Putting the Internet of Things and Machine Learning in the same sentence is like playing buzzword bingo. But truth is, the two concepts actually make sense as a pair. By 2025, it is estimated that IoT will generate over 180 zettabytes of data annually. That’s 180 trillion GBs. And guess what? Machine learning is most effective when using extensive datasets.
To better understand why the IoT needs ML to really take over the world, we’ll start by defining the two concepts separately. We’ll then examine four distinct IoT & ML real-life applications.
What is IoT?
For some reason, the internet of things is often defined in very technical terms. It is however a very simple concept. IoT is about extending the power of the internet to physical things that weren’t traditionally connected. But what power are we talking about?
The value of IoT lies in its capacity to make objects smart. In this context, smart means sending and/or receiving data. Device manufacturers and service providers can then use that data to improve processes, build better products or enhance the customer experience.
The internet of things is growing at an unprecedented rate. Indeed, an estimated 127 new devices connect to the internet every second. Increased connectivity will only accelerate the process. McKinsey expects that by 2022, 100% of the global population will be covered by low-power, wide-area networks (LPWANs). This will allow long-range communications among connected devices while optimizing both costs and power-consumption requirements.
What is Machine Learning
Machine learning is everywhere, and there’s no way to escape it. As a subset of artificial intelligence, ML is the engine behind all those “AI-powered” applications you keep hearing about. But while we always emphasize its results, we rarely look at machine learning from a technical standpoint. It’s a fancy piece of technology that can achieve fantastic results, sure, but how does it really work?
Simply put, machine learning is the science of getting computers to act and improve without being specifically programmed. Tom Mitchell, computer scientist and professor at the Carnegie Mellon University, came up with a simple formalism to better understand the idea of automated improvement:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
- Experience E = how we will collect the data
- Tasks T = which decisions the software will need to make
- Performance P = how you will evaluate the results
Pretty straightforward, isn’t it? Well, not really.
The general idea behind machine learning, i.e. input to output, always remains the same. However, we need to make a crucial distinction between supervised and unsupervised learning.
In supervised learning, you have prior knowledge of what the output should look like. Your goal is to automate a function that best mimics the relationship between input and output observable in the data. We often use it for classification or regression.
Unsupervised learning, on the other hand, does not have a prior expectation of what the output label should look like. The goal here is to infer the natural structure of a dataset. A common task for this method is clustering.
Given the amount of data IoT generates, machine learning is often the best way to derive valuable insights from it. Because IoT impacts virtually every aspect of society, there are countless opportunities for machine learning use cases.
IoT & Machine Learning Applications
Some use cases require machine learning to show their value. Since IoT affects all verticals, we’re going to examine four applications of machine learning for the IoT. To simplify the examples, we’ll assume that the right data is collected.
In the industrial world, asset downtime can cost organizations millions of dollars. Predictive maintenance aims to fix that problem. Indeed, by monitoring equipment to avoid future failure, companies can schedule maintenance when it’s needed. In turn, they can move away from a time-based schedule. Machine learning models can compute TBs of streaming data from various sensors and identify the root cause of equipment issues. This allows maintenance teams to react in a timely fashion and to avoid unplanned downtime.
Consumer IoT companies, such as smart home manufacturers, see engagement as their top priority. Machine learning powered churn analysis aims to identify the cause behind attrition. Organizations have more and more data at their disposal. They can now extract valuable insights from their connected product data. In other words, they are armed to better understand product usage and performance, failures and other critical KPIs. Ultimately, they can reduce attrition. This allows them to implement proactive retention strategies.
The scarcity of clean water resources around the world forces agriculture companies to use it as efficiently as possible. IoT-based smart irrigation management systems can help in achieving optimum water-resource utilization in the precision farming landscape. Sensors can track soil moisture, soil temperature, and environmental conditions. Along with the weather forecast data from the Internet, machine learning models can help agtech companies optimize yield.
Predictive replenishment allows companies to forecast demand and manage stock level in the back store and on the shelves. The objective is to ensure high On-Shelf Availability (OSA). By closing the loop between supply chain and forecast, companies can replenish inventory and optimize warehouse space automatically. Consumer preferences change rapidly, and product demand trends vary wildly depending on the region and time of year. By quickly analyzing large amounts of data on inventory and product sales, the customer can maintain the right quantity of products in all stores.
Because of its very nature, IoT derives its value from data. With the right data science tools, AI experts can run machine learning algorithms on streaming data. This allows organizations to resolve major issues, as exemplified by the wide variety of use cases.
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