Transforming IoT Architecture With AI
Shaping IoT Architecture With AI

Thinkers say Internet of things (IoT) and the artificial intelligence (AI) will transmute business and society more deeply than the digital and industrial revolutions combined, and now we are starting to see how that phase would shape up. One and only one critical factor is where the intelligence exists in and how will IoT architecture will be influenced.

Although many institutes believe that AI’s rightful place is in the cloud as that is the place where they are moving their IT computing power and data, the main requirement for practical IoT is interoperable connections between the different sensors at the periphery of a gateway and bidirectionally from the cloud. This then poses the problem of underdevelopment.

Majority of the machine learning applications and AI that are about to change the industries and revolutionize our world, call for real-time responsiveness. For instance, though we don’t mind the slight delay in Alexa’s slight delay in answering our questions like today’s weather, the responsiveness of self-driving vehicles on the road is a whole different story.

Many AI applications need a lot of computational muscle to process device data and  algorithms. When low latency and real time response is critical, we need edge computing architectures. However, it may not always be the case. AI can still be done in a data warehouse, in the cloud, on an IoT device, or at the edge—or a combination of all of these. To create the most sustainable and efficient IoT architecture, we need to know what type of computing power goes where. This will further enable us to balance the scale economies offered by the cloud with the performance requirement of the AI is being at the edge.

Mostly this is referred as “fluid computing” with different levels of computing intelligence and processing throughout the network architecture.

Securing the IoT Architecture

Unsurprisingly, security is another concern. IoT opens up a lot of gateways for malware attacks as the security protections and encryption are difficult to pack into endpoint devices. While providing low latency, architectures employ secure gateways between the IoT devices and cloud can ease the security risks. If there is a loophole in the security throughout the architecture, the implemented IoT, organizations and AI are vulnerable. This furthermore increases the possibility of AI making bad decisions based on the potentially bad data.

Redundancy can be considered as well. Organizations need to make sure if they have incorporated in enough redundancy into their architectures so in case when something goes down—of course it will—the network can quickly recover.

Development of the new software and hardware is the final point. With AI moving to the edge, we will see manufacturing of more AI- oriented chips for IoT deployment. Large technology powerhouses such as Intel, Microsoft, Google, and Apple are getting in on custom chips also the venture capitalists have started backing up this area.