Generalized Traffic Flow Model for Multi-Services Oriented UAV System

Abderrahmane Abada, Bin Yang, and Tarik Taleb

1. School of Electrical Engineering, Aalto University, Finland
2. Centre for Wireless Communications, University of Oulu, Finland

Unmanned Aerial Vehicles (UAVs) are opening up new opportunities for extensive applications. The traffic flow model is critical to evaluate the traffic needs of various applications in designing and deploying UAV system. However, the traffic flow model has not been explored in multi-services oriented UAV system. To this end, this paper proposes a general traffic flow model for multi-services orientated UAV system. Under such a model, the network services are first categorized into three subsets, each corresponding to one of telemetry, Internet of Things (IoT), and streaming data. According to the Pareto distribution, all UAVs are further partitioned into three subgroups relying on their network usages. We can measure the packet arrival rate for the nine segments, each of which represents one map relationship between a services subset and a UAV subgroup. Therefore, we can also obtain the number of packets for each network service and total data size. Simulation results are presented to illustrate that the number of packets and the data size predicted by our traffic model can well match with these in real scenarios with different network services.

UAV system, multi-services, traffic flow

Abderrahmane Abada, Bin Yang, and Tarik Taleb (2021). Generalized Traffic Flow Model for Multi-Services Oriented UAV System. Journal of Networking and Network Applications, Volume 1, Issue 1, pp. 1–8. https://doi.org/10.33969/J-NaNA.2021.010101.

[1] S. K. Khan, “Performance evaluation of next generation wireless uav relay with millimeter-wave in access and backhaul,” 2019.
[2] N. H. Motlagh, M. Bagaa, and T. Taleb, “Energy and delay aware task assignment mechanism for UAV-based IoT platform,” IEEE Internet Things J., vol. 6, no. 4, p. 6523–6536, Aug. 2019.
[3] B. Yang, T. Taleb, Z. Wu, and L. Ma, “Spectrum sharing for secrecy per-formance enhancement in D2D-enabled UAV networks,” IEEE Network, vol. 34, no. 6, pp. 156–163, Nov./Dec. 2020.
[4] N. H. Motlagh, T. Taleb, and O. Arouk, “Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives,” IEEE Internet of Things J., vol. 3, no. 6, pp. 899–922, Dec. 2016.
[5] C. Benzaid and T. Taleb, “ZSM security: Threat surface and best practices,” IEEE Network, vol. 34, no. 3, pp. 124–133, 2020.
[6] B. de Miguel Molina and M. S. O˜na, “The drone sector in europe,” in Ethics and civil drones. Springer, Cham, 2018, pp. 7–33.
[7] B. Yang, T. Taleb, Y. Shen, X. Jiang, and W. Yang, “Performance, fairness and tradeoff in uav swarm underlaid mmwave cellular networks with directional antennas,” IEEE Trans. Wireless Commun., 2020.[Online]. Available: https://doi.org/10.1109/TWC.2020.3041800
[8] B. Chandrasekaran, “Survey of network traffic models,” Waschington University in St. Louis CSE, vol. 567, 2009.
[9] O. Boxma and J. Cohen, “The single server queue: Heavy tails and heavy traffic,” Self-Similar Network Traffic and Performance Evaluation, pp. 143–169, 2000.
[10] R. R. Marie, J. M. Blackledge, and H. E. Bez, “Characterization of internet traffic using a fractal model,” in Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications. ACTA Press, 2007, pp. 253–258.
[11] M. Grossglauser and J.-C. Bolot, “On the relevance of long-range dependence in network traffic,” IEEE/ACM transactions on networking, vol. 7, no. 5, pp. 629–640, 1999.
[12] A. Abada, B. Yang, and T. Taleb, “Traffic flow modeling for UAV-enabled wireless networks,” Submitted to the 2020 International Con-ference on Networking and Network Applications.
[13] M. Becchi, “From poisson processes to self-similarity: a survey of network traffic models,” Washington University in St. Louis, Tech. Rep, 2008.
[14] R. Jain and S. Routhier, “Packet trains–measurements and a new model for computer network traffic,” IEEE journal on selected areas in Communications, vol. 4, no. 6, pp. 986–995, 1986.
[15] M. Wilson, “A historical view of network traffic models,” Unpublished survey paper. See http://www. arl. wustl. edu/mlw2/classpubs/traffic models, 2006.
[16] W. Fischer and K. Meier-Hellstern, “The markov-modulated poisson process (mmpp) cookbook,” Performance evaluation, vol. 18, no. 2, pp. 149–171, 1993.
[17] J. L. V´ehel and R. Riedi, “Fractional brownian motion and data traffic modeling: The other end of the spectrum,” in Fractals in engineering. Springer, 1997, pp. 185–202.
[18] P. Flandrin, “Wavelet analysis and synthesis of fractional brownian motion,” IEEE Transactions on information theory, vol. 38, no. 2, pp. 910–917, 1992.
[19] P. Pruthi and A. Erramilli, “Heavy-tailed on/off source behavior and self-similar traffic,” in Proceedings IEEE International Conference on Communications ICC’95, vol. 1. IEEE, 1995, pp. 445–450.
[20] B. Hasselblatt and A. Katok, A first course in dynamics: with a panorama of recent developments. Cambridge University Press, 2003.
[21] Z. Hua and Y. Zhou, “Nonlinear chaotic processing model,” arXiv preprint arXiv:1612.05154, 2016.
[22] J. Gordon, “Pareto process as a model of self-similar packet traffic,” in Proceedings of GLOBECOM’95, vol. 3. IEEE, 1995, pp. 2232–2236.
[23] P. M. Dixon, J. Weiner, T. Mitchell-Olds, and R. Woodley, “Bootstrap-ping the gini coefficient of inequality,” Ecology, vol. 68, no. 5, pp. 1548–1551, 1987.
[24] L. Varakin, “The pareto law and the rule 20/80: the distribution of incomes and telecommunication services,” MAC proceedings, vol. 1, pp. 3–10, 1997.
[25] A. Krendzel, Y. Koucheryavy, J. Harju, and S. Lopatin, “Method for estimating parameters of 3g data traffic,” in 2004 IEEE International Conference on Communications (IEEE Cat. No. 04CH37577), vol. 7. IEEE, 2004, pp. 4312–4316.