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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
The Internet of Things (IoT) is the fastest growing technology market. Industries are embracing IoT technologies to improve operational expenses, product life, and people's well-being. An architectural guide is necessary if you want to traverse the spectrum of technologies needed to build a successful IoT system, whether that's a single device or millions of devices.
This book encompasses the entire spectrum of IoT solutions, from sensors to the cloud. We start by examining modern sensor systems and focus on their power and functionality. After that, we dive deep into communication theory, paying close attention to near-range PAN, including the new Bluetooth® 5.0 specification and mesh networks. Then, we explore IP-based communication in LAN and WAN, including 802.11ah, 5G LTE cellular, Sigfox, and LoRaWAN. Next, we cover edge routing and gateways and their role in fog computing, as well as the messaging protocols of MQTT and CoAP.
With the data now in internet form, you'll get an understanding of cloud and fog architectures, including the OpenFog standards. We wrap up the analytics portion of the book with the application of statistical analysis, complex event processing, and deep learning models. Finally, we conclude by providing a holistic view of the IoT security stack and the anatomical details of IoT exploits while countering them with software defined perimeters and blockchains.
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Veröffentlichungsjahr: 2018
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Internet of Things for Architects
Copyright © 2018 Packt Publishing
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Commissioning Editor: Gebin GeorgeAcquisition Editor: Shrilekha InaniContent Development Editor: Sharon RajTechnical Editors: Prashant Chaudhari, Komal KarneCopy Editor: Safis EditingProject Coordinator: Virginia DiasProofreader: Safis EditingIndexer: Aishwarya GangawaneGraphics: Tania DuttaProduction Coordinator: Shantanu Zagade
First published: January 2018
Production reference: 1190118
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78847-059-9
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Contributors
About the author
Perry Lea has spent 21 years at Hewlett Packard as a distinguished technologist and chief architect. He then served as a distinguished member of technical staff and strategic director at Micron Technologies leading a team working on advanced computing devices. He is currently a technical director at Cradlepoint where he leads advancement and research in IoT and fog compute.
Perry has degrees in computer science, computer engineering, and an EE degree from Columbia University. He is a senior member of IEEE and a senior member/distinguished speaker of ACM. He has 8 patents with 40 pending.
About the reviewer
Parkash Karki is a principal architect and product development manager with over 20 years of experience in the IT field. With a BSc (Hons) physics from the University of Delhi and master of computer applications from BIAS, he is PMP certified and also holds other certifications in Microsoft technologies. His has majorly worked on various Microsoft and open source technologies with vast experience in DevOps and Azure Cloud. As a DevOps and Cloud architect, he helps his customers adopt them well. He is very passionate about IoT, artificial intelligence, and automation technologies.
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Table of Contents
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the color images
Conventions used
Get in touch
Reviews
The IoT Story
History of the IoT
IoT potential
Industrial and manufacturing
Industrial and manufacturing IoT use cases and impact
Consumer
Consumer IoT use cases
Retail, financial, and marketing
Retail IoT use cases
Healthcare
Healthcare IoT use cases
Transportation and logistics
Transportation and logistics IoT use cases
Agricultural and environmental
Agricultural and environmental IoT use cases
Energy
Energy IoT use cases
Smart city
Smart city IoT use cases
Government and military
Government and military IoT use cases
Summary
IoT Architecture and Core IoT Modules
IoT ecosystem
IoT versus machine to machine
The value of a network and Metcalfe's and Beckstrom's law
IoT architecture
Role of an architect
Part 1 – Sensing and power
Part 2 – Data communication
Part 3 – Internet routing and protocols
Part 4 – Fog and edge compute, analytics, and machine learning
Part 5 – Threat and security in IoT
Summary
Sensors, Endpoints, and Power Systems
Sensing devices
Thermocouples and temperature sensing
Thermocouples
Resistance Temperature Detectors
Thermistors
Temperature sensor summary
Hall effect sensors and current sensors
Photoelectric sensors
PIR sensors
LiDAR and active sensing systems
MEMS sensors
MEMS accelerometers and gyroscopes
MEMS microphones
MEMS pressure sensors
Smart IoT endpoints
Vision system
Sensor fusion
Input devices
Output devices
Functional examples (putting it all together)
Functional example – TI SensorTag CC2650
Sensor to controller
Energy sources and power management
Power management
Energy harvesting
Solar harvesting
Piezo-mechanical harvesting
RF energy harvesting
Thermal harvesting
Energy storage
Energy and power models
Batteries
Supercapacitors
Radioactive power sources
Energy storage summary and other forms of power
Summary
Communications and Information Theory
Communication theory
RF energy and theoretical range
RF interference
Information theory
Bitrate limits and the Shannon-Hartley theorem
Bit error rate
Narrowband versus wideband communication
The radio spectrum
Governing structure
Summary
Non-IP Based WPAN
Wireless personal area network standards
802.15 standards
Bluetooth
Bluetooth history
Bluetooth 5 communication process and topologies
Bluetooth 5 stack
Bluetooth 5 PHY and interference
Bluetooth packet structure
BR/EDR operation
BLE operation
Bluetooth profiles
BR/EDR security
BLE security
Beaconing
Bluetooth 5 range and speed enhancement
Bluetooth mesh introduction
Bluetooth mesh topology
Bluetooth mesh addressing modes
Bluetooth mesh provisioning
IEEE 802.15.4
IEEE 802.15.4 architecture
IEEE 802.15.4 topology
IEEE 802.15.4 address modes and packet structure
