IoT and Edge Computing for Architects E-Book

Perry Lea

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 867

Veröffentlichungsjahr: 2020

Beschreibung

Learn to design, implement, and secure your IoT infrastructure. Revised and expanded for edge computing.

Key Features

• Build a complete IoT system that's the best fit for your organization
  
  


• Learn about different concepts, tech, and trade-offs in the IoT architectural stack
  
  


• Understand the theory and implementation of each element that comprises IoT design

Book Description

Industries are embracing IoT technologies to improve operational expenses, product life, and people's well-being. An architectural guide is needed 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 IoT devices.

IoT and Edge Computing for Architects, Second Edition encompasses the entire spectrum of IoT solutions, from IoT sensors to the cloud. It examines modern sensor systems, focusing on their power and functionality. It also looks at communication theory, paying close attention to near-range PAN, including the new Bluetooth® 5.0 specification and mesh networks. Then, the book explores IP-based communication in LAN and WAN, including 802.11ah, 5G LTE cellular, Sigfox, and LoRaWAN. It also explains edge computing, routing and gateways, and their role in fog computing, as well as the messaging protocols of MQTT 5.0 and CoAP.

With the data now in internet form, you'll get an understanding of cloud and fog architectures, including the OpenFog standards. The book wraps up the analytics portion with the application of statistical analysis, complex event processing, and deep learning models. The book then concludes by providing a holistic view of IoT security, cryptography, and shell security in addition to software-defined perimeters and blockchains.

What you will learn

• Understand the role and scope of architecting a successful IoT deployment
  
  


• Scan the landscape of IoT technologies, from sensors to the cloud and more
  
  


• See the trade-offs in choices of protocols and communications in IoT deployments
  
  


• Become familiar with the terminology needed to work in the IoT space
  
  


• Broaden your skills in the multiple engineering domains necessary for the IoT architect
  
  


• Implement best practices to ensure reliability, scalability, and security in your IoT infrastructure

Who this book is for

This book is for architects, system designers, technologists, and technology managers who want to understand the IoT ecosphere, technologies, and trade-offs, and develop a 50,000-foot view of IoT architecture. An understanding of the architectural side of IoT is necessary.

Leseprobe

IoT and Edge Computing for Architects

Second Edition

Implementing edge and IoT systems from sensors to clouds with communication systems, analytics, and security

Perry Lea

BIRMINGHAM - MUMBAI

IoT and Edge Computing for Architects

Second Edition

Copyright © 2020 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Producer: Tushar Gupta

Acquisition Editor – Peer Reviews: Suresh Jain

Content Development Editor: Chris Nelson

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Project Editor: Janice Gonsalves

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Presentation Designer: Pranit Padwal

First published: January 2018

Second edition: March 2020

Production reference: 1040320

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Contributors

About the author

Perry Lea is a 30-year veteran technologist. He spent over 20 years at Hewlett-Packard as a chief architect and distinguished technologist of the LaserJet business. He then led a team at Micron as a technologist and strategic director, working on emerging compute using in-memory processing for machine learning and computer vision. Perry's leadership extended to Cradlepoint, where he pivoted the company into 5G and the Internet of Things (IoT). Soon afterwards, he co-founded Rumble, an industry leader in edge/IoT products. He was also a principal architect for Microsoft's Xbox and xCloud, working on emerging technologies and hyper-scale game streaming.

Perry has degrees in computer science and computer engineering, and a D.Engr in electrical engineering from Columbia University. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a senior member/distinguished speaker of the Association for Computing Machinery (ACM). He holds 40 patents, with 30 pending.

Thanks to my wife, Dawn, and family and my friends for being the support team I needed to complete this book. Thanks to my two dogs, Fen and Cardhu, for giving up numerous walks. Andrew: "Look out!"

About the reviewers

Gérald Santucci, who has a PhD in economics (Disequilibrium Theory and Economic Regulation), is an internationally recognized expert on the Internet of Things (IoT), artificial intelligence, and enterprise systems, with a specific interest in ethics, privacy, and data for policy.

He has worked in the European Commission Directorate General for Communications Networks, Content, and Technology (DG_CNECT, formerly DG_XIII and then DG_INFSO), first as an expert in the RACE programme (R&D in Advanced Communications Technologies for Europe, 1986-1988), and then as an official (1988-2017).

