA

Raj Jain, Ph.D., Department of Computer Science Engineering, Washington University, St. Louis, MO, USA

Karthi Jeyabalan, University of Utah, Salt Lake City, UT, USA

William Kao, Ph.D., Department of Engineering and Technology, University of California, Santa Cruz, CA, USA

Joseph Kimchi,