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 developments and emerging trends in the rapidly evolving fields of Big Data, the Internet of Things (IoT) and Machine Learning. As data driven intelligence, connected systems and AI powered technologies continue to transform industries and society, BIOM 2026 aims to bring together researchers, practitioners and industry experts to exchange knowledge, discuss challenges and explore break throughs 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 AI and emerging technologies such as quantum machine learning and blockchain based analytics. BIOM 2026 particularly welcomes work that bridges theory and practice, addresses real world deployment challenges and demonstrates the impact of Big Data, IoT and ML in complex, data intensive environments.

Authors are invited to submit original research articles, project reports, survey papers and industrial 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 Cloud Native Data Platforms
• Data Lakes, Lake houses and Modern Data Architectures
• Large Scale Data Processing Systems (Spark, Flink, Ray)
• High Performance and Parallel Computing for Big Data
• Edge to Cloud Data Pipelines and Streaming Architectures

Big Data Analytics, Mining and Applications

• Large Scale Data Mining and Knowledge Discovery
• Graph Mining, Network Science and Graph Based Analytics
• Spatiotemporal and Geospatial Data Analytics
• Real Time and Streaming Data Analytics
• Domain Driven 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 Privacy Preserving Analytics
• Secure Multiparty Computation and Homomorphic Encryption
• Federated Security and Secure Data Sharing
• Zero Trust 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 Large Scale Pretraining
• Multimodal Learning (Vision Language Sensor 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 and MLOps

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

IoT Systems, Architectures and Connectivity

• IoT Architectures, Protocols and Standards
• Edge and Fog Computing for IoT
• 5G/6G Enabled IoT and Ultra Reliable Low Latency IoT
• IoT Interoperability and Large Scale IoT Platforms
• Resource Efficient IoT Systems

IoT Applications, Sensing and Cyber Physical Systems

• Industrial IoT (IIoT) and Industry 4.0
• Environmental Monitoring and Precision Agriculture
• Wearables, Healthcare IoT and Remote Sensing
• Autonomous Systems and Cyber Physical 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 On Device Machine Learning
• Collaborative and Federated Edge Intelligence
• Resource Efficient ML for Edge and IoT Devices
• Low Latency 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
• Software Defined Networking (SDN) and Network Virtualization
• Data Driven Network Optimization

Digital Twins and Emerging Technologies

• Digital Twins for IoT, Smart Infrastructure and CPS
• Data Driven 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
• Cross Device and Cross Silo Analytics
• Privacy Preserving Collaborative Computation

Sustainable AI and Data Systems

• Green AI and Energy Efficient ML
• Carbon Aware Data Processing
• Sustainable IoT and Edge Systems

Real World Deployments, Benchmarks and Case Studies

• Experimental Results and Deployment Scenarios
• Large Scale 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)