The main focus of integration between MEC (Multi-access Edge Computing) and oneM2M is to enable efficient data processing, low-latency communication, and enhanced service delivery in edge computing environments. The major capability of this integration includes federation of the MEC platforms and interaction with oneM2M instances, allowing for distributed computing and data management across multiple edge nodes.
Both MEC and oneM2M provide some basic APIs and protocols that facilitate federation. The aim of this project is to further those capabilities.
These use cases require a _robust federation and orchestration mechanism_ to ensure seamless operation across multiple MEC/oneM2M instances. The use cases mentioned in clause x.y are further analyzed in this project to provide a comprehensive framework for orchestration ofMEC/oneM2M integration in the following areas:
1.**Handover**: This refers to the process of transferring control of a device or service from one MEC/oneM2M instance to another, ensuring seamless connectivity and service continuity.
2.**Swarm Computing**: This involves the coordination of multiple MEC/oneM2M instances to perform distributed computing tasks, leveraging the capabilities of edge devices and networks.
3.**Federated Learning**: This is a machine learning approach where multiple MEC/oneM2M instances collaboratively train models without sharing raw data, enhancing privacy and reducing latency.
## oneM2M/MEC federation/orchestration - need short description of what is supported now (what API is this)
For federation, we need to first find MEC/oneM2M instances that are registered and/or discoverable.