From July 1, 2016 to June 30, 2017, Gérald performed the function of adviser for cross-cutting policy/research issues, with a focus on knowledge management, the integration of research/innovation, regulation and policy, and the use of big data and data analytics for informing policy.

He was head of the "Knowledge Sharing" unit (2012-2016) and head of the "Networked Enterprise and Radio Frequency Identification (RFID)" unit (2006-2012). Between 2010-2012, Gérald chaired the IoT Expert Group, which was composed of about 40 members from government, industry, and civil society, and held a leadership role in worldwide discussions on IoT identification, architectures, privacy and security, ethics, standards, and governance.

Gérald previously gained extensive experience in the activities of the European Commission through his involvement as the head of unit for a wide range of research and innovation topics, including "eGovernment" (1999-2002), "Trust and Security" (2003), and "eBusiness" (2004-2006).

He has often been regarded as the father of the AIM programme (Advanced Informatics in Medicine).

I would like to gratefully acknowledge and thank Janice Gonsalves, the project editor for this book. Her trust, patience, support, and critical comments helped me throughout.

Paul Deng is a senior software engineer with 10 years of experience in the architecture and development of Internet of Things (IoT) applications. He has in-depth knowledge of building a fault-tolerant, secure, scalable, and efficient IoT communication system. Paul works for Agersens in the development of the eShephered Livestock Management System. Visit him at https://dengpeng.de.

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

IoT and Edge Computing Definition and Use Cases

History of the IoT

IoT potential

Definition of the Internet of Things

Industry and manufacturing

Industrial and manufacturing IoT use cases

Consumer

Consumer IoT use cases

Retail, finance, and marketing

Retail, finance, and marketing IoT use cases

Healthcare

Healthcare IoT use cases

Transportation and logistics

Transportation and logistics IoT use cases

Agricultural and environment

Agricultural and environmental IoT use cases

Energy

Energy IoT use cases

Smart city

Smart city IoT use cases

Military and government

Government and military IoT use cases

Example use case and deployment

Case study – Telemedicine palliative care

Requirements

Implementation

Use case retrospective

Summary

IoT Architecture and Core IoT Modules

A connected ecosystem

IoT versus machine-to-machine versus SCADA

The value of a network and Metcalfe's and Beckstrom's laws

IoT and edge architecture

Role of an architect

Part 1 – Sensing and power

Part 2 – Data communication

Part 3 – Edge computing

Part 4 – 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

High performance IoT endpoints

Vision systems

Sensor fusion

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

802.15 standards

Bluetooth

Bluetooth history

Bluetooth 5 communication process and topologies

Bluetooth 5 stack

Bluetooth stack elements

Bluetooth 5 PHY and interference

BR/EDR operation

BLE roles

BLE operation

Bluetooth profiles

BR/EDR security

BLE security

Beaconing

Bluetooth 5 range and speed enhancement

Bluetooth mesh

Bluetooth mesh

Bluetooth mesh topology

Bluetooth mesh addressing modes

Bluetooth mesh provisioning

Bluetooth 5.1 technology

Bluetooth 5.1 direction finding

Bluetooth 5.1 GATT caching

Bluetooth 5.1 randomized advertising channel indexing

Bluetooth 5.1 periodic advertising sync transfer

Bluetooth 5.1 minor enhancements

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

TCP/IP

WPAN with IP – 6LoWPAN

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

6LoWPAN topologies

6LoWPAN protocol stack

Mesh addressing and routing

Header compression and fragmentation

Neighbor discovery

6LoWPAN security

WPAN with IP – Thread

Thread architecture and topology

The Thread protocol stack

Thread routing

Thread addressing

Neighbor discovery

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

Multefire, CBRS, and shared spectrum cellular

5G

5G frequency distribution

5G RAN architecture

5G Core architecture

5G security and registration

Ultra-Reliable Low-Latency Communications (URLCC)

Fine-grain time-division duplexing (TDD) and low-latency HARQ

Network slicing

5G energy considerations

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

Edge Computing

Edge purpose and definition

Edge use cases

Edge hardware architectures

Processors

Speed and power

Registers

Instruction set architectures (ISAs)

