Glossary

Identifying unusual patterns in data that do not conform to expected behavior, often used in predictive maintenance.

AIoT combines Artificial Intelligence (AI) with the Internet of Things (IoT). While IoT enables devices to collect and exchange data, AI brings the power of intelligent decision-making.

Autonomous or semi-autonomous software programs that use artificial intelligence to perform tasks, make decisions, and learn from data. In IoT, AI agents can analyze sensor data, make predictions, and trigger actions automatically.

Data-driven findings that can directly influence decision-making or operations.

A set of rules and protocols that allow different software components to communicate with each other. In IoT, APIs enable integration between devices, platforms, and applications.

Hardware items that can connect and communicate via the internet or other networks.

Delivery of computing services (servers, storage, databases) over the cloud to enable faster innovation.

Integrated systems that allow devices, platforms, and services to work together seamlessly.

A system or service that is hosted on cloud infrastructure, allowing data storage, processing, and access over the internet. It provides scalability, remote access, and integration for IoT deployments.

A virtual model of a physical object, system, or process. Powered by real-time data from IoT sensors, it allows users to simulate, monitor, and analyze the behavior and performance of the physical counterpart.

Administering, monitoring, and maintaining IoT devices throughout their lifecycle.

Setting up devices to ensure they perform according to operational requirements.

Data that is collected but not used for analysis or decision-making. In IoT, this could include sensor logs, system alerts, or environmental data that is stored but never analyzed.

A business model where equipment is offered as a subscription service rather than a one-time purchase.

A microprocessor-based system designed for specific control functions within a larger system.

Updating device firmware wirelessly without physical intervention.

A device that connects IoT devices to the cloud or other networks, translating data and managing communication.

AI systems capable of generating content such as text, images, or data, useful in simulations and synthetic data.

Generative Asset Intelligence is the dynamic capability of an AI-powered platform to interpret asset data, learn from historical and real-time performance, and autonomously produce insights, explanations, and recommendations that drive operational decisions.

Using GPS or RFID to create virtual geographic boundaries and trigger responses when a device enters/exits.

Attaching geographic location metadata to digital data like images or device logs.

The use of IoT technologies in industrial sectors for automation and predictive analytics.

The ability of systems and devices to work together and share data seamlessly.

IoT applications specifically in healthcare, connecting medical devices and systems.

The analysis of data generated by IoT devices to extract meaningful insights.

Cloud-based infrastructure and services used to develop, manage, and scale IoT solutions.

The implementation of IoT devices and infrastructure into an operational environment.

The various communication methods that link IoT devices to each other, to gateways, or to the cloud. Common types include Wi-Fi, Bluetooth, Zigbee, LoRaWAN, and cellular networks.

A low-power, long-range wireless protocol designed for IoT networks.

Essential, real-time data that is actively used in operational decision-making. It is often processed at the edge and designed for immediate action rather than deep historical analysis.

A type of recurrent neural network (RNN) used in deep learning that is capable of learning long-term dependencies. It is particularly effective for time-series forecasting, sequence prediction, and anomaly detection in IoT applications.

Third-party companies that manage a customer’s IT infrastructure and end-user systems.

Direct communication between devices using any communications channel, including wired and wireless.

A lightweight messaging protocol often used for IoT devices due to its efficiency and ability to handle unreliable networks.

The wireless delivery of new software, firmware, or other data to devices. Essential for managing and updating distributed IoT deployments.

A cloud computing model that delivers infrastructure and software tools for application development.

Using historical data and machine learning to forecast future events or trends.

Maintenance that is scheduled based on predicted equipment failure or degradation.

A set of rules for data communication between devices. Common IoT protocols include MQTT, CoAP, and HTTP.

Tools and processes that allow for the remote oversight of IT systems and devices.

A technology that uses electromagnetic fields to identify and track tags attached to objects. RFID is used in inventory management, asset tracking, and access control systems.

The ability to analyze and act on data as it is generated.

A fast and iterative process of creating working models or MVPs (Minimum Viable Products) for testing and validation. It helps reduce development time and refine product features early on.

Cloud-based software delivery model accessed via a web browser or app.

A company or individual who integrates various systems and ensures they function together.

Artificially generated data used for training AI models or simulating environments when real data is limited or sensitive.

Bridgera’s tagline highlights the transition from basic IoT systems to AI-driven intelligence. It reflects how our platform helps businesses evolve from connected operations to intelligent ones—powered by real-time insights, automation, and AI-enabled decision-making.

Data that is organized into a fixed format, typically rows and columns, making it easy to store, search, and analyze. Structured data is commonly found in relational databases.

A technology that combines telecommunications and informatics to transmit data from remote objects (typically vehicles or IoT devices) to centralized systems for monitoring and control. It often includes GPS, onboard diagnostics, and real-time communication.

Data that does not have a predefined format or organizational structure. It includes text, images, video, audio, and sensor logs, and is more complex to process and analyze than structured data.

A product or service produced by one company that other companies rebrand and sell as their own.

A low-power, low data rate wireless network standard used for short-range communication, often in home automation and industrial control applications.

A low-power wireless communications protocol primarily designed for home automation.