Traffic Flow Modeling for UAV-Enabled Wireless Networks

Abada, Abderrahmane; Yang, Bin; Taleb, Tarik

doi:10.1109/nana51271.2020.00018

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…However, within the scope of this study, one does not delve into traffic model estimation. Several existing studies, including those by [60]- [62], have dedicated efforts to unpack this intricate domain. Their insights suggest that a machinelearning-based model, which adjusts based on real-time data and historical patterns, could be the most efficient way to determine the stochastic information of the network traffic.…”

Section: Discussion On Feasibility Of Practical Implementationmentioning

confidence: 99%

QoE-Aware Cost-Minimizing Capacity Renting for Satellite-as-a-Service Enabled Multiple-Beam SatCom Systems

Kebedew,

Ha,

Lagunas

et al. 2024

IEEE Trans. Commun.

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The advent of Satellite as a Service (SaaS) platforms has empowered satellite service providers (SPs) to rent portions of satellite capacity from infrastructure providers (IPs) to cater to the diverse demands of their users across multiple satellite services. To effectively manage costs and maintain a high Quality of Experience (QoE) for numerous concurrent connections, SPs should secure flexible capacity from IPs. However, the irregular and unpredictable nature of traffic demands from various applications complicates the capacity-renting framework. This study presents a dynamic capacity allocation framework that efficiently handles diverse traffic flows with varying arrival rates, aiming to minimize rental costs while meeting blocking probability and QoE requirements. Utilizing the 𝑀 𝑡 /𝑀 𝑡 /1 queuing model and a continuous-time Markov chain, the technical designs are framed as a statistical optimization problem. In this context, the system waiting-queue lengths are estimated using the transient probabilities of Kolmogorov equations. Subsequently, cumulative distribution functions are employed to re-formulate this stochastic optimization problem into a convex form, which can be tackled through the Lagrangian duality method.Through extensive simulations and numerical assessments, we illustrate our method's efficacy, with the proposed algorithm outperforming benchmarks by reducing costs by up to 9.85% and 3.1%.

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Section: Discussion On Feasibility Of Practical Implementationmentioning

confidence: 99%

QoE-Aware Cost-Minimizing Capacity Renting for Satellite-as-a-Service Enabled Multiple-Beam SatCom Systems

Kebedew,

Ha,

Lagunas

et al. 2024

IEEE Trans. Commun.

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show abstract

“…This paper may be presented in part at the International Conference on Networking and Network Applications, Haikou, December 2020[12].…”

mentioning

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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Mobility, Traffic Models, and Network Management for Internet of Unmanned Things by Using Artificial Intelligence

Sharma

2023

Internet of Things

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Traffic Flow Modeling for UAV-Enabled Wireless Networks

Cited by 5 publications

References 23 publications

QoE-Aware Cost-Minimizing Capacity Renting for Satellite-as-a-Service Enabled Multiple-Beam SatCom Systems

QoE-Aware Cost-Minimizing Capacity Renting for Satellite-as-a-Service Enabled Multiple-Beam SatCom Systems

Generalized Traffic Flow Model for Multi-Services Oriented UAV System

Mobility, Traffic Models, and Network Management for Internet of Unmanned Things by Using Artificial Intelligence

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