IoT vs. Edge vs. Edge AI: Evolution and Use Cases
A deeper look at the differences, evolution, and real world use cases of the above connectivity concepts
While doing research, you’ve no doubt come across a lot of sites that use terms like Edge and IoT interchangeably, with a little bit of indiscriminate “smart” something thrown in there. While these terms are frequently used, they are often misunderstood (and understandably so). Sure, both concepts are related to connectivity and data processing. However, they represent distinct aspects of the digital ecosystem.
So, where does IoT (Internet of Things) end, and the Edge begin (pun intended)? Join us as we follow the footsteps of network evolution and see how each stage has been applied in real world use cases.
The Internet of (Dumb) Things Debunked
IoT, or the Internet of Things simply refers to an intricate network of interconnected systems and devices equipped with sensors and actuators that are capable of communicating and sharing data with the cloud.
Relatively speaking, IoT is DUMB. IoT devices aren’t capable of processing the data they collect, so they have to stream all of it to the cloud regardless how important or valuable that data is. We’ve been connecting dumb devices to the Internet for almost two decades now (the current estimate stands at 15 billion connected IoT devices worldwide) – and misguidedly calling them “smart” simply because they were “connected” devices. We were naively throwing about the term “smart” (smart grid, smart home etc.) rather too liberally, because we didn’t know just how smart technology would become in the not so distant future (i.e. Now).
When talking about IoT, think: data sponges. Like a temperature sensor, wind gauge, microphone or camera that can only sense or ingest something (like the wind, a sound, an image) which becomes data that is immediately passed on (streamed) to the cloud, indiscriminately without deep inspection or filtering.
It seemed like a smart thing to do at the time. But now we know there is an even better way with Edge AI. Because, streaming everything to the cloud, storing it and making the cloud do all the work is extremely costly. Especially since, most of this data is promptly thrown away once it arrives in the cloud, because of its irrelevance.
Internet of Things: Use Cases
Supply Chain Management
Supply chain, logistics management, inventory management and tracking are all made easier by IoT. For instance, IoT sensors or beacons might be of great use in a warehouse when it comes to speeding up packing operations. The same sensors could also track a shipment cross country from the supplier to the distributor. This improved efficiency also leads to reduced operational costs.
Power Grid
Collecting data from street lights or directly from electricity meters in people’s homes enables utilities to optimize energy distribution, better predict power failures and monitor power consumption while reducing truck-rolls to inspect or service equipment. This helps them improve grid reliability, lower operating costs and reduce energy waste.
City Infrastructure
From parking meters to trash cans, IoT and cloud computing assists local governments and various transportation entities by helping with traffic, fleet, and route management using a shared pool of cloud resources. This might involve adjusting traffic signal timings based on congestion, dynamically changing lane directions during rush hours, or providing real-time updates to drivers about optimal routes.
Edge Computing: The New Kid on the Block
Microprocessors are still shrinking; all while still gaining more power. This has led to more powerful tech and a new generation of Edge Endpoints based on IoT. Between IoT and Edge AI is Edge Computing. Interestingly, ‘new kid on the block’ might not be accurate since the tech has been around for 20 years – just didn’t see much use.
At its core, EC represents a much needed departure from the centralized (IoT) model of processing data. No longer dependent on a central, cloud-based system, this approach brings computation and storage nearer to the data source – at the Edge of the Network.
The Edge is sometimes described as a Fog Layer where IoT devices don’t have to push data up all the way up to the cloud, any more. They only push it halfway to these edge computing devices, which act as a proxy, sieving out all the aggregate and passing only what is necessary to the cloud. The main goal here is to optimize bandwidth, reduce latency, as well as bolster privacy and security.
In the IoT scenario, our hypothetical IoT sensor or camera can only take in data and pass it to the cloud indiscriminately. With edge computing, you can harvest data from the same sensor, camera, radio, et cetera and pass all or most of it up to the cloud partially filtered. The cloud mashes it up with other data, and people derive insights, and take action or preventative maintenance.
Use Cases of Edge Computing
Road Traffic Management
Edge computing helps in managing traffic signals and road sensors in real-time to optimize traffic flow, reduce congestion, and improve safety. This is especially crucial in busy urban areas.
Manufacturing Process Control
Perhaps the best use case example of edge computing is process control where all systems and software that exert control over production processes can be managed locally.
