The term “Internet of Things” (IoT) first came up in 1999, and since then, it has changed how businesses operate worldwide. IoT devices sit at the edge of networks where they gather data and send it to centralised services or the cloud. This data is then processed and analysed, enabling better decision-making and improved efficiency.
However, there are potential problems with this process. IoT devices generate vast amounts of data every second, which can strain the system and cause delays. These delays can have serious consequences in industries like healthcare and manufacturing, where immediate data processing is essential for safety and efficiency.
Consider an IoT pressure sensor in an industrial machine. If the pressure rises to dangerous levels and there are delays between the IoT device and data processing, automatic shutoff actions might be taken too late to prevent damage.
This is where edge computing comes in. Edge computing enhances IoT solutions by processing data close to its source. By using edge computing, you can make your IoT setup faster, more reliable, and better suited for real-time data needs.
What is edge computing in IoT?
IoT edge computing is a system where critical data processing occurs at or near the data source. The IoT edge devices process the data locally instead of sending it to a remote server or cloud for processing. This approach improves the speed of decision-making and action triggering.
How are IoT and edge technology connected?
While edge devices offer localised data processing, they still need to connect to external servers or the cloud on the IoT network. This connection is necessary for long-term data storage, comprehensive analysis, and advanced processing tasks that can’t be handled locally. It ensures you get both immediate insights and the benefits of centralised data management and analytics.
Why do we need edge computing in IoT?
Edge computing is essential for optimising workplace IoT. By processing data locally, edge computing addresses many of the limitations of traditional cloud-based IoT solutions.
Here are three key reasons why edge computing is vital for IoT:
Reduced Latency
Edge computing cuts down on the time it takes for data to travel. This reduction in latency is critical for applications needing real-time responses, like industrial automation. By minimising delays, you can make more effective decisions or take prompt actions.
Reducing Strain
Edge computational devices send only necessary data to the cloud. This decreases bandwidth usage and avoids potential bottlenecks, ensuring your network remains efficient and responsive.
Scalability
By distributing data processing, you can deploy more IoT devices without overloading central servers. This approach allows you to grow your IoT network as needed while maintaining smooth and efficient operations.
What is edge computing IoT used for?
Now that you know what is IoT edge computing, let’s unpack what it’s used for. Edge computing IoT is used for a variety of applications across different industries. Here are a few of its use cases:
- Real-time monitoring and control: Edge devices allow for real-time monitoring and control in industrial automation and smart manufacturing. This ensures that systems can react instantly to changes, improving efficiency and safety.
- Predictive maintenance: By analysing data from IoT sensors locally, edge devices can predict equipment failures before they happen. This reduces downtime and maintenance costs, as issues can be addressed proactively.
- Remote operations: Edge computing makes managing remote operations, such as those in oil and gas or agriculture, more efficient. It reduces the need for constant cloud connectivity, enabling quicker decision-making on-site.
What are the key features of edge computing IoT?
Below are the two key features of IoT edge devices:
Cloud platform openness
Cloud platform openness in edge devices refers to the ability to integrate seamlessly with various cloud services. This flexibility allows you to leverage multiple cloud providers and platforms, ensuring that data and applications can move freely between the edge and the cloud. This key feature enhances interoperability and prevents vendor lock-in, giving you the freedom to choose the best cloud solutions for your specific needs.
Gateway openness
Gateway openness involves the compatibility of edge computing devices with different types of IoT gateways. This ensures that devices can connect and communicate effectively, regardless of the underlying technology. By supporting multiple gateways, you can ensure that your edge computing infrastructure is versatile and can adapt to various requirements.
What are the typical applications of edge computing IoT?
Edge computing IoT finds applications in numerous sectors, each benefiting from its unique capabilities.
Power distribution IoT
In power distribution, IoT helps monitor and manage the flow of electricity across the grid. Sensors and devices placed throughout the network collect real-time data on various parameters. With edge computing, this data is processed locally to ensure efficient energy management and quick responses to any issues.
For example, IoT sensors can detect faults or inefficiencies in the grid and immediately alert operators to take corrective actions. This helps in ensuring a stable and reliable supply of electricity. The data collected can then be analysed to predict maintenance needs, preventing future outages.
Smart IES
Smart Industrial and Engineering Systems (IES) use edge computing IoT for real-time monitoring and control of industrial processes. This ensures optimal performance, predictive maintenance, and improved safety in industrial environments.
For example, in a manufacturing plant, edge computing can analyse data from machinery to predict equipment failures and schedule maintenance before a breakdown occurs. This keeps the production line running smoothly and safely.
Is security a risk with edge computing?
Security can be a concern with edge computing, particularly in managing numerous devices spread across different locations. Each device at the edge needs to be secured with access control and data encryption, as well as regularly updated to prevent vulnerabilities. Effective device management strategies are essential to monitor, secure, and control all edge devices, ensuring they remain protected against potential threats.
FAQs about IoT edge computing
What is edge AI in IoT?
Edge AI in IoT refers to the integration of artificial intelligence capabilities at the edge of the network. This means AI algorithms process data directly on IoT devices, enabling real-time insights and actions without relying on cloud-based AI solutions.
What is the role of the edge device in IoT architecture?
The edge device in IoT architecture processes data close to its source. It performs data processing and analysis, reducing the need to send raw data to central servers. This local processing improves response times and reduces bandwidth usage.
What is the difference between edge and cloud IoT?
The main difference between edge and cloud IoT lies in where data processing occurs. Edge IoT processes data locally, near the source. Cloud IoT sends data to centralised servers for processing.