A hybrid approach for data analytics for internet of things

Alturki, Badraddin, Reiff-Marganiec, Stephan and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2017. A hybrid approach for data analytics for internet of things. Presented at: 7th International Conference on the Internet of Things (IoT 2017), Linz, Austria, 22-25 October 2017. Proceedings of the Seventh International Conference on the Internet of Things. ACM, 10.1145/3131542.3131558

Full text not available from this repository.

Abstract

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both in-network level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource-constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	ACM
ISBN:	9781450353182
Last Modified:	07 Nov 2022 10:58
URI:	https://orca.cardiff.ac.uk/id/eprint/134085

Citation Data

Cited 11 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item