Endianness

Processor parallelism

Caches and memory hierarchy

Other processor characteristics

DRAM and volatile memory

Storage and non-volatile memory

Storage classes and interfaces

NAND flash memory design and considerations

Low-speed IO

High-speed IO

Hardware assist and coprocessing

Boot and security modules

Examples of edge hardware

Ingress protection

Operating systems

Operating system choice points

Typical boot process

Operating system tuning

Edge platforms

Virtualization

Containers

Container architecture

An Edge platform ‒ Microsoft Azure IoT Edge

Use cases for edge computing

Ambient computing

Synthetic sensing

Summary

Edge Routing and Networking

TCP/IP network functions at the edge

Routing functions

PAN-to-WAN bridging

Failover and out-of-band management

Edge-level network security

VLANs

VPN

Traffic shaping and QoS

Security functions

Metrics and analytics

Software-defined networking

SDN architecture

Traditional internetworking

SDN benefits

Summary

Edge to Cloud Protocols

Protocols

MQTT

MQTT publish-subscribe

MQTT architecture details

MQTT state transitions

MQTT packet structure

MQTT data types

MQTT communication formats

MQTT 3.1.1 working example

MQTT-SN

MQTT-SN architecture and topology

Transparent and aggregating gateways

Gateway advertisement and discovery

Differences between MQTT and MQTT-SN

Choosing a MQTT broker

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

Comparing fog, edge, cloud, and mist 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

EdgeX

EdgeX architecture

EdgeX projects and additional components

Amazon Greengrass and Lambda

Fog topologies

Summary

Data Analytics and Machine Learning in the Cloud and Edge

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

A brief history of AI and machine learning milestones

Machine learning models

Classification

Regression

Random forest

Bayesian models

Convolutional neural networks

First layer and filters

Max pooling and subsampling

The fundamental deep learning model

CNN examples

Vernacular of CNNs

Forward propagation, CNN training, and backpropagation

Recurrent neural networks

Training and inference for IoT

IoT data analytics and machine learning comparison and assessment

Summary

IoT and Edge Security

Cybersecurity vernacular

Attack and threat terms

Defense terms

Anatomy of IoT cyber attacks

Mirai

Stuxnet

Chain Reaction

Physical and hardware security

RoT

Key management and trusted platform modules

Processor and memory space

Storage security

Physical security

Shell security

Cryptography

Symmetric cryptography

Asymmetric cryptography

Cryptographic hash (authentication and signing)

Public key infrastructure

Network stack – Transport Layer Security

Software-Defined Perimeter

SDP architecture

Blockchains and cryptocurrencies in IoT

Bitcoin (blockchain-based)

IOTA and directed acyclical graph-based (DAG) trust models

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

Thread Group

Zigbee Alliance

Miscellaneous

Protocol consortia

Open Connectivity Foundation and Allseen Alliance

OASIS

Object Management Group

OMA Specworks

Miscellaneous

WAN consortia

Weightless SIG

LoRa Alliance

Internet Engineering Task Force (IETF)

Wi-Fi Alliance

Fog and edge consortia

OpenFog

Eclipse Foundation and EdgeX Foundry

Umbrella organizations

Industrial Internet Consortium

IEEE IoT

Miscellaneous

US government IoT and security entities

Industrial and Commercial IoT and Edge

Commercial and industrial sensor and MEMS manufacturers and vendors

Silicon, microprocessor, and component manufacturers

PAN communication companies

WAN technology companies

Edge computing and solutions companies

Operating system, middleware, and software companies

Cloud providers

Summary

Other Books You May Enjoy

Index

Landmarks

Cover

Index

1

IoT and Edge Computing Definition and Use Cases

You wake up Tuesday, May 17, 2022, around 6:30 A.M. PST, as you always do. You never really needed an alarm clock. You are one of those types with some form of physiological clock. Your eyes open to a fantastic sunny morning as it's approaching 70°F 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 a 5 A.M. flight, you would set it—again, controlled by a cloud agent using the 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 a VR/AR headset since the screen is so much better and larger. You do have an edge 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 whether 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 five 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 policies mandate a zero-emission office space. Each room has proximity sensors to detect not only whether a room is occupied, but also 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 are 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 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 it also hosts 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 a representative called your co-worker 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 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 midday 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, license plate, or a combination of multiple factors 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 manufacturing automation and diagnostics logs didn't pick up any abnormal patterns, as they 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 (strengths, weaknesses, opportunities, and threats) 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. At first, the cost was difficult to justify, but now you believe it may have paid for itself a thousand-fold. 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 has 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 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. Wikipedia defines the IoT this way: 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. (https://en.wikipedia.org/wiki/internet_of_things)