IEEE 802.15.4 start-up sequence
IEEE 802.15.4 security
Zigbee
Zigbee history
Zigbee overview
Zigbee PHY and MAC (and difference from IEEE 802.15.4)
Zigbee protocol stack
Zigbee addressing and packet structure
Zigbee mesh routing
Zigbee association
Zigbee security
Z-Wave
Z-Wave overview
Z-Wave protocol stack
Z-Wave addressing
Z-Wave topology and routing
Summary
IP-Based WPAN and WLAN
Internet protocol and transmission control protocol
IP role in IoT
WPAN with IP – 6LoWPAN
6LoWPAN topology
6LoWPAN protocol stack
Mesh addressing and routing
Header compression and fragmentation
Neighbor discovery
6LoWPAN security
WPAN with IP – Thread
Thread architecture and topology
Thread protocol stack
Thread routing
Thread addressing
Neighbor discovery
IEEE 802.11 protocols and WLAN
IEEE 802.11 suite of protocols and comparison
IEEE 802.11 architecture
IEEE 802.11 spectrum allocation
IEEE 802.11 modulation and encoding techniques
IEEE 802.11 MIMO
IEEE 802.11 packet structure
IEEE 802.11 operation
IEEE 802.11 security
IEEE 802.11ac
IEEE 802.11p vehicle-to-vehicle
IEEE 802.11ah
Summary
Long-Range Communication Systems and Protocols (WAN)
Cellular connectivity
Governance models and standards
Cellular access technologies
3GPP user equipment categories
4G-LTE spectrum allocation and bands
4G-LTE topology and architecture
4G-LTE E-UTRAN protocol stack
4G-LTE geographical areas, dataflow, and handover procedures
4G-LTE packet structure
Cat 0, Cat 1, Cat M1, and NB-IoT
LTE Cat-0
LTE Cat-1
LTE Cat-M1 (eMTC)
LTE Cat-NB
5G
LoRa and LoRaWAN
LoRa physical layer
LoRaWAN MAC layer
LoRaWAN topology
LoRaWAN summary
Sigfox
Sigfox physical layer
Sigfox MAC layer
Sigfox protocol stack
Sigfox topology
Summary
Routers and Gateways
Routing functions
Gateway functions
Routing
Failover and out-of-band management
VLAN
VPN
Traffic shaping and QoS
Security functions
Metrics and analytics
Edge processing
Software-Defined Networking
SDN architecture
Traditional internetworking
SDN benefits
Summary
IoT Edge to Cloud Protocols
Protocols
MQTT
MQTT publish-subscribe
MQTT architecture details
MQTT packet structure
MQTT communication formats
MQTT working example
MQTT-SN
MQTT-SN architecture and topology
Transparent and aggregating gateways
Gateway advertisement and discovery
Differences between MQTT and MQTT-SN
Constrained Application Protocol
CoAP architecture details
CoAP Messaging Formats
CoAP usage example
Other protocols
STOMP
AMQP
Protocol summary and comparison
Summary
Cloud and Fog Topologies
Cloud services model
NaaS
SaaS
PaaS
IaaS
Public, private, and hybrid cloud
Private cloud
Public cloud
Hybrid cloud
The OpenStack cloud architecture
Keystone – identity and service management
Glance – image service
Nova compute
Swift – Object Storage
Neutron – Networking services
Cinder – Block Storage
Horizon
Heat – orchestration (optional)
Ceilometer – telemetry (optional)
Constraints of cloud architectures for IoT
Latency effect
Fog computing
The Hadoop philosophy for Fog computing
Fog Computing versus Edge Computing versus cloud computing
OpenFog Reference Architecture
Application services
Application support
Node management and software backplane
Hardware virtualization
OpenFog node security
Network
Accelerators
Compute
Storage
Hardware platform infrastructure
Protocol abstraction
Sensors, actuators, and control systems
Amazon Greengrass and Lambda
Fog Topologies
Summary
Data Analytics and Machine Learning in the Cloud and in the Fog
Basic data analytics in IoT
Top-level cloud pipeline
Rules engines
Ingestion – streaming, processing, and data lakes
Complex event processing
Lambda architecture
Sector use cases
Machine learning in IoT
Machine learning models
Classification
Regression
Random forest
Bayesian models
Convolutional Neural Networks
First layer and filters
Max pooling and subsampling
Hidden layers and formal description on forwarding propagation
CNN examples
CNN training and backpropagation
RNN
Training and inference for IoT
IoT data analytics and machine learning comparison and assessment
Summary
IoT Security
Cyber security vernacular
Attack and threat terms
Defense terms
Anatomy of IoT cyber attacks
Mirai
Stuxnet
Chain Reaction
Physical and hardware security
Root of Trust
Key management and trusted platform modules
Processor and memory space
Storage security
Physical security
Cryptography
Symmetric cryptography
Asymmetric cryptography
Cryptographic hash (authentication and signing)
Public Key Infrastructure
Network stack – Transport Layer Security
Software defined perimeter
Software-Defined Perimeter architecture
Blockchains and cryptocurrencies in IoT
Bitcoin (blockchain-based)
IOTA (directed acyclical graph-based)
Government regulations and intervention
US Congressional Bill –Internet of Things (IoT) Cybersecurity Improvement Act of 2017
Other governmental bodies
IoT security best practices
Holistic security
Security checklist
Summary
Consortiums and Communities
PAN consortia
Bluetooth SIG
Thread Group
Zigbee Alliance
Miscellaneous
Protocol consortia
Open Connectivity Foundation and Allseen Alliance
OASIS
Object Management Group
IPSO Alliance
Miscellaneous
WAN consortia
Weightless
LoRa Alliance
Internet Engineering Task Force
Wi-Fi Alliance
Fog and edge consortia
OpenFog
EdgeX Foundry
Umbrella organizations
Industrial Internet Consortium
IEEE IoT
Miscellaneous
US government IoT and security entities
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
You probably experience the Internet of Things on a daily basis in your personal and work life. Much of the public’s impression of IoT is from their personal interaction with a Fitbit fitness tracker, an Amazon Echo assistant, or a Google thermostat.
A 2017 search for the keyword IoT on LinkedIn reveals 7,189 job postings related to IoT. Glassdoor shows 5,440 and http://monster.com/ shows more than a thousand requisitions. The IoT market is booming for talent as well as solutions. As is often the case, technologists will take a path of least resistance to binding what had been an unconnected object to the internet. That approach certainly works, but it is different than the role of an architect. An architect needs to understand the big picture of disparate technologies, scaling factors, security, and energy to build an IoT solution that not only works but provides value to their company, customers, and shareholders.
