The Difference Between IoT and Edge Computing (and Why It Matters)

Thousands of bags move through miles of airport conveyor belts every day. Sensors identify suitcases, track their location and ensure they’re routed to the correct destination. As the luggage moves through the system, automated controls make routing decisions in real time, keeping bags moving from check-in to aircraft.

The luggage tracking system is a useful way to think about two technologies that are often discussed together: the Internet of Things (IoT) and edge computing. The terms sometimes are used interchangeably, but they are different.

The distinction matters because collecting information and acting on information are not the same thing. IoT gathers data, such as a bag’s current location. Edge computing helps turn that data into action, including sending an alert that the bag is, say, at baggage claim on carousel 5.

IoT creates visibility

IoT refers to networks of connected physical devices that collect and exchange information. The devices — such as sensors, cameras, radar systems, RFID tags and tracking devices — gather information from the physical world. Their role is to observe and report. Sensors generate information about conditions, location, movement, performance and other real-world events, and they make that information available to other systems.

These components are often incorporated into larger connected systems, such as CCTV networks, medical monitoring equipment, agricultural machinery, household appliances and vehicles.

In the airport example, the sensors and tracking devices that read the bag tag attached to a suitcase provide visibility into where baggage is and what is happening throughout the system.

That visibility is valuable, but it doesn’t solve problems by itself. A separate system determines what to do with the information. Those decisions are made using computational resources either in the cloud or locally, using edge computing.

Edge computing takes action

Edge computing moves processing close to where data is generated. Rather than sending every piece of information to a centralized cloud platform, edge systems analyze data locally and respond immediately. This approach reduces latency, conserves bandwidth and supports real-time operations.

In the airport example, the edge is the control system that uses sensor data to help route bags through the conveyor network, reading the barcode attached to the suitcase. The sensors report what is happening, but the edge system determines what happens next, such as routing the bag to the assigned baggage claim area, then updating a master database about the last recorded status.

It would make little sense for the baggage tracking system to rely heavily on cloud computing. There’s no need to add that network latency.

Edge computing can make better decisions because it has context. It can aggregate information without needing to aggressively filter out extraneous data. As a result, the decisions are both more immediate and more informed.

Another example: Today’s vehicles incorporate cameras, radar, lidar and other sensing technologies that continuously collect information about the vehicle and its surroundings. Those technologies would be considered IoT.

When a vehicle detects an obstacle, identifies a lane departure, or determines that emergency braking is required, the system must decide whether and how to respond. Those decisions cannot send data to a distant cloud platform and wait for instructions. The vehicle compute platforms process sensor data locally and support actions in real time. That’s edge computing.

Cloud platforms still play an important role. They support fleet analytics, predictive maintenance, software updates, and long-term data management. They serve as the central hub for data storage and IoT data management.

Better together

IoT, edge computing, and cloud address different parts of the same challenge. IoT systems collect information from the physical world. Edge computing systems process that information and support action close to where it is generated. Cloud platforms help organizations analyze information and identify broader patterns over time.

The easiest way to think of the distinction is: IoT collects data, edge computing acts on data locally and the cloud analyzes data at scale.

It’s important to note that the three technologies can overlap. IoT systems can include connectivity, software, processing, analytics and actuators. Similarly, edge computing’s role is not only to take action. It provides compute, storage and/or analytics close to the data source; applications running there may make decisions or trigger actions.

Another way to look at the interaction between the different elements is through the lens of the convergence of information technology (IT) with operational technology (OT). For instance, in the airport conveyor example:

• OT manages the conveyor motors, scanners, sensors, controllers and baggage-handling equipment.
• IT is responsible for the applications, databases, networks, analytics and enterprise systems.
• The edge is where the data and workloads from those environments meet.

As connected systems continue to expand, the question is no longer whether organizations can collect more data. The question is how quickly and effectively they use it.