In an infinitely complex world full of data points in the form of wearables, actions taken on mobile devices, photosensors, manufacturing machinery, and so many 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 generate mass quantities of sensor data that may or may not be immediately useful for decision-makers. Each of these data points is 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 including 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. Let’s explore what sensor data is, how it’s stored, and what you need to know about it.

What is sensor data?

Sensor data translates analog data into digital assets that can be manipulated, resulting in sensor data. One of the earliest forms of sensor data, which found widespread acceptance during WWII, can be found in something that’s still in use today: radars.

As the reflections 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. However, modern data management is enabling efficient and cost-effective storage in the long term.

With cloud-based storage solutions, it is more feasible to store captured data points over longer periods of time. Having this historical data on hand allows for time-based analysis, which can be helpful in guiding optimization efforts, defining ideal ranges, and tracking trends over time.

Businesses that employ AI-powered data analysis and reporting tools can make even greater use of historical sensor data, opening the doors to preventative and proactive maintenance and response, reducing costs, and improving accuracy and efficiency throughout their operations.

For example, historical data might reveal an increasing number of false positives or outliers in the weeks leading up to an equipment outage or other issue. This data can help a company get ahead of potential problems, resolving issues before they cause expensive downtime or delays.

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.

As with any type of data a business is dealing with, sensor data should be appropriately secured and governed by a strict data management policy. While sensor data in itself may not be sensitive or heavily regulated, it’s important for organizations to manage their transmission protocols and storage methods to protect data quality and ensure against data leaks and data corruption.

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. To describe the sensor data collection process in the most straightforward terms, it begins with the sensor observing the environment for information.

Sensors are equipped in a way that enables them to detect specific environmental factors. For instance, a temperature sensor has a thermometer built-in, while a humidity sensor has a hygrometer built-in. The special instrumentation inside of a sensor can enable it to accurately track one or many environmental data points.

Through programming, a sensor can be set up to take readings at certain intervals, or it may constantly monitor factors, perhaps only reporting back when a data point enters or exceeds a given range. For instance, a temperature sensor inside of a restaurant’s freezer should constantly monitor the temperature and alert the owner if the temperature begins to rise above a certain limit so they can avoid food spoilage.

Using whatever instrumentation they have on board, sensors are able to observe the environment for physical quantities and convert that data into a signal. In other words, measurements from the real world are translated into sensor data that can be analyzed and manipulated in the digital world.

What type of data do sensors collect?

There are many different types of sensor data, because sensors come in all shapes and sizes and they can be equipped with instrumentation that enables them to measure almost anything in the physical world. Sensor data may come in the form of degrees, percentages, minutes, inches, chemical levels, or just about anything else you can think of.

A single sensor could potentially measure all of these forms of data and more, given that it’s designed to do so. However, sensors generally have a very close range as they’re generally installed to measure one or two very specific data points. For instance, businesses use sensors in server rooms to measure environmental factors like temperature and humidity to ensure the servers aren’t getting too warm.

With a variety of data formats in mind, let’s explore some of the best examples of sensors and sensor data on the market today.

Sensor Data examples

There are 10 key types of sensors being used in IoT devices to capture data, and these represent the best examples of sensor data in current times:

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

Many more types of sensors exist, and their use is varied across industries. Custom sensors can also be designed based on a business’s unique needs, particularly if the business needs level sensors to detect specific substances or chemicals. However, regardless of the type of sensor being used, 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?

Time series data is basically a series of data points, each of which is linked to a specific moment (or second) in time. Understanding how specific data points change over time is the basis for many statistical and business analyses. For example, a combination of the changes in relative humidity and temperature over time helps dictate what we think of as “weather.”

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 information that can be used to make decisions 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 will remain 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 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.


What are examples of sensor data?

Sensors can be instrumented to track a variety of data points, but the most common include temperature, humidity, pressure, proximity, speed, rotation, light, and gas or chemical levels.

How do sensors collect data?

Using whatever instrumentation they have on board, sensors are able to observe the environment for physical quantities and convert that data into a signal. For instance, a sensor with a thermometer can measure temperature and transmit that information to a data center for storage and analysis.

What type of data do sensors collect?

Depending on what a sensor is designed to track, it may collect data in the form of degrees, percentages, minutes, inches, chemical levels, or just about anything else you can think of. If something in the physical world can be measured, a sensor can be designed and instrumented to collect data about it.

How to read O2 sensor live data

The best sensing technology features live data, which means it will relay data in real time as it receives it. To read O2 sensor live data, you first need to establish boundaries or markers for normal behavior. If the data exceeds the normal range, you’ll know there’s an issue. For instance, oxygen sensors tend to generate 0.45 volts when the air/fuel mixture is at a point of equilibrium, but can generate up to 0.90 volts when the fuel mixture is too rich.


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