IoT vs. Edge vs. Edge AI: A Quick Compare and Contrast

Frequently used but often misunderstood, we compare and contrast the above connectivity concepts

Ah yes, the Internet of Things – if only we knew then what we know now, we wouldn’t have thrown the term “smart” around ever so willy-nilly. Kind of like when one monkey teaches the other how to lure termites out with a slim stick – there’s always a smarter way lurking patiently in your peripherals. Still, let’s not be too hard on ourselves; all tech had to start somewhere, right?

In addition to being one of the most profoundly ambiguous phrases of all time, IoT is quite broken from a functionality and security perspective. While there is merit to the idea of internet-enabling regular products, how “smart” that is, has definitely been blown out of proportion.

See, IoT is just a term for devices that are connected to the cloud. As such, using the term “Smart” is quite misleading as technology advancements in the IT field have clearly demonstrated. Instead of smart homes or smart grids, we should have aptly called them “connected homes” or “dumb connected grids that are unbelievably inefficient”.

Edge Computing (EC) is what brings the “smarts” to the table. EC moves compute and some or all decision making to the edge of the network, making IoT data collection, analysis and decision-making around that data more efficient.

But we’re not done yet, the next evolution, which is what we are going through right now, is Edge AI (essentially IoT and Edge Computing combined with AI thrown in). Edge AI refers to the deployment of artificial intelligence algorithms and AI models directly on local edge devices. This allows for real time processing of data and analysis without constantly relying on cloud infrastructure or even needing a real-time connection to the internet.

So let’s break this down for you and draw some clean lines between these technologies. The aim of the diagram below is to show you exactly where the “smart” stuff is happening in each scenario (depicted by the brain).

In a Nutshell

IoT devices like a temperature sensor, light sensor or a camera can only take in data and stream it to the cloud indiscriminately, regardless how valuable that data is or (in most cases) isn’t. For example, a camera at a traffic intersection watching for jaywalkers needs to stream everything to the cloud, where AI in the cloud analyzes the video for Jaywalking behavior.

Whereas, in contrast Edge Computing systems could harvest data from the same sensor, camera, wireless radio, et cetera and pass just a subset of the data to the cloud, where it may be mashed up with other data A.K.A. BIG Data, and people derive insights, and take action – which in some cases might be preventative, proactive, or corrective maintenance.

These two alternatives already exist, but in 2024 and beyond a new possibility is emerging:

Enter Edge AI – now the same device (or at least a new generation of it) that was previously harvesting data is smart enough to filter and process ALL or most of it of the data and derive its own insights and act upon it…ON ITS OWN!

So in the Jaywalking scenario above, the camera itself can distinguish jaywalking from anything else, and only push that information to the cloud while discarding everything else.

Or taking another example, say you have an industrial motor which is showing signs of imminent failure. In IoT and Edge, we get early warning and can take remedial action before a crisis. But the RISK of failure is not affected until people take action on the insights.

Whereas, in the Edge AI scenario, the device can take action on its own: perhaps slow the motor speed, or turn it off to let it cool down then start it again. Edge AI could do something in real-time based on the data, which leads to a better outcome than the preceding IoT or Edge Computing enabled solutions can achieve.

Table Contrasting IoT vs. Edge Computing vs. Edge AI

Features	IoT with Cloud	Edge Computing	Edge AI
How and Where Data is Stored and Processed	“Big Data” repositories in the cloud	Data processing and storage closer to the source or edge of a network	Complete on-device data processing, storage, and decision making
Network Architecture	Hierarchical and centralized structure	Distributed and decentralized	Decentralized/ on-device
Data Processing and Storage	Cloud-based storage and processing	Network edge / Fog layer data processing	Complete on-device data processing
Productivity & Performance	Larger compute power and storage, Shared pool of cloud computing resources	Reduced latency enables faster response time and lower network load	Highly diminished latency, Decreased bandwidth, Reduced workload on network
Real-Time Processing	Delayed regular data in the cloud, not in real time	Near real-time data processing at intermediate layer	Real-time processing and decision-making on the device
Security	Frequent data transmission is a high security risk	Intermediate data processing lowers security risk	Lowest security risk since all data is processed on-device
Best Use Cases	Industrial machines, City services: roads, sewers, lighting, power, parking; Industrial machines. esp. low data needs	Security systems, Surveillance, Assembly line status monitoring	Autonomous vehicles, Smart devices, Healthcare, Lifestyle, Farming

Beat your Competition to the Edge with embedUR

Whether it’s IoT, Edge Computing, or Edge AI, it’s important to note that each approach has strengths and weaknesses. Likewise, they all have a place in today’s technology landscape. The best option for you will depend on specific requirements and use cases.

The real battle now lies in the technical challenges of building intelligent edge, AKA Edge AI solutions, which depend on a high level of specialized expertise to overcome. However, most startups, even established companies lack the depth of engineering resources necessary.

For engineering managers and CTOs with limited resources, the problem extends beyond simply understanding new technologies; it also involves effectively integrating them into product pipelines and meeting aggressive schedules.

Here’s where embedUR comes in. We are the perfect partner for navigating this challenging environment because of our extensive knowledge of embedded systems, the edge computing revolution, cutting-edge technologies like TinyML and much more.

IoT vs. Edge vs. Edge AI: A Quick Compare and Contrast