In-depth analysis: five powerful skills behind the Internet of Things

Sensors are playing an increasingly important role in the development of the Internet of Things. At present, the demand for sensor products has increased significantly, and the focus has gradually shifted to the field of high-tech MEMS sensors. The accuracy of MEMS sensors determines the quality of the information collected.

First, a brief introduction to MEMS sensors, a technology known as the "Micro-Electro Mechanical System", was predicted by scholars in the 1980s to "provide another technological revolution for human society." Compared with traditional sensors, it has the characteristics of small size, light weight, low cost, low power consumption, high reliability, suitable for mass production, easy integration and intelligentization. At the same time, the feature size on the order of micrometers makes it possible to perform functions that are not possible with some conventional mechanical sensors. At present, MEMS technology has been widely used in high-tech fields such as aerospace, electronics, biology, medicine, etc., and has become a key technology related to national defense security, technological development, and economic prosperity.

2015 was an unusual year for the global MEMS industry, and the top 30 MEMS companies had a different fate during the year. Five MEMS companies grew faster than 20% in 2015: Avago Technologies was 41%; InvenSense was 33%; TSMC was 29%; Qorvo was 28%; Sound Technology (AAC) is 22%. The only change in the list of the top 30 global MEMS companies in 2015 was that TSMC replaced ULIS, the manufacturer of infrared imaging products. ULIS lost its position in the 30th place in the 2014 list due to the multi-million dollar revenue gap with TSMC in 2015.

Internet of Things

The number of sensors has surged across the surface of the Earth and around people's lives, providing a variety of data messages around the world. These price-sensitive sensors are the driving force behind the development of the Internet of Things (IoT) and our society is facing a digital revolution. However, connecting and acquiring data from sensors is not always straightforward or so easy. Here are 5 tips. To assist in mitigating the first war between engineers and transmission interfaces to sensors.

Tip 1 - Start with the bus tool

In the first step, the engineer should take the first time to connect to the sensor by means of a bus tool to limit the unknown. A bus tool connects a personal computer (PC) to the sensor's I2C, SPI or other protocol that allows the sensor to "talk". A PC application associated with the bus tool provides an embedded microcontroller (MCU) driver that is known to work with the source to send and receive data and is not unknown or certified. In the working environment of the bus tool, the developer can send and receive messages to get an understanding of how the part works, before attempting to operate at the embedded level.

Tip 2 - Write the transport interface code in Python

Once the developer has tried to use the sensor of the bus tool, the next step is to write the application code for the sensor. Instead of jumping directly to the microcontroller's code, write the application code in Python. Many bus tools configure plug-ins and sample code in scripting (wriTIng scripts), which is usually one of the languages ​​available in .NET. Writing an application in Python is quick and easy, and it provides a way to test the sensor in the application, which is not as complex as testing in an embedded environment. Having high-level code will make it easy for non-embedded engineers to mine sensor scripts and tests without the need for an embedded software engineer.

Tip 3 - Test the sensor with Micro Python

One of the advantages of writing the first piece of application code in Python is that by calling Micro Python, the application calls to the bus tool application programming interface (API) for easy replacement. Micro Python runs in real-time embedded software, with many sensors for engineers to understand its value. Micro Python runs on a Cortex-M4 processor, and it's a great environment to debug application code from it. . Not only is it simple, there is no need to write I2C or SPI drivers here, as they are already covered in the Micro Python library. (You can read the Using Micro Python for real-TIme software development or 5 Advantages of using Micro Python for Embedded Software Programming on the EDN website for more details.)

Tip 4 - Using Sensor Vendor Code

Any sample code that can be "searched" from the sensor manufacturer, the engineer needs to go a long way to understand how the sensor works. Unfortunately, many sensor vendors are not experts in embedded software design, so don't expect to find a beautiful architecture and elegant example that can be put into production. Just use the vendor code to learn how this part works, and then the frustration of refactoring will emerge until it can be cleanly integrated into the embedded software. It may start like "spaghetTI", but using the manufacturer's understanding of how its sensors work will help reduce many of the weekends that have been destroyed before the product launch.

Tip 5 - Use a sensor fusion library

The chance is that the sensor's transmission interface is not too new, and no one has done this before. All libraries known, such as the "sensor fusion library" provided by many chip manufacturers, help developers quickly master, and even better, avoiding the reincarnation of their reinvention or drastic modification of the product architecture. Many sensors can be integrated into a common type or category, and these types or categories will allow the driver to be developed smoothly and, if handled properly, are almost universal or less reusable. Look for these sensor fusion libraries and learn about their strengths and weaknesses.

Final thought

When sensors are integrated into embedded systems, there are many ways to help improve design timeliness and ease of use. When developers start designing, through a high-level abstraction and learning how sensors work before integrating sensors into a lower-level system, they will never “walk the wrong way”. The many resources that exist today will help developers “win”, without having to start from scratch.

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