Autonomous Systems Poised to Become the Norm in IT
Autonomous systems are becoming more prevalent in today’s world. In several years, autonomous IT systems could be the norm rather than a novelty.
An autonomous system comprises one or more networks that a single entity or organization manages. These self-contained systems can act based on data. For a system to truly be autonomous, it must be able to gather information, find a solution based on this information and execute an action to achieve a goal. Consider connected cars, which collect information not only about themselves, but also other connected systems and surrounding entities, including traffic lights, other vehicles, pedestrians and so on. This kind of autonomous system is necessary to ensure traffic safety and optimal traffic flows.
While autonomous systems are hardly the norm today, they are becoming the model for many Internet of Things-connected systems. IoT is pushing the limits of IT through automated deployment, monitoring and problem resolution of IT resources. As system developers apply automation to IoT systems, we will see growing use of policy-driven configuration, automatic and comprehensive monitoring, as well as automated remediation.
Autonomous Systems Gain Ground in Various Sectors
Consider one of the most well-known IoT application areas: self-driving cars. Autonomous vehicles are an oft-cited IoT application. According to a May 2019 Canalys report, 7% of cars sold in the U.S. in Q1 2019 were capable of some autonomous driving. The popularity of partially autonomous vehicles will continue to grow.
Industrial IoT applications encompass various use cases, with techniques such as digital twinning providing ways of managing remote devices. IoT devices in manufacturing environments collect data on the state and performance of equipment and make on-the-spot decisions about adjustments to operating parameters.
IoT for businesses can extend to agriculture as well. Fully monitored greenhouses allow for hands-off plant care, and drones and monitoring devices can be a boon for crop fields by alerting farmers to potential problems.
Creating Autonomous Systems: How They Work
While the application of IoT systems is as varied as the businesses that use them, there is a shared architecture among most IoT deployments that enables greater automation than would otherwise be possible.
A typical IoT deployment data pipeline consists of four distinct stages. First, the sensors on IoT field devices collect data. Depending on the application, these sensors collect data such as temperature, air pressure and proximity. Next, analog sensor data is converted into digital information that can travel over a network. This stage usually happens on the same device that contains the sensor, called the data acquisition device. The third stage is to move the digitized data to an edge device. An edge device can further process the data and prepare it for storage and analysis in a data center. During this stage, the edge system will remove redundant or unnecessary data points and organize data into categories for ingestion. In the final stage, the data enters an on-premises or cloud-based data center. The ingested data might need more compute-intensive processing before it can be analyzed or stored for later use.