This repository is a comprehensive, curated list of resources for edge computing, targeting researchers, developers, and practitioners. It provides an extensive catalog of frameworks, simulators, tools, and datasets to facilitate the development and evaluation of edge computing applications and systems.

How It Works

The list is organized into categories such as Simulators, Frameworks, Networks, Test Data/Benchmarks, Tools, and Edge-AI frameworks. Each entry includes a brief description of the project's purpose, key features, and often its underlying technology or methodology. The curation aims to cover a wide spectrum of edge computing paradigms, from vehicular networks and IoT to AI inference and distributed systems.

Quick Start & Requirements

This is a curated list, not a runnable project. Each listed item will have its own installation and usage instructions, typically found via the provided links.

Highlighted Details

• Extensive coverage of simulation tools, including those based on OMNeT++, ns-3, CloudSim, and Mininet, for network and system modeling.
• A broad selection of edge AI frameworks and libraries for optimizing and deploying machine learning models on resource-constrained devices.
• Numerous datasets and benchmarking suites for evaluating the performance of edge computing solutions across various scenarios.

Maintenance & Community

This is a community-curated list, with contributions likely coming from various academic institutions and individuals in the edge computing field. The "Awesome" list format suggests ongoing community effort to maintain and expand the resource catalog.

Licensing & Compatibility

The licensing of individual projects listed varies widely, from permissive licenses like MIT and Apache 2.0 to more restrictive ones. Users must consult the specific license for each tool or framework they intend to use.

Limitations & Caveats

As a curated list, the repository itself does not offer direct functionality. The quality and maturity of individual projects vary, and users must perform their own due diligence on each listed item. The alphabetical ordering within categories may not always reflect the most relevant or performant options.