Many IoT projects fail or are stuck in R&D for two reasons. First, building a robust system is difficult from a security and robustness perspective. Second, often is the case that an IoT solution technically works, but it is not manageable from the perspective of the purchasing IT manager. As we place more things on the internet, we as architects need to consider the enterprise and industrial IT world is a 50-year-old mature industry. Placing an IP address on a lightbulb is certainly possible, but not necessarily manageable from the customer perspective. This book attempts to address the IoT from an enterprise/industrial/commercial perspective rather than a hobbyist perspective.
This book covers IoT from an architectural and holistic point of view from sensor to cloud including all the physical transports and transformation between the two. Because this book is an architectural guide, it attempts to maintain enough depth to teach another architect the constraints and discipline of an underlying system. There are countless books and tutorials on IoT specifics, such as MQTT protocol, cloud design and DevOps, power and battery design, and RF signal analysis. These are all important components for an IoT system, and a qualified architect should be able to span the breadth to design a robust system. However, the architect must understand when to pull up from design details to continue to provide value as an architect.
It isn't expected that a reader come to this book with an inherent knowledge of every engineering domain. This book touches on radio frequency signaling, power and energy, and circuit theory. On the other side of the aisle, the book goes into internet protocol programming and cloud provisioning. Finally, it will dive deep into machine learning applications such as convolutional neural networks. Having all the skills to bring these technologies together is an architects function. This book helps you get to that level, but it doesn't expect you to come with a deep understanding of each science.
What you can do with IoT is incredible as it will usher in the next major revolution in manufacturing, healthcare, government, and enterprise. It will have major impact, yet inevitable, to the world GDP, employment, and markets. It also poses the greatest challenges and risk in security as you will learn.
Of those thousands of jobs listed, many are for IoT architects, technologists, and principals to build IoT solutions rather than widgets. This book will help you learn and apply technologies for those types of projects.
Additionally, it’s fun. Designing a device for monitoring your home lighting or controlling thousands of streetlights in a city from the other side of the globe or on an airplane is a significantly powerful technology, made for techno-junkies but applied by architects.
Who this book is for
This book is aimed at for architects, system designers, technologists, and technology managers who want to understand the IoT ecosphere, various technologies, and trade-offs and develop a 50,000-foot view of IoT architecture.
What this book covers
Chapter 1, The IoT Story, introduces you to the growth, the importance, and the impact of the IoT from a narrative and historical perspective. You will also learn of use cases in various areas including industrial IoT, smart cities, transportation, and healthcare.
Chapter 2, IoT Architecture and Core IoT Modules, presents the overall picture of the combination of technologies covered in this book. Each segment has a purpose and can unknowingly affect each other. This is an important chapter for an architect to under the “big picture” of inter-related technologies. This chapter also explores the ways to place a value on IoT.
Chapter 3, Sensors, Endpoints, and Power Systems, explores billions of edge endpoints and sensor technologies that will be placed on the internet. Fundamentals of sensor designs, architectures, and power systems are taught.
Chapter 4, Communications and Information Theory, will review important material on the dynamics and mathematical models that define communication systems important to IoT. You will understand the theory behind architectural decisions in selecting the proper forms of telecommunication.
Chapter 5, Non-IP-Based WPAN, discusses all the major non-IP-based protocols and technologies at the IoT Edge. This chapter includes a deep review of the new Bluetooth 5 architecture, Zigbee, Z-Wave, and mesh topologies for sensor networks.
Chapter 6, IP-Based WPAN and WLAN, will complete the near-range communication with a treatment of IP-based communication, including 6LoWPAN, Thread, and IEEE 802.11 standards. This chapter also details new 802.11 protocols such as 802.11p for vehicular communication and 802.11ah for IoT.
Chapter 7, Long-Range Communication Systems and Protocols (WAN), covers wide area network and long-range communication transport data from things to the cloud. This chapter covers in detail all the cellular LTE standards, LoRaWAN, Sigfox, as well as new LTE narrowband and 5G architectures.
Chapter 8, Routers and Gateways, discusses the importance of edge routing and gateway functions. This chapter explores routing systems, gateway functions, VPNs, VLANs, and traffic shaping, and it covers software-defined networking.
Chapter 9, IoT Edge to Cloud Protocols, introduces you to the prevalent IoT to cloud protocols, such as MQTT, MQTT-SN, CoAP, AMQP, and STOMP. You will learn how to use them and, importantly, which to use.
Chapter 10, Cloud and Fog Topologies, explores the fundamentals of cloud architectures using OpenStack as a reference. You will learn of cloud constraints and how fog computing (using frameworks such as the OpenFog standard) seeks to solve these problems.
Chapter 11, Data Analytics and Machine Learning in the Cloud and in the Fog, covers the technologies and use cases for analyzing the myriad of IoT data efficiently using tools, such as rules engines, complex event processing, and lambdas. This chapter also explores machine learning applications for IoT data and where it makes sense to use them.
Chapter 12, IoT Security, covers security from a holistic view for every IoT component covered in this book. You will understand the theory and architecture of protocol, hardware, software-defined perimeter, and block-chain security.
Chapter 13, Consortiums and Communities, details the numerous industrial, academic, and government consortiums defining the standards and rules around the Internet of Things.
To get the most out of this book
There are several examples of hardware design and coding examples in this book. Most of the coding examples are pseudo-code based on Python syntax. Working examples are also based on Python 3.4.3 that is usable on Mac OS X, Linux, and Microsoft. In areas (such as Chapter 9, IoT Edge to Cloud Protocols, libraries such as MQTT (such as Paho) are freely available for use in Python.
Having familiarity with some foundational calculus, information theory, electrical properties, and computer science can only help us gain a deeper insight into IoT from an architectural perspective.
Some examples show scripting within Chapter 10, Cloud and Fog Topologies, use OpenStack or Amazon AWS/Greengrass. In those cases, acquiring a cloud account is helpful but not strictly needed to understand the architectural goals.
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it from https://www.packtpub.com/sites/default/files/downloads/InternetofThingsforArchitects_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The insert operation places a modification in the working memory."
