AI is reshaping the landscape of intelligent connected devices. As the AIoT (Artificial Intelligence of Things) market accelerates, we’re learning that relying solely on cloud-based processing introduces serious limitations, from network latency and energy use to privacy concerns and reliability gaps.
The solution? Bringing AI closer to the data source with edge computing. But connectivity remains essential. Rather than reducing communication to a secondary function, connected AI strengthens the link between smart devices and the networks that support them, enabling seamless coordination, adaptive intelligence, and real-time responsiveness.
What You Will Learn
Discover how connected AI is transforming device intelligence, connectivity, and collaboration across the AIoT landscape. In this article, you’ll learn:
- Why cloud-only AI is no longer sufficient for intelligent IoT systems
- How edge AI and connectivity work together to optimize performance
- Real-world examples of AI enhancing wireless communication protocols
- How communication data itself is becoming a sensor for AI systems
- The role of connected AI in federated learning and distributed edge systems
Why Connected AI Matters for Edge AI Innovation
Forecasts project that 6 billion IoT devices (based on TinyML chipsets) will ship in 2030, across many application domains from agriculture to smart homes, transportation, wearables and healthcare, smart cities and utilities. Depending on application these must support a range of communication protocols, all the way from cellular and Wi-Fi to Bluetooth and 802.15.4. It is fair to assume that anyone building an AIoT product will want to offer more than just table stakes – an AI function and a connectivity function side by side – if they can differentiate through enhanced leverage of an integrated solution. What might that look like?
How Edge AI Enhances Communication Performance
In crowded communication environments it becomes very important for an AIoT device to select the most available channel with the least interference, and to be able to alter this choice adaptively as ambient conditions and traffic demands change at and between access points or base stations. In simpler times this channel estimation/optimization was handled through precomputed lookup tables, but now AI management has become essential to keep up with these more complex demands.
AI for Channel Optimization and Network Reliability
This optimization is not only important for throughput. Safety-critical applications, such as automotive apps through V2X or in surgical and industrial robotics, all depend on ultra-low latency. This is a key component of the 5G cellular standard (and beyond) and requires guarantees from both network and end-user devices, increasingly served by AI-based channel optimization.
Another emerging application is positioning, especially valuable to locate moving devices (packages, shared bikes) in a smart city. Communication between base stations and edge devices can provide time-of-flight and angle of arrival data, though accuracy can be compromised by reflections and other factors. AI can mitigate these limitations through learning over time.
Using Connected AI to Turn Connectivity Into a Sensor
This is a very exciting area, using communication (particularly Wi-Fi and cellular) as an additional sensing input to an AIoT device. By monitoring channel state information (CSI), commonly compromised by blockages and moving objects, then collating these inputs from multiple devices around a room/office/building/city, such a system can detect objects or people moving, even down to the level of detecting breathing rates.
This sensing input depends heavily on intelligent processing to separate interesting signals from noise, to eliminate reflections and don’t-care movement (such as overhead ceiling fans, or pets) from human activity. Applications extend from home security to gesture recognition, to non-intrusive health monitoring.
Exactly how much of a role AIoT plays in this application is still evolving but it is becoming clear, as in so many other applications, that some level of AI processing on edge devices – smart speakers, smart TVs, smart power sockets, etc. – will be essential given their natural distribution around the environment. Local intelligence is also essential to pre-process and reduce what must be sent to a central hub for final classification.
Connected AI in Federated Learning and Distributed Systems
Local Model Training Meets Smart Communication
A different but equally interesting example can be found in federated learning. For fleets of systems – might be cars, or autonomous cars, or autonomous office cleaning devices – learning must be an ongoing process and can’t depend on shipping massive amounts of data through backhaul to a cloud-based training network.
Training instead should start to be developed locally on each edge system, then that enhanced AI model can be shared between the local nodes and shipped back to a cloud-based training consolidator. This is the “federated” part of federated learning. Each edge system is responsible for contributing its own learning to the greater good. AI at the edge obviously plays a role, and so does communication because it must handle efficiently local training upload and eventually revised global training download.
Companies who can offer technology and expertise in both communications and AI are very rare. We have this expertise and technologies at Ceva and are accustomed to providing fully integrated solutions or options in which AI and communications sit side by side. If you would like a discussion on trends we are seeing and how we might be able to help, checkout our website and give us a call.
Key Takeaways
- Connected AI isn’t just about data transfer. It enables smarter, faster, and more adaptive decision-making at the edge
- Edge AI improves connectivity by dynamically optimizing channels, reducing latency, and supporting positioning
- Communication technologies (like Wi-Fi and cellular) are now inputs for AI, unlocking new applications like motion detection and health monitoring
- Federated learning shows how edge devices and communication systems must collaborate to scale AI in real-world, distributed environments
- Companies that can deliver both AI processing and communication IP together, like Ceva, are positioned to lead in the AIoT era
Where Ceva Makes the Difference
Explore how Ceva’s Edge AI and Connectivity IP solutions work together to power the next generation of AIoT devices.
Talk to Our Experts
FAQs
What is connected AI?
Connected AI refers to the integration of edge AI processing and wireless connectivity, allowing intelligent devices to collaborate and learn in real time.
How does edge AI support communication optimization?
Edge AI enables real-time analysis of network conditions, dynamically selecting the best channels and reducing latency in communication-critical environments.
Can communication signals be used as sensors?
Yes. Technologies like Wi-Fi and cellular can be leveraged as non-visual sensing inputs through connected AI, detecting motion, presence, and even vital signs.
What is federated learning, and why is it important for AIoT?
Federated learning allows edge devices to train AI models locally, preserving data privacy and reducing transmission costs while still enabling collective intelligence.
Moshe Sheier
