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Mourad ELGORMA published in AI, AI Applications by Industry, AI Ethics & Responsible AI, Computer Vision, Deep Learning (DL),

NVIDIA Computex 2026 Keynote: Vera Rubin, Vera CPU, RTX Spark and the Future of AI PCs

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Technology News 2026

NVIDIA Computex 2026 Keynote: Vera Rubin, Vera CPU, RTX Spark and the Future of AI PCs

NVIDIA’s Computex 2026 keynote, presented by CEO Jensen Huang during GTC Taipei at COMPUTEX, introduced one of the most important technology roadmaps of the year. The presentation focused on a new era of computing powered by artificial intelligence, agentic AI, AI factories, personal AI computers, robotics and open-source AI tools.

In the keynote highlight video, NVIDIA presented several major announcements, including the Vera Rubin AI computing platform, the Vera CPU, the RTX Spark superchip, a deeper collaboration with Microsoft to reinvent Windows PCs, and new tools for building secure personal AI agents.

Event: Computex 2026 Date: June 1, 2026 Speaker: Jensen Huang Topic: AI Computing

Quick Summary

The main message of NVIDIA’s Computex 2026 keynote is clear: the future of computing will be based on AI agents. These agents will not only answer questions. They will be able to reason, plan, use tools, interact with software, search files, generate content, write code, manage workflows and assist users in real time.

To make this possible, NVIDIA is building a complete ecosystem: powerful GPUs, new CPUs, AI superchips for personal computers, secure runtime software, networking for AI factories, open-source agent tools, robotics platforms and enterprise AI infrastructure.

1. Context: Why NVIDIA’s Computex 2026 Keynote Is Important

Computex is one of the world’s most important technology exhibitions, especially for hardware, semiconductors, laptops, servers, AI infrastructure and consumer electronics. In 2026, NVIDIA used this event to present its vision for the next stage of artificial intelligence.

The keynote was not only about a new graphics card or a single processor. It was about a complete transformation of computing. According to NVIDIA’s direction, computers are moving from passive machines to intelligent systems capable of understanding tasks and helping users complete them.

Important Idea

The most important concept in the keynote is agentic AI. This means AI systems that can take a user request and execute multiple steps to achieve a goal. For example, an AI agent may read documents, generate a report, open software, search files, write code and check results.

2. NVIDIA Vera Rubin: A New Platform for Agentic AI Factories

One of the most powerful announcements was the NVIDIA Vera Rubin platform. This platform is designed to power the next generation of large-scale artificial intelligence systems. NVIDIA describes Vera Rubin as a foundation for agentic AI factories, where massive computing systems generate intelligence at industrial scale.

In simple words, Vera Rubin is not just one chip. It is a complete AI infrastructure platform that combines CPUs, GPUs, networking, storage acceleration and security technologies into a rack-scale AI supercomputer.

AI Infrastructure

Rack-Scale System

Vera Rubin is designed as a large integrated system, not as an isolated component. It connects compute, memory, networking and security for high-performance AI workloads.

Agentic AI

Built for Agents

AI agents require long reasoning chains, tool use, memory, context processing and repeated actions. Vera Rubin is optimized for these workloads.

Networking

Spectrum-X Ethernet Photonics

NVIDIA introduced advanced networking technologies to help AI factories scale to very large numbers of GPUs.

Security

Confidential Computing

Security is central because AI factories process sensitive data, models, prompts, agent memory and business information.

Main Technologies Inside Vera Rubin

  • NVIDIA Vera CPU: a CPU designed for AI agents and data center workloads.
  • NVIDIA Rubin GPU: the GPU part of the new AI computing generation.
  • NVIDIA NVLink: high-speed communication between GPUs and system components.
  • ConnectX SuperNIC: advanced networking interface for large-scale AI systems.
  • BlueField DPU: data processing, networking, storage and security acceleration.
  • Spectrum-X Ethernet: networking fabric for large AI factories.

Why Vera Rubin Matters

Modern AI is becoming more expensive and more complex. Large language models, reasoning models, multimodal systems and AI agents require more compute, faster networking and better memory management. Vera Rubin aims to reduce cost per token, improve performance and support the next generation of AI services.

3. NVIDIA Vera CPU: A CPU Designed for AI Agents

NVIDIA also presented the Vera CPU, described as a CPU built for AI agents. This is a very important strategic move because NVIDIA is widely known for GPUs, but the AI era also requires strong CPUs to coordinate complex workloads.

GPUs accelerate mathematical operations, model inference and training. However, AI agents also need CPUs for orchestration, data handling, software execution, networking, memory management and interaction with tools. This is where the Vera CPU becomes important.

1

AI Agent Receives a Task

The user asks the AI agent to perform a complex operation, such as creating a report, analyzing files or building a workflow.

2

CPU Coordinates the Workflow

The CPU helps manage system operations, tool calls, memory, files, permissions and communication between different software components.

