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ASIANComNet 2026: Asian Conference on Communication and Networks

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ASIANComNet
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Submission Date:
2026-08-31 Extended
Notification Date:
2026-09-10
Conference Date:
2026-10-11
Location:
Hanoi, Vietnam
Viewed: 10562   Tracked: 1   Attend: 0

Conference Partner Index (CP-I)

45.1 / 100
Ranked #2,822 of 5,687 conferences · Top 50%

#434 of 864 in Networks & Communications

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
23
Public record completeness (15%)
35

Inputs used: Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 8

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-25

Call For Papers

ASIANComNet 2026 (Asian Conference on Communication and Networks) is an academic conference held in Hanoi, Vietnam on 2026-10-11. The paper submission deadline is 2026-08-31 (extended). Acceptance notifications are sent on 2026-09-10.

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
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