Edge Prototypes: Definition and Advantages

Edge prototypes are devices with local processing and decision-making capabilities, without the need to send data elsewhere for subsequent processing on other devices.

To better understand how edge prototypes work, we can refer to an example:

Smart wristbands for padel

Imagine a wristband equipped with sensors that enable real-time data capture. Using this data, it would be possible to identify the types of strokes performed by athletes, as well as provide a diagnosis of those movements. We’re talking about a prototype powered by an artificial intelligence model capable of performing this analysis. 

In this prototype example, an edge prototype architecture could be used, or alternatively, a cloud-based or mobile device analysis architecture outside the smart wristband.

Edge Architecture. Methodology for Edge Prototypes

Data is captured through the wristband. On the same device, mathematical analysis is performed to identify and diagnose the padel strokes executed by the athlete. The results of the analysis are then sent to a mobile application, where the user can view and store their performance data. 

Cloud Computing Architecture

The smart wristband for padel would similarly capture data through its electronic sensors. Then, without any local analysis, it would send the data streams to the cloud (server), where the intelligent model would run to identify the types of padel strokes performed and provide a diagnosis. After the cloud-based data analysis, the results generated by the AI model could be retrieved by the mobile application through highly intuitive user interfaces.

Data Analysis on a Device External to the Wearable

The smart wristband for padel players would also be responsible for capturing the data generated by the athletes. Through a communication protocol between the wristband and a mobile device, data packets could be transmitted and later analyzed by a mathematical model running on the mobile device’s processing capabilities. The results would then be displayed within the same mobile application. 

As you can see, the definition of edge prototypes refers to devices capable of completing both data capture and analysis without relying on external processing.

Key Advantages of Edge Computing Devices

The ability to deliver immediate responses is one of the main advantages of prototypes that use edge computing architectures. It eliminates the need to transfer information through communication protocols.

Many prototypes are intended to be used in environments with limited connectivity. In such cases, intelligent functions can only be enabled through processing that does not rely on servers or external devices.

In edge prototypes, the risks associated with data transmission are avoided. For this reason, the chances of data being intercepted are significantly reduced. We could think, for example, of biometric analysis devices at an airport. With a cloud-based data processing architecture, there would be a much higher risk of data being intercepted and used maliciously.

In prototypes like the padel sports example described, it’s true that the data eventually appears in a mobile application. Therefore, at some point, information must be transferred from the smart wristband to the app. However, this transmission only involves a simple and short data string, avoiding network overload.

Edge prototypes offer significant savings in operational costs. When processing is carried out in the cloud, there is often a need to pay high server fees, which can be avoided by shifting processing tasks to the edge device itself.

Disadvantages of Devices with Local Processing – Edge

It is true that most edge prototypes use processors whose capabilities—due to space and power consumption constraints—are lower than those found in other devices, such as smartphones or computers. Therefore, it is essential to evaluate the processor specifications and the complexity of the mathematical operations to be handled by the prototypes.

Obviously, when the processor is required to perform complex tasks, it is common to observe power consumption spikes during data analysis processes. This could lead to the need for larger and higher-capacity batteries. However, there are strategies to manage processor activation and usage cycles in order to maintain strict control over energy consumption.

The level of complexity involved in developing prototypes capable of performing complex local processing with artificial intelligence models requires advanced skills and knowledge of embedded systems, which often leads to significantly higher investment in the product development process.

Edge Products vs. Cloud Computing

It is truly difficult to determine whether it is better to develop a product using edge computing methodologies or cloud computing without understanding the specific use cases and the prototype’s constraints.

Based on the advantages and disadvantages of both methodologies, at Let’s Prototype we conduct preliminary studies before manufacturing a prototype, which help reveal key trade-offs for making informed decisions.

In our product development methodology for innovative electronic devices, we design lab-level experiments that allow us to evaluate processor capabilities both inside and outside the device, as well as measure the complexity of processing tasks and the power required to execute them efficiently. Based on these findings, we can anticipate the necessary electronic components for the prototype and the potential aesthetic design implications that may result from these conclusions.

One of the most common mistakes made by product development companies is launching into rapid prototyping without first analyzing these possibilities. If you're planning to develop an electronic product, we can help you define the best way to build a prototype before taking the first step and investing in non-competitive solutions.

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