There is an explosion in the amount of data being generated by digital devices. The traditional model of processing and storing all data in the cloud is becoming too costly and often too slow to meet the requirements of the end user. This is motivating a move towards an edge computing approach that facilitates the processing of device data closer to the source.
Gartner Group suggests: “Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2022, Gartner predicts this figure will reach 75%.” The change to edge computing may have a profound impact on an organization’s IT and OT systems, and how new digital products are built.
To help better understand the movement towards edge computing, let’s look at some of the benefits.
- A key consideration for edge computing is to overcome network latency. If the IoT application requires sub-second response time, then waiting for a request to the cloud may become an issue. For instance, a safety critical control system operating an industrial machine might need to stop immediately if a human is too close. The processing of human recognition by a sensor and the processing of the decision to stop a machine can not be delayed by a network interaction. A delayed response time may cause serious human harm or damage to the machine. Similarly, autonomous vehicles or augmented reality applications need a response time below 20ms. This can’t be delivered by regular communication with the cloud. Moving the processing of the sensor data to an edge gateway is a way to avoid network latency and achieve a desired response time.
- Cost is also a driving factor for edge computing. The bulk of telemetry data that is generated by sensors and actuators is likely not relevant for the IoT application. The fact a temperature sensor reports a 20ºC reading every second might not be interesting until the sensor reports a 40ºC reading. Edge computing allows for the data to be filtered and processed before it is sent to the cloud. This reduces the network cost of data transmission. It also reduces the cloud storage and processing cost of data that is not relevant to the application.
- Deploying analytics algorithms or machine learning models to an edge gateway allows for the computational processing to be performed on smaller data sets. Edge computing will often be more computational efficient to process this type of data.
The role of cloud computing in IoT
Cloud computing continues to play an important role in many other functions, such as central data storage, data analytics and machine learning across fleets of devices, fleet or device management, and integration of device data with back-end enterprise systems. Cloud computing also supports edge computing by hosting the ability to remotely manage edge gateways.Read about Bosch IoT Suite
- Many digital products need to be autonomous in their operation. This allows them to achieve the required safety, reliability, and user experience needs. Edge computing provides the ability to have local storage and local computation. Thereby, the device can continue to function even if it is not connected to the network.
- Finally, edge computing can improve the security and privacy of an IoT application. Edge computing can reduce the number of sensors and actuators connected to the internet. This reduces the potential attack vector of security attacks. Local data processing and filtering by an edge gateway can also reduce the amount of sensitive and private information that is sent through a network. Thereby, it addresses privacy needs or regulations for the application.
Edge computing for IoT: A guide on how it complements the cloud
It is important to consider edge computing as complementary to cloud computing. To help our customers better understand edge computing and how it relates to cloud computing, we have recently published a guide on how edge computing complements the cloud in IoT. We hope you will enjoy it.