How does IoT work?

IoT devices are empowered to be our eyes and ears when we can’t be there physically. Equipped with sensors, the devices capture the data that we could see, hear or perceive. They then share the data as directed and we analyze it to help us inform and automate our subsequent actions or decisions.

Here are 4 stages of process:

. Capture the data. Through sensors, IoT devices capture data from their environments. This could be as simple as temperature or as complex as a real-time video feed.

. Share the data. Using available network connections, IoT devices make this data accessible through a public or private cloud, as indicated

. Process the data. At this point, the software is programmed to do something based on that data – such as turning on a fan or sending a warning.

.Act on the data. Accumulated data from all devices on an IoT network is analyzed. This provides powerful insights to inform reliable business decisions and actions.

How have IoT technologies evolved?

IoT devices generate more than 40 zettabytes of data per year – which is equivalent to 40 trillion gigabytes. Although we cannot quantify digital data in physical terms, we can say that if all that data were converted to floppy disks from the 1990s – and laid on a carpet  – it would cover more than half of the earth’s surface. For IoT to evolve, a specific set of technologies had to come together and advance at the same time. And in a chicken-and-egg fashion, it can be difficult to say which technological development came first in the evolution of IoT.

Connectivity – This massive growth in the volume of IoT data could only have happened with internet and cloud connectivity robust enough to send and receive it. Many IoT devices today depend on a local Wi-Fi network for their ability to transmit complex and voluminous data. But as 5G and other cellular networks improve, a recent McKinsey article outlines the impact it can have and how it can decouple IoT devices from Wi-Fi networks.

Sensor Technology – With the constant increase in demand for IoT sensor innovation, the market has grown from a few expensive niche vendors to a highly globalized and price-competitive sensor manufacturing industry. Since 2004, the average price of IoT sensors has fallen by more than 70%, accompanied by an increase in demand for improving the functionality and diversity of these products.

Computing power:  The 40 zettabytes of data currently generated by IoT devices is expected to nearly double in the next five years – and exponentially after that. To use and take advantage of all this data, modern companies demand increasing amounts of memory and processing power.

Artificial intelligence and machine learning:  These technologies give companies the ability not only to manage and process large amounts of IoT data but also to analyze and learn from it. The larger and more diverse the datasets, the more robust and accurate the insights, and intelligence that advanced AI-powered analytics can deliver. The rise of IoT devices has grown a lot, along with the advancement of artificial intelligence and your appetite for the data they deliver.

Cloud Computing:  Just as connectivity was an integral part of the development of the Internet of Things, the rise of cloud computing has also been closely linked to its evolution. With the ability to provide high-volume storage and processing power on-demand,  IoT cloud services paved the way for IoT devices to collect and transmit increasingly large and complex datasets. Private cloud solutions have also made it possible for companies to manage higher volumes and types of IoT data while maintaining the security of a closed system.

Edge Computing – Devices within an IoT network are often geographically widely distributed, yet all transmit data to a single central system. As IoT data volumes grow larger and larger, they can begin to monopolize a company’s cloud bandwidth and capacity. In addition, the data takes time to be captured, transmitted, processed, and received at its final destination. This lag – known as “latency” – adds further inefficiency, especially for companies where data processing is very time-sensitive. Edge Computing solutions decentralize the processing power of a system by bringing it closer to the data source. This is accomplished by integrating localized computing systems, as well as building processing capabilities within the IoT devices themselves.