Scope 

6^th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) serves as a premier global forum for presenting innovative ideas, research developmentsandemerging trends in the rapidly evolving fields of Big Data, the Internet of Things (IoT) and Machine Learning. As data driven intelligence, connected systemsandAIpowered technologies continue to transform industries and society, BIOM 2026 aims to bring together researchers, practitionersandindustry experts to exchange knowledge, discuss challengesandexplore breakthroughs shaping the next generation of intelligent systems.

The conference encourages contributions that advance the state of the art in large scale data processing, distributed and federated learning, edge intelligence, 5G/6G enabled IoT, trustworthy and robust AI, digital twins, data centric AIandemerging technologies such as quantum machine learning and blockchainbased analytics. BIOM 2026 particularly welcomes work that bridges theory and practice, addresses real world deployment challengesanddemonstrates the impact of Big Data, IoTandML in complex, dataintensive environments.

Authors are invited to submit original research articles, project reports, survey papersandindustrial case studies that illustrate significant advances in the field. Submissions may address any of the conference themes, including, but not limited to, the topics listed below.

Big Data Systems, Infrastructure and Platforms
• Distributed and CloudNative Data Platforms
• Data Lakes, LakehousesandModern Data Architectures
• LargeScale Data Processing Systems (Spark, Flink, Ray)
• HighPerformance and Parallel Computing for Big Data
• EdgetoCloud Data Pipelines and Streaming Architectures

Big Data Analytics, Mining and Applications
• LargeScale Data Mining and Knowledge Discovery
• Graph Mining, Network ScienceandGraphBased Analytics
• Spatiotemporal and Geospatial Data Analytics
• RealTime and Streaming Data Analytics
• DomainDriven Analytics (Healthcare, Finance, Climate, etc.)

Data Management, Governance and Quality
• Data Integration, Cleaning and Wrangling
• Data Governance, Lineage and Compliance
• Data Quality, Bias Detection and Fairness
• Metadata Management and Semantic Technologies
• DataCentric AI and Data Quality Engineering

Security, Privacy and Trust in DataDriven Systems
• Big Data Security, Privacy and Trust
• Differential Privacy and PrivacyPreserving Analytics
• Secure Multiparty Computation and Homomorphic Encryption
• Federated Security and Secure Data Sharing
• ZeroTrust Architectures for IoT and Edge Systems

Machine Learning and AI for Big Data
• Scalable Machine Learning Algorithms
• Distributed, Federated and Split Learning
• Deep Learning Architectures and Optimization
• Foundation Models and LargeScale Pretraining
• Multimodal Learning (VisionLanguageSensor Fusion)
• AutoML, Neural Architecture Search and Model Compression
• Causal Inference and Causal Machine Learning

Trustworthy, Robust and Safe Machine Learning
• Adversarial Machine Learning and Robustness
• Safe and Reliable ML Systems
• ML under Distribution Shift
• Explainable and Interpretable ML
• ML Risk Assessment and Governance

ML Systems, Deployment andMLOps
• Scalable Training and Inference Systems
• ML Model Deployment, Monitoring and Drift Detection
• ML Observability and Lifecycle Management
• Data/Model Versioning and Reproducibility
• RealTime ML and Online Learning

IoT Systems, Architectures and Connectivity
• IoT Architectures, Protocols and Standards
• Edge and Fog Computing for IoT
• 5G/6GEnabled IoT and UltraReliable LowLatency IoT
• IoT Interoperability and LargeScale IoT Platforms
• ResourceEfficient IoT Systems

IoT Applications, Sensing and CyberPhysical Systems
• Industrial IoT (IIoT) and Industry 4.0
• Environmental Monitoring and Precision Agriculture
• Wearables, Healthcare IoT and Remote Sensing
• Autonomous Systems and CyberPhysical Systems
• Sensor Fusion and Intelligent Sensing

Advanced IoT Security and Resilience
• Lightweight Cryptography for IoT
• Secure Firmware, OTA Updates and Device Hardening
• Intrusion Detection for IoT and Edge Systems
• Resilient IoT Architectures and Fault Tolerance

Edge Intelligence and Distributed AI
• Edge AI and OnDevice Machine Learning
• Collaborative and Federated Edge Intelligence
• ResourceEfficient ML for Edge and IoT Devices
• LowLatency AI and RealTime Inference

Networking for Big Data, IoTand ML
• 5G/6G Networks for DataIntensive Applications
• Network Slicing and QoS for IoT and ML Workloads
• SoftwareDefined Networking (SDN) and Network Virtualization
• DataDriven Network Optimization

Digital Twins and Emerging Technologies
• Digital Twins for IoT, Smart Infrastructure and CPS
• DataDriven Simulation and Predictive Modeling
• Blockchain for IoT, Data Integrity and Secure Analytics
• Quantum Machine Learning and Quantum Data Processing
• Generative AI for IoT and Big Data Applications

Federated Analytics and Collaborative Intelligence
• Federated Data Mining and Knowledge Discovery
• CrossDevice and CrossSilo Analytics
• PrivacyPreserving Collaborative Computation

Sustainable AI and Data Systems
• Green AI and EnergyEfficient ML
• CarbonAware Data Processing
• Sustainable IoT and Edge Systems

RealWorld Deployments, Benchmarks and Case Studies
• Experimental Results and Deployment Scenarios
• LargeScale System Benchmarking and Performance Evaluation
• Industrial Applications and Technology Transfer

Selected papers from BIOM 2026, after further revisions, will be published in the special issue of the following journals

• International Journal of Data Mining & Knowledge Management Process (IJDKP)
• International Journal of Database Management Systems (IJDMS)
• Machine Learning and Applications: An International Journal (MLAIJ)
• Advances in Vision Computing: An International Journal (AVC)
• International Journal of Grid Computing & Applications (IJGCA)
• International Journal of Ambient Systems and Applications (IJASA)
• International Journal on Web Service Computing (IJWSC)