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Deriving Machine to Machine (M2M) Traffic Model from Communication Model

Deriving Machine to Machine (M2M) Traffic Model from Communication Model

Barakat, Basel ORCID: 0000-0001-9126-7613, Keates, Simeon ORCID: 0000-0002-2826-672X, Arshad, Kamran and Wassell, Ian J. (2018) Deriving Machine to Machine (M2M) Traffic Model from Communication Model. In: The Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT 2018), 31 October - 01 November 2018, Philadelphia University, Amman, Jordan. (In Press)

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Abstract

The typical traffic models proposed in literature can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model for M2M communications that generates the traffic. Therefore, the proposed model is able to capture a wider picture than the state-of-the-art traffic models. The proposed model illustrates the behaviour of M2M uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analyzed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of- the-art traffic models using simulations. The simulation results show that the proposed model has a significantly higher accuracy in estimating the number of transmitted packets compared with the liteature model.

Item Type: Conference or Conference Paper (Paper)
Uncontrolled Keywords: Machine to Machine Communication; Communication Model; Communication System Traffic; Traffic Model; Stochastic Process.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Faculty of Engineering & Science > Future Technology and the Internet of Things
Last Modified: 06 Nov 2018 11:33
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/22044

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