History of the IoT

The term "IoT" can most likely be attributed to Kevin Ashton in 1997 and his work at Procter 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 will have 1 trillion Internet-connected devices by 2030. 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:

YearDeviceReference

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: the first Ethernet-connected network printer.

http://hpmuseum.net/display_item.php?hw=350

1993

Internet-connected coffee pot at University of Cambridge (the 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 Special Interest Group (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 the 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:

Figure 1: Analysis of keyword searches for IoT, patents, and technical publications

IoT potential

The IoT is already affecting 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 ways 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 3 percent (The route to a trillion devices, ARM Ltd 2017) to 4 percent (The Internet of Things: Mapping Value Beyond the Hype, McKinsey and Company 2015) 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. 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 IoT deployment growth rate in the near term is about 20 percent year over year.

Figure 2: Analysts and industry claims of the number of connected objects

These numbers should at first glance impress the reader. For example, if we took a very conservative stance and predicted that only 20 billion newly connected devices would 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 (M2M) and connected objects will 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 then took 15 additional years for 1 billion people to be regular users of the Internet. IoT is looking to add 6 billion connected devices per year. This, of course, is swaying the industry.

Figure 3: The disparity between human population growth versus connected thing growth. The trend has been a 20 percent growth of connected objects versus a nearly flat 0.9 percent human growth. Humans will no longer drive network and IT capacity.

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 (for example, green energy solutions)Reducing costs (for example, in-home patient healthcare)Reducing time to market (for example, factory automation)Improving supply chain logistics (for example, asset tracking)Reducing production loss (for example, theft or spoilage of perishables)Increasing productivity (for example, machine learning and data analytics)Cannibalization (for example, 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.

Definition of the Internet of Things

One should look at some of these claims with a degree of skepticism. It is nearly impossible to quantify the exact number of devices that are Internet-connected. Additionally, we have to separate those devices that are naturally Internet-connected like smartphones, PCs, servers, network routers, and IT infrastructure. We should also not include in the realm of IoT those machines that have had presence in offices, homes and workplaces for decades that are essentially connected through some form of networking. We do not include office printers, copiers, or scanners as part of the IoT spectrum.

This book will examine IoT from the perspective of connecting devices that have not necessarily been connected to each other or the Internet. These devices may have historically not had much if any computational or communication abilities. By that, we imply that devices historically have had cost, power, space, weight, size, or thermal limits.

As we see in the history of IoT devices, connecting traditionally unconnectable objects like refrigerators at Carnegie Mellon has been possible since the early 1980s, but the cost was significant. It required the processing power of a DEC PDP11 mainframe computer. Moore's Law demonstrates the increases in the number and density of transistors in silicon chipsets, while Dennard scaling improves the power profile of computers. With these two trends, we now produce devices that utilize more powerful CPUs and increased memory capacity and run operating systems capable of executing a full network stack. It is only with these requirements being met that the IoT has become an industry unto itself.

The basic requirements of a device to be considered part of the IoT:

Computationally capable of hosting an Internet protocol software stackHardware and power capable of utilizing a network transport such as 802.3Not a traditional Internet-connected device, such as a PC, laptop, smartphone, server, data center appliance, office productivity machine, or tablet computer

We also include "edge" devices in this book. Edge devices themselves can be IoT devices or can "host" IoT devices. Edge devices as detailed later in this book will generally be managed computer nodes that extend closer to the sources of data generation or data action. They may not be typical servers and clusters found in data centers but space, power, and environmentally hardened devices that are in the field. For example, a data center blade would consist of electronics optimized for the climate-controlled atmosphere of a server farm with hot and cold aisles, heat exchangers, and uninterruptible power supplies. Edge devices may be found outside and exposed to weather elements and in areas where constant and consistent power is not an option. Other times, they may include traditional server nodes, but outside the constraints of a datacenter.

Given these qualifiers, the true size of the IoT market is smaller than analyst forecasts. When we divide traditional IT and Internet-connected devices from IoT devices, we see a different growth rate as shown in the following figure.

Figure 4: Separating sales volume of IoT devices by definition from non-IoT devices (for example, IT equipment and mobile computing)

Further analysis into actual components that are used in IoT devices reveals another interesting pattern. As already mentioned, most Internet-connected devices require a certain level of performance and hardware to communicate through standard protocols. Yet the following graphic shows a difference in the number of communication chips and processors versus the number of sensors that are shipping. This reinforces the concept that there is a large fan-out from sensors to edge computers and communication devices.

