MLNS2
The MLNS2 (Machine Learning, Network, System and Security) is an Inria International Team involving three Inria teams (AVALON, WIDE, and TRiBE), the ENS Lyon (LIP), and the Université of Yaoundé I (Cameroon).
The aim of this collaboration is to adequately design and investigate efficient approaches to fight against simbox 
frauds and malware proliferation. Addressing such challenges require mul- tidisciplinary knowledge such as Machine Learning, Network, System, and Security (MLNS2). For more details on the project, please visit our website.
MAGICO
MAGICO (Machine–Learning Enabled Next–Generation IoT Communications) is an Inria International Team involving Inria team TRiBE, the IIT (BHU) Varanasi, and the IIT Guwahati (India).
The aim of this collaboration is to focus on modern communications for the Internet of
Things (IoT). Traditionally the wireless network systems have been designed in “layers” (e.g.
OSI Layers). Designing next–generation communications for IoT requires revisiting this
separation of layers, for some important use–cases, such as Industrial IoT and/or massive
Machine Type Communications. The teams aims to do so through the use of Machine Learning techniques to improve the performance of the communication methods. For more details on the project, please visit our website.
Embrace
EMBRACE was an Inria International Team between TRiBE and three 
Brazilian Universities (UFMG, UFG, and UTFPR), funded between 2015 and 2019.
The EMBRACE project addressed the topic of designing efficient solutions for 5G networks taking into account human behavior, uncertainty, and heterogeneity of networking resources.
Although the funding period has ended, the collaboration are still on-going with exchanges of students and researchers scheduled to happen in 2020. For more details on the project, please visit our website.
