Edge AI, also known as Edge computing with Artificial Intelligence, is a new technology that combines AI with edge computing. This means that instead of processing data in the cloud, it will be done on the devices themselves, i.e., at the edge of the network. This has numerous advantages, one of them being improved performance, reliability, and reduced latency. On the other hand, IoT or the Internet of Things, has been one of the fast evolving technologies in the recent past, and according to estimates, it is expected to be worth more than $1.1 trillion by the end of 2026. The popularity and advancement of both technologies bring in focus the impact that Edge AI will have on the IoT landscape.
One of the primary reasons why Edge AI is expected to change the IoT landscape is its ability to enhance the reliability and speed of devices. By processing data on the device, there will be fewer delays and inconsistencies, leading to prompt responses. Moreover, devices can learn from data generated by sensors and other devices, and over time it can improve its efficiency without resorting to communication with the cloud. With edge computing alone, there was a significant increase in a device’s performance, and with AI added to the mix, it takes the game to another level entirely.
Another advantage of edge AI in the IoT landscape is enhanced data security. As data will not only be processed on the device but also remain on the device, the chances of data breaches and theft during data transmission are reduced. Moreover, with AI, devices can analyze and detect anomalies and mitigate risks faster than humans, which is a critical feature in today’s digital age.
Edge AI also allows IoT-enabled devices to work independent of any central cloud network. This means that even if there are connectivity issues, the devices can still function as they have the required processing power. So, devices can operate in remote areas, disconnected from any network, and not lose any functionality. This will benefit businesses that operate in harsh environments or have a need for devices functioning in remote areas.
Furthermore, Edge AI also aids in reducing costs. As data processing takes place on the device, businesses do not have to bring in and maintain extra infrastructure for data processing. This can save businesses significant amounts of money on cloud storage and maintenance costs, and the expenditure on hardware can be significantly reduced too.
Conclusion:
Edge AI is certainly going to cause a paradigm shift in the way IoT is viewed and performed. The advantages are too many to ignore, and companies that adopt this technology will be one step ahead of their competitors in the coming years. The advantages of integrity, security, reliability, and cost-efficiency are too compelling to be ignored. And as Edge AI continues to evolve, we can only expect to see more benefits from this amazing technology.
