Industrial Internet of Things (IIoT) Glossary
The Industrial Internet of Things (IIoT) refers to the application of Internet of Things (IoT) technologies and concepts in industrial settings, particularly in the context of manufacturing and other industrial processes. IIoT is a key component of Industry 4.0, the fourth industrial revolution characterized by the integration of advanced digital technologies, such as sensors, automation, and data analytics, into industrial operations.
By connecting industrial assets, processes, and systems through the use of IIoT, manufacturers can gain unprecedented visibility, control, and optimization of their operations, leading to improved efficiency, productivity, and quality. Knowing IIoT glossary terms is important for understanding the technology, its applications, and its impact on the industrial landscape.
A list of key IIoT terms for technical content managers and writers
IIoT-related terms such as "smart factory," "predictive maintenance," "digital twin," and "edge computing" are essential for comprehending the capabilities and implications of this technology.
Understanding these terms can help manufacturers, engineers, and decision-makers effectively plan, implement, and leverage IIoT solutions to enhance their manufacturing processes, reduce downtime, optimize asset performance, and ultimately, gain a competitive advantage in the market.
- Additive Manufacturing - Production methods like 3D printing that construct objects by adding layers of material.
- Agricultural Robots - Robots to handle essential farming tasks like harvesting crops, pruning, weed control, monitoring, etc.
- Analytics - The discovery, interpretation, and communication of meaningful data patterns. Used to gain insights and drive better decisions.
- Anomaly Detection - Identifying abnormal conditions or outliers in data that may indicate problems or faults.
- Asset Performance Management - Leveraging data analytics to optimize performance of physical assets such as production equipment.
- Augmented Reality - Overlaying digital information and visualizations onto the physical environment. Used for training, maintenance, etc.
- Automation - Use of control systems and information tech to reduce need for human work in processes.
- Big Data - Extremely large data sets that require new tools and methods to capture, store, analyze, and visualize. IIoT generates vast amounts of big data.
- Cloud Computing - Storing and accessing data and programs over the internet rather than a local server or computer. Enables ubiquitous access to shared resources.
- Closed Loop Quality Management - Using real-time data to adjust processes, immediately applying quality feedback.
- Collaborative Robotics - Robots designed to safely work alongside humans in shared workspaces.
- Condition Monitoring - Continuously monitoring assets to assess health and detect early signs of problems.
- Connectivity Protocols - Standardized rules that allow devices to communicate such as MQTT, OPC UA, Modbus, etc.
- Context Awareness - Ability of systems to understand situational conditions and adapt accordingly.
- Cybersecurity - Protection of internet-connected systems, hardware, software, and data from cyberattacks.
- Dark Factory - Highly automated plants with few or no human workers. Enabled by IIoT and smart machines.
- Data Historian - Centralized software program that logs and stores industrial time-series data.
- Data Integration - Combining data from disparate sources and applications into meaningful information.
- DataOps (Data Operations) - A set of practices, principles, and tools that aim to improve the collaboration, integration, and automation of data pipelines and analytics processes. DataOps is closely related to the concept of the Industrial Internet of Things (IIoT).
- Deep Learning - A type of machine learning that uses neural networks modeled after the human brain. Excels at pattern recognition from large amounts of data.
- Digital Factory - Simulation models of manufacturing facilities used for designing, programming, and testing processes.
- Digital Supply Networks - Information sharing and connectivity across a supply chain network.
- Digital Thread - Communication framework that connects data flows between software systems and physical assets.
- Digital Twin - A virtual representation of a physical object or system that models its state in real time for understanding and simulation.
- Digital Twin Aggregation - Bringing together multiple digital twins into larger connected systems.
- Distributed Control System (DCS) - Control systems with embedded controllers distributed throughout a plant.
- Drone Surveying - Using aerial drones to survey, map and collect data on crops and land. Edge
- Computing - Processing data at or near the source of data generation rather than relying on the cloud. This reduces latency and bandwidth use.
- Edge Gateway - An edge computing device that serves as an access point between controllers, machines, sensors and the cloud.
- Embedded Software - Software applications built into mechanical, electrical or electronic hardware devices.
- Energy Management - Monitoring energy use and patterns to minimize waste and costs.
- Enterprise Integration - Unifying systems, processes and data across an organization for efficiency and insights.
- Fleet Management - Monitoring vehicle location, usage, maintenance and performance. Fog
- Computing - Distributed data processing complementing cloud and edge computing.
- Gateway - A bridge device that connects controls, sensors, and other devices with higher level networks or cloud systems.
- Human-Machine Interface (HMI) - User interface that allows monitoring and control of equipment.
