Call for Paper

Author Guidelines

Policy Announcement by IEEE: 

The IEEE reserves the full right to exclude a paper from distribution after the conference (including its removal from IEEE Explore) if the paper is not presented at the conference.
Furthermore, papers are reviewed on the basis that they do not contain plagiarized material and have not been submitted to any other conference at the same time (double submission). These matters will be dealt with very seriously and the IEEE will take action against any author who engages in either practice. For more details on the IEEE policy, visit the following links.
To be published in the AsianComNet 2024 Conference Proceedings and to be eligible for publication in  IEEE Xplore®, one of the authors of an accepted paper should register for the conference at the FULL or LIMITED ( Non-Student or Student) rate and the paper must be presented by one of authors of that paper at the conference unless permission is sought and granted by TPC Chair for a substitute presenter in advance of the event and who is qualified both to present and answer questions.
A non-refundable registration fee must be paid prior to uploading the final paper in the IEEE format and publication-ready version of the paper. Accepted and presented papers will be published in the  AsianComNet 2024 Conference Proceedings and will be submitted to IEEE Xplore®.
During the initial paper submission process,  the authors should ensure that the author list and the paper title of the submitted pdf file are an exact match to the author list and paper title on the submission system registration page. In particular, the Conference registration page must include all co-authors, not just the submitting author. Papers that fail to comply with this rule might result in papers being withdrawn from the review process.  Also, note that the author list of an accepted paper can NOT be changed in the final manuscript and only PDF files will be accepted for the review process. All submissions must be done through the Conference submission system.
Page Length Limit: 
1. The page length limit for all initial submissions for review is SIX (6) printed pages (10-point font) and must be written in English.
2. All final accepted papers must have a maximum paper length of six (6) printed pages (10-point font) including figures. No more than two (2) additional printed pages (10-point font) may be included in final submissions and the extra pages (the 7th and 8th pages) will incur an over-length page charge of US$50 for each page. All final papers must be submitted to the IEEE Conference eXpress website and the authors can refer to the acceptance letter for the instructions on how to upload final papers.
3. The authors may use one of the templates for Microsoft Word A4, US letter and latex in the following link.
IEEE conference templates
4. If the authors have any questions regarding the submission of manuscripts, they can contact one of the Track Chairs of the Tracks that the authors will be submitting a paper.


For the regular technical program, without any restriction in scope, topics of interest include, but are not limited to:

➢Track1: Mobile computing, communications, 5G and beyond

Track Chair: Nakkeeran R, Pondicherry University

• Spectrum sensing, fusion, decision-making, and allocation
• Signaling process, PHY/link layer protocols, and optimization
• Resource optimization, network, and distributed network computing
• Dynamic spectrum access, spectrum sharing, spectrum management
• AI/ML for cognitive radio network
• VNF/SDN (NFV, VIM, VNFs, service function chaining, network slicing, and Open Flow)
• Quantum communications, and network computing resources
• Quantum Computing in Disrupting the Moore’s Law
• Application of Quantum Computing in Cyber Security
• Quantum Computing in Large Scale AI.
• Sensor networks, mesh networks, MIMO, massive MIMO, mmWave, V2X, 5G and 6G
• Edge computing, IoT connectivity, and energy harvesting
• LEO (Low Earth Orbit) satellite/HAPS (High Altitude Platform System) Communication & its scale and applications
• LEOS/HAPS communication integration with terrestrial mobile networks
• LEOS/HAPS communication network reliability
• Industrial IoT, e.g., manufacturing, logistics, and supply chain
• Industry control network, networking theory, and algorithms
• Wireless embedded sensor systems, body sensors, smart cities & security
• Cognitive radio and Soft defined radio
• Future generation communications and pervasive computing
• Peer-to-peer network computing and overlaying networks
• Directional antenna and networking
• FDMA/OFDMA modulations, synchronization, and power optimization
• Security & privacy, attacking models, confidentiality & security in communications
• Services, middleware, and multimedia on wireless networks
• QoS, reliability, performance, and communication theory
• Wireless network simulations, implementation, and applications
• Optical Networks and free space optical communications

➢Track2: IoT and applications

Track Chair: R. Kishore, SSN College of Engineering

• Embedded Computer and System and Sensors and Actuators

• Software for IoT, Storage and Data Management for IoT and Computing for IoT

• Smart Transportation, Connected Car, Automotive and Intelligent Transportation

• Highway, Rail Systems and Emergency vehicle notification systems

• Automatic road enforcement and Smart Traffic Light

• Smart Collision Avoidance

• Smart Tele-healthcare, Healthcare, e-Health, Assisted Living, Smart Telecardiolgy and Telenursing

