Artificial Intelligence

How to Derive and Analyze Data from the IoT

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It’s a cliche, but it’s true — we are now more connected than we have ever been. Our homes have become our caretakers. Our cities are getting smarter and smarter. Devices can be programmed to start your pot of coffee in the morning and lock the door as you leave for work. As we continue to move towards automation through the Internet of Things, we are amassing tons and tons of data that can be analyzed and implemented to create smoother operating systems and conditions.

What is the IoT?

The idea that machines could be connected is nothing new. People have been wiring devices together since at least the ‘70s. But, the Internet of Things was a term coined by Kevin Ashton in 1999. He was describing the emerging trend of connecting devices together across the burgeoning internet. So, the true IoT hasn’t been around all that long.

The IoT is, simply put, a system of interconnected digital devices. This system of interconnected devices shares a network with which to exchange massive amounts of data. The beauty of the IoT is that it doesn’t require humans to communicate with each other or directly with computers in order to be functional and useful. The IoT encompasses any device that uses the internet to exchange information with other devices. As you can imagine, the IoT has virtually innumerable amounts of priceless data that can be derived and analyzed to learn more about consumer behavior, the functionality of systems, and how to improve the relationship between people and devices.

The IoT means something different to everyone, depending on where it’s located and what it’s being used for. But, at this point, it’s safe to say that virtually any device that uses the internet to communicate is part of the IoT. It’s definition shifts as we find new uses for it.

Types of IoT Data

Recent estimates put the number of IoT-connected devices at 75.44 billion world-wide by 2025. That is an incredible number of computing devices working across the globe, every minute of every day! Not only does this mean that these devices are likely to become more affordable and easy to use in the coming years, but it also means that the data that the IoT will be producing will become astronomical in the next five years. There has never been a better time to learn how to derive and analyze data from the IoT.

It’s not just home devices that are utilizing the IoT. Cities have been using the IoT to streamline public transportation, monitor traffic conditions, learn more about resource usage from smart water and electricity meters, and there are tons of new uses being discovered every year. The IoT can, in part, gives us types of data like:

  • Location data
  • Automation data
  • Status data
  • Business data
  • Web data
  • Health data

This is nowhere near an exhaustive list, but it does illustrate the potential the IoT holds to make our world a more seamless, smart place to live. For instance, the oil and gas industry has begun to use infrared video feeds in the field linked together on the IoT to monitor for corrosive conditions. By using this technology and data they are able to keep workers safe by catching problems before they become catastrophic. From the health industry to the software industry, the IoT has the potential to wield data that can change the course of human history.

The IoT is a world in and of itself. Business leaders and scientists are coming up with new ways to derive data every single day. While the applications of the IoT for personal use are undoubtedly essential and beneficial, the IoT becomes revolutionary when it’s applied to entire ecosystems where we work, live, and do business. When the IoT is scaled to meet the needs of entire cities, the data produced is virtually gold.

How Data is Derived from the IoT

Because there are so many types of devices that can connect through the IoT, the number of ways to collect data is equally expansive. For example, smart devices generally operate by noting events. Events are moments like a doorbell camera catching the mail person dropping off a package or someone asking their smart thermometer to turn up the heat. These events produce data that is sent across the network to an application that connects everything together. Then this application can pool that data to be delivered back across the network for a number of uses and functions. The data can be processed in a number of ways but the most commonly used protocols are typically MQTT and HTTP (there are other common protocols).

Data can either be sent across the network in real-time or as a batch of information. It’s critical that the data be delivered to wherever it’s going with accurate time measurements. When the IoT is used in mechanical settings, the data has to illuminate errors in the precise order of events or it may become impossible to pinpoint where a computerized machine is hitting a point of critical failure. So, the real heart of the IoT is its ability to accurately measure and time events.

This is also true for traffic cameras. If the IoT delivered inaccurate data on traffic patterns, it could really throw off traffic light timing, which in turn can increase traffic and accidents.