A block of code is set as follows:
rule "Furnace_On"whenSmoke_Sensor(value > 0) && Heat_Sensor(value > 0)theninsert(Furnace_On())end
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
rule "Furnace_On"when
Smoke_Sensor(value > 0) && Heat_Sensor(value > 0)
theninsert(Furnace_On())end
Any command-line input or output is written as follows:
aws greengrass create-function-definition --name "sensorDefinition"
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Internet Key Exchange (IKE) is the security protocol in IPsec."
Get in touch
Feedback from our readers is always welcome.
General feedback: Email [email protected] and mention the book title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].
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The IoT Story
You wake up Tuesday, May 17, 2022, around 6:30 AM PST, as you always do. You never really needed an alarm clock, you are one of those types with some form of physiological clock. Immediately after, your eyes open to a fantastic sunny morning as it's approaching 70° C outside. You will take part in a day that will be completely different than the morning of Wednesday, May 17, 2017. Everything about your day, your lifestyle, your health, your finances, your work, your commute, even your parking spot will be different. Everything about the world you live in will be different: energy, healthcare, farming, manufacturing, logistics, mass transit, environment, security, shopping, and even clothing. This is the impact of connecting ordinary objects to the Internet, or the Internet of Things (IoT). I think a better analogy is the Internet of Everything.
Before you even awakened, a lot has happened in the IoT that surrounds you. Your sleep behavior has been monitored by a sleep sensor or smart pillow. Data was sent to an IoT gateway and then streamed to a cloud service you use for free that reports to a dashboard on your phone. You don't need an alarm clock, but if you had another 5 A.M. flight you would set it—again, controlled by a cloud agent using if this, then that (IFTTT) protocol. Your dual zone furnace is connected to a different cloud provider and is on your home 802.11 Wi-Fi, as are your smoke alarms, doorbell, irrigation systems, garage door, surveillance cameras, and security system. Your dog is chipped with a proximity sensor using an energy harvesting source that lets him open the doggy door and tell you where he is.
You don't really have a PC anymore. You certainly have a tablet-style computer and a smartphone as your central creation device, but your world is based on using VR/AR Goggles since the screen is so much better and larger. You do have a fog computing gateway in your closet. It's connected to a 5G service provider to get you on the Internet and WAN because wired connections don't work for your lifestyle—you are mobile, connected, and online no matter where you are, and 5G and your favorite carrier make sure your experience is great in a hotel room in Miami or your home in Boise, Idaho. The gateway also performs a lot of actions in your home for you, such as processing video streams from those webcams to detect if there's been a fall or an accident in the house. The security system is being scanned for anomalies (strange noises, possible water leaks, lights being left on, your dog chewing on the furniture again). The edge node also acts as your home hub, backing up your phone daily because you have a tendency to break them, and serves as your private cloud even though you know nothing about cloud services.
You ride your bike to the office. Your bike jersey uses printable sensors, and monitors your heart rate and temperature. That data is streamed over Bluetooth Low Energy to your smartphone simultaneously while you listen to Bluetooth audio streamed from your phone to your Bluetooth earphones. On the way there, you pass several billboards all displaying video and real-time ads. You stop at your local coffee shop and there is a digital signage display out front calling you out by name and asking if you want the last thing you ordered yesterday: a 12 oz Americano with room for cream. It did this by a beacon and gateway recognizing your presence within 5 feet and approaching the display. You select yes, of course. Most people arrive at work via their car and are directed to the optimal parking space via smart sensors in each parking slot. You, of course, get the optimal parking space right out front with the rest of the cyclists.
Your office is part of a green energy program. Corporate mandated policies on a zero-emission office space. Each room has proximity sensors to detect not only if a room is occupied, but who is in the room. Your name badge to get in the office is a beaconing device on a 10-year battery. Your presence is known once you enter the door. Lights, HVAC, automated shades, ceiling fans, even digital signage is connected. A central fog node monitors all the building information and syncs it to a cloud host. A rules engine has been implemented to make real-time decisions based on occupancy, time of day, and the season of the year, as well as inside and outside temperatures. Environmental conditions are ramped up or down to maximize energy utilization. There are even sensors on the main breakers listening to the patterns of energy and making a decision on the fog nodes if there are strange patterns of energy usage that need examination.
It does all this with several real-time streaming edge analytics and machine learning algorithms that have been trained on the cloud and pushed to the edge. The office hosts a 5G small cell to communicate externally to the upstream carrier, but they also host a number of small-cell gateways internally to focus signals within the confines of the building. The internal 5G acts as a LAN as well.
Your phone and tablet have switched to the internal 5G signal, and you switch on your software-defined network overlay and are instantly on the corporate LAN. Your smartphone does a lot of work for you; it is essentially your personal gateway to your own personal area network surrounding your body. You drop into your first meeting today, but your co-worker isn't there and arrives a few minutes late. He apologizes, but explains his drive to work was eventful. His newer car informed the manufacturer of a pattern of anomalies in the compressor and turbocharger. The manufacturer was immediately informed of this and called the owner to inform him that the vehicle has a 70 percent chance of having a failed turbo within two days of his typical commute. They scheduled an appointment with the dealership, and have the new parts arriving to fix the compressor. This saved him considerable cost in replacing the turbo and a lot of aggravation.
For lunch, the team decides to go out to a new fish taco place downtown. A group of four of you manage your way into a coupe more comfortable for two than four and make your way. Unfortunately, you'll have to park in one of the more expensive parking structures. Parking rates are dynamic and follow a supply and demand basis. Because of some events and how full the lots are, the rates doubled even for mid-day Tuesday. On the bright side, the same systems raising the parking fees also inform your car and smartphone exactly which lots and which space to drive to. You punch in the fish taco address, the lot and capacity pop up, and you reserve a spot before you arrive. The car approaches the gate, which identifies your phone signature and opens up. You drive to the spot and the application registers with the parking cloud that you are in the right spot over the correct sensor.
