Sensor data is time series data

In an infinitely complex world full of data points in the form of wearables, actions taken on mobile devices, photosensors, manufacturing machinery, and more — how can mere humans expect to understand and make sense of the astounding number of outputs from our various systems? In short, we can't.

Sensor data is generated when a device detects and responds to some type of input from the physical environment. Often coming together in a network, sensors are able to generate mass quantities of sensor data that may or may not be immediately useful for decision-makers. Each of these data points are captured at a specific moment in time — effectively transforming sensor data into time series data that can be analyzed across this additional dimension.

With the application of AI and advanced algorithms, sensor data can be translated through a business logic lens into actionable information.

The Internet of Things (IoT) is increasingly driven by sensor data, with devices taking measured actions based on everything from wind speed and direction, vital body functions, illumination intensity, and temperature. Each data point must be recorded, transmitted, secured and ultimately utilized, providing an ongoing challenge for organizations. Gaining a greater understanding of how sensor data and time series data differ can help developers and decision-makers take full advantage of these powerful — yet byte-sized — data points.

What is sensor data?

In the simplest of terms, sensors are utilized to translate analog data into digital assets that can be manipulated. One of the earliest forms of sensor data that found widespread acceptance during WWII can be found in something that's still in use today: radars. As the reflection of radio waves are detected, radar equipment is able to translate those data points and provide users with a meaningful physical display of the details. These powerful tools can successfully identify trends and patterns in the physical world by capturing and cataloging specific data points.

How is sensor data stored?

There is a tremendous volume of sensor data generated each minute, making storing these unique points an increasingly difficult task. Traditionally, some of these sensor data points would be stored only for a limited period of time. With cloud-based storage solutions, it becomes more feasible that captured data points are stored over longer periods of time to allow for time-based analysis.

How can you transmit sensor data?

How sensor data is transmitted often depends on the sensitivity of the data that is being transferred. In the world of industrial IoT (IIoT), sensor data is often transmitted wirelessly back to a central location for storage and analysis. Transmission options vary based on process needs and can be limited by loss-tolerance, priority, completeness, and timeliness. Sensor data transmission and management protocol (SMTP) is widely used to provide basic frameworks around sensor data that include timestamps.

How do sensors collect data?

Capturing and transmitting digital information about the physical (analog) world quickly becomes a complicated process that varies based on the types of sensors being deployed.

Types of sensor data

There are 10 key types of sensors being used in IoT devices to capture data:

  1. Temperature sensors measure changes in temperature and convert these environmental inputs into data points.
  2. Humidity sensors capture the amount of water vapor in a particular system.
  3. Pressure sensors are used to detect fluctuations or drops in pressure for systems that include gases and liquids.
  4. Proximity sensors provide motion detection for non-contact objects, often using infrared beams or electromagnetic fields.
  5. Level sensors are able to detect substance levels in a variety of applications, such as food and beverage manufacturing and water treatment.
  6. Accelerometers measure external inputs to determine the rate of an object's changing velocity over time — or acceleration.
  7. Gyroscope sensors are often used in video games and mobile devices, capturing the speed and rotation around an axis.
  8. Gas sensors are used by industries and in homes, monitoring the presence of specific gasses that could indicate a change in air quality.
  9. Infrared sensors work by measuring the heat emitted in their area and can detect or emit infrared radiation.
  10. Optical sensors are common in smartphones and self-driving automobiles, converting light rays into digital signals.

It is vital to understand how IoT sensor data is captured at a specific moment in time, creating a relentless stream of time series data.

What is time series data?

Understanding how specific data points change over time is the basis for many statistical and business analyses — a combination of the changes in relative humidity and temperature over time help dictate what we think of as "weather", for instance. Each time an action is performed within a network, logs are created that allow users to trace unique activities after they occurred. Even things such as economic data are technically time series data points, often compared to past results and projected into the future.

Time series data versus sensor data

There is a fair amount of confusion around the difference between time series data and sensor data. When you consider the various types of sensor data and how they are used, and the fact that sensors can capture changes in the physical world at a single point in time — it quickly becomes clear that sensor data is time series data!

These critical data points that sensors capture are most valuable when analyzed over time, providing valuable information that can be used in decision making or to drive additional business logic steps. This type of complex data structure is a requirement for IoT interactions, where devices can be outfitted with UID (unique identifiers) to transfer large quantities of data directly over a network. The volume of data produced, captured, and transmitted can be overwhelming, making this one of the next Big Data challenges for organizations. Separating the valuable data points to find the signals within the noise is a key concern for data scientists in the coming years.

How to reduce the noise of sensor data analytics

One of the considerations with data is how to look past any outliers or noise and shine a light on meaningful state changes or shifts in output. Reducing noise in any sensor system allows you to acquire greater fidelity in the sensor data, helping reduce the possibility of inaccurate conclusions based on unclean data. Powerful artificial intelligence platforms allow users to peer through data points, performing analytics to more quickly detect and identify anomalies.

Even with advanced AI tools available, developers still need fast, convenient, and secure access to logs, metrics, and events. Rebuilding this type of application from scratch would be cost-prohibitive for individual developers or teams, making sensor data analytics applications a pivotal component for advanced analysis and the use of sensor data.

Sensor data analytics applications

Continuously tracking, storing, cataloging, and tagging specific time series data can introduce a level of uncertainty that would be unacceptable for developers without some form of translation tool that helps make the sensor data portable across cloud and enterprise networks. With sensor data being used to power industry and customer experiences throughout the world, developers can provide extremely creative solutions to some of the toughest programming challenges — but only if their target data points are accessible and secure.

A purpose-built time series database, InfluxDB is invaluable for a wide range of potential use cases, including IIoT, network monitoring, and application monitoring (use case examples across industries can be found here). With real-time visibility across systems, developers can build structures that take advantage of the wealth of sensor data that is being generated each minute with fewer limitations. Ready to learn more about InfluxDB and see how this revolutionary tool can help dramatically improve the speed, security, and portability of your time series data?

Get started with InfluxData by signing up for a free InfluxDB Enterprise trial online (which allows you to run InfluxDB on-prem or in the Cloud). Alternately, the fast, serverless InfluxDB Cloud (managed database as a service) is fully elastic in terms of scalability, providing developers with an easy-to-use service with an open API for convenience. InfluxDB and Telegraf, the plugin-driven server agent, can be used together for data collection from IoT sensors, databases, and other systems.

Available as InfluxDB open source, InfluxDB Cloud & InfluxDB Enterprise

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