Edge Computing for Smart Homes

Designing devices for smart homes can be a huge challenge. There are numerous limitations to be overcome, but the sensible use of sensors can help smooth the way. Devices for smart homes can relate to lighting, kitchen appliances, security, heating/cooling, and entertainment. With the advancement in technology for smart homes, engineers need to be more intuitive and develop more capabilities for making products more intelligent. Among the expectations from homeowners are faster response, higher performance, higher levels of accuracy, and easier integration of multiple devices.

Today, there are widely varying intelligent devices in modern intelligent home technology. Most often, these produce massive amounts of data that must be processed quickly. Although there are limitations to improving the technology for smart homes, contextual data can address them by using a combination of sensors, with the device processing them on the field rather than doing it in a cloud.

Just like in any technology, the fundamental systems and components of smart home technology are also constantly improving. Engineers must continuously develop better solutions as soon as they recognize the limitations. Among the several limitations, three major ones that plague smart home technology are accuracy, latency, and compatibility.

Accuracy is an extremely important factor in smart home technology. Everything affects accuracy, starting from the sensors that are necessary to collect data to the artificial intelligence tools that process the data. This is leading engineers to collect data using innovative new approaches, including using algorithms to combine multiple sensors for processing the data so that they can achieve a higher level of accuracy.

For instance, a smart home security system may involve radar, computer vision, and sound detection to accurately predict the presence of a person. Engineers are also using AI tools and algorithms for finding the most efficient methods of processing data. However, this leads us to the next limitation—latency.

Latency negatively impacts any type of smart home technology. Home security, for instance, needs collecting data from multiple sensors, and analyzing them as fast as possible. The impact on latency increases as there is an increase in the data gathered, transmitted, and processed.

With end users having multiple smart systems working concurrently, compatibility challenges are bound to crop up, impacting overall performance and functionality. This is one reason for engineers to move their focus from systems that depend on platforms, manufacturers, and devices. Rather, they are moving more of the functionality and processing to the devices themselves. This is where edge computing is helping them—addressing all three challenges at a time.

In smart home technology, edge computing transfers most of the processing and analysis from the cloud to the device itself. In simpler terms, data processing takes place as close to the sensor as possible.

For instance, home security cameras are notorious for reporting false positives, eventually causing the owners to ignore accurate alerts. One way of improving the accuracy is by improving the quality of the lens and image sensors. The other is by using edge computing to differentiate between the movement of animals and leaves being moved by winds.