What is sensor data? Examples of sensors and their uses

How sensor data works

Sensors gather and generate information based on the physical conditions surrounding them. Sensors usually include the following:

• A processor to convert physical signals into digital data.
• A connector between the sensor and the system analyzing the data.
• Communications capabilities to transmit data to people or machines.
• A power source.

IoT is a large physically connected and wireless sensor network containing an array of IoT devices with sensors attached. Both wired and wireless sensor systems combine specialized transducers with a communications infrastructure to monitor and record conditions at various locations. IoT devices communicate with one another without human intervention.

IoT sensor data exists in three stages on the network that involve elements of data management:

1. Creation. Sensors gather and transmit data used for a variety of smart devices. They do this by collecting signals and turning them into data.
2. Transmission. Data generated is sent to other machines using network protocols, such as Message Queueing Telemetry Transport, Hypertext Transfer Protocol and Constrained Application Protocol. Transmission methods vary based on loss-tolerance, security and timeliness requirements.
3. Storage. Data is stored in various formats and accessed for subsequent use, data analysis and forecasting. In some cases, it's sent in real time immediately after creation. In others, it's stored for a period of time before being sent to its next destination in batches. Storage and bandwidth limitations can dictate the amount of data transmitted and the way it's sent. Cloud-based storage is used for high-volume sensor data.

Types of sensors

Sensors are usually named after the physical parameter they measure. The following is a list of sensor types and how they work:

• Temperature sensors. These include thermocouples that indicate temperature by measuring a change in voltage, infrared sensors that detect emitted infrared energy and infer temperature based on intensity, and semiconductors that detect temperature based on the conductivity of a semiconductor.
• Proximity sensors. These detect the presence or absence of a nearby object or material. Inductive proximity sensors sense the presence of a metallic object using an electromagnetic field. Photoelectric ones use a beam of light to detect objects. Ultrasonic sensors use sound to detect the presence of objects.
• Motion sensors. Accelerometers, gyroscopes and other motion sensors detect physical movement. They can use technology found in proximity sensors and are often used in security systems to detect the presence of people.
• Gas sensors. These detect carbon dioxide and other gases, calculating the amount of an element in the air. Other examples of gas sensors are air quality sensors, which detect chemicals that indicate air pollution; breathalyzers, which detect alcohol in the air; and humidity sensors, which measure the air's water content.
• Level sensors. These include point level sensors that measure the level of a liquid or dry material and indicate whether it's above or below what it should be. Continuous level sensors provide continuous level readings.
• Light sensors. Light-dependent resistors and other similar sensors measure changes in circuit resistance to determine changes in light intensity.
• Pressure sensors. These are devices such as a strain gauge, which has a spring element that changes shape as force is applied, affecting resistance and changing the pressure reading. Differential pressure sensors measure the difference between two pressures connected to each side of the sensor.
• Chemical sensors. These include chlorine residual sensors, which measure the amount of chlorine in water, and pH sensors, which check the hydrogen-ion activity in a solution to measure its acidity.
• Biomedical sensors. Also called biometric sensors, these include medical devices, such as optical heart rate sensors, which use light-sensitive diodes to determine volume changes in the capillaries above a person's wrist. They can include pulse oximeters that shine a light-emitting diode light through the finger of a patient, analyze the character of the light and use that data to determine the amount of oxygen in the blood.

Examples of sensor data

One of the earliest implementations of sensor data was in World War II, where radar was used to detect objects that previously weren't within the range of sight. The following sensor examples and types of sensor data processing techniques show numerous and diverse sensor applications and use cases:

• Accelerometers. These detect changes in gravitational acceleration in devices, such as smartphones and game controllers, to determine acceleration, tilt and vibration.
• Photosensors. These detect the presence of visible light, infrared transmission and ultraviolet energy.
• Lidar. Light detection and ranging is a laser-based method of detection, range-finding and mapping. It typically uses a low-power, eye-safe pulsing laser in conjunction with a camera.
• Charge-coupled devices. CCDs store and display the data for an image in such a way that each pixel is converted into an electrical charge. The intensity of the charge in a CCD is related to a color in the color spectrum.
• Smart grid sensors. These provide real-time data about grid conditions, detecting outages, faults and load, as well as triggering alarms. They're important to the functioning of smart cities.
• Gyroscope sensors. These capture the speed and rotation around an axis of an object. For example, gyroscope sensors enable mobile phones to sense the speed they're going and the direction they're facing.
• Infrared sensors. These measure heat in the surrounding air and detect infrared radiation. They are used in gas warning devices, flame detectors and precision temperature measurement.

Time series data vs. sensor data

The terms time series data and sensor data are similar in meaning. Sensor data can be time series data and vice versa.

The term sensor data emphasizes the data source and the method of data collection. This data comes from sensors.

The term time series data emphasizes the fact that a given data reading or data point represents some aspect of the physical world over a period of time. Time series data is a series of data collected at different points in time and almost always includes a timestamp.

Time series data doesn't always refer to sensor data. For example, stock market data over a period of time is time series data that doesn't come from sensor readings. Time series data can deliver data in batches or in a continuous stream.

Sensor data use cases

The following are examples of how sensor data is used in real-world settings:

• Air quality monitoring. Sensors measure environmental pollutants like particulate matter and other gases. Such systems are often found in urban areas where pollution causes health risks.
• Industrial automation. These ensure that critical manufacturing systems perform at optimum efficiency and with minimal errors. These smart manufacturing systems use automation sensors to detect equipment malfunctions, and improve production activities and safety.
• Agriculture efficiency. Farmers use smart farming systems that monitor metrics such as air temperature, humidity and soil moisture to ensure that crops produce acceptable yields.
• Wearable devices. Smart watches provide a wealth of health-related data. They use sensors that track metrics such as heart rates, number of steps and sleep patterns.

Streamed data can be hard to store and manage because of the volume of data created. Often, it requires the use of artificial intelligence to process. Learn how big data and AI work together to process data and train AI and machine learning algorithms.