That afternoon, you need to go to the manufacturing site on the other side of town. It's a typical factory environment: several injection molding machines, pick-and-place devices, packaging machines, and all the supporting infrastructure. Recently, the quality of the product has been slipping. The final product has joint connection problems and is cosmetically inferior to last month's lot. After arriving at the site, you talk to the manager and inspect the site. Everything appears normal, but the quality certainly has been marginalized. The two of you meet and bring up the dashboards of the factory floor.
The system uses a number of sensors (vibration, temperature, speed, vision, and tracking beacons) to monitor the floor. The data is accumulated and visualized in real time. There are a number of predictive maintenance algorithms watching the various devices for signs of wear and error. That information is streamed to the equipment manufacturer and your team as well. The logs and trend analysis didn't pick up any abnormal behavior, and had been trained by your best experts. This looks like the type of problem that would turn hours into weeks and force the best and brightest in your organization to attend expensive daily SWOT team meetings. However, you have a lot of data. All the data from the factory floor is preserved in a long-term storage database. There was a cost to that service, and at first it was difficult to justify, but you think it may have paid for itself a thousandfold. Taking all that historical data through a complex event processor and analytics package, you quickly develop a set of rules that model the quality of your failing parts. Working backward to the events that led to the failures, you realize it is not a point failure, but several aspects:
The internal temperature of the working space rose 2° C to conserve energy for the summer months
The assembly slowed down output by 1.5 percent of due to supply issues
One of the molding machines was nearing a predictive maintenance period and the temperature and assembly speed pushed its failing case over the predicted value
You found the issue, and retrained the predictive maintenance models with the new parameters to catch this case in the future. Overall, not a bad day at work.
While this fictional case may or may not be true, it's pretty close to reality today. The IoT is defined by Wikipedia: https://en.wikipedia.org/wiki/Internet_of_things as The Internet of things (IoT) is the inter-networking of physical devices, vehicles (also referred to as "connected devices" and "smart devices"), buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data.
History of the IoT
The term IoT can most likely be attributed to Kevin Ashton in 1997 with his work at Proctor and Gamble using RFID tags to manage supply chains. The work brought him to MIT in 1999 where he and a group of like-minded individuals started the Auto-ID center research consortium (for more information, visit http://www.smithsonianmag.com/innovation/kevin-ashton-describes-the-internet-of-things-180953749/). Since then, IoT has taken off from simple RFID tags to an ecosystem and industry that by 2020 will cannibalize, create, or displace five trillion out of one hundred trillion global GDP dollars, or 6% of the world GDP. The concept of things being connected to the Internet up through 2012 was primarily connected smartphones, tablets, PCs, and laptops. Essentially, things that first functioned in all respects as a computer. Since the humble beginnings of the Internet starting with ARPANET in 1969, most of the technologies surrounding the IoT didn't exist. Up to the year 2000, most devices that were associated with the Internet were, as stated, computers of various sizes. The following timeline shows the slow progress in connecting things to the Internet:
Year
Device
Reference
1973
Mario W. Cardullo receives the patent for first RFID tag
US Patent US 3713148 A
1982
Carnegie Mellon internet-connected soda machine
https://www.cs.cmu.edu/~coke/history_long.txt
1989
Internet-connected toaster at Interop '89
IEEE Consumer Electronics Magazine (Volume: 6, Issue: 1, Jan. 2017)
1991
HP introduces HP LaserJet IIISi: first Ethernet-connected network printer
http://hpmuseum.net/display_item.php?hw=350
1993
Internet-connected coffee pot at University of Cambridge (first internet-connected camera)
https://www.cl.cam.ac.uk/coffee/qsf/coffee.html
1996
General Motors OnStar (2001 remote diagnostics)
https://en.wikipedia.org/wiki/OnStar
1998
Bluetooth SIG formed
https://www.bluetooth.com/about-us/our-history
1999
LG Internet Digital DIOS refrigerator
https://www.telecompaper.com/news/lg-unveils-internetready-refrigerator--221266
2000
First instances of Cooltown concept of pervasive computing everywhere: HP Labs, a system of computing and communication technologies that, combined, create a web-connected experience for people, places, and objects
https://www.youtube.com/watch?v=U2AkkuIVV-I
2001
First Bluetooth product launched: KDDI Bluetooth-enabled mobile phone
http://edition.cnn.com/2001/BUSINESS/asia/04/17/tokyo.kddibluetooth/index.html
2005
United Nation's International Telecommunications Union report predicting the rise of IoT for the first time
http://www.itu.int/osg/spu/publications/internetofthings/InternetofThings_summary.pdf
2008
IPSO Alliance formed to promote IP on objects, first IoT-focused alliance
https://www.ipso-alliance.org
2010
The concept of Smart Lighting formed after success in developing solid-state LED light bulbs
https://www.bu.edu/smartlighting/files/2010/01/BobK.pdf
2014
Apple creates iBeacon protocol for beacons
https://support.apple.com/en-us/HT202880
Certainly, the term IoT has generated a lot of interest and hype. One can easily see that from a buzzword standpoint, the number of patents issued (https://www.uspto.gov) has grown exponentially since 2010. The number of Google searches (https://trends.google.com/trends/) and IEEE peer-reviewed paper publications hit the knee of the curve in 2013:
IoT potential
The IoT will touch nearly every segment in industrial, enterprise, health, and consumer products. It is important to understand the impact, as well as why these disparate industries will be forced to change in the way they build products and provide services. Perhaps your role as an architect forces you to focus on one particular segment; however, it is helpful to understand the overlap with other use cases.
As previously mentioned, there is an opinion that the impact of IoT-related industries, services, and trade will affect three percent (The route to a trillion devices, ARM Ltd 2017: https://community.arm.com/cfs-file/__key/telligent-evolution-components-attachments/01-1996-00-00-00-01-30-09/ARM-_2D00_-The-route-to-a-trillion-devices-_2D00_-June-2017.pdf) to four percent (The Internet of Things: Mapping Value Beyond the Hype, McKinsey and Company 2015: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/The%20Internet%20of%20Things%20The%20value%20of%20digitizing%20the%20physical%20world/Unlocking_the_potential_of_the_Internet_of_Things_Executive_summary.ashx) of global GDP by 2020 (extrapolated). Global GDP for 2016 was $75.64 trillion dollars, with an estimate that by 2020 it will rise to $81.5 trillion. That provides a range of value from IoT solutions of $2.4 trillion to about $4.9 trillion.