3

GPU Accelerates AI Processing

The GPU processes model inference, reasoning, generation, image/video tasks and other AI-heavy operations.

4

Result Is Delivered to the User

The system returns a final result after multiple steps of reasoning, tool usage and verification.

Simple Explanation

The Vera CPU can be understood as the coordinator of AI work. It helps the system manage tasks, while GPUs provide the heavy acceleration needed for AI models.

4. RTX Spark: Bringing AI Agents to Personal Computers

Another major announcement was NVIDIA RTX Spark, a new superchip designed for Windows PCs in the age of personal AI. This is one of the most interesting announcements because it brings NVIDIA’s AI strategy from huge data centers to laptops and desktops.

RTX Spark is designed to allow users to run powerful AI workloads locally on their devices. Instead of sending every request to the cloud, some AI models and agents can run directly on the PC. This can improve privacy, reduce latency and make AI tools more responsive.

Local AI

On-Device Agents

Personal AI agents can run directly on laptops and desktops, helping users with files, apps, creative tasks and code.

Performance

AI Acceleration

RTX Spark combines NVIDIA AI and graphics technologies to accelerate local AI workloads, graphics, video and creative applications.

Privacy

Less Cloud Dependency

Local processing can help keep sensitive data on the user’s device instead of sending everything to cloud servers.

Creators

Creative Workflows

RTX Spark targets creators, AI developers and gamers who need high performance in portable devices.

Technologies Mentioned Around RTX Spark

  • CUDA: NVIDIA’s parallel computing platform used by developers and AI researchers.
  • RTX: NVIDIA’s graphics and AI acceleration platform.
  • TensorRT: software for optimizing AI inference performance.
  • DLSS: AI-powered graphics performance and image quality technology.
  • OptiX: ray tracing and rendering acceleration technology.
  • FP4: low-precision AI computation for efficient model execution.
  • Unified memory: memory architecture useful for large local AI workloads.
RTX Spark = AI acceleration + graphics + local agents + Windows integration + creator workflows

5. NVIDIA and Microsoft: Reinventing Windows PCs

NVIDIA and Microsoft announced a collaboration to bring personal AI agents to Windows PCs. The idea is to transform the PC from a simple application launcher into a more intelligent assistant capable of helping users complete tasks.

For more than 40 years, users interacted with PCs mainly through clicking, typing and opening applications. With AI agents, the interaction model changes. A user may describe a goal in natural language, and the computer can help execute the task.

Traditional Windows PC AI-Native Windows PC
The user manually opens applications. The AI agent can help select tools and execute steps.
The user searches files manually. The AI agent can semantically search local files.
Most advanced AI depends on cloud services. Some AI models and agents can run locally on the device.
Security is mainly application-based. Agent security needs identity, containment, policy and user control.
The PC is mainly a tool. The PC becomes a digital teammate.

Security Note

Personal AI agents must be controlled carefully because they may access files, applications and private information. This is why NVIDIA and Microsoft highlighted security primitives, containment, policies and user control.

6. OpenShell, OpenClaw, NemoClaw and the New AI Agent Ecosystem

NVIDIA’s keynote also focused on software tools for AI agents. Hardware alone is not enough. To build useful AI agents, developers need models, runtimes, policies, safety layers and development frameworks.

NVIDIA introduced or highlighted several tools and projects around personal and physical AI agents, including OpenShell, OpenClaw, NemoClaw and other open AI resources.

Runtime

OpenShell

OpenShell is designed to help AI agents run more securely on personal devices, with policy controls and user-defined permissions.

Agents

OpenClaw

OpenClaw is part of the growing open-source agent ecosystem, allowing developers to build and deploy agent-based workflows.

Blueprints

NemoClaw

NemoClaw provides resources for building agent workflows and safer agent systems across local, cloud and edge environments.

Models

Open AI Models

NVIDIA’s ecosystem includes open models and tools for enterprise AI, physical AI, robotics and reasoning workloads.

What Is an AI Agent?

An AI agent is a software system that can understand a goal, plan actions, use tools, interact with applications and complete tasks with some level of autonomy. Instead of giving only one answer, an agent can perform a workflow.

7. AI Factories: The New Infrastructure of Intelligence

Jensen Huang often uses the concept of an AI factory. In a traditional factory, raw materials are transformed into physical products. In an AI factory, data and energy are transformed into intelligence.

This concept is important because advanced AI requires much more than a single server. It requires thousands of GPUs, high-speed networking, storage, power, cooling, security, software orchestration and continuous optimization.

Factory Type Input Process Output
Traditional Factory Raw materials Machines and assembly lines Physical products
AI Factory Data, energy and compute AI models, GPUs, CPUs, networking and software Intelligence, predictions, agents and digital services

Main Components of an AI Factory

  • Compute: GPUs, CPUs and accelerators for training and inference.
  • Networking: high-speed links to connect thousands or millions of compute units.
  • Storage: systems for data, model checkpoints, embeddings and context memory.
  • Security: protection for models, data, prompts, agents and enterprise workflows.
  • Software: orchestration, runtime, AI frameworks and developer tools.
  • Energy efficiency: essential for reducing operational cost and environmental impact.

