While the integration of artificial intelligence into the Internet of Things is an exciting technological development, it’s also part of a shift that experts believe will double the value of IoT to nearly $1 trillion over the next five years.
The rise of the AI of Things, or AIoT, promises to transform our devices from simple data collectors into intelligent decision-makers. This evolution, however, brings some hurdles.
While processing at the edge (on a network closer to the source) offers benefits like enhanced privacy, improved autonomy and faster real-time processing, it also presents challenges. We must consider hardware limitations, increased complexity and the delicate balance between device capability and cost efficiency.
Going forward, developers must weigh these factors carefully, accounting for the immense potential and practical constraints of bringing AI to devices at the edge.
What Is AIoT?
The AI of Things integrates artificial intelligence with the Internet of Things. Introducing AI into the mix improves data management, privacy and IoT efficiency.
How Is AI Changing IoT and Edge Computing?
Two evolutions are happening at the same time here.
First, there’s an ongoing edge revolution in IoT. As I’ve written previously, edge computing fundamentally changes how devices process data.
Instead of relying on distant cloud servers, edge computing allows devices to process information locally or within a nearby network. This approach significantly reduces latency and resource consumption, enabling faster data delivery and increased efficiency.
Second, AI is working its way into almost every industry, and IoT is no different. From predictive maintenance to smart assistants, devices integrated with AI achieve a new level of autonomy and usefulness.
The edge takes this up another level. In an AI-enhanced edge scenario, the device generates data, processes it locally and then uses it to make decisions onboard. This represents a leap forward in the capabilities of devices, allowing them to not just collect and transmit data, but to interpret and independently act on it.
Gartner reports that edge AI and generative AI will be among the most valuable contributors to IoT through 2024 and beyond. Things like native AI applications, code generation tools and platforms will be especially important for businesses looking to deliver targeted business outcomes based on local data.
By next year, Gartner estimates 95 percent of new industrial IoT deployments will include analytics and AI-edge inference capabilities, up from less than 30 percent in 2022.
Benefits of AIoT
This is a big shift and happening quickly. Edge AI is on its way to revolutionizing data processing in connected devices, promising enhanced speed, reduced latency and new possibilities for next-generation products.
At the forefront of these advantages is the enhancement of privacy. By processing data locally on the device, edge AI minimizes the need to transmit sensitive information to cloud servers, addressing a growing concern among consumers and businesses alike.
This approach is particularly important for devices that handle personal data, such as smart home assistants or health monitoring wearables, where privacy breaches could have serious consequences.
Beyond privacy, edge AI enables near real-time processing and decision-making, a game-changer for applications requiring split-second responses. Additionally, it grants devices a level of autonomy previously unattainable, allowing them to function effectively even in areas with unreliable network connectivity.
But, not all that glitters is connected device gold, and it’s worth considering the shortcomings in AIoT.
Challenges of AIoT
For starters, there are hardware limitations for connected devices. AI models require significant processing capabilities and, even when scaled down, can cause power consumption problems.
Juxtapose this with the industry’s growing preference for real-time operating systems, cheaper chips and resource-constrained devices and you have a recipe for technical headaches.
Additionally, incorporating AI often requires a complete overhaul of device architecture, especially for smaller IoT devices. This necessity for redesign can slow the adoption of edge AI across various sectors.
The limitations of edge computing introduce additional considerations. Edge devices and networks typically offer far less storage capacity than cloud infrastructure, a problem when AI models potentially range from gigabytes to terabytes in size.
Consequently, developers face a balancing act between the sophistication of AI models and the practical constraints of the edge. This tension forces tough decisions, often requiring compromises that can impact the overall effectiveness of AI integration.
Designers Must Tackle AIoT’s Challenges
Let’s wait and see how technology catches up with the problems above. After all, we’re already seeing smart security cameras running edge AI to detect faces and make security decisions on the user’s behalf.
This is a nascent industry that’s coming along in leaps and bounds, and we shouldn’t be surprised if developers find new ways to downsize the technology, improve power consumption and enhance efficiency.
But, for now, these are drawbacks that will need to be designed for. Expect a mixture of cloud and edge device connectivity to provide a big data solution in the meantime.
IoT is already well-regarded for decreasing business downtime, preventing costly maintenance and improving long-term decision-making. Adding AI into the mix, with an eye on safety and efficiency, promises to only increase the industry’s value proposition.
I look forward to seeing whether the industry embraces a proliferation of many small devices with limited intelligence, or if the landscape is dominated by numerous simple sensors overseen by a centralized, highly intelligent house assistant that processes their data and issues commands.
Whatever happens next, harder, better, faster and stronger devices are on the horizon. To get there, designers must tackle the key challenges of system complexity and hardware limitations.
