Living on the edge

The edge is the space of computing topology nearest to where the analog interfaces the digital and vice versa. The edge of the first computing systems, such as 1945's Electronic Numerical Integrator and Computer (ENIAC) general-purpose computer, was simply the interfaces used to input instructions and receive printed output. You couldn't access these interfaces without being directly in front of them. With the advent of remote access mainframe computing in the 1970s, the edge of computing moved further out to public terminals that fit on a desk and connected to mainframes via coaxial cable. Users could access the common resources of the local mainframe from the convenience of a lab or workstation to complete their work with advanced capabilities such as word processors or spreadsheets.

The evolution of humans using the edge for computing continued with increases in compute power and decreases in size and cost. The devices we use every day, such as personal computers and smartphones, deliver myriad outcomes for us. Some outcomes are delivered entirely at the edge (on the device), but many work only when connected to the internet and consume remote services. Edge workloads for humans tend to be diverse, multipurpose, and handle a range of dynamisms. We could not possibly enumerate everything we could do with a smartphone and its web browser! These examples of the edge all have in common that humans are both the operator and recipient of a computing task. However, the edge is more than the interface between humans and silicon.

Another important historical trend of the edge is autonomous functionality. We design computing machines to sense and act, then deploy them in environments where there may be no human interaction at all. Examples of the autonomous edge include robotics used in manufacturing assembly, satellites, and weather stations. These edge workloads are distinct from human-driven workloads in that they tend to be highly specialized, single-purpose, and handle little dynamism. They perform a specific set of functions, perform them consistently, and repeat them until obsolescence. The following figure provides a simplistic history of both human-driven interfaces and autonomous machines at the edge over time:

Figure 1.1 – A timeline of cyber-physical interfaces at the edge from 1950 to 2020

In today's technological advances of wireless communications, microcontrollers and microprocessors, electrical efficiency, and durability, the edge can be anywhere and everywhere. Some of you will be reading this book on an e-reader, a kind of edge device, at 10 km altitude, cruising at 900 kph. The Voyager 1 spacecraft is the most distant manmade edge solution, continuing to operate at the time of this writing 152 AU from Earth! The trend here is that over time, the spectrum of capabilities along the path to and at the edge will continue to grow, as will the length of that path (and the number of points on it!) and the remoteness of where those capabilities can be deployed. The following diagram illustrates the scaling of entities, compute power, and capabilities across the topology of computing:

Figure 1.2 – Illustration of computing scale from the cloud to a sensor

Our world is full of sensors and actuators; over time, more of these devices are joining the IoT. A sensor is any component that takes a measurement from our analog world and converts it to digital data. An actuator is any component that accepts some digital command and applies some force or change out into the analog world. There's so much information out there to collect, reason about, and act upon. Developing edge solutions is an exciting frontier for the following reasons:

• There is a vast set of possibilities and problems to solve in our world today. We need more innovation and solutions to address global outcomes, such as the 17 sustainable development goals defined by the United Nations (UN).
• The shrinking cost factor to develop edge solutions lowers the barrier to experiment.
• Tools that put solution development in the reach of anyone with a desire to learn are maturing and becoming simpler to use.

This book will teach you how to develop the software of edge solutions using modern edge-to-cloud technologies, including how to write software that interacts with physical sensors and actuators, how to process and exchange data with other local devices and the cloud, and how to get value from advanced ML technologies at the edge. More important than the how is the why—in other words: why do we build the solutions this way? This book will also explain the architectural patterns and tips for building well-architected solutions that will last beyond the time of particular technologies and tools.

Implementation details such as programming languages and frameworks come and go with popularity, necessity, and technological breakthroughs. The patterns of what we build and why we build them in particular ways stand the test of time and will serve you for many of your future projects. For example, the 1995 Design Patterns: Elements of Reusable Object-Oriented Software by Gamma, Helm, Johnson, and Vlissides is still guiding software developers today despite the evolution of tools that the authors used at the time. We, the authors, cannot liken ourselves to these great thinkers or their excellent book, but we refer to it as an example of how we approached writing this book.

Common concepts for edge solutions

For the purposes of this book, we will expand the definition of the edge as any component of a cyber-physical solution operating outside of the cloud, its data centers, and away from the internet backbone. Examples of the edge include a radio switch controlling a smart light bulb in a household, sensors recording duty cycles and engine telemetry of a tractor-trailer at a mining site, a turnstile granting access to subway commuters, a weather buoy drifting in the Atlantic, a smartphone using a camera in a new augmented reality (AR) game, and of course, Voyager 1. The environmental control system running in a data center to keep servers cool is still an edge solution; our definition intends to highlight those components that are distant from the gravity of the worldwide computing topology. The following diagram shows examples of computing happening at various distances further from the gravity of data centers in this computing topology:

Figure 1.3 – Examples of the edge at various distances

A cyber-physical solution is one that combines hardware and software for interoperating the digital world with the analog world. If the analog world is a set of properties we can measure about reality and enact changes back upon it, the digital world is the information we capture about reality that we can store, transmit, and reason about. A cyber-physical solution can be self-contained or a closed loop such as a programmable logic controller (PLC) in an industrial manufacturing shop or a digital thermostat in the home. It may perform some task autonomously or at the direction of a local actor such as a person or switch.

An edge solution is, then, a specific extension of a cyber-physical solution in that it implies communication or exchange of information with some other entity at a point in time. It can also operate autonomously, at the direction of a local actor or a remote actor such as a web server. Sensors deliver autonomous functionality in the interest of a person who needs the provided data or are used as input to drive a decision through a PLC or code running on a server. Those decisions, whether derived by computers or people, are then enacted upon through the use of actuators. The analog, human story of reacting to the cold is I feel cold, so I will start a fire to get warm, while the digital story might look like the following pseudocode running a local controller to switch on a furnace:

if( io.read_value(THERMOMETER_PIN) < LOWER_HEAT_THRESHOLD ) {
     io.write_value(FURNACE_PIN, HIGH);
}

We will further define edge solutions to have some compute capability such as a microcontroller or microprocessor to execute instructions. These have at least one sensor or actuator to interface with the physical world. Edge solutions will at some point in time interact with another entity on a network or in the physical world, such as a person or another machine.

Based on this definition, what is the edge solution that is nearest to you right now?

Sensors, actuators, and compute capability are the most basic building blocks for an edge solution. The kinds of solutions that we are interested in developing have far more complexity to them. Your responsibility as an IoT architect is to ensure that edge solutions are secure, reliable, performant, and cost-effective. The high bar of a well-architected solution means you'll need to build proficiency in fundamentals such as networking, cryptography, electrical engineering, and operating systems. Today's practical production edge solutions incorporate capabilities such as processing real-time signals, communicating between systems over multiple transmission media, writing firmware updates with redundant failure recovery, and self-diagnosing device health.

This can all feel overwhelming, and the reality is that building solutions for the edge is both complex and complicated. In practice, we use purpose-built tools and durable patterns to focus invested efforts on innovation and problem solving instead of bootstrapping and reinventing the wheel. The goal of this book is to start small with functional outcomes while building up to the big picture of an E2E solution. The learnings along the way will serve you on your journey of building your next solution. While this book doesn't cover every topic in the field, we will take every opportunity to highlight further educational resources to help you build proficiency beyond the included focus areas. And that's just about all in terms of building solutions for the edge! Next, let's review how ML fits in.