8. Physical AI, Robotics and Autonomous Machines

Another key theme of the keynote was physical AI. Physical AI refers to AI systems that understand and interact with the real world. Examples include robots, autonomous vehicles, industrial machines, smart factories and humanoid robots.

Unlike chatbots, physical AI must understand space, movement, objects, safety, sensors and real-world actions. This requires simulation, world models, robotic platforms and powerful AI computing.

Robotics

Humanoid Robots

NVIDIA is investing in platforms that help researchers and companies build more capable humanoid robots.

Autonomous Vehicles

Robotaxis

Physical AI is also important for autonomous driving, robotaxis and intelligent transport systems.

Simulation

Digital Twins

Before robots operate in the real world, they can be trained and tested in simulated environments.

Industry

Smart Factories

Physical AI can help factories monitor machines, optimize processes and automate complex operations.

9. Summary Table of the Main NVIDIA Computex 2026 Announcements

Technology Category Main Purpose Why It Is Important
Vera Rubin AI infrastructure platform Power large-scale agentic AI factories Supports next-generation AI reasoning, inference and data center workloads
Vera CPU Processor Coordinate AI agents and data center tasks Shows NVIDIA’s move beyond GPUs into full AI computing systems
RTX Spark PC superchip Bring AI agents to Windows laptops and desktops Enables local AI, better privacy, faster response and creator workflows
Microsoft Collaboration Software and ecosystem Create AI-native Windows experiences Could redefine how users interact with PCs
OpenShell Agent runtime Run agents securely on personal devices Provides policy, privacy and user-control mechanisms
Physical AI Tools Robotics and simulation Support robots, AVs and industrial AI Extends AI from digital tasks to real-world actions

10. Why This Keynote Matters for the Future

NVIDIA’s Computex 2026 keynote matters because it shows the direction of the technology industry. AI is no longer limited to chatbots or cloud-based services. It is becoming a complete computing layer inside personal computers, enterprise systems, data centers, robots and industrial machines.

For Developers

Developers will need to learn how to build AI agents, connect models to tools, manage local inference, secure workflows and optimize applications for AI hardware.

For Researchers

Researchers can explore new topics such as agentic AI, local AI inference, AI security, robotics, physical AI, efficient model deployment, AI networking and high-performance computing.

For Businesses

Businesses will increasingly treat AI as infrastructure. They will need to think about compute capacity, data security, cost per token, local vs cloud AI, productivity workflows and automation.

For Normal PC Users

The PC may become more intelligent. Instead of only opening applications manually, users may ask the computer to perform tasks, organize information, create content and interact with software automatically.

Key Takeaway

NVIDIA is positioning itself at the center of the next computing revolution: AI agents running everywhere, from giant AI factories to personal laptops.

11. Frequently Asked Questions

What was announced at NVIDIA Computex 2026?

NVIDIA announced several technologies, including the Vera Rubin platform, Vera CPU, RTX Spark for AI PCs, Microsoft Windows AI collaboration, OpenShell for secure agents and tools for physical AI and robotics.

What is NVIDIA Vera Rubin?

Vera Rubin is NVIDIA’s AI computing platform designed for large-scale AI factories, agentic AI workloads, reasoning models and high-performance inference.

What is NVIDIA RTX Spark?

RTX Spark is a new NVIDIA superchip designed to bring AI agents and powerful local AI capabilities to Windows laptops and compact desktop PCs.

Why is Microsoft involved?

Microsoft is working with NVIDIA to build a Windows experience for personal AI agents, including security, containment and local AI execution.

What is agentic AI?

Agentic AI refers to AI systems that can perform multi-step tasks. They can reason, plan, use tools, interact with apps and complete workflows instead of only answering simple questions.

What are AI factories?

AI factories are large-scale computing infrastructures that transform data and energy into intelligence using GPUs, CPUs, networking, storage and AI software.

12. Sources and Further Reading

You can add these links at the end of your WordPress article as official and useful sources:

  • NVIDIA GTC Taipei at COMPUTEX 2026: https://www.nvidia.com/en-tw/gtc/taipei/computex/
  • NVIDIA Vera Rubin full production announcement: https://nvidianews.nvidia.com/news/vera-rubin-full-production-agentic-ai-factory
  • NVIDIA and Microsoft reinvent Windows PCs: https://nvidianews.nvidia.com/news/nvidia-microsoft-windows-pcs-agents-rtx-spark
  • NVIDIA Vera CPU announcement: https://nvidianews.nvidia.com/news/nvidia-unveils-vera-the-cpu-for-agents
  • NVIDIA open-source agent tools for physical AI: https://nvidianews.nvidia.com/news/nvidia-releases-major-collection-of-open-source-agent-tools-and-skills-for-physical-ai
  • YouTube keynote highlight video: https://www.youtube.com/watch?v=ugNnw4lAMWA
“`

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Mourad ELGORMA published in IoT, SysAdmin Linux, SYSTEM ADMINISTRATIONS

Installation of Node-RED Dashboard on Ubuntu

Installation of Node-RED Dashboard on Ubuntu

A complete step-by-step guide to install Node-RED on Ubuntu, add the modern FlowFuse Dashboard package, start the service, open the dashboard, and solve common access problems.

