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Deep learning in edge computing for massive parallel processing of IoT data streams

The focus of this collection is to combine deep learning and edge computing to reveal latest research findings and developments in the field of IoT-edge computing.  We also expect submissions from fundamental results to real life applications and case studies, motivated by the need of massively deep learning algorithms in edge computing to achieve efficiency and availability of IoT data streams processing.

Edited by:

Arun Kumar Sangaiah, Vellore Institute of Technology, India
Patrick Siarry, Université Paris-Est Créteil, France
Prem Prakash Jayaraman, Swinburne University of Technology, Australia
Faiyaz Doctor, School of Computer Science and Electronic Engineering, University of Essex, UK

  1. Cloud storage with sharing services is increasingly popular among data owners. However, it is difficult for the users to know if the cloud server providers (CSPs) indeed protect their data. To verify data inte...

    Authors: Yange Chen, Hequn Liu, Baocang Wang, Baljinnyam Sonompil, Yuan Ping and Zhili Zhang
    Citation: Journal of Cloud Computing 2021 10:3
  2. With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-e...

    Authors: Chen Ling, Weizhe Zhang, Hui He and Yu-chu Tian
    Citation: Journal of Cloud Computing 2020 9:43