IIoT stands for the Industrial Internet of Things and refers to a network of connected devices in the industrial sector. It is a subset of the Internet of Things (IoT). The defining characteristic of connected devices on IIoT networks is that they transfer data without human-to-human or human-to-computer interaction. The terms IIoT and IoT refer to proprietary, standalone networks, as well as broader, global networks.
A histogram is used to summarize discrete or continuous data, grouping data points into specified range values, called “bins.” The histogram is similar to a vertical bar graph; however, the histogram shows no space between the bars. Creating a histogram provides a visual representation of data distribution, and have the ability to displace a large amount of data and the frequency of data values. The median distribution of the data can be determined by a histogram, as well as showing any outliers or gaps in the data.
MQTT (Message Queueing Telemetry Transport) is a simple, lightweight message publishing and subscribing network protocol. It is the standard protocol for Internet of Things (IoT) and Industry 4.0 messaging.
It is lightweight, low bandwidth, and functions well in high latency and unreliable environments, making it ideal in production environments. Devices send data (publish) to an MQTT Broker with a topic and a data payload, and devices can subscribe to that topic and subtopics and receive updates containing that data from the broker when the data changes. Topics can be defined to use several levels of depth and devices can subscribe to topics using wildcards allowing for dynamic changes when required.
Zero-Trust is a security concept and cybersecurity framework allowing an organization to aggressively defend itself, its data, and user permissions using an advanced system of automated security protocols. Zero-Trust is centered on the belief that organizations should not automatically trust anything inside or outside their perimeters and instead must verify anything and everything trying to connect to their systems before granting access.
The EDM Correlation data analysis tool is designed to help users perform root cause analysis across multiple devices or systems. It compares the activity of one system over a given period with the activity of multiple other systems and identifies the most highly related data points. This allows EDM to create an intuitive visualization of related events across an entire network of mixed technology enabling root cause analysis to be performed more efficiently than ever before.
Forecasting uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Choosing appropriate forecasting techniques depends on the type of data being used and behavior of the data. For example, forecasting techniques used for “financial data” may not be appropriate for time series data like “computer server data” or “data center network activity data” (though underlying forecasting principles stay the same).