IoT Edge Computing Devices

1. Introduction

a) Introduction to IoT

The Internet of Things (IoT) describes different types of physical devices that collect data using sensors. The idea is that one of these “things” is just an embedded device that could be as simple as a light sensor measuring light intensity and then broadcasting the data in real time or at predefined intervals to a central entity. People can use the data collected from these devices to make intelligent decisions. A device embedded with a light sensor can tell us when to turn on or off the lights on our driveway based on sunlight intensity, as an example. But IoT devices can also broadcast sensor data to a more powerful system that can run AI/ML models on that data instantly to predict future behaviors. The Internet of Things has changed the way we interact with the world and has, in turn, revolutionized the way we use and process data.

b) Introduction to edge computing

As electronic devices get smaller, the number of connected devices increases. It’s estimated that the number of IoT devices will increase by 24 billion by the end of 2030.

IoT devices are used across many industries to improve processes, and many industries rely on analyzing and processing this data in real (or near real) time. The challenge is that IoT data is so massive that it’s actually measured in zettabytes, which is equal to one trillion gigabytes. With IoT data coming from all over the world across billions of devices, processing this data in real-time requires compute closer to where the data is generated. This is where edge devices or edge compute modules come in. 

An edge device or edge compute module can be the same IoT device that is collecting the data or a standalone device near the IoT devices themselves. Rather than offloading the processing tasks to a distant cloud VPS or a server, the data can be processed on the edge node so that instant decisions can be made. These decisions can be time or security-critical ones like setting off an alarm or avoiding an obstacle in the case of smart cars.

Figure 1: Typical Industrial Environment with Sensor Nodes

In Figure 1, sensor nodes are spread across a typical industrial environment, which might include temperature-sensing nodes, current-sensing nodes, and vibration-sensing nodes. All the data acquired from the sensor nodes is shared with the edge node or edge compute module near the machine, so the edge node can do real-time and on-prem calculations and set the machine parameters accordingly.

2. How IoT and edge computing works together

IoT devices can sometimes be edge devices as well. IoT devices have sensors with limited processing power, connected to a network that communicates between the devices and the cloud/edge. Edge devices, in comparison, have nodes with enough compute power and storage to process data on their own. Some use cases that make use of IoT and edge devices include:

• Smart homes
• Self-driving cars
• Industrial IoT (IIoT) devices

a) IoT edge computing architecture

Edge computing is now being used extensively in many IoT settings. While there are no industry standards for how to design a system with edge compute nodes, there are some commonalities in most IoT systems.

Figure 2: Common Components in an IoT Solution with Edge Computing Devices

Layer 3 is a gateway or wireless gateway layer that is responsible for acquiring the processed data from the edge devices and forwarding it to the cloud server on layer 4 to permit data storage at scale. This ensures that the historical data is always available for visualization. Layer 4 is a traditional cloud-based VPS and can have (but is not limited to) a database, front-end dashboards, microservices, and a back-end.

While edge nodes can be made to run machine learning / deep learning (ML/DL) models, it is important to note that model training is sometimes done in the cloud or on a machine equipped with powerful resources because some edge nodes are not made for this task.

b) Edge Gateways

A simple IoT gateway or edge gateway is a device that connects IoT sensors or sensor networks to the cloud. For example, edge gateway can be used for converting data acquired from long-range communication protocol (LoRa) sensor nodes to TCP data to be sent to a central server. In Figure 2, we can regard the IoT nodes as LoRa nodes, creating a mesh network of LoRa node devices. A mesh network eliminates the need for each IoT node to send data to the server directly, congesting the network. In a mesh network, all the IoT nodes share the data among themselves, and only a specified number of IoT nodes (LoRa nodes), out of the whole mesh network, send the data to the server or gateway.

3. Use Cases / Examples of Edge Computing in IoT

There are multiple uses cases and examples of edge computing being used in IoT:

• Real-time industrial environment monitoring
• Security networks based on facial recognition
• Machine fault detection and prevention

4. Benefits of Edge Computing in IoT

Edge computing provides a number of benefits when used in an IoT solution:

• Faster data processing with lower data communication latency between the sensor nodes and the edge devices
• Native security (because the data remains within the premises of the deployment)
• Lower operation costs (because the cloud services are not being used extensively in the edge computing use-case)

Conclusion

IoT edge computing devices allow faster data processing with lower processing costs. With the number of IoT devices increasing every year, it is a natural choice for IoT solution architects to make use of IoT edge computing devices to provide the most optimal and cost-effective IoT solutions.

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