Method for estimating parameters of 3G data traffic

Krendzel, Andrey; Koucheryavy, Yevgeni; Harju, Jarmo; Lopatin, S. I.

doi:10.1109/icc.2004.1313361

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…We use σ i to denote the percentage of network usage produced by the ith subgroup for the ith network service. Then, using the Lorenz curves, the subgroups can be determined as [25]…”

Section: B Subgroups For Uavsmentioning

confidence: 99%

Generalized Traffic Flow Model for Multi-Services Oriented UAV System

Abada¹,

Yang²,

Taleb³

2021

J-NaNA

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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.

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“…We use σ i to denote the percentage of network usage produced by the ith subgroup for the ith network service. Then, using the Lorenz curves, the subgroups can be determined as [25]…”

Section: B Subgroups For Uavsmentioning

confidence: 99%

Generalized Traffic Flow Model for Multi-Services Oriented UAV System

Abada¹,

Yang²,

Taleb³

2021

J-NaNA

View full text Add to dashboard Cite

show abstract

“…We assume that UAVs from the 3 rd subgroup will generate 90% of video streaming data, and 90% of IoT data will be generated by UAVs from the 2 nd and 3 rd subgroups. Then, using the Lorenz curves, the subgroups can be determined as [26] F 3 = 0.9…”

Section: B Subgroups For Uavsmentioning

confidence: 99%

Traffic Flow Modeling for UAV-Enabled Wireless Networks

Abada

Yang

Taleb

2020

2020 International Conference on Networking and Network Applications (NaNA)

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This paper investigates traffic flow modeling issue in multi-services oriented unmanned aerial vehicle (UAV)-enabled wireless networks, which is critical for supporting future various applications of such networks. We propose a general traffic flow model for multi-services oriented UAV-enable wireless networks. Under this model, we first classify the network services into three subsets: telemetry, Internet of Things (IoT), and streaming data. Based on the Pareto distribution, we then partition all UAVs into three subgroups with different network usage. We further determine the number of packets for different network services and total data size according to the packet arrival rate for the nine segments, each of which represents one map relationship between a subset of services and a subgroup of UAVs. Simulation results are provided to illustrate that the number of packets and the data size predicted by our traffic model can well match with these under a real scenario.

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“…When 3G starts, the popularity of services leads to dramatic changes in network traffic characteristics. Consequently, some methods of traffic prediction have been proposed in this era [3]- [8]. Authors in [3] took the notion of self-similarity that took place in 3G networks into account and proposed a method to estimate the main parameters of network traffic.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, some methods of traffic prediction have been proposed in this era [3]- [8]. Authors in [3] took the notion of self-similarity that took place in 3G networks into account and proposed a method to estimate the main parameters of network traffic. In [4], the authors proposed an order-k Markov model to predict traffic patterns in 3G networks.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Traffic Prediction in Emerging Mobile Networks

Jia¹,

Wan²,

Liang³

et al. 2015

jcm

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With the increasing popularity of mobile devices and applications, emerging mobile network traffic exhibits special characteristics in temporal scale e.g., there is a scale variance between the network traffic on weekdays and on weekends. Although most existing methods have been applied to data traffic prediction, few of them take such characteristic into consideration. In this paper, by using real data in mobile networks, we adopt the entropy theory to reveal that the duration of time-series given for prediction doesn't always have a positive impact and that the uncorrelated preceding time-series also deteriorates the prediction accuracy. In view of this, partitioning the network traffic prediction into weekdays' and weekends' perspective, we propose a method to predict the data traffic. Finally, we evaluate the proposed method through predicting the data traffic for a future time according to the historical data traffic in a real mobile network. In comparison with the work based on ARMA (Auto Regressive Moving Average) method, our proposed method can reduce the Mean Absolute Percentage Error (MAPE) by 35.7% and 43.8% on weekdays' and weekends' prediction, respectively.  Index Terms-Mobile networks, data traffic prediction, entropy theory, time-series, real data I.

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Method for estimating parameters of 3G data traffic

Cited by 6 publications

References 10 publications

Generalized Traffic Flow Model for Multi-Services Oriented UAV System

Generalized Traffic Flow Model for Multi-Services Oriented UAV System

Traffic Flow Modeling for UAV-Enabled Wireless Networks

A New Method for Traffic Prediction in Emerging Mobile Networks

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