The language of edge computing

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition. These tasks include learning from data, recognizing patterns, understanding language, problem-solving, and decision-making. AI systems use algorithms and computational models to process information, adapt to new inputs, and improve their performance over time.

CAN bus is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer. It was originally developed by Bosch in the mid-1980s for automotive applications as a method to enable reliable and efficient communication between various in-vehicle systems without the need for excessive wiring.

DBSCAN is a popular density-based clustering algorithm used for identifying clusters in data with varying shapes and sizes, while also detecting noise or outliers. Unlike other clustering methods such as K-Means, DBSCAN does not require the number of clusters to be specified in advance and is well-suited for datasets with noise and complex cluster shapes.

Edge AI refers to the deployment of artificial intelligence (AI) algorithms and machine learning models directly on edge devices, such as smartphones, IoT devices, and embedded systems, rather than relying on cloud-based data centers. This enables real-time data processing and decision-making at or near the source of data generation, significantly reducing latency, bandwidth usage, and the dependency on constant network connectivity.

Edge Analytics refers to the process of collecting, analyzing, and deriving insights from data at the edge of the network, where the data is generated, rather than sending it to a centralized data center or cloud for processing. This approach allows for real-time analysis and decision-making, enabling faster responses and reducing the need for large-scale data transmission over networks.

Edge cloud, also known as the edge cloud computing or edge computing cloud, is a distributed computing infrastructure that brings cloud computing capabilities closer to the edge of the network. It is designed to process and store data locally on devices or edge servers, rather than sending it to a centralized cloud data center for processing.

Edge Computing is a distributed computing paradigm that brings computation and data storage closer to the physical location where data is generated, typically at the "edge" of the network. Instead of relying on centralized data centers or cloud servers, edge computing enables data processing and analysis to occur locally on devices like sensors, IoT devices, and gateways. This significantly reduces latency, optimizes bandwidth usage, and enables real-time data processing.

An edge computing platform is a comprehensive solution designed to process and analyze data at or near the source of data generation, such as IoT devices, sensors, and other edge devices, rather than relying solely on cloud or data center resources. This approach enables real-time data processing, reduced latency, optimized bandwidth usage, and greater data privacy, making it essential for applications that require immediate insights and actions.

An edge network is a decentralized computer network that is designed to bring data processing and storage closer to where it's needed, such as the end user, rather than in a centralized location like a cloud data center. This enables faster data processing and reduced latency, which is crucial for real-time applications like video streaming, online gaming, and autonomous vehicles.

HDF5, or Hierarchical Data Format version 5, is a widely-used open-source file format and set of tools designed to store and organize large amounts of data. It is highly flexible and efficient, making it the format of choice for handling complex data structures and massive datasets in scientific computing, engineering, and data analytics. Files with the .h5 extension are commonly associated with this format.

The Industrial Internet of Things (IIoT) embodies the convergence of connected devices with advanced analytics in industrial settings, facilitating automation, real-time monitoring, and informed decision-making. Key sectors leveraging IIoT include manufacturing, energy, transportation, and healthcare.

K-Means is a popular unsupervised machine learning algorithm used for clustering data into distinct groups based on their similarities. It works by partitioning a dataset into K clusters, where each data point belongs to the cluster with the nearest mean. K-Means is widely used in various fields, including market segmentation, image compression, and pattern recognition.

Kafka is a distributed streaming platform and messaging protocol designed for building real-time data pipelines and streaming applications. Developed by LinkedIn and later open-sourced under Apache, the Kafka protocol is widely used for high-throughput, low-latency, and fault-tolerant data transmission across various industries.

MQTT is a lightweight messaging protocol designed for low-bandwidth, high-latency environments, commonly used in machine-to-machine (M2M) communication and Internet of Things (IoT) applications. It was originally developed by IBM in the late 1990s to connect oil pipeline sensors over satellite connections with remote servers.

ONNX, or Open Neural Network Exchange, is an open-source format designed to facilitate the interoperability between different deep learning frameworks. Created by Microsoft and Facebook, ONNX provides a common framework for representing machine learning models, enabling models to be easily transferred and used across different platforms without needing to be retrained or significantly modified.

Predictive maintenance is a type of maintenance strategy that uses data analytics and machine learning algorithms to predict equipment failures before they occur.

Random Forest is a versatile and widely-used machine learning algorithm based on the ensemble learning method. It operates by constructing multiple decision trees during training and then combining the predictions from each tree to produce a final output. This approach improves accuracy, reduces overfitting, and increases robustness, making Random Forest suitable for both classification and regression tasks.

The Raspberry Pi is a small, affordable, and versatile single-board computer developed by the Raspberry Pi Foundation. Its primary aim is to promote teaching of basic computer science in schools and in developing countries. However, its low cost and open design have made it extremely popular among hobbyists and the maker community, who use it for everything from home automation to custom media centers.

Real-time analytics refers to the process of analyzing data as soon as it is generated or received, in order to make fast and informed decisions based on the insights gained. In manufacturing and automotive industries, real-time analytics is critical for optimizing processes, identifying potential issues before they escalate, and improving overall efficiency.

SQL, or Structured Query Language, is a standardized programming language used to manage and manipulate relational databases. It is widely employed for querying, updating, and organizing data stored in databases, making it one of the most essential tools for working with structured data.

Servitization is the process by which a company shifts its focus from selling physical products to providing a comprehensive suite of services around those products.

Stream analytics is a type of data analysis that focuses on processing and analyzing data in real-time, as it is generated or "streamed" from various sources. The main goal of stream analytics is to extract valuable insights and patterns from these continuous data streams, allowing organizations to make faster and more informed decisions.

A stream model is a framework used to represent continuous data flows as events processed in real-time. This model is essential for the dynamic analysis and management of streaming data in various applications.

Streaming data refers to the continuous flow of real-time data generated by various sources, such as sensors, devices, applications, and user interactions, which is transmitted and processed in near real-time. Unlike traditional batch data, which is collected and stored for later analysis, streaming data is processed as it arrives, enabling immediate insights and actions. Streaming data is essential for applications that require real-time analytics, monitoring, and decision-making.

TensorFlow Lite is a lightweight, open-source framework designed for deploying machine learning models on mobile, embedded, and edge devices. It is a part of the TensorFlow ecosystem, specifically optimized to run machine learning inference tasks on devices with limited computational power, such as smartphones, IoT devices, and embedded systems.

Zero Trust is a security concept and model that requires all users, devices, and applications to be authenticated and authorized before they are allowed to access a network or resources within it.

ZeroMQ, often abbreviated as 0MQ or ZMQ, is a high-performance asynchronous messaging library, aimed at use in distributed or concurrent applications. It provides a message queue, but unlike middleware solutions like AMQP or MQTT, ZeroMQ functions at a lower level, resembling a concurrency framework more than a traditional message broker.