• Telerehabilitation and Teletrauma

• Smart Agriculture and Environmental Monitoring

• Smart Manufacturing

• Utilities Management and Operation Logistics and Supply Chain Optimization

• Production Flow Monitoring Health of Machinery

• Industrial IoT

• Smart Cities, Smart Home, Building Management and Operation Automation

• Smart Grid and Energy Management

• Connectivity for IoT

• Software Defined Networks

• Sensor Network and Massive IoT

➢Track3: Image processing

 Track Chair: Somporn Tiacharoen, KMUTNB

• AI for computer vision and image processing
• NLP, image, vision learning, and deep learning
• Texture image representation and classification
• Image filtering and enhancement
• Image segmentation
• Object detection and recognition
• Tracking and motion analysis
• Image synthesis, 3D reconstruction and modeling
• Stereo vision and depth estimation
• Face recognition and biometrics
• Scene understanding and semantic segmentation
• Image and video compression
• Image and video restoration and super-resolution
• Optical character recognition
• Medical image analysis and processing
• Document analysis and recognition
• Video analysis and summarization
• Augmented reality and virtual reality
• Color, multispectral, and hyperspectral imaging
• Medical image computing
• Sensing, representation, modeling, and registration
• Stereoscopic, multiview, and 3D processing
• Biometrics, forensics, and security

➢Track4: Privacy, Security for Networks

Track Chair: Chana Jan-Im, Royal Thai Army

• Privacy enhancement, policy, access control, and regulation
• Privacy with surveillance, big data, machine learning, and IoT
• Privacy for healthcare, human-computer interaction, and other applications
• Network security, cybersecurity risk assessment, malware analysis
• Cryptography, cryptographic algorithm, post-quantum cryptography
• Attacks, DDoS, ransomware, and cybersecurity attacks and detection
• Cyber network, configuration, cloud, IoT, and wireless communications
• Multistage attacks, data security, AI, and intrusion detection
• Risk assessment, management, and network monitoring
• Blockchain, cryptocurrency, smart contracts, identity management, and voting
• Blockchain applications, e.g., smart grid, healthcare, industrial control systems
• Cyber authentication and access control
• Deep learning for attack behavior, prediction, and game theory
• AI/ML and deep learning for security and privacy

➢Track5: Dedicated Technologies for Wireless Networks

Track Chair: Iacovos Ioannou, University of Cyprus

• AI/ML forIntegrated sensing and communication and Intelligent reflecting surfaces

• AI/ML for link layer and MAC layer

• AI/ML for mobility and network management

• AI/ML formolecular networks

• AI/ML foroptical networks

• AI/ML for over-the-air computation

• AI/ML forthe physical layer

• AI/ML for resource management and network optimization

• AI/ML for semantic communications

• Wireless communications and networks to assist AI/ML services

• AI/ML for modulation and coding schemeoptimization

• AI/ML for transceiver design, source coding, channel decoding,channel estimation and prediction

• AI/ML for non-conventional communication channels (e.g., high-dimensional and molecular channels)

• AI/ML for radio environment awareness and decision-making

• AI/ML for massive connectivity and ultra-reliable and low latency communications (URLLC)

• AI/ML for massive MIMO and large intelligent surfaces (LIS)

• AI/ML for cell-free wireless systems

• AI/MLfor vision-aided wireless communications, positioning, and location-based services

• AI/ML for joint communication and control and semantic communications

• AI/MLfor non-linear signal processing and physical layer security

➢Track6: Emerging Trends of AI/ML

Track Chair: Ravikumar Balakrishnan, Intel, USA

• Data sets for 5G/6G testbeds and trials

• Distributed AI/ML for communication networks

• Distributed multi-agent reinforcement learning aided wireless networks

• Edge learning for wireless networks

• Federated learning for wireless communications

• Distributed intelligence in wireless communications

• Standardization of AI/ML in network architectures.

• AI/ML in network planning and 5G and beyond use case

• AI/ML in Network Diagnostics

• AI/ML in Network characteristics forecasts

• AI/ML techniques for security incident identification and forecast

• AI/ML techniques for precise synthesizing and efficient mobile traffic forecast

• AI/ML–aided forecasting techniques for QoS improvement, and QoE inference

• AI/ML techniques for multi-tenant environments service level agreement forecast

• AI/ML techniques for Complex event recognition and forecasting

• AI/ML techniques for Network Optimization and Control

• AI/ML techniques for Transport and FH/BH networks

• AI/ML techniques for E2E slicing

• AI/ML techniques for E2E service assurance

• AI/ML techniques for Resource reservation

• AI/ML techniques for Resource allocation (jointly through slice-based demand prediction)

• AI/ML techniques for autonomous slice management -slice isolation, and slice Optimization

• AI/ML solutions for control and orchestration

• AI/ML techniques for cross-layer optimization framework

• AI/ML solutions for anomaly detection, and management analytics

• AI/ML- aaS in network management and orchestration

• AI/ML solutions for Management of traffic, Dynamic load balancing, Efficient per-flow scheduling, MEC, and NFV orchestrators, Resource allocation for service function chaining, and Dynamic resource sharing in NFV infrastructure.


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