So, no matter what protocols are used to collect data from the IoT, it’s vital that the data is accurate and in order of events.

Metadata and telemetry — the basics

Metadata is data about the device being connected to the IoT. It rarely, if ever, changes. This is information like serial and model numbers, unique identifiers, and the date it was manufactured. This static data is typically easy to derive from somewhere on the device. It is likely available through the manufacturer-provided app as well.

Telemetry, on the other hand, is data collected by the device about the status of that device. This data does change, though it is typically read-only data. This information comes from sensors located in or on the device and these sensors let the user know what’s going on around the device. Telemetry can be compared to the senses humans possess. Some sensors can technically smell (like a smart carbon monoxide detector) and some can see (in the case of a smart doorbell). Either way, this data is useful for all sorts of reasons and it can come at varying intervals, but again — the data must be delivered in an accurate order of events in order to be truly useful. This data is fundamentally pretty simple, but it’s still useful. It can tell users and tech specialists how and what devices are being used across organizations and what this data is sensing.

Analytic systems for processing big data

As businesses adapt to using the IoT to make operations cheaper and easier, they are amassing lots and lots of data. Data is functionally useless in these numbers unless you have systems in place to analyze it. The vast majority of businesses use a combination of techniques and systems to analyze this massive data. Techniques and systems like cloud computing and Apache Hadoop bring real value to this endless stream of data coming into businesses every day.

This big data doesn’t just require extremely smart processing systems to be useful, it also requires big data engineers to set up:

  • Infrastructure
  • Integration of all the different types of data sources
  • Crafting reliable data
  • Data security

The list goes on and on. So, while machines are getting smarter and the IoT is more seamless and independent than ever, engineers play a vital role in deriving and analyzing data from the IoT.

AI Data Analytics

Another exciting development in the world of big data is AI (artificial intelligence). If you’ve noticed that Google search has gotten much smarter over the years, you aren’t imagining things. Google’s algorithm is programmed to become more adept at natural language processing — that is the more people search for things, the more competent Google gets at properly interpreting the request and delivering accurate results that people are looking for. 

This is just one example of the potential for AI to interpret large quantities of data from the IoT with relative ease. This frees up resources to be used elsewhere. However, AI data sorting is still very new and it’s still essential to have engineers and other specialists available to maintain and monitor these AI programs and devices.

RFID Tags and Cloud-Based Data Management Software Systems

RFID (Radio-frequency identification) chips and tags are small sensors that can be inserted into almost anything to tract the location of that device (or even people!). RFID tags have become hugely popular in the last few years and more corporations are incorporating them into their business model to improve efficiency and to reduce overhead costs. RFID tags help locate expensive equipment and track employee hours.

Obviously, as the number of devices that are connected to the IoT grows, the amount of data also grows. One way many companies have learned to collect and analyze this precious data is through having a cloud-based data management system. Cloud computing makes it possible to access this data from virtually anywhere. It also makes it easy to store this data and call up what information is needed very quickly, without overwhelming hardware and software systems.

Cloud data analytics allow you to look at specific, single points of data or whole batches of data at a time. There are several types of cloud solutions available to help process this data.

The Future of the IoT

As the population grows and gets older, the need for the IoT will only grow. Caring for the growing number of seniors with medical needs will require huge numbers of trained medical professionals. The IoT could have the potential to allow many of these folks to live in their own homes safely. It can keep traffic flowing, buses running on time, and monitor water and power usage for better resource management and allocation. There is no denying that this data is powerful and will require trained, highly-skilled engineers and specialists to monitor and make sense of it for down-stream use.

In business, the IoT is an invaluable asset that allows companies to optimize their resources and systems to save money and time. This, in turn, can save the customer money and time. The IoT is just in its infancy. What started as a term casually-coined during the 90s has become a movement. We have just scratched the surface of what the IoT is capable of, and the future looks brighter than ever for those who choose to work with and for the IoT its complimenting data analytics systems.

Related: What is Knowledge representation in Artificial Intelligence?

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