Developments in AI for the internet of things

Developments in AI for the internet of things

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AI is everywhere. Including at the edge.

Edge AI is driving important shifts in the Internet of Things (IoT) landscape by integrating artificial intelligence tech directly into IoT devices (also known as edge devices). 

This makes data processing from those devices faster, more secure, and more efficient overall – reducing the volume of data that has to be processed in centralised facilities and maximising the value of the data generated by IoTs. 

Local data processing with edge AI enables:

  • Faster inference of meaning from data, and faster decision-making as a result
  • Reduced latency due to lower volumes of traffic on a network
  • Improved security, with fewer opportunities for threat actors to intercept data in transit
  • Increased energy efficiency 

And AI allows IoTs to perform more sophisticated tasks

Instead of just collecting and transmitting data, AI enables IoT devices to perform tasks that make them increasingly useful. 

For example, facial and gesture recognition capabilities can be integrated into smart home devices; and predictive maintenance tools can operate in factory automation IoTs. Security cameras can leverage IA for real-time threat detection; and smart appliances can offer personalised experiences for users. 

The Wevolver’s 2024 State of Edge AI Report focuses on the different industry use cases for this technology, and explores what businesses in those industries can do now to embrace the shift and stay competitive while edge AI continues to grow exponentially. 

And also this year, a growing number of studies are going into more detail about how different sectors can leverage edge AI to increase efficiencies and quality of work – such as this study which proposes a roadmap for integrating edge AI into farming, and this exploration of the potential of edge AI for secure healthcare devices. 

Developers are working to overcome the challenges 

Edge AI holds numerous opportunities – but implementing it comes with a unique set of challenges. In particular, IoTs tend to have limited computational power, so it’s not always possible to operate AI systems without additional power sources. 

There are questions around the data quality and the risk of data loss when it’s stored on an IoT, rather than a centralised data pool. And while edge AI can increase security by minimising the transmittance of data, it also raises its own security questions – with local vulnerabilities putting devices and the data they store at risk.

In spite of those obstacles, edge AI represents a shift in the organisation and scalability of smart tech solutions. It’s a field of research and development that will take real strides forward over the next few years; with potential applications across all critical industries

In the future, AI will exist at all our edges 

Experts expect around 43 billion IoT devices to be in operation by 2027 – and edge AI will play an important role in enhancing the capabilities of those devices and making them a truly valuable source of information and insight. 

As well as enhancing the capacities of existing IoT use cases, edge AI is driving the emergence of new possibilities across a wide range of industries. In the future, edge AI will be working in our washing machines and in our rivers, and everywhere in between. 

Join us at DeepFest 2025 to discover the latest developments in edge AI. We can’t wait to see you there.


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