Embedded system design for hardware agnostic-software patterns

Background:
Embedded systems are the backbone of many smart devices, from vehicles to industrial machines. However, traditional embedded software is often tightly coupled with specific hardware, making it hard to update, scale, or repurpose across platforms. This project tackles that challenge by designing a flexible, software-defined ecosystem that is hardware-agnostic. The goal is to decouple applications from underlying hardware through modern technologies like containerization, cloud environments, and peer-to-peer (P2P) communication.​The team is also addressing a shift from centralized (client-server) architectures to decentralized, peer-based systems, enabling more resilient and scalable networks for connected devices.

Methods:
The project adopts a container-based approach, where each software component runs in isolated environments. This ensures that individual applications can be updated or managed without disrupting the entire system.

Key technologies and design methods include Over-The-Air (OTA) updates to remotely deliver new features or fixes, cloud-based development environments to streamline testing and deployment, cluster computing for embedded systems that enable devices to pool their computing resources, integration with the Robot Operating System (ROS) for containerized robotic applications, and implementation of Peer-to-Peer (P2P) communication to enhance decentralization and reduce single points of failure.

The project timeline extends from January 2024 through December 2025. Recent accomplishments include working prototypes for both P2P communication and cluster computing.
‍
Findings:
So far, the team has demonstrated working P2P communication and cluster computation capabilities among embedded systems, confirming the feasibility of this decentralized, container-based architecture. These advances pave the way for scalable deployment across varied hardware platforms.

Notably, the migration from centralized to decentralized systems has improved system resilience, provided greater flexibility in updates and deployment, and enabled support for dynamic, distributed computing models.

However, challenges remain. Key issues include managing long-range communication efficiently, handling intermittent connectivity between devices, and coordinating multiple edge devices as part of a coherent centralized logic model.