Figure 5: Trend in sales of sensors, processors, and communication ICs within IoT sales

What is notable is that most IoT installations are not a single device that has the capabilities of running an Internet hardware and software stack. Most sensors and devices have no capabilities of reaching the Internet directly. They lack the processing capabilities, memory resources, and power distribution required for full Internet connectivity. Rather, much of what is really the IoT relies upon gateways and edge computers in a hub-and-spoke model. There is a large fan-out of devices that connect to edge computers through local personal area networks, non-IP networks (Bluetooth), industrial protocols (ModBus), legacy brownfield protocols (RS232), and hardware signals.

Industry and manufacturing

Industrial IoT (IIoT) is one of the fastest-growing 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 in real time. These systems historically have been on-premises computers and servers to manage factory floor performance and output. We call these systems supervisory control and data acquisition (SCADA). 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 actual real-time decisions for OT. This means latency is a major issue for IoT on a factory floor.

Additionally, downtime and security are top concerns. 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 on 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

Following are the industrial and manufacturing IoT use cases and their impact:

Preventative maintenance on new and preexisting factory machineryThroughput increase through real-time demandEnergy savingsSafety systems such as thermal sensing, pressure sensing, and gas leaksFactory floor expert systems

Consumer

Consumer-based devices were one of the first segments to adopt things being connected to the Internet. Consumer IoT first took the form of 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 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 the 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 with the healthcare market, which has 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 securityWearables: Health and movement trackers, smart clothing/wearablesPets: Pet location systems, smart dog doors

Retail, finance, 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. These include traditional banking services and insurers, but also leisure and hospitality services. The 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 provide better customer experiences while minimizing overhead and loss in the cost of sales.

Retail, finance, and marketing IoT use cases

Some of the 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 manufacturing 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 are 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 careLearning models of predictive and preventative healthcareDementia and elderly care and trackingHospital equipment and supply asset trackingPharmaceutical tracking and securityRemote field medicineDrug researchPatient fall indicators

Transportation and logistics

Transportation and logistics will be a significant, if not the leading driver in IoT. The use cases involve using devices to track assets 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 afford any downtime. We will also see service trucks that can track assets such as workers' tools, construction equipment, and other valuable assets. 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 awarenessMunicipal vehicle planning, routing and monitoring (snow removal, waste disposal)Cold storage transportation and safety of food deliveryRailcar identification and trackingAsset and package tracking within fleetsPreventative maintenance of vehicles on the road

Agricultural and environment

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, world economies are becoming more affluent. Even as famines are less common than 100 years ago, 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. Remote 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 (cornfields). This has impacts on data communication systems that we will need to consider later in 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 yieldSmart lighting in nesting or poultry farming to improve yieldLivestock health and asset trackingPreventative maintenance on remote farming equipment via manufacturerDrone-based land surveysFarm-to-market supply chain efficiencies with asset trackingRobotic farmingVolcanic and fault line monitoring for predictive disasters

Energy

The energy segment includes the monitoring of energy production at the source of production to the consumer. 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 responses 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 gainsRemote solar panel monitoring and maintenanceHazardous analysis of nuclear facilitiesSmart electric, gas, and water meters in a citywide deployment to monitor usage and demandTime-of-use tariffsReal-time blade adjustments as a function of weather on remote wind turbines

Smart city

"Smart city" is a phrase used to imply connected and intelligent infrastructure, citizens, and vehicles. 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 have embraced IoT connectivity to 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.

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 cities are also impacted by government mandates and regulations (as we will explore later); therefore, there are ties to the government segment.

Smart city IoT use cases

Some of the smart city IoT use cases are as follows:

Pollution control and regulatory analysis through environmental sensingMicroclimate weather predictions using citywide sensor networksEfficiency gains and improved costs through waste management service on demandImproved traffic flow and fuel economy through smart traffic light control and patterningEnergy efficiency of city lighting on demandSmart snow plowing based on real-time road demand, weather conditions, and nearby plowsSmart irrigation of parks and public spaces, depending on weather and current usageSmart cameras to watch for crime and real-time automated AMBER AlertsSmart parking lots to automatically find best parking spaces on demandBridge, street, and infrastructure wear and usage monitors to improve longevity and service

Military and government

City, state, and federal governments, as well as the military, have a keen interest 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 counterattacks 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