University of Oulu

Y. Li et al., "Gamma-modulated Wavelet model for Internet of Things traffic," 2017 IEEE International Conference on Communications (ICC), Paris, 2017, pp. 1-6. doi: 10.1109/ICC.2017.7996506

Gamma-modulated wavelet model for Internet of Things traffic

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Author: Li, Yuhong1; Huang, Yuanyuan1; Su, Xiang2;
Organizations: 1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
2Centre for Ubiquitous Computing, University of Oulu, Oulu, Finland
3Network Technology Laboratory, Huawei Technologies Co., Ltd., Nanjing, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2019-08-20


Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to re-think its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-4673-8999-0
ISBN Print: 978-1-4673-9000-2
Pages: 1 - 6
DOI: 10.1109/ICC.2017.7996506
Host publication: 2017 IEEE International Conference on Communications (ICC)
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: The work is partly supported by Huawei HIRP program.
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