Engineering Lessons in Transitioning from the Internet of People to the Internet of Things

IoT can save companies a lot of money and create better products for end users.

Timothy Chou’s book Precision: Principles, Practices and Solutions for the Internet of Things. (Image courtesy of Amazon.)

Timothy Chou’s book Precision: Principles, Practices and Solutions for the Internet of Things. (Image courtesy of Amazon.)

With the growth of the Internet of Things (IoT) products, engineers might want to think more like software developers than product designers.

At least, that is the opinion of Timothy Chou, author of Precision: Principles, Practices and Solutions for the Internet of Things.

“Increasingly, machines are software with sensors and actuators on them,” said Chou. “Tesla is a good example. You get new feature releases on a car. In the old world, that wasn’t possible. In the new world, it becomes the way you think about the machine.”

In the past, you might not know if there was an issue with your product. Your user base might just kick the product to the curb without submitting any customer feedback.

Today, you can look at the data coming back from your products and inform your customers that there was a problem and that it was fixed with an IoT software patch before they even realized there was a problem.

Traditionally, these problems could only be addressed with the next model release. This is one lesson that the transition to the Internet of Things can teach us.

The Internet of People Is Becoming an Internet of Things

There is a lot more product revolutions engineers can learn when they dig deeper into the framework of the IoT—something that Chou has done in great detail.

Many lessons revolve around the idea that we no longer have to design tools for the Internet of People (IoP). As the IoT grows, machines will be mostly talking to other machines. As a result, the opportunities for data collection and product improvements are immense.

“Most of the technology we have built until now has been for IoP not IoT applications: ERP, purchasing, HR, CRM, e-commerce. All of these applications believe they are talking to people at the end,” explained Chou. “But people aren’t things and things aren’t people.

“Even now on the Internet, there are more things than people—almost 100 times the global population,” he added. “Things have more to say; humans interact by talking and typing, but that pales in comparison to what machines can do. The sensors can sample at 10000 hertz. Humans can’t type that fast. Things can be programmed; people can’t, and they can go where people cannot.”

That is a good point. Many IoT applications still have quite a lot of human-in-the-loop which Chou notes is much more of an IoP thought process. Many connected devices tools require humans to fill in forms and update the conditions of the connected device. Many more need to connect to a cell phone to gain any IoT or IoP functionality.

Instead, much of this information can be gathered from the thing itself and processed through analytics. And the functionality that the cell phone would give can also be built into the device. And with the price of sensors dropping, bringing the connection capabilities to the product itself is becoming much more feasible and affordable.

“Engineers should think about the operating environment for the thing,” said Chou. “Show me a cell phone, and you will meet a saturated market, even in China. Show me an air purifier with cell phone technology and now you have a new market. The challenge for the person building the machine is to think about the machine not just form the electrical chemical and mechanical way but from the software. I suggest that engineers learn about software.”

Chou has broken up this IoT framework into five layers which he outlined in 12 use cases in his book. Chou also offered advice for engineers who will be creating their own IoT frameworks:

  • Things: Think before connecting a product to the Internet. Will it add value?
  • Connect: Think of how to gain insights into areas where it was impossible or hard to get to in the past like internal body sensors or between mechanical equipment.
  • Collect: More data isn’t always better. Try to think about what you will use the data for and what lessons you might learn. Otherwise, the data likely won’t be used.
  • Learn: If you have thousands of sensors collecting data every second, your data scientist will not be able to visualize this. Think about the useful information you can pull out of the data swarm like preventative maintenance or optimization. And process data at the network edge before sending it all to the cloud.
  • Do: These are the business reasons for the IoT. Think of transferring to product-as-a-service (PAAS) contracts like how GE is leasing engines to airlines. Also, think of how to improve the user experience with methods to update and service equipment via the IoT.

Four Engineering Lessons Learned from the Internet of Things

When engineers leave a lot of the data collection and analysis to the IoT framework, they will be able to optimize their products in various ways.

One of the more obvious examples will be the reduction of consumables. When the IoT is used to maximize the usage of a product, a lot less fuel and raw materials will be used up.

Chou used the example of an airline. When the scheduling of a fleet of planes is created by the system, then it can be optimized. Ideally, the IoT system can use the least number of planes traveling the shortest distances to move the same number of passengers.

Many futurists have theorized that a similar setup with autonomous cars in a grand Uber-like system could one day eliminate the need for individuals to own cars in cities. The car will just be there when you need to get to work and then move on to the next customer once you arrive at your destination. At that point, you are not just reducing fuel consumption. You are also reducing the number of cars on the road.

Autonomous cars are also a great example of a second lesson from the IoT: the elimination of human error. In theory, if all vehicles were autonomous, there would be an increase in transportation safety as the human element is eliminated from the equation. Chou notes that this is especially true when looking at rail transportation where human error is a leading cause of crashes.

But the elimination of human error through the IoT will help more than just the transportation industry. Many manufacturing and data acquisition positions will also benefit from such a system.

“People get wrapped up in consumerism. What’s the taxi driver going to do?” joked Chou. “You are missing the point. People think IoT is as trivial as an IoT thermostat or a tennis racket isn’t successful. You have to think way bigger picture—the infrastructure of the planet.”

Additionally, IoT systems are also able to detect things that a human cannot. Chou says this fact leads to a third lesson the IoT infrastructure teaches us: IoT products can improve customer service.

Take an electricity grid. By adding sensors to the system in the field, engineers will be able to keep track of the phase angles at various locations. With this information, the grid can predict blackouts and improve the quality of service for customers.

At the end of the day, the best customer service for a product is one that doesn’t need customer service because the product noticed the problem before the customer even realized it was there.

Finally, IoT products can be healthier for both the planet and the end user. Chou used an example of a farm that uses sensors to determine when to add water, pesticides and fertilizers to a crop. By implementing an IoT system, farmers can even determine the best time to harvest crops based on shelf life and nutrient levels.

In other words, IoT systems can be much more efficient and better for the environment than traditional processes. Many farms have been known to drastically reduce their carbon footprint by implementing IoT systems.

Clearly, there are a lot of lessons that engineers can learn from implementing an IoT system to their products. But choosing which platform to use to create this system can be a challenge. To learn more about that, read: 10 Questions to Ask an IoT Platform Provider.

Written by

Shawn Wasserman

For over 10 years, Shawn Wasserman has informed, inspired and engaged the engineering community through online content. As a senior writer at WTWH media, he produces branded content to help engineers streamline their operations via new tools, technologies and software. While a senior editor at, Shawn wrote stories about CAE, simulation, PLM, CAD, IoT, AI and more. During his time as the blog manager at Ansys, Shawn produced content featuring stories, tips, tricks and interesting use cases for CAE technologies. Shawn holds a master’s degree in Bioengineering from the University of Guelph and an undergraduate degree in Chemical Engineering from the University of Waterloo.