Tag Archives: Edge Computing

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.

What is Edge Computing?

Many IT professionals spend most of their careers within the safe and controlled environments of enterprise data centers. However, managing equipment in the field is a different ball-game altogether.

Pandemics such as the COVID-19 are increasingly transforming the world. The emerging ecosystem is confronting and challenging this transformation. Among this mayhem, edge computing is entering as a key transition phase, with the massive shift towards home-based work. Along with the generation of new opportunities for distributing computing, key players are deploying increasing numbers of edge data centers for navigating the sharp economic downturn.

The major benefit of edge computing is it acts on data at the source. This distributed computing framework works by bringing enterprise applications closer to sensors acting as data sources within the IoT system and connecting them with local edge servers and cloud storage systems. Edge computing can deliver strong business benefits with its better bandwidth availability, improved response times, and faster insights.

Edge computing has the potential to enable new services and technologies with its low-latency wireless connectivity. This could transform global business and society. According to some technologists, edge computing can bring in a new era of powerful mobile devices with no limit on their ability to compute power and data.

Consultants and futurists are projecting a growth of up to US$4.1 trillion for the edge economy by 2030. Linux Foundation, in their report Edge 2020 claim edge investment will take wing after 2024, and the power footprint of the deployed edge IT and data center facilities reaching 102, 000 MW by 2028. They expect the annual capital expenditures to reach US$146 billion by then.

In the technology world, however, there are divided opinions regarding the short-term prospects of edge computing. Although there is no doubt about the usefulness of edge computing, people are skeptical about the time frame for edge computing to become profitable. Therefore, starting with 2020, investors and end-users are looking intently at the economics of edge computing and focusing more on its near-term cost-benefits rather than on its long-term potentials.

There is a huge opportunity in edge data centers, as edge computing plays out over several years, with long deployment horizons and gradual adoption of technologies boosting the market. However, executives do not expect the revolution to go through cheaply, with the build-out of edge computing pressurizing the economics of digital infrastructure. This may create repeatable form factors leading to more affordable deployments. Experts are confident that most edge data facilities will be highly automated, remotely managed, and require no human intervention.

At the present, it is difficult to say which edge projects will succeed. With product segmentation and a fluid ecosystem, even promising ventures can struggle as they try to locate profitable niches. While investors are wary of speculative projects, it is reasonable to expect well-funded platform builders and stronger incumbents will acquire promising edge players, especially those running short of funding.

Tower operators are also influencing the competitive landscape. Their massive real estate holdings and financial strengths are positioning the tower operators as potentially important players in the edge computing ecosystem.