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Deadlines

  • Paper Submission
    Sep. 10, 2018 Sep. 24, 2018
  • Author Notification
    Nov. 10, 2018
  • Camera‐ready copies
    Nov. 25, 2018

Aims and scope of the track

To address several open research issues regarding sustainability of future Fog/Edge systems, this track aims at solicit contributions highlighting challenges, state-of-the-art, and solutions to a set of currently unresolved key questions including – but not limited to – performance, modelling, optimization, energy-efficiency, reliability, security, privacy and techno-economic aspects of Fog/Edge systems. Through addressing these concerns while understanding their impacts and limitations, technological advancements will be channeled toward more sustainable/efficient platforms for tomorrow’s ever-connected systems.

For the past thirty two years, the ACM Symposium on Applied Computing has been a primary  forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2019 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and is hosted by University of Cyprus, in Limassol (Cyprus). The SFECS track at ACM SAC 2018 solicits contributions on the joint research and practice of Sustainable Computing and Fog/Edge Computing. 

 

Topics

  • Fog/Edge architectures for data sensing and processing
  • Interoperability among Fog/Edge systems
  • Big data analytics in Fog/Edge systems
  • Optimization models and techniques to process data in Fog/Edge systems
  • Social aspects in adopting Fog/Edge paradigms
  • Security and privacy issues in Fog/Edge systems
  • Dynamic resource, service and context management in Fog/Edge computing systems
  • Resources and power management in sustainable Fog/Edge networks
  • Energy Efficient routing protocols and data management schemes
  • High-performance and parallel learning in sustainable computing
  • Privacy-preserving decision-making, learning and classification in sustainable computing
  • Sustainable future generation enterprise deep learning based on Fog/Edge Computing
  • Sustainability of Fog/Edge platforms for next generation robots’ systems
  • Simulation and modeling of Fog/Edge systems
  • Performance evaluation of Fog/Edge applications, platforms and systems
  • Quality of service/experience (QoS/QoE)