Green, Pervasive, and Cloud Computing: 14th International Conference, Gpc 2019, Uberlândia, Brazil, May 26-28, 2019, Proceedings

ISBN-13: 9783030192228 / Angielski / Miękka / 2019 / 255 str.

Miani, Rodrigo

Opis

This book constitutes the proceedings of the 14th International Conference on Green, Pervasive, and Cloud Computing, GPC 2019, held in Uberlândia, Brazil, in May 2019.The 17 full papers included in this volume were carefully reviewed and selected from 38 initial submissions. They are organized in the following topical sections: machine learning; Internet of Things and mobility; cloud and related technologies.

Spis treści

Machine Learning.- Evaluating the Four-way Performance Trade-off for Stream Classification.- Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence.- U-control Chart based Differential Evolution Clustering for Determining the Number of Cluster in k-Means.- A Distributed Algorithm for Scalable Fuzzy Time Series.- Internet of Things and Mobility.- Mobility Aware RPL (MARPL): Mobility to RPL on Neighbor Variability.- A Method Based on Dispersion Analysis for Data Reduction in WSN.- Autonomic IoT Battery Management with Fog Computing.- Evaluating Post-Quantum Signatures for IoT devices.- Performance Analysis of a System for Vehicle Identification using LoRa and RFID.- Automating Mockup-based Usability Testing on the Mobile Device.- Cloud and Related Technologies.- Network and Cloudlet Selection for Computation Offloading on a Software-Defined Edge Architecture.- Cloud Computing Adoption in the Government Sector in Brazil: An Exploratory Study with Recommendations from IT Managers.- Dynamic Resource Allocation in Hybrid Mobile Cloud Computing for Data-Intensive Applications.- A Multi-Device Cloud-based Personal Event Management System.- CNTC: A Container Aware Network Traffic Control Framework.- BlockP2P: Enabling Fast Blockchain Broadcast with Scalable Peer-to-Peer Network Topology.- AutoCVSS: An Approach for Automatic Assessment of Vulnerability Severity Based on Attack Process.