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Linux in Real-Time Embedded Systems: Leveraging Open-Source Power for Edge AI and IoT Applications

A Research Activity Presented to
The Faculty, College of Computer Studies in
Pambayang Dalubhasaan ng Marilao

by
Victoriano, Mark Joseph M.
Bachelor of Science in Information Technology – 22B
January 20, 2026
Edge AI and Real-Time OS
II. Executive Summary

This report investigates the role of Linux in real-time embedded systems, particularly in the context of Edge AI and IoT applications. Linux, with its open-source nature, offers significant flexibility and scalability, making it ideal for resource-constrained environments where real-time performance is critical. The report highlights how Linux's kernel and real-time patches, such as PREEMPT-RT, are tailored to meet low-latency, high-throughput requirements. 
The integration of AI-driven tasks in Edge computing is also explored, focusing on Linux’s capability to manage distributed data and perform on-device inference. The growing importance of Linux in Edge AI for IoT devices is discussed, with a forward-looking view of how Linux will continue to drive innovation in these areas over the next five years.

III. Introduction to the Technology

3.1. Definition and Context
Linux-based real-time embedded systems leverage the power of the Linux kernel to enable low-latency, real-time processing on devices with constrained resources. These systems are often used in Edge AI applications where computing occurs closer to the data source (i.e., at the "edge" of the network), minimizing latency and bandwidth usage. Embedded Linux systems are particularly suited for Internet of Things (IoT) devices, autonomous vehicles, robotics, and industrial automation, where both real-time performance and AI-driven functionality are required.

3.2. Problem Solved
Traditional operating systems, particularly those designed for general-purpose use, do not meet the performance requirements of real-time applications. The challenge in Edge AI is to perform AI inference locally on devices with limited processing power while ensuring low-latency responses and reliability. Real-time Linux systems, with customizations such as PREEMPT-RT and lightweight distributions (e.g., Yocto, Buildroot), solve these challenges by providing real-time task scheduling, efficient resource management, and the ability to perform on-device AI inference without relying heavily on cloud processing.

IV. Architectural and Technical Analysis
4.1. Core Architectural Component
The most critical architectural component of Linux in real-time embedded systems is the real-time kernel, enhanced by the PREEMPT-RT patch. This patch transforms the standard Linux kernel into a preemptive, real-time system capable of handling tasks with tight deadlines. The real-time kernel allows for more predictable execution times, reducing the chance of latency spikes. Linux also utilizes lightweight file systems (e.g., UBIFS or JFFS2) for flash storage, minimizing memory usage while providing reliable access to system resources.
For Edge AI, the kernel must support hardware accelerators (such as TPUs or GPUs) and provide the necessary software interfaces for AI workloads. Linux in this context supports distributed processing, allowing edge devices to collaborate, share models, and synchronize AI tasks, whether in IoT sensor networks or autonomous vehicle fleets.

4.2 Comparison Table
Feature/ Function	Traditional OS Approach	Linux Real-Time Embedded System Approach
Resource Scheduling	Static, time-sliced CPU allocation	Dynamic, priority-based scheduling with PREEMPT-RT for real-time tasks
Real-Time Task Handling	Non-real time task handling (e.g., best-effort scheduling)	Real-time task handling with low-latency guarantees, using real-time Linux features
Memory Management	Traditional memory paging and swapping	Optimized memory allocation tailored for constrained devices (e.g., Buildroot, minimal overhead)
AI Inference	Cloud-based inference with high latency	Cloud-based inference with high latency
IoT/Edge Communication	Reliant on cloud for data processing and model updates	Distributed, local data processing and model updates in real-time between edge devices
4.3 Performance/ Security Implication
The major performance implication of using Linux in real-time embedded systems is the latency reduction through real-time scheduling. In IoT applications, for example, the ability to respond to sensor inputs in near real-time is critical. PREEMPT-RT ensures that tasks are executed within their deadlines, providing deterministic behavior that is not possible with standard Linux.
From a security perspective, embedded Linux systems, especially in Edge AI, must address concerns related to local AI processing and data privacy. The use of encrypted communications and secure boot processes helps protect against potential vulnerabilities. Additionally, ensuring that the edge devices are securely isolated from the cloud while executing AI inference is paramount

V. Future Impact and Conclusion
5.1. Potential Applications
1.	Internet of Things (IoT): Real-time, AI-powered Linux systems will drive the next generation of IoT devices, enabling smart cities, healthcare, and industrial automation. By processing data locally, devices can respond faster to real-world events, such as monitoring and controlling environmental parameters in real-time.

2.	Autonomous Vehicles: Linux in real-time systems will enable autonomous vehicles to make real-time decisions based on local sensor data. By running AI models directly on the vehicle's embedded systems, latency is minimized, allowing for safer and faster decision-making during critical events (e.g., obstacle detection, navigation).

5.2. Conclusion
Linux-based real-time systems are poised to play an integral role in the development of Edge AI applications, particularly within the realm of IoT and autonomous systems. The flexibility of Linux, combined with real-time capabilities and AI model integration, allows for efficient resource management and local data processing, essential for real-time decision-making. As the demand for Edge AI continues to grow, Linux’s adaptability will be a key driver in the success of real-time applications, shaping the future of autonomous systems and intelligent devices over the next five years. 
References
•	Harrison, M. (2021). Security in Embedded Linux: Techniques for Securing IoT Devices. IEEE Internet of Things Journal, 8(7), 5535-5546. https://doi.org/10.1109/JIOT.2021.3048912
•	Peng, P., & Ge, Y. (2020). Real-Time Linux for Embedded Systems: A Comprehensive Overview. Journal of Embedded Systems, 15(2), 54-65.
•	U-Boot Documentation. (2021). U-Boot: The Universal Bootloader for Embedded Systems. Retrieved from https://u-boot.org
•	Yocto Project Documentation. (2021). What is Yocto? Retrieved from https://www.yoctoproject.org
•	Buildroot Documentation. (2021). Building Embedded Linux Systems with Buildroot. Retrieved from https://buildroot.org

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