IMPORTANT INFORMATION FOR COMPSAC 2022 AUTHORS 

Because of uncertainties related to Covid 19, COMPSAC 2022, currently planned as an in-person event to be held in Turin, Italy, may be conducted in a hybrid format – with some sessions in-person and some over the Internet. If the conference format is hybrid, authors of accepted papers will be able to decide to present their papers either virtually or in-person in Turin. 

However, if the pandemic worsens, the conference will be completely virtual as it was in 2020 and 2021, and all presentations will be done virtually. We will announce in the submission acceptance letters to authors whether the conference will be hybrid or virtual. Registration fees will be adjusted to reflect the format of the conference.

The International Workshop on Artificial Intelligence for Intelligent Network Management (AINet – 2022)

Goal of the workshop:

The workshop aims to foster cooperation among telecom and network researchers, AI communities in order to exchange the latest industrial experience and research ideas on intelligent network management.

Workshop theme:

Artificial intelligence (AI) enables the simulation of human intelligence in computing machines that can be programmed to behave like humans and imitates their actions. The term can be associated with any machine that shows human traits such as learning new patterns and problem-solving. The main characteristic of artificial intelligence is its power to think rationally and take appropriate action to achieve a specific goal. Machine learning, which is a subset of artificial intelligence, refers to a computing paradigm that can automatically learn from a set of data with minimal human intervention. After that, it can take any input and classify or predict the outcomes.

Network technologies such as Software Defined Networking (SDN), Network Function Virtualization (NFV), and 5G / 6G, are continuously evolving to support the exponential growth of connected devices and unique performance expectations such as reliability, dependability, and scalability. The downside of those technologies is that they are changing faster than we can manage them. To address this problem, cognitive network (CN) is increasingly used, which refers to a network as a cognitive process that can take input as real-time network conditions, process them using artificial intelligence and act on those network conditions.

Therefore, research is required to understand and improve the potential and suitability of artificial intelligence in the context of network management. This will provide a deeper understanding and better decision-making based on largely collected and available network data. It will also present opportunities for improving artificial intelligence algorithms on aspects such as reliability, dependability, and scalability and demonstrate the benefits of these methods in management and control systems.

Scope of the workshop:

This workshop aims at gathering the recent advances in AI for Intelligent network management. We hope this workshop will inspire new thoughts and contributions to this specific topic. Topics include but are not limited to the following:

  • Deep and Reinforcement learning for networking and communications in 5G networks
  • Data mining and big data analytics in 5G networking
  • Protocol design and optimization using AI/ML in 5G
  • Self-learning and adaptive networking protocols and algorithms for 5G
  • Intent & Policy-based management for intelligent networks
  • Innovative architectures and infrastructures for intelligent networks
  • AI/ML for network management and orchestration in 5G systems
  • AI/ML for network slicing optimization in 5G systems
  • AI/ML for service placement and dynamic Service Function Chaining in 5G systems
  • AI/ML for C-RAN resource management and medium access control
  • Decision making mechanisms
  • Routing optimization based on flow prediction in 5G systems
  • Data-driven management of software defined networks for 5G networks
  • Methodologies for network problem diagnosis, anomaly detection and prediction
  • Reliability, robustness and safety based on AI/ML techniques
  • Network Security based on AI/ML techniques in 5G
  • AI/ML for IoT
  • Open-source networking optimization tools for AI/ML applications
  • Experiences and best-practices using machine learning in operational networks
  • Novel context-aware, emotion-aware networking services
  • Machine learning for user behavior prediction
  • Modeling and performance evaluation for Intelligent Network
  • Possible use cases of 6G
  • QoS management with AI/ML in 6G
  • Scope of AI/ML in 6G network platform

Likely participants: Telecom and network researchers, AI communities are called to participate and exchange ideas and techniques.

Please visit Information for Authors for formatting instructions, page limits, and IEEE paper templates.

Important dates for submission and notification are listed here.

Workshop Organizers

Deepak Puthal, Newcastle University
Email: deepak.puthal@ieee.org

Amit Kumar Mishra, University of Cape Town
Email: akmishra@ieee.org

Arif Ahmed, Ericsson
Email: arif.ahmed@ericsson.com

Ananya Choudhury, Maastricht University
Email: ananya.choudhury@maastro.nl

Program Committee

(to be confirmed)

Nikumani Choudhury, BITS Pilani, India

Biswapratap Singh Sahoo, Samsung R&D, India

Sambit Kumar Mishra, SRM University, India

Prabha Sundaravadivel, University of Texas at Tyler, USA

Ashish Nanda, Deakin University, Australia

Mukesh Prasad, University of Technology Sydney, Australia

Chi Yang, Huazhong University of Science and Technology, China

Xuyun Zhang, Macquarie University, Australia

Prasanth Yanambaka, Central Michigan University, USA

Ayan Mondal, University Rennes, France

Kumar Yelamarthi, Tennessee Tech University, USA

Amey Kulkarni, NVIDIA Inc., USA

Pradip Sharma, University of Aberdeen, UK

Meriam Gay Bautista, Lawrence Berkeley National Laboratory, USA