Improving fast velocity and large volume data processing in IoT/M2M platforms

Pohan Peng, Fuchun Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In IoT/M2M platforms such as those defined by ETSI and oneM2M, all data collected by sensors are transmitted and stored in the platforms before they are processed. These data consume much of platform resources including network bandwidth, storage, CPU and memory. Nevertheless, in reality not all these data are needed by the IoT/M2M applications. In this paper, we propose to add a Streaming Data Preprocessor in the IoT/M2M platform to deal with fast velocity and large volume data from the IoT/M2M sensors. We experiment and compare the data processing capability of our enhanced platform with the one without a preprocessor based on an ETSI M2M-compliant platform OpenMTC. Our experiment was conducted for a factory management use case from which we show that our enhanced system demonstrates better performance.

Original languageEnglish
Title of host publication2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages565-570
Number of pages6
ISBN (Electronic)9781509041305
DOIs
StatePublished - 6 Feb 2017
Event3rd IEEE World Forum on Internet of Things, WF-IoT 2016 - Reston, United States
Duration: 12 Dec 201614 Dec 2016

Publication series

Name2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016

Conference

Conference3rd IEEE World Forum on Internet of Things, WF-IoT 2016
CountryUnited States
CityReston
Period12/12/1614/12/16

Keywords

  • ETSI M2M
  • IoT/M2M Preprocessing
  • IoT/M2M Streaming Data Processing
  • OpenMTC

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  • Cite this

    Peng, P., & Lin, F. (2017). Improving fast velocity and large volume data processing in IoT/M2M platforms. In 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016 (pp. 565-570). [7845402] (2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WF-IoT.2016.7845402