- IIoT (Industrial Internet of Things) - Refers to the application of Internet of Things (IoT) technologies and principles within industrial and manufacturing environments. IoT is the network of physical objects, devices, sensors, and other items embedded with electronics, software, sensors, and connectivity that enables data exchange and monitoring.
- Industrial 5G - 5G networks tailored for industrial environments, delivering ultra reliable low latency
- Inventory Management - Tracking and controlling inventory to improve visibility and reduce costs.
- Lean Manufacturing - An operational philosophy focused on reducing waste and optimizing efficiency.
- Legacy Systems - Outdated computing systems, software, and applications that remain in use though often inefficient.
- Logistics - Planning and coordinating activities like procurement, shipping, warehousing, packaging, inventory management, etc.
- Loss Prevention - Using IIoT monitoring to identify leaks, overflows, breaks or losses to prevent waste and environmental harm.
- Low Power Wide Area Networks - Long range wireless networking for IoT devices with low bandwidth needs.
- Machine Learning - An application of AI that provides systems the ability to automatically learn and improve from experience without explicit programming.
- Maintenance as a Service - Outsourcing equipment maintenance to a service provider using IIoT monitoring.
- Manufacturing Execution System (MES) - Systems that manage and monitor work on the factory floor.
- Mass Customization - Flexible manufacturing techniques to produce customized products at mass market prices.
- MQTT (Message Queuing Telemetry Transport) - is a lightweight, publish-subscribe network protocol used for machine-to-machine (M2M) communication and the Internet of Things (IoT). It was designed to be a low-power, low-bandwidth, and secure protocol that is easy to implement.
- Natural Language Processing - Algorithms that analyze and derive meaning from human language. Used for document analysis, chatbots, voice control, etc.
- Network Edge - Computing at the extremes of the network closest to the data sources.
- Networking - Communication between nodes like controllers, sensors, and actuators via protocols like WiFi, Bluetooth, LPWAN, etc.
- OPC UA - Open platform communications unified architecture. Standard for secure industrial interoperability.
- Plant Asset Management - Software tools to optimize reliability and uptime of physical assets.
- Platform as a Service (PaaS) - Cloud platform for developing, running, and managing applications without building infrastructure.
- Predictive Analytics - Using data, statistical algorithms and ML models to identify future outcomes.
- Predictive Maintenance - Using data to forecast equipment failures before they occur to minimize downtime.
- Prescriptive Analytics - Moving beyond predictive analytics to recommend actions to take advantage of predictions.
- Precision Agriculture - Farming management based on observing and responding to variability in crops using sensors, robots, GPS, etc.
- Prognostics - Predicting future states and remaining useful life of assets based on data analytics.
- Product as a Service - Business model where customers pay for product usage rather than ownership. Enabled by smart connected products.
- Product Life Cycle Management (PLM) - Monitoring all aspects of a product throughout its life.
- Remote Monitoring - Monitoring assets and conditions from a central location.
- RFID - Radio Frequency Identification tags attached to objects that emit signals for tracking and identifying items.
- Robotics - Use of robots assisted by IIoT connectivity, sensory data, and analytics.
- ROI - Return on Investment. The financial benefit realized versus costs for a project or initiative.
- Root Cause Analysis - A method of problem solving to identify the root causes rather than just symptoms.
- SCADA - Supervisory Control and Data Acquisition (SCADA) - Systems that monitor, control and alarm industrial processes.
- SCM - Supply Chain Management (SCM) - Coordinating flows of goods, data, and finances across a distribution network.
- Sensors - Devices that detect events or changes in quantities like temperature, pressure, motion, etc. and provide corresponding outputs.
- Smart Agriculture - Using IIoT and data analytics to improve farming operations and crop yields.
- Smart Factory - Next generation of connected manufacturing facilities using IIoT, automation, ML, etc.
- Smart Grid - Electrical grid enhanced with digital communications, automation and ML to improve efficiency and reliability.
- Smart Machines - Machines augmented with computing power, connectivity, data analytics, and self-directed capabilities.
- Smart Products - Products made intelligent by sensors, connectivity, data and embedded software.
- Smart Water - Using sensors, meters, data analytics and automation to efficiently monitor and manage water distribution.
- Supply Chain Management (SCM) - Coordinating flows of goods, data, and finances across a distribution network.
- System Integration - Connecting disparate sub-systems into a unified whole with coordinated functions.
- Time-Sensitive Networking - Standards ensuring reliable data delivery within precise time constraints.
- Virtual Sensors - Using software algorithms to provide inferred measurements that physical hardware sensors cannot.
- Vertical Farming - Growing crops in vertically stacked layers in a controlled indoor environment. Enabled by IIoT.
- Warehouse Management System (WMS) - Software to efficiently manage warehouse operations like inventory tracking.