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Ubuntu Node-RED FlowFuse Dashboard Port 1880 IoT Dashboard
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Introduction

“`

Node-RED is a powerful visual programming tool used for automation, IoT systems, MQTT projects, sensor monitoring, and dashboard creation. In this tutorial, we will install Node-RED on Ubuntu and add the modern dashboard package: @flowfuse/node-red-dashboard.

The old package node-red-dashboard is deprecated. For a new installation, it is better to use FlowFuse Dashboard, which provides modern dashboard nodes such as gauges, charts, buttons, text widgets, and templates.

Objective Install Node-RED on Ubuntu, install FlowFuse Dashboard, restart Node-RED, and verify access to the dashboard from a web browser.
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Table of Contents

  1. Update Ubuntu
  2. Install useful dependencies
  3. Install Node-RED
  4. Start Node-RED manually
  5. Install FlowFuse Dashboard
  6. Restart Node-RED
  7. Enable Node-RED at startup
  8. Open the dashboard
  9. Create a simple test dashboard
  10. Allow port 1880 in the firewall
  11. Useful commands
  12. Troubleshooting

1. Update Ubuntu

“`

First, update the package list and upgrade the system packages. This step is important because Node-RED and Node.js depend on updated system libraries.

Update Ubuntu packages
sudo apt update
```

sudo apt upgrade -y
“`
Tip If the upgrade takes some time, wait until it finishes completely before installing Node-RED.
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2. Install Required Tools

“`

Install useful tools required for compiling packages and downloading installation scripts.

Install dependencies
sudo apt install -y build-essential git curl
Package Purpose
build-essential Provides compiler tools needed by some Node.js packages.
git Used to download and manage source code repositories.
curl Used to download scripts and files from the terminal.
“`

3. Install Node-RED

“`

Use the official Node-RED installation script for Debian and Ubuntu-based systems. This script installs or updates Node.js and Node-RED automatically.

Install Node-RED using the official script
bash <(curl -sL https://raw.githubusercontent.com/node-red/linux-installers/master/deb/update-nodejs-and-nodered)

During the installation, the script may ask if you want to continue installing or updating Node.js and Node-RED. Type:

Answer during installation
y
Important Let the installation finish completely. Do not close the terminal while Node-RED and Node.js are being installed.
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4. Start Node-RED Manually

“`

After installation, you can start Node-RED manually using this command:

Start Node-RED manually
node-red

When Node-RED starts successfully, open the editor in your browser:

Open Node-RED locally
http://localhost:1880

If you are connecting from another computer or phone on the same network, use the Ubuntu machine IP address:

Open Node-RED from another device
http://ADRESSE_IP_UBUNTU:1880

To find the IP address of your Ubuntu machine, run:

Find Ubuntu IP address
hostname -I
“`

5. Install FlowFuse Dashboard

“`

To install the modern Node-RED dashboard, go to the Node-RED user directory:

Go to Node-RED user folder
cd ~/.node-red

Now install the FlowFuse Dashboard package:

Install FlowFuse Dashboard
npm install @flowfuse/node-red-dashboard
Successful Installation Example If the installation finishes correctly, you may see a message similar to: added packages, audited packages, and found 0 vulnerabilities.
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6. Restart Node-RED

“`

After installing the dashboard package, restart Node-RED so the new dashboard nodes appear inside the editor.

Restart Node-RED using Node-RED command
node-red-restart

If Node-RED is running as a systemd service, restart it with:

Restart Node-RED service
sudo systemctl restart nodered

To check the service status:

Check Node-RED service status
sudo systemctl status nodered
“`

7. Enable Node-RED at Ubuntu Startup

“`

To make Node-RED start automatically when Ubuntu boots, enable the Node-RED service:

Enable Node-RED service at startup
sudo systemctl enable nodered.service

Start the service manually if it is not already running:

Start Node-RED service
sudo systemctl start nodered.service
Result Node-RED will now launch automatically every time your Ubuntu machine starts.
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8. Useful Node-RED Management Commands

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Node-RED provides simple commands to start, stop, restart, and read logs.

Command Description
node-red-start Start Node-RED.
node-red-stop Stop Node-RED.
node-red-restart Restart Node-RED.
node-red-log Display Node-RED logs and errors.
Node-RED management commands
node-red-start
```

node-red-stop node-red-restart node-red-log

9. Open the Node-RED Dashboard

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After restarting Node-RED, open the Node-RED editor:

Node-RED editor URL
http://ADRESSE_IP_UBUNTU:1880

In the left sidebar, you should see new dashboard nodes such as:

  • ui-button
  • ui-text
  • ui-gauge
  • ui-chart
  • ui-template

The dashboard page is usually available at:

Dashboard URL
http://ADRESSE_IP_UBUNTU:1880/dashboard
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10. Create a Simple Dashboard Test

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To verify that the dashboard works, create a very simple test flow.

