Cloud computing happens in some centralized location with a huge resource in the pay-as-you-go model. Edge computing happens on the device itself. This comparison arises in this era of big data and IoT for different reasons.
Edge computing is the data processing power at the edge of a network which is pushing the computing applications, data, and services away from the centralized nodes. A good example of use-case of both can be the autonomous cars. Edge computing approach forces leveraging resources such as prototyping boards, laptops, smartphones, tablets and so on. There are many examples where edge computing offers more advantages. Edge computing makes the industrial Internet of Things applications fail-proof. When data analysis is done at the edge, that is called edge analytics. With our brief description, it should be obvious to the readers that the intention of edge computing is not to replace cloud computing. Edge computing is closely related to fog computing. The advantages of edge computing are more responsive processing, less dependency on network connectivity, more reliability, dedicated processing and possibly more data security. Practically, cloud computing becoming a partial subset of edge computing.
What is the difference between edge computing and traditional on-premise applications? Edge devices are typically much lower powered with limited storage and computing ability. Edge computing is a way of optimising cloud computing by involving the computing resources at the edge of the network. Edge computing is limited to basic data visualization, basic data analytics, data caching, buffering, streaming, pre-processing, cleansing, filtering, M2M communication. Problem of edge is obvious – if there are multiple devices at the edge, then they required to using same version of code, software etc. So, like software development, we need to follow the principles of development.
The concept of edge computing, fog computing are important to be known to the developers related to the internet of things and big data. The goal of our website is to empower the makers. In traditional electronics, we should strictly use ESP32 like boards for prototyping. With the advent of the internet of things, we connected the boards to some platform which helps to perform the goal. The concept of edge computing is replacing ESP32 with Raspberry Pi in complex works. As edge computing forces the methodologies of software engineering, developing in that way requires updated working knowledge of agile, DevOps and so on.
ESP32-CAM has the ability of face detection. IBM Watson also has visual recognition service. If we combine both, the change of failure becomes low. We can code to compare the prediction on the device. That approach is forcing to use Raspberry Pi more than ESP32. That does not mean ESP32 like SoC are dying. Raspberry Pi and ESP32, both can be used for edge computing. In today’s world, the need of computing & storage power from the edge devices usually huge.