A sophisticated data-collection device, the sensor is a crucial—and fascinating—component of the Internet of Things (IoT). This article explores the evolution of sensor technology, the main challenges facing the industry today, and how IoT sensor data analytics can transform the way your building works.

The Evolution Of IoT Sensor Data (& How It Benefits You)
The Evolution Of IoT Sensor Data (& How It Benefits You)

Michael Davies | Iota Communications

A sophisticated data-collection device, the sensor is a crucial—and fascinating—component of the Internet of Things (IoT). This article explores the evolution of sensor technology, the main challenges facing the industry today, and how IoT sensor data analytics can transform the way your building works.


IoT Sensor Data: Yesterday To Today

The purpose of the modern-day sensor is simple: To collect analog data from the physical world and translate it into digital data assets. This idea isn’t new—throughout history we’ve developed similar technologies for the very same purpose, although each incarnation was slightly different.

Radar, short for radio direction and ranging, was an early way of gathering and translating data from the physical world. Radar generates and transmits radio waves, then “listens” for reflections. Any detected reflections are directed into a piece of electronic equipment that processes and displays them in a meaningful form. This type of detection system played a critical role in World War II, helping Allied forces identify hostile aircraft and ships.

A different type of sensing, or detection, technology was valuable during the Iraq war. When the Kuwaiti oil fires began, the smoke rendered night vision and other enhanced visioning tools useless. The solution was a forward-looking infrared camera that used thermal imaging to see and measure thermal energy emitted from objects. Its ability to penetrate through smoke and fog to capture the heat signature of approaching vehicles made it easier for soldiers to distinguish between friend and foe.

Innovations like these represented the start of miniaturizing technology for purposes of translating the analog world. There are a host of parameters in the world around us that we take for granted—it’s hot outside, the wind is blowing, the water is cold—but without tools, we’re not capable of measuring those things with any reliability. Sensors give us the ability to measure movement, waves, heat, light, and much more. Translating an analog situation into a digital one gives us the power to catalog the data associated with the physical world, and identify trends and patterns.


Modern IoT Sensors & Data Capture

So what does today’s analog-to-digital translation process look like?

  • Sensors are measuring just about any aspect of the physical world. The calibration of sensors allows them to be tailored to application-specific functions, so they can measure things like temperature, vibration, electricity, and air composition with accuracy. Often, sensor data is tasked with capturing information relevant to a particular task, so the data can be used to make process improvements for the purpose of saving money or increasing efficiency.
  • Sensors are connected through gateways, which enable them to relay the collected data to a server in the cloud.
  • From there, the information is transmitted to your computer or cell phone so you have instant access to all monitored activities taking place.
  • Then, humans (with the help of machines) can catalog, label, and store the data to look for patterns and trends regarding the intended process improvement, providing broad insights about the surrounding environment.
  • Continuous tracking of the associated parameters allows for continuous process improvements.

These tools are revolutionizing the way we’re capturing data—and what we’re able to do with it. If you consider the sheer number of IoT sensors deployed and the associated data being accumulated, the amount of information gathered is staggering. What kind of revolution is this change bringing about? Granular remote monitoring and the extraction of information is helping business owners make smarter decisions by lowering transaction costs and providing feedback loops to drive efficiency performance. Given the level of uncertainty around running a business, this new reality is invaluable. The better your system of monitoring, measuring, and cataloging data, the better you’ll understand the story of your business. That leads to a greater degree of confidence and more reliable decision-making going forward.


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IoT Sensor Data Analytics For Facilities: How Decisions Are Made

While the sensors provide access to the data itself and the network connection pulls data from the building to a repository, another critical piece of the IoT is sensor data analysis. If you can’t analyze the data collected by the sensors properly, you can’t act on it.

For facilities, IoT sensor data analytics start with an initial determination of the building’s load profile—its energy usage over the course of a day. Using sensors attached to building devices, we can determine things like:

  • What’s the building’s total energy use?
  • How much energy is being consumed by lighting, heating/cooling equipment, ventilation, etc.?
  • What equipment/machinery has the largest load profile?

Armed with that information, the next step is to normalize it—benchmark and compare it to other facilities of the same size. Keep in mind that, depending on building locations, there may be a difference in energy usage during different times of the year (more energy used in summer, for example). Take weather into consideration and develop a correlation model on energy consumption. Activity and time of day also play a role in understanding how a company uses energy.

Analytics tools usually involve statistical algorithms, and, more recently, machine learning capabilities. These sophisticated technologies can drill into the details of energy use and outside factors to provide you with “what-ifs.” For example, time series forecasting is used to separate cyclical and seasonal factors from the underlying trends in the data, allowing you to reduce peak energy through better scheduling—or take action during critical peak demand events. As a result, you can pinpoint future savings opportunities more closely, and help direct machine utilization and scheduling based on the input and analytics that are applied.


Challenges Facing The IoT Sensor Industry

In my experience, reliability is the number one challenge regarding sensors.

Battery power is one aspect of reliability that must be addressed. Currently sensors must be either battery powered, or they must draw power from an AC outlet or the equipment they reside on. As has been noted in Forbes:

“Batteries and wires may be a feasible solution to power IoT now, but as the sheer volume of IoT rises globally, it will be virtually impossible to keep up. A failing or depleted battery within an IoT sensor, M2M or factory automation could cost a company revenue and increase safety and liability issues. Wireless power—without pads, over distance—is critical.”

For companies engaged in demand response efforts, for example, a non-working sensor could thwart attempts to avoid peak demand thresholds and cause the entire efficiency program to fail. And even though researchers are currently working on ways to extend battery life to 10 years—an ambitious and admirable goal—that may not actually solve the problem: The incredible number of IoT devices predicted for the future would still require that more than 270 million batteries be changed every day.

Cellular disruptions and power outages also pose reliability problems. A power outage might not impact the network inside a building, but it will disrupt the sensors’ ability to get a signal out—unless the building is equipped with backup power to PCs and modems.


The Next Frontier Of Data Collection & Analysis In IoT

What’s next for IoT data collection and analysis? The IoT Edge.

Edge computing allows data to be analyzed at the point of the device, rather than transmitting it to the cloud. Tesla and similar players in the autonomous driving vehicle market have been among those doing early experimentation with edge computing. That’s because self-driving cars are the perfect use case: They can’t rely on the cloud for data processing because transmitting the information takes too long—a car can’t wait seconds to make a decision to brake. For autonomous driving to work, information processing must be done at the vehicle level (where the vehicle is “the edge.”)

The same concept could easily be put to use within buildings, to make decisions where you simply don’t have time to render a decision by sending data to the cloud.

Demand response again serves as a good example: If you receive new information from a utility, you may need to act immediately in order to avoid incurring an additional charge. Or, problems or complications with sophisticated machinery or production lines could be identified and addressed immediately. These types of decisions could, in fact, be orchestrated on a local server rather than processed in the cloud. Some of our clients already do this for security reasons, simply because they don’t want certain information leaving their premises. Setting up a server in a company’s IT room ensures that everything stays local, but it can also serve as a way to reduce data analysis and reaction times.

As tasks and processes become more complex and more time-dependent, implementing edge analytics may be beneficial for some companies.


Interested in working with a partner to decipher your IoT sensor data?

Iota can help. We’ve designed and implemented data monitoring and efficiency programs for numerous Fortune 100 clients, and we can do the same for you. Using our line of IoT sensors and our advanced analytics platform, our knowledgeable team will help you design a remote monitoring program that’s right for your facility and your energy goals.


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The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow

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