Author: Julia Mitchell

In 2021, the global automotive IoT market was valued at US $115 billion, and is projected to grow at a GAGR of about 90%!

This impressive growth reveals some tendencies of what a vehicle becomes for humankind. First, end-users who are getting used to having their cars always know the optimal route and are ready to offer their favorite podcasts to listen to. The second is commercial transportation that uses vehicle data to make money. Accordingly, the automotive IoT solutions market is abundant with various technical bells and whistles, from 300-meter range lidars to heads-up display units which are actively advertised. Such solutions are really impressive, but best-in-class does not mean the best option for your business. For instance, you can equip the vehicle with extremely high-resolution computer vision cameras to identify obstacles on the route, but why do you need them if the well-trained neural network can perform the same much cheaper?

If you are wondering about the implementation of Enterprise IoT solutions, you understand that this process is rapidly developing all over the globe. Accoring to McKinsey Digital, 127 devices hooked up to the Internet for the first time every second in 2021, and in North America alone the worth of smart factories is expected to reach $500 billion in 2022.

This trend is not surprising, as EIoT implementation helps to achieve a level of worker safety that was unattainable before, as well as new business models, and, therefore, new revenue flows. Using IoT devices, you will be able to get more information about manufacturing processes, employee and client behavior, and data that will help predict breakdowns and prevent downtime of equipment. Sounds tempting? All these benefits are achievable, but they depend on the company's ability to correctly assess the risks of EIoT implementation.

In the recent ten years, the Internet of Things has shown itself as a breakthrough solution for delivering more informed business strategies, improving customers’ experience, managing assets, automating processes, performing predictive maintenance, and so on. A large role here was played by embedded solutions – hardware and software systems which contribute to the high performance of the whole IoT ecosystem. However, there is no universal method of organizing and deploying the embedded IoT system to guarantee its complete success. On a case-by-case basis, embedded IIoT solutions providers, manufacturers, developers, and business owners have to decide which “things” to equip, how to customize solutions, and how to save without causing harm. However, the future seems to belong to the IoT embedded. 

As McKinsey reports, by 2030, total revenue for 5G IoT embedded modules will increase more than 50 times! This is partly due to deep learning processors and neural processors contributing to exponential growth in performance and energy efficiency of embedded computing systems. However, to make it real, manufacturers of the ready-made solutions have to study deeply the demand to produce off-the-shelf modules for customers’ specific needs. Despite there being an abundance of ready-made embedded systems on the market today, they are not always in strict adherence to customers' business goals. At some point, the question might arise of reducing the cost of a solution in mass production and of a unique form factor or minimizing the dimensions of the devices. All these factors reinforce the need for custom embedded hardware and software development. This article focuses on the main points to consider while designing an embedded system within IIoT solutions to make it capable of improving business.