Track 1: Mobile computing, communications, 5G and beyond

• 5G and 6G Technologies
• Cell-free Networks
• Cloud-RAN, Programmable RAN
• Ultra Large Cell Technologies for 5G and beyond 5G networks
• 5G and Beyond Small Cell Technologies
• Network Slicing and Multi-service Architectures
• Cloud-based 5G and Beyond Mobile Architectures
• 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
• Ultra-reliability and Low-latency communications
• Terahertz for Future Networks
• Digital Twins of Complex Systems with 5G & Future Networks
• Tactile Internet
 
Track 2: IoT and applications

• IoT technologies for energy monitoring, efficiency, harvesting, etc.
• IoT Architecture with embedded AI
• AI for IoT edge computing
• Low-power AI for IoT and Distributed AI for IoT
• IoT with SDGs (Sustainable Development Goals)
• Intelligent Transportation Systems
• Big Data and Information Integrity in IoT
• Non-Terrestrial Networks for IoT/AI
• Beyond 5G, 6G technologies for IoT/AI
• Digital Twins in IoT applications
• Cryptography, Key Management, Authentication, and Authorization for IoT
• Biometrics Applications in Enhancing IoT Security and Privacy
• Blockchain for Securing 6G-enabled IoT-based Applications
• Security Awareness and Effective Training Approaches in IoT
• Applying Machine Learning Techniques in IoT Security
• Blockchain and Distributed Ledger Technology for IoT Security and Privacy
• Blockchain-based Security and Privacy in Resilient IoT-enabled 5G and Beyond
• Strategies for Proactive Cybersecurity Incident Prevention and Response in IoT
• Edge Computing and Intelligence in AI and IoT
• Machine Learning for IoT Applications
• Mobile deployment of Large Language Models (LLMs)
• LLMs for AIoT applications
• AI and IoT Solutions for Smart Cities
• Security and Privacy in AI-driven IoT Systems
• 5G and Its Impact on AI and IoT
• Human-Machine Interaction in IoT Environments
• IoT Sensors and Actuators: Innovations and Advances
• AI-driven Predictive Maintenance in IoT
• Energy-Efficient AI Algorithms for IoT Devices
• IoT in Healthcare: Applications and Challenges
• Industrial IoT (IIoT) and AI for Manufacturing
• AI and IoT in Precision Farming
• Ethical Considerations in AI-powered IoT Systems
• IoT Standards and Interoperability
• Robotic Process Automation (RPA) in IoT
• AI-driven Automation in Supply Chain Management
• IoT Analytics and Big Data Processing
• AI in Edge Devices: Challenges and Solutions
• Wireless Sensor Networks in AI and IoT
• IoT for Environmental Monitoring and Sustainability
• AI and IoT in Transportation and Logistics
• Cross-domain Integration of AI and IoT Technologies

Track 3: Privacy, Security for Networks

• 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

Track 4: Dedicated Technologies for Wireless Networks

• AI/ML-based physical layer technologies for B5G and 6G
• Beamforming in a massive MIMO system based on AI/ML
• AI/ML-based non-orthogonal multiple access (NOMA) techniques
• AI/ML-aided Channel modeling
• AI/ML in network design and planning
• AI/ML for coverage and capacity optimization
• AI/ML-based network load balancing and traffic steering
• Intelligent network slicing
• AI/ML for network deployment automation
• AI/ML for service quality assurance and improvement
• AI/ML self-driving networks
• AI/ML for network energy saving and efficiency improvement
• Reinforce Learning for Autonomous Networks and Federated Learning in Networking
• Artificial intelligence-generated content (AIGC) for wireless security
• Large language model (LLM) for wireless security
• Machine learning/deep learning-driven device identification using radio frequency fingerprint, Physical layer channel features, and network traffic features
• Deep learning enhanced physical layer security
• Deep learning-enhanced RF security
• Adversarial machine learning in wireless communications, including adversarial erosion attacks, poisoning attacks, and Trojan/backdoor attacks
• Defensive and anticipatory aspects of adversarial machine learning in wireless communications
• AI/ML for Security and privacy of deep learning-based wireless sensing
• AI/ML for Intrusion and anomaly detection for wireless networks

Track 5: Emerging Trends of AI/ML

• 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.

Track 6: Signal Processing for Wireless Communications

• Channel estimation, acquisition, and equalization
• Compressive sensing and sparse signal processing algorithms
• Decentralized. cooperative signal processing and Distributed signal processing for edge learning and computing
• Interference management techniques in communications systems
• Localization, positioning, and tracking techniques
• Architectures for signal demodulation and decoding
• Signal processing for integrated communications and sensing, artificial intelligence, data analytics, and machine learning
• Signal processing for green communications, energy harvesting, and wireless power transfer
• Signal processing for millimeter,THz communication systems, multi-antenna, MIMO, and/or multi-user systems
• Signal processing for optical communications and semantic communications
• Signal processing for security enhancement, particularly physical layer security and privacy
• Signal processing for sensor networks, smart cities, and IoT applications
• Signal processing for single-carrier, OFDM / OFDMA, multicarrier systems including new waveforms
• Signal processing for smart grid and powerline communications
• Signal processing for software defined and cognitive radio
• Signal processing for emerging wireless hardware architectures (e.g., reconfigurable intelligent
surfaces, metasurface-based antennas, holographic MIMO)
• Signal processing techniques for commercial/standardized and emerging systems
• Signal processing techniques for full-duplex communications and physical-layer network slicing
• Signal transmission, detection, synchronization, spatial transmission and distributed transmission techniques
• Spectrum sensing, shaping, and management techniques
• Signal processing for emerging technologies in 6G, e.g., CoMP, OTFS, VLC, UAV, integrated sensing
and communication and semantic communications
 
Track 7: Pattern Recognition, Computer Vison and Image Processing

• 3D imaging from multi-view and sensors
• 3D imaging from single images
• Adversarial attack and defense mechanisms
• Biometrics and Computational Imaging
• Computer vision for societal good
• Computer vision theory
• Datasets and evaluation
• Machine learning, Deep learning architectures, and techniques
• Document analysis and understanding
• Efficient and scalable vision
• Embodied vision: Active agents, simulation
• Event-based cameras and Explainable computer vision
• Face, body, pose, gesture, and movement detection
• Image and video synthesis and generation, and Low-level vision
• Medical imaging and biological vision, cell microscopy
• Multimodal learning and Optimization methods
• Photogrammetry and remote sensing, Physics-based vision and shape-from-X
• Categorization, detection, retrieval, and Representation learning
• Computer Vision for Robotics
• Understanding of Scene Analysis
• Segmentation, grouping, and shape analysis
• Self-, semi-, meta-, and unsupervised learning
• Transfer learning,  low-shot learning, continual, and long-tail learning
• Transparency, fairness, accountability, privacy, and ethics in vision
• Action and event understanding, Low-level analysis, motion, and tracking
• Vision + graphics, Vision, language, and reasoning
• Vision applications, systems, and services
• 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

Track 8: Communication and Networking Technologies for Smart Agriculture

• Embedded Systems Solutions and Pervasive Computing for Smart Agriculture.
• Artificial intelligence in Smart Agriculture.
• Communications and Networking Technologies to enable Smart Agriculture.
• Novel systems, Models, Solutions, and Applications to minimize CO2 emissions.
• Technologies and Applications to assist in Agricultural Productivity and Resilience to Climate Change.
• Technologies and Applications for a sustainable Agrifood chain.
• Technologies and Applications to preserve soil, water, and biodiversity and to Sustain Environmental Protection