AINet 2023: The IEEE International Workshop on Artificial Intelligence for Intelligent Network Management

Technical Program

*= in-person presentation in Torino

CNN-based perceptual hashing scheme for image groups suitable for security systems
Yusei Sugawara, Zhaoxiong Meng, Tetsuya Morizumi, Sumiko Miyata, Kaito Hosono and Hirotsugu Kinoshita

Secure Deduplication with Dynamic Key Management in Fog Enabled Internet of Things
Jay Dave, Nikumani Choudhury, Utkarsh Tiwari, Samyu Kamtam and Kudapa Sai Rohith

Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage
Sultan Almansoori, Mohamed Alzaabi, Mohammed Alrayss, Deepak Puthal, Joy Dutta and Aamna Al Shehhi

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

Paper Templates

IEEE Paper templates are available in MS Word 2003 and LaTex. All submissions must use US 8.5×11 letter page format.

Important Dates

Main Conference/Symposium
Main conference/symposium papers due: 15 January 2023
Extended to 15 February 2023
Notification: 7 April 2023
Camera-ready and registration due: 7 May 2023 Updated: 18 May 2023

Journal then Conference Submissions
Due date: April 7, 2023
Notifications: April 30, 2023

Workshops, Fast Abstract, SRS Programs
EXTENDED: Workshop papers due: 21 April 2023
UPDATED: Notifications: 7 May 2023
UPDATED: Camera-ready and registration due: Updated: 18 May 2023

Submission Link

Please submit your paper on EasyChair

IEEE Conference Publishing Policies

All submissions must adhere to IEEE Conference Publishing Policies.

IEEE Cross Check

All submission will be screened for plagiarized material through the IEEE Cross Check portal.

Workshop Organizers

Workshop Co-Chairs

Deepak Puthal, Newcastle University

Amit Kumar Mishra, University of Cape Town

Arif Ahmed, Ericsson

Ananya Choudhury, Maastricht University

Program Committee

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