International School on

Data Science and IoT

University of Catania, 9 -13 September 2019

 
 

WORKSHOP on DSIoT

The Workshop on Data Science and IoT is a side event, with respect to the school, sponsored by the School and technically co-sponsored by the IEEE Circuits and Systems Chapter, and IEEE SMC Chapter of the Italy Section.
Its purpose is to allow school participants to share reasearch activities and is open to all people and it is free of charge. During the workshop slots for presentations by the participants are scheduled, and an award for the best presentation is also included.

Easychair has been adopted to manage papers; click here if you are an author or reviewer.

Selected papers will be invited to be revised and extended to be published in a Book published in the Springer Book Series on IoT (Scopus-indexed).

The Workshop is planned on September, 10 (Tuesday) from 14:00 to 17:30.
See the School program for details.

Here is the list of presentations:

  1. Ibrahim Arif and Nevena Ackovska – IoT aided Smart Home Architecture for Anomaly Detection
  2. Annamaria Ficara, Giacomo Fiumara, Pasquale De Meo and Antonio Liotta – Correlations among Game of Thieves and other centrality measures in large networks: preliminary results.
  3. Barbara Attanasio, Alessandro Di Stefano, Aurelio La Corte and Marialisa Scatà – A modeling approach based on Multiplexity and EGT for resource sharing in Fog/Cloud Computing
  4. Georgios Georgiadis, Andreas Komninos, Andreas Koskeris and John Garofalakis – Improving Hydroponic Agriculture through IoT-enabled Collaborative Machine Learning
  5. Fabrizio Formosa and Michele Malgeri – Optimization strategy in data transmission on Narrowband IoT with LWM2M
  6. Marco Vigo and Michele Malgeri – Analysis of GPS power consumption in constrained-resources devices
  7. Muneer Bani Yassein – An Energy-efficient Techniques for Constrained Application Protocol (CoAP): Challenges and Open Issues
  8. Qimeng Li, Raffaele Gravina and Giancarlo Fortino – A Collaborative BSN-enabled Architecture for Multi-user Activity Recognition