About the IOEMLA workshop

IOEMLA covers novel applications of the Internet of Everything and Machine Learning techniques.

The Internet of Everything (IoE) encompasses not only machine- to-machine but also people-to-people and people-to-machine connections. It is estimated that the total number of devices supported by the IoE could reach 50 billion by the end of 2020. This rapid growth is being fueled by the increasing availability of network access, the creation of more inexpensive smart devices with sensors and network capabilities built into them, the rapid growth in smartphone penetration, and the creativity and innovation of people who can see and capitalize on the almost unlimited opportunities.

Topics of interest include, but are not limited to, the following scope

Application Areas in:

  • Smart Society
  • Smart Cities
  • Smart Manufacturing
  • Smart Grid
  • Smart Home and building
  • Smart Clothes: wearable technology
  • e-Health
  • Social Networks
  • Agriculture and food
  • Education
  • Transport


Topics of interest:

  • IoE Multimedia and Societal Impacts
  • Application and experimental experiences in smart cities
  • Human mobility modelling and analytics
  • LargeĀ­-scale visualization of urban data
  • Machine learning for predictive models
  • Parallel and distributed computing of big urban data
  • Safety, security, and privacy for smart cities
  • Smart buildings, grids, transportation, and utilities
  • IoT and AI business models
  • Privacy and security issues, and relevant policies concerning IoE
    • Optimization for cloud computing, networking, and applications
    • Cloud storage design and networking
    • Cloud system and storage security
    • Cloud network virtualization techniques
    • Modelling for cloud system, network and storage
    • Performance analysis for cloud system, network and storage
    • Big data storage and networking in the Clouds
    • Mobile Cloud system design
    • Fog and Edge Computing
    • Data cleaning approach and technology for network big data
    • Data governance method for network big data
    • Business intelligence based on network big data
    • Modelling and data mining for network big data
    • Data privacy for network big data
    • Community detection method for big data networks
    • Large data analysis of social network based on information sensing platform
    • Deep learning for Internet-based monitoring and control systems
    • Deep learning for collaborative factory automation
    • Deep learning for distributed industrial control and computing paradigms
    • Deep learning for real-time control software for industrial processes
    • Deep learning for control of wireless sensors and actuators
    • Deep learning for systems interoperability and human-machine interface
    • Real-time communication interfaces and protocols for fog computing
    • Fog computing for real-time monitoring in industries
    • The future for fog computing in industries: challenges and open issues
    • CPS-based design optimization of transportation
    • Dynamic modelling and controls of transportation Cyber-Physical Systems
    • Data-centric management of Internet of Things and cyber-physical systems