  1. Add an inject node.
  2. Add a ui-text or ui-gauge node.
  3. Connect the inject node to the dashboard node.
  4. Double-click the dashboard node.
  5. Create a new Page and a new Group.
  6. Click Deploy.
  7. Open the dashboard URL in your browser.
Open dashboard test page
http://ADRESSE_IP_UBUNTU:1880/dashboard
Expected Result You should see your dashboard widget displayed in the browser.
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11. Allow Port 1880 in the Firewall

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If you cannot access Node-RED from another PC or phone on the same network, the firewall may be blocking port 1880.

Allow Node-RED port 1880 using UFW:

Allow port 1880
sudo ufw allow 1880/tcp
```

sudo ufw reload
“`

Then try opening Node-RED again:

Access Node-RED from browser
http://ADRESSE_IP_UBUNTU:1880
Network Note Your Ubuntu machine and your phone or PC must be connected to the same local network.
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12. Quick Command Summary

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Here is the complete list of essential commands used in this tutorial.

Complete command summary
sudo apt update && sudo apt upgrade -y
```

sudo apt install -y build-essential git curl bash <(curl -sL https://raw.githubusercontent.com/node-red/linux-installers/master/deb/update-nodejs-and-nodered) cd ~/.node-red npm install @flowfuse/node-red-dashboard sudo systemctl enable nodered.service sudo systemctl restart nodered.service

13. Troubleshooting

“`
Problem Solution
Dashboard nodes do not appear Restart Node-RED and check logs using node-red-log.
Dashboard inaccessible from another device Check the Ubuntu IP address using hostname -I and allow port 1880/tcp with UFW.
Node-RED does not start Run sudo systemctl status nodered and node-red-log to read the errors.
npm install fails Check internet connection, update Ubuntu, and make sure you are inside ~/.node-red.
Useful troubleshooting commands
hostname -I
```

node-red-log sudo systemctl status nodered sudo ufw allow 1880/tcp sudo ufw reload

Final Result

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After completing this tutorial, Node-RED and FlowFuse Dashboard should be installed and running on your Ubuntu machine.

Node-RED Editor http://ADRESSE_IP_UBUNTU:1880
Dashboard Page http://ADRESSE_IP_UBUNTU:1880/dashboard
Dashboard Package @flowfuse/node-red-dashboard
Default Port 1880
Useful Log Command node-red-log
Installation Completed You can now create professional dashboards for IoT data, MQTT sensors, Raspberry Pi projects, LoRa monitoring, and real-time system visualization.
“`

Prepared for: Elgorma Mourad – Node-RED Dashboard installation tutorial for Ubuntu.

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Mourad ELGORMA published in IoT, OPERATING SYSTEMS, OS Linux, SysAdmin Linux, SYSTEM ADMINISTRATIONS

How to Create or Install an Operating System for Raspberry Pi 4 Using Raspberry Pi Imager and a Memory Card

How to Create or Install an Operating System for Raspberry Pi 4 Using Raspberry Pi Imager and a Memory Card

A complete beginner-friendly tutorial to install Raspberry Pi OS on a microSD card, configure hostname, username, password, Wi-Fi, SSH, timezone, and first boot settings for Raspberry Pi 4.

“`
Raspberry Pi 4 Raspberry Pi OS Pi Imager microSD Card SSH Setup
“`

Introduction

“`

Before using a Raspberry Pi 4, you need to install an operating system on a bootable storage device, usually a microSD card. The easiest and safest method is to use Raspberry Pi Imager, the official tool provided by Raspberry Pi.

Raspberry Pi Imager allows you to select the Raspberry Pi model, choose the operating system, select the memory card, and configure important settings before the first boot. These settings include the username, password, Wi-Fi network, country, timezone, keyboard layout, and SSH access.

Download Raspberry Pi Imager
Important The microSD card will be completely erased during this process. Make sure you do not have important files on it before continuing.
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Image Suggestion 1 Add a screenshot of the official Raspberry Pi Imager download page here.
In WordPress, upload your screenshot to Media Library, then replace this box with an image.

Table of Contents

  1. Required hardware and software
  2. Download Raspberry Pi Imager
  3. Insert and prepare the microSD card
  4. Select Raspberry Pi 4 as the device
  5. Select Raspberry Pi OS
  6. Select the memory card
  7. Configure advanced OS settings
  8. Write the OS image to the card
  9. Boot Raspberry Pi 4 for the first time
  10. Connect using SSH
  11. Update the system after installation
  12. Troubleshooting common problems

1. Required Hardware and Software

“`

Before starting, prepare the following components:

Component Recommended Choice
Raspberry Pi Board Raspberry Pi 4 Model B
Memory Card microSD card, preferably 32 GB or more
Card Reader USB microSD card reader or built-in laptop SD reader
Power Supply Official Raspberry Pi USB-C power supply recommended
Internet Wi-Fi or Ethernet connection
Software Raspberry Pi Imager
Recommendation For Raspberry Pi OS with desktop, use at least a 32 GB microSD card. For server use, Raspberry Pi OS Lite can run on smaller cards, but 16 GB or 32 GB is still more comfortable.
“`

2. Download Raspberry Pi Imager

“`

Go to the official Raspberry Pi software page and download Raspberry Pi Imager for your computer operating system. It is available for Windows, macOS, and Linux.

Official Raspberry Pi Imager Download Page

Install Raspberry Pi Imager on Windows

  1. Download the Windows installer from the official page.
  2. Open the downloaded file.
  3. Follow the installation steps.
  4. Launch Raspberry Pi Imager from the Start Menu.

Install Raspberry Pi Imager on Linux

On Debian-based systems, you can install it from the terminal if it is available in your repository:

Install Raspberry Pi Imager on Linux
sudo apt update
```

