TU Berlin

Main document

Literature Database Entry


Daniel Happ, Sanjeet Raj Pandey and Vlado Handziski, "Migrating IoT Processing to Fog Gateways," Proceedings of 1st GI/ITG KuVS Fachgespräch Fog Computing (FG-FC 2018), Darmstadt, Germany, March 2018.


Internet-connected sensor devices usually send their data to cloud-based servers for storage, data distribution and processing, although the data is often mainly consumed locally to the source. This creates unnecessary network traffic, increases latency and raises privacy concerns. Fog and edge computing instead propose to migrate some of those functions to the edge of the network. In particular, on premise gateways have the potential to offer more privacy preserving and low-latency local storage and processing capabilities. In this study, we outline our ongoing efforts to combine the benefits of fog and cloud sensor data processing. We present our work-in-progress towards a system that automatically selects the most suitable execution location for processing tasks between cloud and fog. We present a protocol for migration of processing tasks from one system to another without service interruption, and propose a reference architecture. We additionally introduce an analytical cost model that serves as basis for the placement selection and give advice on its parametrization. Finally, we show initial performance results, gathered with an early prototype of the proposed architecture.

Quick access

Authors' Version PDF (PDF on this web site)
BibTeX BibTeX


Daniel Happ
Sanjeet Raj Pandey
Vlado Handziski

BibTeX reference

    author = {Happ, Daniel and Pandey, Sanjeet Raj and Handziski, Vlado},
    title = {{Migrating IoT Processing to Fog Gateways}},
    address = {Darmstadt, Germany},
    booktitle = {1st GI/ITG KuVS Fachgespr{\"{a}}ch Fog Computing (FG-FC 2018)},
    month = {3},
    year = {2018},

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.


Featured Paper