The scale of connected objects is unprecedented. Speculation of industry growth is imperiled with risks. To help normalize the impact, we look at several research firms and reports on the number of connected objects by the year 2020. The range is large, but still in the same order of magnitude. The average of these 10 analyst forecasts is about 33.4 billion connected things by 2020-2021. ARM recently conducted a study and forecast that by 2035 one trillion connected devices will be operational. By all accounts, the projects growth rate in the near term is about 20 percent year over year.
These numbers should impress the reader. For example, if we took a very conservative stance and predict that only 20 billion newly connected devices will be deployed (excluding the traditional computing and mobile products), we would be saying that 211 new Internet connected objects will come online every second.
Why this is of significance to the technology industry and IT sector is the fact that world population currently has a growth rate of roughly 0.9 percent to 1.09 percent per year (https://esa.un.org/unpd/wpp/). World population growth rate peaked in 1962 at 2.6 percent year over year, and has steadily been declining due to a number of factors. First and foremost, improvement in world GDP and economies has a propensity to reduce birth rates. Other factors include wars and famine. That growth implies that human-connected objects will plateau and machine to machine and connected objects will be represent the majority of devices connected to the internet. This is important because the IT industry applies value to a network not necessarily by how much data is consumed, but by how many connections there are. This, generally speaking, is Metcalfe's law, and we will talk about that later in this book. It is also worth noting that after the first public website went live at CERN in 1990, it took 15 years to reach 1 billion people on Earth over the Internet. IoT is looking to add 6 billion connected devices per year. This, of course, is swaying the industry:
It should be noted that economic impact is not solely revenue generation. The impact from IoT or any technology comes in the form of:
New revenue streams (green energy solutions)
Reducing costs (in-home patient healthcare)
Reducing time to market (factory automation)
Improving supply chain logistics (asset tracking)
Reducing production loss (theft, spoilage of perishable)
Increasing productivity (machine learning and data analytics)
Cannibalization (Nest replacing traditional thermostats)
In our discussion throughout this book, it should be at the top of our minds as to what value an IoT solution delivers. If it is simply a new gadget, there will be a limited market scope. Only when the foreseeable benefit outweighs the cost will an industry thrive. Generally speaking, the target used should be a 5x improvement over a traditional technology. That has been my goal in the IT industry. When considering the cost of change, training, acquisition, support, and so on, a 5x differential is a fair rule of thumb.
We now detail the sectors of industry and how IoT will affect them.
Industrial and manufacturing
Industrial IoT (IIoT) is one of the fastest and largest segments in the overall IoT space by the number of connected things and the value those services bring to manufacturing and factory automation. This segment has traditionally been the world of operations technology (OT). This involves hardware and software tools to monitor physical devices. Traditional information technology roles have been administered differently than OT roles. OT will be concerned with yield metrics, uptime, real-time data collection and response, and systems safety. The IT role will concentrate on security, groupings, data delivery, and services. As the IoT becomes prevalent in industry and manufacturing, these worlds will combine especially with predictive maintenance from thousands of factory and production machines to deliver an unprecedented amount of data to private and public cloud infrastructure.
Some of the characteristics of this segment include the need to provide near real-time or at real-time decisions for OT. This means latency is a major issue for IoT on a factory floor. Additionally, downtime and security are a top concern. This implies the need for redundancy, and possibly private cloud networks and data storage. The industrial segment is one of the fastest-growing markets. One nuance of this industry is the reliance of brownfield technology, meaning hardware and software interfaces that are not mainstream. It is often the case that 30-year-old production machines rely on RS485 serial interfaces rather than modern wireless mesh fabrics.
Industrial and manufacturing IoT use cases and impact
Following are the industrial and manufacturing IoT use cases and their impact:
Preventative maintenance on new and pre-existing factory machinery
Throughput increase through real-time demand
Energy savings
Safety systems such as thermal sensing, pressure sensing, and gas leaks
Factory floor expert systems
Consumer
Consumer-based devices were one of the first segments to adopt things being connected on the internet. Consumer IoT came into form as a connected coffee pot at a university in the 1990s. It flourished with the adoption of Bluetooth for consumer use in the early 2000s. Now millions of homes that have Nest thermostats, Hue lightbulbs, Alexa assistants, and Roku set-top boxes. People too are connected with Fitbits and other wearable technology. The consumer market is usually first to adopt these new technologies. We can also think of these as gadgets. All are neatly packaged and wrapped devices that are essentially plug and play.
One of the constraints in the consumer market is the bifurcation of standards. We see, for example, several WPAN protocols have a footing like Bluetooth, Zigbee, and Z-wave (all being non-interoperable).
This segment also has common traits in the healthcare market, with wearable devices and home health monitors. We keep them separate for this discussion, and healthcare will grow beyond simple connected home health devices (for example, beyond the functionality of a Fitbit).
Consumer IoT use cases
The following are some of the consumer IoT use cases:
Smart home gadgetry
: Smart irrigation, smart garage doors, smart locks, smart lights, smart thermostats, and smart security.
Wearables
: Health and movement trackers, smart clothing/wearables.
Pets
: Pet location systems, smart dog doors.
Retail, financial, and marketing
This category refers to any space where consumer-based commerce transacts. This can be a brick and mortar store or a pop-up kiosk. Additionally, this category refers to why we include financial institutions and marketing fields in this area. These include traditional banking services and insurers, but also leisure and hospitality services. Retail IoT impact is already in process, with the goal of lowering sales costs and improving customer experience. This is done with a myriad of IoT tools. For simplicity in this book, we also add advertising and marketing to this category.
This segment measures value in immediate financial transactions. If the IoT solution is not providing that response, its investment must be scrutinized. This drives constraints on finding new ways to either save costs, or drive revenue. Allowing customers to be more efficient allows retailers and service industries to move customers quickly, and to do so with less staffing resources.
Retail IoT use cases
Some of the retail IoT use cases are as follows:
Targeted advertising, such as locating known or potential customers by proximity and providing sales information.
