Abstract
This chapter presents data and traffic analyses in 5G networks. We setup experiments with Zigbee sensors and measure different traffic patterns by changing the environmental conditions and number of channels. Due to the differences in read, write operations, message fragmentations and backoff of the Carrier Sense Multiple Access/Collision Avoidance algorithm we demonstrated that the traffic flows are changing dynamically. This leads to different behaviour of the network domain and requires special attention to network design. Statistical analyses are performed using Easyfit tool. It allows to find best fitting probability density function of traffic flows, approximation toward selected distributions as Pareto and Gamma and random number generation with selected distribution. Our chapter concludes with future plan for distribution parameters mapping to different traffic patterns, network topologies, different protocols and experimental environment.
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Acknowledgments
Our thanks to ICT COST Action IC1303: Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE), ICT COST Action IC1406: High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), project No ИФ-02-9/15.12.2012, Gateway Prototype Modelling and Development for Wired and Wireless Communication Networks for Industrial and Building Automation and project on irrigation controller development.
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Goleva, R. et al. (2016). Data and Traffic Models in 5G Network. In: Mavromoustakis, C., Mastorakis, G., Batalla, J. (eds) Internet of Things (IoT) in 5G Mobile Technologies. Modeling and Optimization in Science and Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-30913-2_20
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DOI: https://doi.org/10.1007/978-3-319-30913-2_20
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