sudo apt install rpi-imager -y
“`

Install Raspberry Pi Imager on macOS

  1. Download the macOS version from the official page.
  2. Open the downloaded file.
  3. Drag Raspberry Pi Imager to the Applications folder.
  4. Open it from Applications.
“`
Image Suggestion 2 Add a screenshot of Raspberry Pi Imager main window with the three buttons: Device, OS, and Storage.
Suggested caption: Raspberry Pi Imager main interface.

3. Insert the microSD Card

“`

Insert the microSD card into your computer using a card reader. Make sure your computer detects the card correctly.

Warning All data on the selected microSD card will be deleted. Double-check that you select the correct storage device inside Raspberry Pi Imager.
“`

4. Open Raspberry Pi Imager

“`

Open Raspberry Pi Imager. You will usually see three main options:

  • Choose Device: select your Raspberry Pi model.
  • Choose OS: select the operating system to install.
  • Choose Storage: select the microSD card.
“`

5. Choose the Raspberry Pi Device

“`

Click Choose Device, then select:

Option Value
Device Raspberry Pi 4

Selecting the correct board helps Raspberry Pi Imager recommend compatible operating systems.

“`

6. Choose the Operating System

“`

Click Choose OS. For most users, the recommended option is:

Use Case Recommended OS
Desktop use with screen, mouse, and keyboard Raspberry Pi OS 64-bit
Server, SSH, MQTT, Node-RED, Linux lab Raspberry Pi OS Lite 64-bit
Beginner with graphical interface Raspberry Pi OS with Desktop
For Raspberry Pi 4 Projects If your goal is to use the Pi as a server, MQTT broker, Node-RED server, or Linux administration lab, Raspberry Pi OS Lite 64-bit is a clean and lightweight choice.
“`
Image Suggestion 3 Add a screenshot showing the Raspberry Pi OS selection menu.
Suggested caption: Selecting Raspberry Pi OS inside Raspberry Pi Imager.

7. Choose the Storage Device

“`

Click Choose Storage, then select your microSD card.

Be Careful If you have multiple USB drives connected, remove unnecessary drives before writing the image. This reduces the risk of erasing the wrong device.
“`

8. Configure Advanced OS Settings

“`

Before writing the operating system, click Next. Raspberry Pi Imager may ask if you want to apply OS customization settings. Choose Edit Settings or open the settings menu if available.

These settings are very important because they allow you to prepare the Raspberry Pi before the first boot.

Recommended General Settings

Parameter Recommended Value
Hostname raspberrypi or pi4-server
Username mourad
Password Choose a strong private password
Wireless LAN Enable if you want Wi-Fi connection
SSID Your Wi-Fi network name
Wi-Fi Password Your Wi-Fi password
Wireless LAN Country DZ for Algeria, or select your country
Timezone Africa/Algiers
Keyboard Layout us, fr, or your preferred layout

Recommended Services Settings

Parameter Recommended Value
Enable SSH Yes
SSH Authentication Password authentication for beginners, SSH key for better security
Username for SSH mourad
Security Note Do not use weak passwords such as 123456, raspberry, admin, or your phone number. Use a strong password with letters, numbers, and symbols.
“`
Image Suggestion 4 Add a screenshot of the OS customization settings: hostname, username, password, Wi-Fi, locale, and SSH.
Suggested caption: Configuring username, password, Wi-Fi, timezone, and SSH before first boot.

