ML-IoT2020: Machine Learning for Sensing, Communication and Networking in IoT Como, Italy, June 23-26, 2020 |
Conference website | https://sites.google.com/view/ml-iot2020/home?authuser=0 |
Submission link | https://easychair.org/conferences/?conf=mliot2020 |
International Workshop on
Machine Learning for Communication and Networking in IoT (ML-IoT)
In conjunction with IEEE SECON 2020 (https://secon2020.ieee-secon.org)
22-26 June 2020, Como,Italy
Recent advances in sensing, communications and networking have enabled the support of real-life applications within several Internet of Things (IoT) verticals including smart city, smart home, smart transportation, smart utilities and E-health. In order to support the ever-increasing number of IoT devices and heterogeneous applications, it is crucial to design resource-efficient and scalable upcoming 5G and beyond systems in a way that they can operate in the complex wireless environment while supporting the emerging Machine-Type Communications (MTC)/IoT traffic. Towards enabling the effective design and operation of these IoT systems, the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques has recently caught the attention of various research communities, industries and the related stakeholders. On one hand, some researchers argue that employing ML in communication systems offers little benefits because communication systems were primarily designed for bandwidth, power and complexity optimization. On the other hand, big data-driven solutions, including Deep Learning (DL) can be highly advantageous for data-driven prediction, analysis and performance improvement by utilizing time-dependent properties of network elements. This could be achieved by adapting the ML/DL models to the dynamicity of the underlying communication environment along with the training data generated by the IoT devices. In addition, DL techniques may provide significant benefits towards automating the conventional acquisition/sensing/processing/computing tasks in communication networks supporting heterogeneous IoT applications, and towards enhancing the security and privacy of IoT systems. However, there are several issues to be addressed for the applications of ML/DL techniques in the complex IoT environments involving resource-constrained IoT/MTC devices, including heterogeneity, limited computational capability, distributed nature, and distinct traffic characteristics.
This workshop focuses on recent research activities related to AI and ML in sensing, communication and networking tasks for various IoT verticals, and aims to open a debate on the applications of AI and ML/DL techniques in emerging IoT networks and applications. In this direction, we invite researchers from the academia, industry, and governmental organizations to submit their novel works on system architectures, learning models, theoretical models/algorithms, system level simulations/experimental results, hardware demonstration results, and standardization activities in the related areas.
The main topics of interest for this special issue include, but not limited to the following:
Supervised, unsupervised and reinforcement learning for IoT systems
Deep learning for IoT Applications
Active learning in wireless IoT systems
Learning-assisted resource allocation and management for IoT
ML for access congestion management in edge IoT networks
Fuzzy logic based learning for IoT applications
ML/DL for bioinformatics/E-healthcare/smart city/smart home
Machine learning for traffic management in IoT
ML-assisted decision automation in Industrial IoT
Knowledge acquisition, discovery and learning for IoT
Learning through mobile data mining in IoT systems
Distributed and parallel learning algorithms in IoT applications
Feature extraction and classification in IoT Applications
Computational learning theory for IoT
Hybrid learning algorithms for IoT systems
ML-assisted security enhancement for IoT systems
ML-assisted intrusion and malware detection for IoT
Learning-assisted privacy preservation and trust for IoT
Adversial ML for IoT systems
ML-assisted attack prediction and detection in IoT systems
ML-assisted cyber security in IoT networks
Human-centric AI and explainable AI for IoT systems
Accepted and presented papers will appear in the conference proceedings of IEEE SECON 2020 and will be submitted for inclusion in IEEE Xplore®.
Important Dates:
Review paper submission: 15 March 2020
Notification of acceptance: 20 April 2020
Camera-ready submission: 1 May 2020
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
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All papers need to be submitted electronically through this link (https://easychair.org/conferences/?conf=mliot2020) in PDF format. Submitted papers do not necessarily overlap with papers that have been submitted to a journal or a conference with proceedings.
Papers must be clearly presented in English, must not exceed 4-5 pages including tables, figures, references and appendices.
Committees
Program Committee
1. Dr. Shree Krishna Sharma, SnT, University of Luxembourg, Email: shree.sharma@ieee.org
2. Dr. Waleed Ejaz, Thompson Rivers University, Canada, Email: waleed.ejaz@ieee.org
3. Dr. Lewis Nkenyereye, Sejong University, Korea, Email: nkenyele@sejong.ac.kr
Organizing committee
Dr. Adnan Shahid, Ghent University, Belgium
Dr. Najam Ul Hasan, Dhofar University, Oman
Dr. Entenam Unayes Khandakar, Victoria University, Australia
Dr. Ali Kashif Bashir, University of Faroe Islands, Faroe Islands
Dr. Saleem Aslam, Bahria University, Pakistan
Dr. Tadilo E. Bogale, North Carolina A&T State University, NC, USA
Dr. Syed Junaid Nawaz, COMSATS University Islamabad, Pakistan
Dr. Eva Lagunas, SnT, University of Luxembourg, Luxembourg
Mr. Sabin Bhandari, Western University, Canada
Dr. Bruce Ndibanje, CTILab Co.,Ltd, Korea;
Dr. Bayu Adhi Tama, Pohang University of Science and Technology, Korea
Dr. Muhammad Khuram Shahzad, National University of Science and Technology, Pakistan
Dr. Mohammad Shojafar, Ryerson University, Canada
Contact
All questions about submissions should be emailed to shree.sharma@ieee.org or waleed.ejaz@ieee.org