Shifting attitude towards the Internet of Things and smart homes

the (gold) The “Artificial Internet of Things,” a tech ecosystem, emerged during the pandemic. Then the smart home was developed.

AIoT combines connected objects (IoT) with the artificial intelligence (AI) used in these objects.

The past 12 months have been difficult. The pandemic has wreaked havoc all over the world and people are now realizing that Covid-19 is here to stay.

We are now accepting this fact and looking for ways to adapt our lives and our interactions with the world. To ensure that people live safe, productive and happy lives, governments, industries and businesses are constantly changing the status quo.

People had to change the way they worked and where they worked. Over the past year, working from home has become the norm. Companies can continue to allow employees to work remotely as long as employees remain productive. Working from home has re-emphasized the importance of work and the value of our homes. Discussions about technology-enabled smart homes are more topical than ever.

Smart homes and all the technologies used are still a very small industry. In the past year, research has identified the barriers preventing artificial intelligence from becoming a reality. Electronics engineers identified important market-level as well as device-level problems in this paper. Then the researchers ran the same study a year later to see how things had improved. the address? What is the address? No results have been reported.

AI has security issues due to its reliance on data. The more information a device needs, the brighter it is. Engineers have found that processing data locally can solve privacy issues. Homes can keep their data within their walls without sharing it with third parties in the cloud. Reducing third-party cookies reduces the risk of data leakage.

smart home

A smart home can be used to store data so that a remote cybercriminal will not become an ordinary thief to steal it. Although this is unlikely to happen, device manufacturers should ensure that data processing on their devices is secure.

You can enjoy better data and decision security by using various device-level security features such as secure key storage, fast encryption, and real number randomization.

Engineers have found connectivity to be a major barrier to deploying AI. However, only 27% of industry professionals see connectivity as a significant barrier to technology, and 38% are concerned about the technology’s ability to overcome latency issues. For example, home health care monitoring cannot be held back by poor communication when it comes to making decisions about potentially life-altering conditions, such as heart attacks. However, using on-device processing makes network response time irrelevant.

If the industry wants to develop applications that do not suffer from latency, it should move to on-device computing. Product makers can now run specific AIoT chips in nanoseconds, allowing products to think quickly and make decisions accurately.

AIoT

Engineers also mentioned the expansion issue last year. Engineers know that the number of connected devices is constantly increasing, which puts additional pressure on the cloud infrastructure. About 25% of engineers believe that scalability is a barrier to the success of cutting-edge technology in 2020. However, experts are beginning to recognize the deep-rooted scalability benefits of the Internet of Things.

The cloud is no longer an edge computing factor, eliminating any potential scaling and growth issues. Today, less than a fifth of engineers believe that cloud infrastructure can hamper advanced AI.

The good news? The electronics industry has nothing to do to ensure the scalability of the Internet of Things. One of the major technical barriers to expanding the Internet of Things is the need for cloud computing to manage billions of additional devices and petabytes in the future—which has now been eliminated.

Increase power capacity and reduce power consumption

The AIoT market has grown over the past year. Technically it has also improved. The on-device AI processing capabilities are improved while reducing power requirements and expenses. Chip owners can now customize chips for different AIoT needs at an affordable price.

How can engineers transition to AIoT chips as a realistic option for product manufacturers?

The development environment is a critical consideration. New chip architectures often mean immature and untested proprietary programming platforms that engineers have to learn and get comfortable with.

Instead, engineers should look for locations that can afford to use industry-standard methods they are familiar with. Industry-standard approaches include full programming capability and runtime environments such as FreeRTOS, TensorFlow Lite, and C. Engineers can program chips quickly using easy-to-use platforms without learning new languages, tools, or technologies.

It is essential to have a single programming environment that can handle all the computing requirements of an IoT system. Capability for computing demands will always be key to enabling the design agility needed to bring fast and secure AI home in the new post-Covid era.

Image credit: Kindel Media; pixels. thank you!

Diana Ritchie

Editor in Chief at ReadWrite

Diana is the ReadWrite editor. Previously, she worked as an editor at Startup Grind and has over 20 years of experience managing and developing content.

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