9. Write the Operating System to the microSD Card

“`

After selecting the device, OS, storage, and configuration settings, click Write or Next.

Raspberry Pi Imager will:

  • Download the selected operating system if needed.
  • Erase the microSD card.
  • Write the OS image to the card.
  • Verify that the image was written correctly.

When the process finishes, Raspberry Pi Imager will show a success message. You can then safely remove the microSD card from your computer.

“`

10. Insert the microSD Card into the Raspberry Pi 4

“`

Make sure the Raspberry Pi is powered off. Insert the prepared microSD card into the microSD slot of the Raspberry Pi 4.

Then connect:

  • USB-C power supply
  • Ethernet cable if using wired network
  • HDMI screen if using desktop mode
  • Keyboard and mouse if needed
Tip If you configured Wi-Fi and SSH in Raspberry Pi Imager, you can use the Raspberry Pi headless, without screen, mouse, or keyboard.
“`

11. First Boot

“`

Power on the Raspberry Pi 4. The first boot may take a little longer than normal because Raspberry Pi OS expands the filesystem and applies your configuration.

After the first boot, your Raspberry Pi should connect to your network automatically if Wi-Fi or Ethernet was configured correctly.

“`

12. Find the Raspberry Pi IP Address

“`

To connect to your Raspberry Pi by SSH, you need its IP address. You can find it using one of these methods:

Method 1: Check Your Router

Open your router admin page and look for connected devices. Search for the hostname you configured, for example raspberrypi or pi4-server.

Method 2: Use ping with Hostname

Ping Raspberry Pi hostname
ping raspberrypi.local

Method 3: Scan Your Local Network

On Linux, you can use:

Check local network devices
ip neigh
“`

13. Connect to Raspberry Pi Using SSH

“`

If SSH was enabled in Raspberry Pi Imager, you can connect from your computer using:

SSH using hostname
ssh mourad@raspberrypi.local

Or using the IP address:

SSH using IP address
ssh mourad@192.168.1.104

Replace 192.168.1.104 with the real IP address of your Raspberry Pi.

First SSH Connection The first time you connect, your computer may ask you to confirm the device fingerprint. Type yes and press Enter.
“`

14. Update Raspberry Pi OS After Installation

“`

After logging in for the first time, update the system packages.