Control systems include data processing equipment, process sensors, actuators, networks to connect equipment, and algorithms to relate process variables to product attributes. Edge computing turns multiple machines into one synchronized body controlled by a single mind.
Industrial Automation
In manufacturing plants, edge computing is used to process data from machinery sensors locally. This allows for real-time monitoring and immediate responses to potential issues, reducing downtime and improving efficiency.
Banking
With edge enabled ATMs, transactions are processed right there on the spot as opposed to waiting for a central server far away. This makes the withdrawals and deposits faster and more efficient, leading to higher customer satisfaction.
Edge AI: A New Era of Network Evolution
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.
Compared to IoT and EC, the same sensor or camera that was harvesting data is now smart enough to filter and process ALL or most of it and get its own insight and act upon it…ON ITS OWN! Edge AI could do something in real-time based on the data, and that results in better outcomes than the preceding IoT and EC solutions can achieve.
All this while addressing latency, security, and costs concerns.
Wearable devices, drones, self-driving cars, smart home appliances and high tech cameras are among the technologies leveraging Edge AI at the moment to promptly deliver real time information at crucial moments. Edge AI is quickly growing in popularity as different industries find new ways to harness the power of AI to unlock new opportunities for innovation. But deployment obstacles, a shortage of production-ready AI chips, and lack of expertise are holding many companies back from fulfilling their Edge AI dreams.
Edge AI Use Cases
Autonomous Vehicles
Edge AI is vital for processing sensor data in autonomous vehicles in real-time. It enables swift decision making for tasks like lane tracking, obstacles, and collision avoidance without re;ying on a constant internet connection.
Smart Surveillance
Today’s home starts its smarts right out the door (pun intended) with AI enabled innovations like the ring camera. These ring doorbells record video and audio, sending alerts to the homeowner to potential security threats. The difference between IoT solutions and Edge AI is that the latter often includes intelligent features like face recognition, package and animal detection.
By enabling features like facial recognition, behavior analysis and object detection, Edge AI allows for intelligent video analytics in surveillance systems. This bolsters the efficiency and accuracy of security monitoring while reducing the need for constant streaming to centralized servers.
Smart Homes
Edge AI is literally finding a home in smart devices and appliances such as voice assistants. These appliances process data locally and respond quickly to user commands, detect anomalies and automate tasks.
Agriculture
Edge AI in agriculture helps farmers monitor soil conditions, optimize crop yield and manage resources more efficiently. By analyzing data from drones, satellite imagery and sensors, edge AI allows for precision agriculture techniques such as pest detection, yield prediction, and targeted irrigation.
Numerous other industries are also increasingly trying to develop edge AI applications so as to minimize costs, automate processes, optimize operations and substantially improve decision making.
Edge AI Today
As you’re reading this, forward thinkers in every industry see the opportunity that Edge AI presents – and they are scrambling to figure out what products they can build to be first movers who create and capture new market opportunities.
The opportunities are abundant. Execution is the challenge, because it requires a lot of unique skills and resources to bring these types of products to market. Many companies who are excited and enthusiastic about the possibilities unfortunately are stuck because they can’t find the right resources to do this and they don’t have it in house. So in reality, there’s not a lot of deployments at the moment; something that will definitely change in the near future.
Live on the Edge with embedUR
To sum it up, all three approaches (IoT, Edge Computing, Edge AI) have strengths and weaknesses and all have a place in today’s technology landscape. The best option for you will depend on specific requirements and use cases. The cloud is best for applications requiring large scale processing and storage while Edge Computing and Edge AI are ideal for apps with real-time processing and urgent decision making requirements.
While not insurmountable, the technical challenges of building intelligent edge, AKA smart edge, AKA Edge AI solutions depend on a high level of specialized expertise to overcome. However, most startups, even established companies lack the depth of engineering resources necessary.
As embedded experts with an IoT, Edge Computing and communications background, we’ve been tackling similar integration challenges arising from intelligent edge solutions for the past ten years. Ever since we all misguidedly started calling dumb IoT things smart.
With nearly two decades of experience in embedded wireless solutions, and more than a decade in IoT, embedUR is uniquely equipped with the engineering know-how and resources to scale this next technological challenge that is Edge AI.
Note the problem for engineering managers and CTOs, with limited resources, 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, Edge Computing; the new era of Network Evolution, and cutting-edge technologies like TinyML.
Book a discovery call today and let’s explore how to bring your big idea to market, together.
