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SS on Emerging Data-driven Approches for Network Optimization
IEEE CAMAD 2020
https://camad2020.ieee-camad.org/
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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 networking
– Energy-efficient federated learning
– Incentive mechanisms of federated learning
– In-network computation for next generation wireless networks
** IMPORTANT DATES **
Submission Deadline: May 20th
Notification Acceptance: July 5th
Camera-Ready due: July 31st
** SUBMISSION INSTRUCTIONS **
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/).