Update Raspberry Pi OS
sudo apt update
```

sudo apt full-upgrade -y
“`

After the update, reboot the Raspberry Pi:

Reboot Raspberry Pi
sudo reboot
“`

15. Optional: Open Raspberry Pi Configuration Tool

“`

You can use raspi-config to configure system options after installation.

Open Raspberry Pi configuration menu
sudo raspi-config

Useful options include:

  • Enable or disable SSH
  • Change hostname
  • Configure Wi-Fi country
  • Set timezone
  • Enable interfaces such as I2C, SPI, Serial, or Camera
  • Expand filesystem if needed
“`

16. Optional: Enable Interfaces for IoT Projects

“`

If you plan to use your Raspberry Pi 4 for IoT, sensors, LoRa, MQTT, or serial communication, you may need to enable hardware interfaces.

Interface Use Case
SSH Remote terminal access
SPI LoRa modules, displays, ADC modules
I2C Sensors such as BME280, INA219, OLED displays
Serial GPS modules, Arduino communication, LoRa HAT serial mode
VNC Remote desktop access

Open the configuration tool:

Enable interfaces
sudo raspi-config

Then go to:

Menu path
Interface Options → Enable SSH / SPI / I2C / Serial
“`

17. Recommended Basic Security After Installation

“`

After installing Raspberry Pi OS, apply these basic security steps:

  • Use a strong password.
  • Keep the system updated.
  • Disable services you do not use.
  • Use SSH keys instead of password login for better security.
  • Do not expose SSH directly to the internet without protection.

You can check your current username with:

Check current user
whoami

You can check the hostname with:

Check hostname
hostname
“`

18. Troubleshooting Common Problems

“`

Problem 1: Raspberry Pi Does Not Boot

  • Check that the microSD card was written successfully.
  • Use a good-quality power supply.
  • Try another microSD card.
  • Make sure you selected Raspberry Pi 4 in Raspberry Pi Imager.

Problem 2: Cannot Connect by SSH

  • Make sure SSH was enabled in Raspberry Pi Imager.
  • Check that the Raspberry Pi is connected to the same network as your computer.
  • Check the IP address from your router.
  • Try using the IP address instead of raspberrypi.local.
Example SSH command
ssh mourad@192.168.1.104

Problem 3: Wi-Fi Does Not Connect

  • Check the SSID and Wi-Fi password.
  • Make sure the wireless country is correct.
  • Try Ethernet first, then fix Wi-Fi from the terminal.

Problem 4: Wrong Keyboard Layout

Open the configuration tool and change the keyboard layout:

Keyboard configuration
sudo raspi-config
“`

Final Result

“`

At the end of this tutorial, your Raspberry Pi 4 is ready with Raspberry Pi OS installed on the microSD card. You should now have:

  • A bootable Raspberry Pi OS microSD card.
  • A configured username and password.
  • Wi-Fi or Ethernet network access.
  • SSH access enabled.
  • Correct timezone and keyboard layout.
  • An updated Raspberry Pi system ready for projects.
Example Username mourad
Example Hostname raspberrypi
Example SSH Command ssh [email protected]
Recommended Timezone Africa/Algiers
“`

Official Links

“`

Use the official Raspberry Pi website to download Raspberry Pi Imager and read the official documentation:

“`

This tutorial explains how to create and configure a Raspberry Pi 4 operating system using Raspberry Pi Imager and a microSD card.

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Mourad ELGORMA published in Uncategorized

Fostering new Vertical and Horizontal IoT Applications with Intelligence Everywhere

Intelligence Everywhere is predicated on the seamless integration of IoT networks transporting a vast amount of data streams through many computing resources across an edge-to-cloud continuum, relying on the orchestration of distributed machine learning models. The result is an interconnected and collective intelligent ecosystem where devices, systems, services, and users work together to support IoT applications. This paper discusses the state-of-the-art research and the principles of the Intelligence Everywhere framework for enhancing IoT applications in vertical sectors such as Digital Health, Infrastructure, and Transportation/Mobility in the context of intelligent society (Society 5.0). It also introduces a novel perspective for the development of horizontal IoT applications, capable of running across various IoT networks while fostering collective intelligence across diverse sectors. Finally, this paper provides comprehensive insights into the challenges and opportunities for harnessing collective knowledge from real-time insights, leading to optimised processes and better overall collaboration across different IoT sectors.

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Mourad ELGORMA published in Uncategorized

Towards Smart Healthcare: Challenges and Opportunities in IoT and ML

The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. The traditional healthcare system, centered around hospitals and clinics, has proven inadequate in the face of such challenges. Intelligent wearable devices, a key part of modern healthcare, leverage Internet of Things technology to collect extensive data related to the environment as well as psychological, behavioral, and physical health. However, managing the substantial data generated by these wearables and other IoT devices in healthcare poses a significant challenge, potentially impeding decision-making processes. Recent interest has grown in applying data analytics for extracting information, gaining insights, and making predictions. Additionally, machine learning, known for addressing various big data and networking challenges, has seen increased implementation to enhance IoT systems in healthcare. This chapter focuses exclusively on exploring the hurdles encountered when integrating ML methods into the IoT healthcare sector. It offers a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios: IoT-based, ML-based, and the implementation of machine learning methodologies in the IoT-based healthcare industry. This compilation will assist future researchers, healthcare professionals, and government agencies by offering valuable insights into recent smart healthcare advancements.

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Mourad ELGORMA published in Uncategorized

Introduction to IoT

The Internet of Things has rapidly transformed the 21st century, enhancing decision-making processes and introducing innovative consumer services such as pay-as-you-use models. The integration of smart devices and automation technologies has revolutionized every aspect of our lives, from health services to the manufacturing industry, and from the agriculture sector to mining. Alongside the positive aspects, it is also essential to recognize the significant safety, security, and trust concerns in this technological landscape. This chapter serves as a comprehensive guide for newcomers interested in the IoT domain, providing a foundation for making future contributions. Specifically, it discusses the overview, historical evolution, key characteristics, advantages, architectures, taxonomy of technologies, and existing applications in major IoT domains. In addressing prevalent issues and challenges in designing and deploying IoT applications, the chapter examines security threats across architectural layers, ethical considerations, user privacy concerns, and trust-related issues. This discussion equips researchers with a solid understanding of diverse IoT aspects, providing a comprehensive understanding of IoT technology along with insights into the extensive potential and impact of this transformative field.

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Mourad ELGORMA published in Uncategorized

Marine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing

Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging to efficiently and reliably collect and compute a huge amount of maritime data. In this paper, we propose a hybrid low-Earth orbit (LEO) and unmanned aerial vehicle (UAV) edge computing method in space-air-sea integrated networks for marine IoT systems. Specifically, two types of edge servers mounted on UAVs and LEO satellites are endowed with computational capabilities for the real-time utilization of a sizable data collected from ocean IoT sensors. Our system aims at minimizing the total energy consumption of the battery-constrained UAV by jointly optimizing the bit allocation of communication and computation along with the UAV path planning under latency, energy budget and operational constraints. For availability and practicality, the proposed methods were developed for three different cases according to the accessibility of the LEO satellite, “Always On," “Always Off" and “Intermediate Disconnected", by leveraging successive convex approximation (SCA) strategies. Via numerical results, we verify that significant energy savings can be accrued for all cases of LEO accessibility by means of joint optimization of bit allocation and UAV path planning compared to partial optimization schemes that design for only the bit allocation or trajectory of the UAV.

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