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 stack
• Hardware and power capable of utilizing a network transport such as 802.3
• Not 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 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 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 security
• Wearables: Health and movement trackers, smart clothing/wearables
• Pets: 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 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 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 awareness
• Municipal vehicle planning, routing and monitoring (snow removal, waste disposal)
• Cold storage transportation and safety of food delivery
• Railcar identification and tracking
• Asset and package tracking within fleets
• Preventative 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 yield
• Smart lighting in nesting or poultry farming to improve yield
• Livestock health and asset tracking
• Preventative maintenance on remote farming equipment via manufacturer
• Drone-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 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 gains
• Remote solar panel monitoring and maintenance
• Hazardous analysis of nuclear facilities
• Smart electric, gas, and water meters in a citywide deployment to monitor usage and demand
• Time-of-use tariffs
• Real-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 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 parking spaces on demand
• Bridge, 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 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 personnel tracking and location services
• Synthetic sensors to monitor hostile environments
• Water level monitoring to measure dam and flood containment