Hi, I’m Omar!

You’ve probably seen me in other programming articles. Recently, I published a medical telemetry article that explains what telemetry analytics is and how it is applied to the medical field. Now, in this article, we will discuss how Telemetry works as a data collection method and analytical process. We will review different definitions as well as some examples of where telemetry analytics is applied.

What is telemetry?

In a very summarized way, telemetry is the collection of data through sensors, which, in turn, send the information to remote locations. These sensors can be used within different applications, for example, sensors measuring the temperature at a specific depth in the ocean, sensors that record the speed of a remote-controlled vehicle, or sensors connected to a patient’s brain to record brain activity.

Telemetry Technician

A telemetry technician is commonly associated with the person in charge of monitoring patient vital signs (EKG) in a hospital. But this definition could be related to any person who monitors signals with some special telemetry equipment.

Telemetry technicians must have specific knowledge of the equipment they monitor. Knowing how to interpret signals such as ECG (QRS), coordinates, and numerical data. In the case of the medical telemetry technician, there is The National Telemetry Association (NTA), which offers certifications and training in this type of monitoring.

Telemetry Data

Telemetry data are all those values obtained during the process of reading and transmitting devices (sensors, electrodes, etc.). These data are reinterpreted by telemetry software, which helps translate each value into a language understandable to humans.

Telemetry Sensors

Sensors are devices located in the study source. These obtain electrical data, temperature, and/or pressure. These sensors transmit information wirelessly (in the case of remote studies, using the mobile phone network, GPS, or satellite) or wired, as in the case of ECG electrodes.

Also known as telemetry applications, they are programs in charge of collecting, translating, and interpreting the values obtained during the data transmission process. These systems are usually divided into several sub-processes (Services, APIs, Databases, etc.), depending on the logic or requirements of the developers.

Telemetry API

In software engineering, an API (Application Programming Interface) is software that allows communication between two components or programs for the transmission of information. Applying this logic to telemetry, an API would help store information collected by a telemetry mechanism in a database on a remote server. A current example (Software Telemetry), is the AWS Telemetry Lambda API:

This API assists in the transmission of log data to the Lambda service, which in turn transmits the data directly to the extension.

Connected user experiences and telemetry

This process is one of the most common that you could find, and this is because it belongs to the Windows operating system. This service manages the transmission, diagnostic, and usage information to improve the experience and quality of the Windows platform.

These two practically go hand in hand, therefore, I think it is good to explain and differentiate both concepts. Observability is the ability to comprehend and understand the results obtained by a system (Telemetry).

The objective is to explain the reason for these results (for example, the performance of a racing car), to optimize the studied entity, or to avoid potential problems related to the source study (for example Earthquakes, Fires, Heart Attacks, etc.). While telemetry collects the values, observability tries to explain the reason for those values.

Monitoring

Monitoring is a study complement for observability. Monitoring is a continuous review process, whose objective is to verify the efficiency and effectiveness of applications or projects in execution. When an anomaly is found within the monitoring, the observability will analyze the results, to explain the errors obtained and thus be able to find a solution to the problem.

Telematics is another concept related to telemetry analytics. While telemetry obtains data remotely, Telematics are much more complex programs that are responsible for analyzing data remotely, providing more detailed results and interpretations. Usually, telematics technology is used to track vehicles, making use of the mobile network and GPS.

Among other more interpretable data, we can obtain Speed, Braking, Location, Fuel Usage, and other driver behaviors. It could be said that telematics is the set of telemetry software, APIs, and Telematics dashboards.

Telemetry Tracing

This concept is closely related to the previous concepts. Tracing allows us to observe the journey of a request through different threads in different servers and/or microservices. This helps to know where our petition is in the entire process. If an error occurs, we will be able to locate the exact point and analyze the problem. In some Azure DevOps processes, specifically pipelines, you can see a representation of a process and track where your request is at:

If you look at the image above, you may notice some similarities to the AWS diagram shown above. These tracking processes can be very useful in telematics systems, since they show a trace of the origin of the data transmission, up to the server that stores said information.

IoT protocols are a set of rules for communication with the Internet. Many devices that transmit or receive data over the Internet are governed by this type of protocol. Within these devices are many remote sensors used for telemetry.

Sensors that work with IoT protocols have certain requirements, such as the integrated battery, Low-Code Footprint (lightweight computing and wireless transmission without requirements), Low Bandwidth, and Local Intelligent IoT Gateways (a physical device or virtual platform that connects sensors, IoT modules, and smart devices to the cloud).

Protocols:

• MQTT: The Message Queuing Telemetry Transport (MQTT) protocol runs on top of TCP/IP and requires an intelligent IoT gateway. It is used in devices with integrated components and low power consumption. 

• CoAP: Constrained Application Protocol (CoAP) can be routed through IP networks, and is used in devices that use low-power networks. 

• HTTP: Hypertext Transfer Protocol, a protocol that can be routed through the Internet and that allows the transfer of data between various devices. This protocol runs on top of other network protocol layers but can be insecure.

• HTTPS: Hypertext Transfer Protocol Secure, It is a protocol used for a much more secure data transfer, since it makes use of certificates and encrypted data.

The topic of telemetry analytics is quite extensive, as can be the case with IoT protocols, so I decided to show synthesized definitions. It is incredible to imagine how telemetry has affected the life of the human being; from the study of our vital signs to extremely complex data (temperature, vibration, pressure, voltage, altitude, speed, time, among other magnitudes, etc.).

Added to this, the development of software for monitoring, telemetry analytics, communication, and storage of this information turns out to be a whole new world. The subject of telematics systems is attractive. It’s amazing to specify the type of behavior of a driver, just by collecting data on speed, fuel levels, braking, and more.

It was quite nice to read and write this article, which is a topic that everyone should keep an eye on since telemetry analytics is implemented daily in our lives without even noticing it. Thank you very much for your attention. Bye!