You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

IoT and Fog Analytics for Industrial Robot Applications

Fulltext:


Publication Type:

Conference/Workshop Paper

Venue:

The 25th IEEE International Conference on Emerging Technologies and Factory Automation


Abstract

The rapid development of IoT, cloud and fog computing has increased the potential for developing smart services for IoT devices. Such services require not only connectivity and high computing capacity, but also fast response time and throughput of inferencing results. In this paper we present our ongoing work, investigating the potential for implementing smart services in the context of industrial robot applications with focus on analytic inferencing on fog and cloud computing platforms. We review different use cases that we have found in the literature and we divide them into two suggested categories, "distributed deep models" and "distributed interconnected models". We analyze the characteristics of IoT data in industrial robot applications and present two concrete use cases of smart services where inferencing in a fog and a cloud architecture, respectively, is needed. We also reason about important considerations and design decisions for the development process of analytic services.

Bibtex

@inproceedings{Lager5942,
author = {Anders Lager and Alessandro Papadopoulos and Thomas Nolte},
title = {IoT and Fog Analytics for Industrial Robot Applications},
month = {September},
year = {2020},
booktitle = {The 25th IEEE International Conference on Emerging Technologies and Factory Automation},
url = {http://www.es.mdh.se/publications/5942-}
}