[hpc-announce] [CFP] IEEE CAMAD'20: SS on Emerging Data-driven Approches for Network Optimization

Claudio Fiandrino claudio.fiandrino at imdea.org
Wed Apr 29 08:28:58 CDT 2020

*[Please accept our apologies if you receive multiple copies of this 

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SS on Emerging Data-driven Approches for Network Optimization



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The foundation of 5G and beyond mobile networks lies in the convergence 
between networking and computing. The most appealing realization of such 
convergence is the application of artificial intelligence (AI) and 
machine learning (ML) to optimize network functions. The latter has 
generated an increasing interest from academia and industry paving the 
path for the transformation from the 5G paradigm "connected things" into 
a "connected intelligence" vision for beyond 5G and 6G mobile networks. 
To this end, the role of AI/ML is to support zero-touch configuration 
and orchestration, thereby enabling self-configuration and 
self-optimization of the mobile network. Mobile networks are indeed 
becoming increasingly complex, heterogeneous, dynamic and dense, which 
makes extremely hard to model correctly their behavior. Model-free 
solutions that AI enable can overcome such challenge.

This Special Session seeks contributions from experts in areas such as 
network programming, distributed systems, machine learning, data 
science, data structures and algorithms, and optimization to discuss the 
latest research ideas and results on the application of AI/ML to 
networking. Specifically, this Special Session welcomes contributions in 
the following major areas (indicative list, other related topics will 
also be considered):

- Machine learning (ML) and big data analytics in networking
- Case studies showing (dis)advantages of AI/ML techniques for 
networking over traditional ones
- Edge-driven data analytics and applications to smart cities
- AI/ML assisted network optimization
- Resource-efficient machine learning for mobile networks
- Measurements and analysis of network traffic for AI/ML systems
- Efficient ML data structures, algorithms and network protocols to 
process network monitoring data
- Approaches for privacy-aware network traffic data collection
- Architectures for federated learning and its applications to 
- Energy-efficient federated learning
- Incentive mechanisms of federated learning
- In-network computation for next generation wireless networks


Submission Deadline: May 20th
Notification Acceptance: July 5th
Camera-Ready due: July 31st


Prospective authors are invited to submit a full paper of not more than 
six (6) IEEE style pages including results, figures and references. 
Papers should be submitted via EDAS. Papers submitted to the conference, 
must describe unpublished work that has not been submitted for 
publication elsewhere. All submitted papers will be reviewed by at least 
three TPC members, while submission implies that at least one of the 
authors will register and present the paper at the conference. 
Electronic submission will be carried out through the EDAS web site at 
the following link: https://edas.info/newPaper.php?c=27371&track=101982

All accepted papers will be included in the conference proceedings and 
IEEE digital library (http://ieeexplore.ieee.org/).

Claudio Fiandrino, PhD
Post-Doc Researcher

Web: http://people.networks.imdea.org/~claudio_fiandrino/
Phone: (+34) 91 481 6932

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