How Machine Learning is Applied to IoT

How Machine Learning Can Accelerate Your IoT Initiative

As businesses invest more and more resources into IoT solutions, connecting vast digital infrastructures and adding new functionality, they do it all in hope of achieving one simple goal: more data.

Data became a new business currency and fuel for new processes. The IoT applications are the easiest means of gathering this precious resource. However, vast amounts of data are just numbers, and it is the interpretation of data that really matters. Getting actionable insights and putting that data to work is what drives enterprises forward and drives innovation.

This is where the concept of machine learning (ML) comes into play. The ability of the machine to make cognitive decisions, which are based on the historical data can bring IoT applications to a new level of efficiency. Combing the internet of things and machine learning will help businesses to implement data-driven initiatives based on hidden patterns and anomaly detection.

The Growing Demand For IoT Data Analysis

The IoT infrastructure is probably the best candidate for ML implementation, as these devices tend to generate frequent and large data samples, and the efficiency of machine learning depends purely on the quality and quantity of historical data.

 In the last couple of years, the field of machine learning for IoT has entered a high growth phase, driven largely by industries such as manufacturing, logistics, transportation, and other industrial sectors. Today, the IoT market is dominated by smart city initiatives and the industrial internet of things sectors, which own more than 50% of the market.

In most other industries there is still a void in the understanding of how to implement the internet of things solutions. But as the use cases become more and more apparent, companies will form concrete objectives and the demand for IoT systems with advanced machine learning capabilities will rise dramatically.

ML & IoT Most Exciting Use Cases

There are many examples of how ML can work together with with the internet of things.

Advanced Logistics Planning

Logistics is all about getting the right supply at the right time in the right place at minimal cost. It is a complex managerial process that involves many variables:

• Warehouse capacity;
• Transportation time and cost;
• Inventory control;
• Taxes;
• Etc.

The machine learning and IoT are now can be implemented to track and analyze various environmental factors, and monitor sensitive goods to avoid damage or loss. Environmental sensing provides you with real-time data on certain condition like humidity in the storage area, the temperature of the product. This can be in turn used to detect any quality issues in the supply chain by identifying patterns and fixing them on the spot.

Predictive Maintenance

Predictive maintenance is one of key factors that determine the cost of operation. Basically, it provides your business with the ability to foresee a possible failure in the device. Industrial IoT relies on the continuous and smooth work of sensors.

ML is used to analyze such parameters as temperature and humidity in order to predict the performance of the devices and alert operators when the hardware is malfunctioning or needs replacement.

Another benefit of IoT monitoring with predictive maintenance is the ability to identify poor performance due to the lack of optimization in device usage. This particular use case is widely used in high-power electronics and heavy machinery, where even a minimal increase in the efficiency can result in millions of dollars worth of savings.

Autonomous Vehicles

Self-driving cars are safer, cleaner and more efficient future of transportation. And, unlike previous examples, it has a direct and clearly visible impact on our everyday lives. That is why this particular field of research in ML and IoT has gained such momentum lately.

The ability of a car to drive on its own relies on an incredible amount of real-time sensing data. Moreover, if we are talking about maximizing efficiency, cars need to be able to communicate with other cars and parking operator systems. Processing all this information on the spot and making optimized response while underway on the road needs ML and deep learning (DL) algorithms to power computer vision.

IoT & ML Development Services

There are obviously more use cases for the combination of machine learning and IoT. The fuse between the two elevates strengths of each and helps to breach the potential gaps such as IoT security or limited access to distributed data sources.

If your organization decide to pursue the use of ML and DL together with IoT, you will soon find that the hidden patterns and insights in your data are endless gold mines. Internet of things solutions can transform many of your basic outdated processes into intelligent and highly optimized workflows that facilitate growth, innovation and a competitive edge.

Bio

Darya Tsygankova is a Marketing Manager at HQSoftware, a software development company with the main focus on IoT application development, VR, and AR solutions.

Author’s Website URL: https://hqsoftwarelab.com/