Beaconing, such as proximity sensing customers, traffic patterns, and inter-arrival times as marketing analytics.
Asset tracking, such as inventory control, loss control, and supply chain optimizations.
Cold storage monitoring, such as analyze cold storage of perishable inventory. Apply predictive analytics to food supply.
Insurance tracking of assets.
Insurance risk measurement of drivers.
Digital signage within retail, hospitality, or citywide.
Beaconing systems within entertainment venues, conferences, concerts, amusement parks, and museums.
Healthcare
The healthcare industry will contend with industrial and logistics for the top spot in revenue and impact on IoT. Any and all systems that improve the quality of life and reduce health costs is a top concern in nearly every developed country. The IoT is poised to allow for remote and flexible monitoring of patients wherever they may be. Advanced analytics and machine learning tools will observe patients in order to diagnose illness and prescribe treatments. Such systems will also be the watchdogs in the event of needed life-critical care. Currently, there are about 500 million wearable health monitors, with double-digit growth in the years to come.
The constraints on healthcare systems are significant. From HIPAA compliance to the security of data, IoT systems need to act like hospital quality tools and equipment. Field systems need to communicate with healthcare centers 24/7, reliably and with zero downtime if the patient is being monitored at home. Systems may need to be on a hospital network while monitoring a patient in an emergency vehicle.
Healthcare IoT use cases
Some of the healthcare IoT use cases are as follows:
In-home patient care
Learning models of predictive and preventative healthcare
Dementia and elderly care and tracking
Hospital equipment and supply asset tracking
Pharmaceutical tracking and security
Remote field medicine
Drug research
Patient fall indicators
Transportation and logistics
Transportation and logistics will be significant, if not the leading driver in IoT. The use cases involve tracking the asset on devices being delivered, transported, or shipped, whether that's on a truck, train, plane, or boat. This is also the area of connected vehicles that communicate to offer assistance to the driver, or preventative maintenance on behalf of the driver. Right now, an average vehicle purchased new off a lot will have about 100 sensors. That number will double as vehicle-to-vehicle communication, vehicle-to-road communication, and automated driving become must-have features for safety or comfort. This has important roles beyond consumer vehicles, and extends to rail lines and shipping fleets that cannot absorb any downtime. We will also see service trucks that can track assets within a service vehicle. Some of the use cases can be very simple, but also very costly, such as monitoring the location of service vehicles in the delivery of stock. Systems are needed to automatically route trucks and service personnel to locations based on demand versus routine.
This mobile-type category has the requirement of geolocation awareness. Much of this comes from GPS navigation. From an IoT perspective, the data analyzed would include assets and time, but also spatial coordinates.
Transportation and logistics IoT use cases
Following are some of the transportation and logistics IoT use cases:
Fleet tracking and location awareness
Railcar identification and tracking
Asset and package tracking within fleets
Preventative maintenance of vehicles on the road
Agricultural and environmental
Farming and environmental IoT includes elements of livestock health, land and soil analysis, micro-climate predictions, efficient water usage, and even disaster predictions in the case of geological and weather-related disasters. Even as the world population growth slows down, world economies are becoming more affluent. Hunger and starvation crises are rare. That said, the demand for food production is set to double by 2035. Significant efficiencies in agriculture can be achieved through IoT. Using smart lighting to adjust the spectrum frequency based on poultry age can increase growth rates and decrease mortality rates based on stress on chicken farms. Additionally, smart lighting systems could save $1 billion annually on energy versus the common dumb incandescent lighting currently used. Other uses include detecting livestock health based on sensor movement and positioning. A cattle farm could find animals with the propensity of sickness before a bacterial or viral infection were to spread. Edge analysis systems could find, locate, and isolate heads of cattle in real time, using data analytics or machine learning approaches.
This segment also has the distinction of being in remote areas (volcanoes) or sparse population centers (corn field). This has impacts on data communication systems that we will need to consider in later Chapter 5, Non-IP Based WPAN and Chapter 7, Long-Range Communication Systems and Protocols (WAN).
Agricultural and environmental IoT use cases
Some of the agricultural and environmental IoT use cases are as follows:
Smart irrigation and fertilization techniques to improve yield
Smart lighting in nesting or poultry farming to improve yield
Livestock health and asset tracking
Preventative maintenance on remote farming equipment via manufacturer
Drones-based land surveys
Farm-to-market supply chain efficiencies with asset tracking
Robotic farming
Volcanic and fault line monitoring for predictive disasters
Energy
The energy segment includes the monitoring of energy production at source to and through the usage energy at the client. A significant amount of research and development has focused on consumer and commercial energy monitors such as smart electric meters that communicate over low-power and long-range protocols to reveal real-time energy usage.
Many energy production facilities are in remote or hostile environments such as desert regions for solar arrays, steep hillsides for wind farms, and hazardous facilities for nuclear reactors. Additionally, data may need real-time or near real-time response for critical response to energy production control systems (much like manufacturing systems). This can impact how an IoT system is deployed in this category. We will talk about issues of real-time responsiveness later in this book.
Energy IoT use cases
The following are some of the use cases for energy IoT:
Oil rig analysis of thousands of sensors and data points for efficiency gains
Remote solar panel monitoring and maintenance
Hazardous analysis of nuclear facilities
Smart electric meters in a citywide deployment to monitor energy usage and demand
Real-time blade adjustments as a function of weather on remote wind turbines
Smart city
Smart city is a phrase used to imply connecting intelligence to what had been an unconnected world. Smart cities are one of the fastest growing segments, and show substantial cost/benefit ratios especially when we consider tax revenues. Smart cities also touch citizens' lives through safety, security, and ease of use. For example, several cities such as Barcelona are fully connected and monitor trash containers and bins for pickup based on the current capacity, but also the time since the last pickup. This improves the trash collection efficiency allowing the city to use fewer resources and tax revenue in transporting waste, but also eliminates potential smells and odors of rotting organic material. Smart cities are also impacted by government mandates and regulations (as we will explore later), therefore there are ties to the government segment.
