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.
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:
Mario W. Cardullo receives the patent for first RFID tag
US Patent US 3713148 A
Carnegie Mellon internet-connected soda machine
Internet-connected toaster at Interop '89
IEEE Consumer Electronics Magazine (Volume: 6, Issue: 1, Jan. 2017)
HP introduces HP LaserJet IIISi: first Ethernet-connected network printer
Internet-connected coffee pot at University of Cambridge (first internet-connected camera)
General Motors OnStar (2001 remote diagnostics)
Bluetooth SIG formed
LG Internet Digital DIOS refrigerator
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
First Bluetooth product launched: KDDI Bluetooth-enabled mobile phone
United Nation's International Telecommunications Union report predicting the rise of IoT for the first time
IPSO Alliance formed to promote IP on objects, first IoT-focused alliance
The concept of Smart Lighting formed after success in developing solid-state LED light bulbs
Apple creates iBeacon protocol for beacons
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:
Analysis of keyword searches for IoT, patents, and technical publications
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.
Different analyst and industry claims on the number of connected things
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:
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 (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 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.
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-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).
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.
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.
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.
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.
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 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.
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).
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
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.
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 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.
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
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.
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
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.