Database of Things 1 (Machbase Database)
The Internet of things is a network of physical devices--peripherals, appliances, vehicles, wearable, and other items--embedded with electronics, software, and sensors that enable these objects to connect and exchange data over the internet or, sometimes, a private network.
These devices often include sensors, digitally controlled actuators, and communication terminals especially engineered for physical, moving objects. The ability to collect, process and analyze data from these devices and derive valuable insights is the catalyst that began the new trends driving much of the modern tech industry, industry 4.0.
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Along with these new trends, however, come new technical challenges. The operation of and interactions between thousands of physical objects generates huge volumes of data at a mind-boggling pace. As technology continues to improve and integration becomes increasingly common, more information and data is generated, collected, analyzed and turned into intelligent insights.
This article will describe the types of IoT data generated, unique characteristics of IoT data, and how to overcome the immense challenge of processing it all.
IoT Data Types And Characteristics
In the early days of the Internet of Things, the only data available was via RFID. Nowadays, the improved development of communication and computing technology offers a wider variety of data sources and types--modern sensors and actuators can record almost anything imaginable. Here are the different types of IoT data available, and their characteristics.
RFID (Radio Frequency Identification)
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Radio-frequency identification or RFID technology uses electromagnetic fields to automatically identify and track tagged objects or equipment.
A typical RFID setup consists of an IC chip that stores electronic information and an antenna that transmits and receives the data wirelessly via radio waves. Active RFID often features a battery or other local power source that allow them to interact with an RFID reader hundred of meters away. Unlike with barcodes, RFID chips don’t need to be lined up and scanned directly by their readers, allowing easy interaction and data processing.
Barcodes may still be the method of choice in distribution, but since RFID technology is fairly cheap, it’s beating out barcodes nearly everywhere else. RFID technology has a wide variety of uses including travel, smart devices, factory operations, warehousing, farming, and healthcare.
RFID allows the tracking and recording of positional information through time series data, providing invaluable information for logistical applications.
The problem with RFID technology is security. RFID data can be read from your devices at a distance, often without you knowing. When that data can be personal or confidential in nature, that can be a huge problem.
Log Data
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Log data is information automatically generated by software and hardware, and it plays a very important role in managing IoT devices.
At a base level, log data records the time each log is generated. In addition, log data often also includes various environmental information, such as the ID information of IP/MAC address, the system usage/load information, and the temperature/humidity information from the input records. Since log data is unique, a special conversion process--such as parsing of log messages--is required in order for the information to be represented as a schema of relational DBMS.
Typically, log data is generated in a text form and is automatically deleted when a certain capacity is reached. Obviously, that won’t do. Since ongoing data collection is important for IoT, a different approach is required in order to collect and analyze data long-term.
Another challenge that can make log data difficult to process is that it can be recorded in various formats depending on which programs generate it.
Location And Environmental Data
As we mentioned regarding RFID, location information from moving objects is extremely useful. Another important type of environmental information is weather data. In general, positional information is obtained by using data from global positioning systems (GPS). Since GPS information is obtained through satellites, it is difficult to obtain highly accurate positional data using that technology alone. However, using GPS in conjunction with local positioning technology makes it possible to obtain more accurate and detailed information.
The location data of non-moving equipment can also be treated as very important information. For example, the combination of environmental and location information, such as temperature, humidity, and air pressure from floating sensors in the ocean can be useful for weather forecasts and disaster alarms.
Currently, location and environmental data are being studied in combination with other technologies such as geographic information systems and mobile computing in order to discover additional use cases.
Sensor Data And Time Series Data
Nowadays, every mobile phones is equipped with numerous sensors such as cameras, GPS and acceleration sensors. There are also many sensors in equipment used in factories and public services, such as roads, railways, seaports, and airports. As more and more smart devices are connected, the opportunity to collect and analyze useful IoT data also continues to rise.
Each device and sensor has unique identifiers allowing simultaneous data recording and measurement. This special data, recorded in “Timestamp, Sensor identifier, Sensor value” format, can be sequentially stored based on the input time for data analysis later. This data is called the time series sensor data.
Through analysis, time series sensor data can offer a wealth of real-world data from IoT device sensors that can be used to solve a multitude problems previously impossible to address.
Sensor and Control Data
In time series, sensor data can be collected by actuators in real time while control signal data manages actuator recordings. Since this data keeps changing in real time, a large amount of data is generated and it can be difficult to store and analyze.
This type of data can be used in accident analysis, defective product prediction, quality improvement and production control.
Historical Data
When sensor data includes the time it is collected, it can be categorized as historical data. Since it is time-based, the volume of historical data can increase very quickly depending on the length of the data collection period.
That said, when being collected for detailed analysis, it’s possible to selectively shorten the data collection period in order to avoid larger the data amounts, which can be an issue unless you’re able to resolve within your DBMS.
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Database of Things 1 (Machbase Database)
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