One of the characteristics of smart city deployment may be the number of sensors used. For example, a smart camera installation on each street corner in New York would require over 3,000 cameras. In other cases, a city such as Barcelona will deploy nearly one million environmental sensors to monitor electric usage, temperature, ambient conditions, air quality, noise levels, and parking spaces. These all have low bandwidth needs versus a streaming video camera, but the aggregate amount of data transmitted will be nearly the same as the surveillance cameras in New York. These characteristics of quantity and bandwidth need to be considered in building the correct IoT architecture.
Smart city IoT use cases
Some of the smart city IoT use cases are as follows:
Pollution control and regulatory analysis through environmental sensing
Microclimate weather predictions using citywide sensor networks
Efficiency gains and improved costs through waste management service on demand
Improved traffic flow and fuel economy through smart traffic light control and patterning
Energy efficiency of city lighting on demand
Smart snow plowing based on real-time road demand, weather conditions, and nearby plows
Smart irrigation of parks and public spaces, depending on weather and current usage
Smart cameras to watch for crime and real-time automated AMBER Alerts
Smart parking lots to automatically find best space parking on demand
Bridge, street, and infrastructure wear and usage monitors to improve longevity and service
Government and military
City, state, and federal governments, as well as the military, have a keen interesting in IoT deployments. Take California's executive order B-30-15 (https://www.gov.ca.gov/news.php?id=18938), which states that by 2030 greenhouse gas emissions affecting global warming will be at levels 40 percent below 1990 levels. To achieve aggressive targets like this, environmental monitors, energy sensing systems, and machine intelligence will need to come into play to alter energy patterns on demand while still keeping the California economy breathing. Other cases include projects like the Internet Battlefield of Things, with the intent of providing efficiencies for friendly, personal, and counter-attacks on enemies. This segment also ties into the smart city category when we consider the monitoring of government infrastructures like highways and bridges.
The government's role in the IoT also comes into play in the form of standardization, frequency spectrum allocation, and regulations. Take, for example, how the frequency space is divided, secured, and portioned to various providers. We will see throughout this text how certain technologies came to be through federal control.
Government and military IoT use cases
Following are some of the government and military IoT use cases:
Terror threat analysis through IoT device pattern analysis and beacons
Swarm sensors through drones
Sensor bombs deployed on the battlefield to form sensor networks to monitor threats
Government asset tracking systems
Real-time military personal tracking and location services
Synthetic sensors to monitor hostile environments
Water level monitoring to measure dam and flood containment
Summary
Welcome to the world of the IoT. As an architect in this new field, we have to understand what the customer is building, and what the use cases require. IoT systems are not a fire-and-forget type of design. A customer expects several things from jumping on the IoT train.
First, there must be a positive reward. That is dependent on your business, and your customer's intent. From my experience, a 5x gain is the target and has worked well for the introduction of new technologies to pre-existing industries. Second, IoT design is, by nature, a plurality of devices. The value of IoT is not a single device or a single location broadcasting data to a server. It's a set of things broadcasting information and understanding the value the information in aggregate is trying to tell you. Whatever is designed must scale or will scale, therefore that needs attention in upfront design.
We now start exploring the topology of an IoT system as a whole then break down individual components throughout the rest of the book.
Remember, data is the new oil.
IoT Architecture and Core IoT Modules
The IoT ecosphere starts with the simplest of sensors located in the remotest corners of the earth, and translates analog physical effects into digital signals (the language of the internet). Data then takes a complex journey through wired and wireless signals, various protocols, natural interference, and electromagnetic collisions, before arriving in the ether of the internet. From there, packetized data will traverse various channels arriving at a cloud or large data center. The strength of IoT is not just one signal from one sensor, but the aggregate of hundreds, thousands, potentially millions of sensors, events, and devices.
This chapter starts with a definition of IoT versus machine-to-machine architectures. It also addresses the architect's role in building a scalable, secure, and enterprise IoT architecture. To do that, an architect must be able to speak to the value the design brings to a customer. The architect must also play multiple engineering and product roles in balancing different design choices.
This book will cover everything from the transformation of physical to digital sensing, power systems and energy storage, to manage billions of devices to near meter, near kilometer, and extreme range communication systems and protocols, to network and information theory, to internet protocols for the IoT, to the role of edge routing and gateways. The book then turns to the application of data through cloud and fog computing, as well as advanced machine learning and complex event processing. The final content investigates security and the vulnerability of the largest attack surface on earth.
IoT ecosystem
These industries will rely on the hardware, software, and services provided by the bulk of the IT industry. Nearly every major technology company is investing or has invested heavily in IoT space. New markets and technologies have already formed (and some have collapsed or been acquired). Throughout this book, we will touch on nearly every segment in information technology, as they all have a role in IoT:
Sensors
: Embedded systems, real-time operating systems, energy-harvesting sources,
Micro-Electro-Mechanical Systems
(
MEMs
).
Sensor communication systems
: Wireless personal area networks reach from 0 cm to 100 m. Low-speed and low-power communication channels, often non-IP based have a place in sensor communication.
Local area networks
: Typically, IP-based communication systems such as 802.11 Wi-Fi used for fast radio communication, often in peer-to-peer or star topologies.
Aggregators, routers, gateways
: Embedded systems providers, cheapest vendors (processors, DRAM, and storage), module vendors, passive component manufacturers, thin client manufacturers, cellular and wireless radio manufacturers, middleware providers, fog framework providers, edge analytics packages, edge security providers, certificate management systems.
WAN
: Cellular network providers, satellite network providers,
Low-Power Wide-Area Network
(
LPWAN
) providers. Typically using internet transport protocols targeted for IoT and constrained devices like MQTT, CoAP, and even HTTP.
Cloud
: Infrastructure as a service provider, platform as a service provider, database manufacturers, streaming and batch processing manufacturers, data analytics packages, software as a service provider,
data lake
providers, Software-Defined Networking/Software-Defined Perimeter providers, and machine learning services.
Data analytics
: As the information propagates to the cloud en-mass. Dealing with volumes data and extracting value is the job of complex event processing, data analytics, and machine learning techniques.
Security
