How machine learning helps unlock the full potential of IoT for business development

Connected devices are now a new normal in our lives, which means smart virtual assistants, fridges, routers, clocks, or doorbells are mundane, everyday things. Internet of things (IoT) systems continue to grow in popularity due to the lockdown. During the pandemic, many industries have needed to transform their communications, delivery, maintenance, and other processes.

Gartner’s recent survey reports that despite the impact of COVID-19, 47% of businesses will enlarge their investments in IoT. So, why are IoT solutions indispensable for unleashing the full potential of your business?

Under the hood of IoT and ML

Let’s go over the working principles of IoT and machine learning (ML) systems, which consist of the following key components:

• Hardware parts
• Processors, sensors, storage places and software
• Data reception components and protocols
• Analytics to glean critical insights from data

Developing a custom IoT solution depends on a plethora of edge devices located in offices, homes, warehouses, aboard ships or anywhere else.

To address complicated requirements, vendors of network automation and orchestration tools create a wide range of IoT solutions and services that support different types of edge devices. Many solutions only work in constrained IoT environments, based on operator or device models, or limited third-party platforms. Some also focus on specific verticals, such as utility management, smart grids in retail, cargo tracking in logistics, or energy management.

You should be wondering where machine-learning algorithms fit into the picture. IoT systems collect data through its many sensors, but massive amounts of data would be useless without adequate processing methods. Raw data requires thorough analyses and appropriate organizing before you can make valuable use of it.

Machine-learning and deep-learning algorithms allow computers to mimic how the human brain works, processing big data and generating inference. But machines require algorithms to perform tasks, learn and self-regulate in some cases. By adopting more and more nuances of human-like behavior, they might become more sophisticated.

IoT and machine-learning applications work as machine-to-machine (M2M) communication systems. А computer network protocol connects one machine — be it a mobile phone, mobile gateway, or an electronic component — to other IoT devices, like sensors, without having a human user interface in sight.

The training process of machine learning models and IoT systems starts with raw data that a computer receives as input, then finds correlations and provides an output that makes sense. The more data it processes, the more complicated the tasks it can perform, generating valuable feedback. An analogy that could be made is that IoT and machine learning are connected similarly to our bodies and minds, which work together to collect and analyze data from our senses.

Benefits of IoT implementation for your business

An obvious application of machine learning for IoT is the automation of data processing. But it is not the only possible use case combining IoT and machine learning. Another widely used capability of machine learning or deep learning and IoT is the predictive potential they provide to make the most out of raw data. Let’s have a look at some notable use cases.

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