Ubiquitous Cloud-based Mobile Edge Computing

  in Special Issue   Posted on September 28, 2020

Information for the Special Issue

Submission Deadline: Fri 15 Jan 2021
Journal Impact Factor : 0.757
Journal Name : Distributed and Parallel Databases
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/10619/updates/18204752
Journal & Submission Website: https://www.springer.com/journal/10619

Special Issue Call for Papers:

Massive growth of networked technologies like IoT, 5G networks, cloud computing and edge networks has given rise to major advances in big data storage, processing and communication technologies to process and extract the required data from the wide range of available structured and unstructured data sources. These advances mandate an urgent need to develop effective approaches to readily process the large amount of data obtained from big data applications. Big data analytics plays a significant role in various domains such as smart cities, healthcare, smart surveillance, automotive applications and in other real-time monitoring applications. Despite these advances, the big data research community faces significant challenges in data availability, security, privacy and trust.

The existing cloud computing solutions has certain limitations like centralized processing architectures and open environment with a restrictive control of the service provider, which poses a serious threat to the available information’s security and privacy. To develop and deploy solutions that overcome such data security challenges, Edge computing techniques are increasingly being incorporated in cloud based data storage and processing models. Hence, the current trend is to extend the traditional cloud computing model with edge computing model with advanced 5G networks to intermediate the connection between cloud computing systems and their users. This makes the destination networks to remain closer to the data sources when compared with the traditional big data processing technologies that are solely based on the cloud computing paradigm. This type of data processing at the network edge enhances the security, privacy and trust of the big data analytics models by providing secured authentication and accountability models. As this technology still remains at the nascent stage in the real-time applications, further research exploration of this cloud-based edge computing techniques is needed to deploy innovative data processing applications to the big data research community.

This special issue aims to gather the state-of-the-art research works that integrates cloud and edge computing model to enhance the security, privacy and trust of Big data analytics framework.

Potential topics include, but are not limited to:

  • Secured authentication using edge computing models
  • Edge-based innovative solution design patterns for big data analytics framework
  • 5G networking protocols for edge based big data analytics framework
  • Data access control mechanisms using secured edge computing models
  • Edge based big data services and analytics
  • Cloud-edges for building a secured big data analytics framework
  • Security models for big data analytics applications
  • Innovative threat detection techniques
  • Data protection and integrity models
  • Edge driven data access control techniques
  • Privacy-preserving big data analytics techniques

Deadline for submissions: January 15 2021

Guest Editors

A. Pasumpon Pandian, Vaigai College of Engineering, India (Lead Guest Editor)
Noureddine Bouhmala, University of Southeast Norway (USN), Norway
Xavier Fernando, Ryerson University, Canada
V. Suma, Dayananda Sagar College of Engineering, India

Submission Instructions

The submitted manuscripts must be written in English and describe original research not published nor currently under review by other journals or conferences. Parallel submissions will not be accepted. All submitted papers, if relevant to the theme and objectives of the special issue, will go through a vigorous peer-review process. Submissions should (i) conform strictly to the Instructions for Authors available on the Journal website and (ii) be submitted through the Editorial Management system available at: http://www.editorialmanager.com/DAPD

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