Traffic modeling for aggregated periodic IoT data

Hosfeld, Tobias; Metzger, Florian; Heegaard, Poul E.

doi:10.1109/icin.2018.8401624

Cited by 35 publications

(23 citation statements)

References 24 publications

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“…The average coefficient of variation over all n nodes of APP can be numerically derived and fitted. The numerical derivation was conducted in our previous work [33] and led toC…”

Section: )mentioning

confidence: 99%

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Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

et al. 2019

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As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm-Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm-Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.

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“…The average coefficient of variation over all n nodes of APP can be numerically derived and fitted. The numerical derivation was conducted in our previous work [33] and led toC…”

Section: )mentioning

confidence: 99%

“…Only in the (unrealistic) case that the additional network delay δ is much larger than the period T then the aggregated process leads to a Poisson process, as the sending period T has no significant influence anymore. Detailed results can be found in [33].…”

Section: Impact Of Network Transmission Times (Ngi/d/1)mentioning

confidence: 99%

Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

et al. 2019

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“…It is important to note that our model is based on the average complexity and thus it does not depend on the distribution. Furthermore, the assumption of homogeneous task periodicities is reasonable for modeling the surveillance of homogeneous physical phenomena and in manufacturing systems as discussed in [22]. We leave the case of heterogeneous periodicities to be subject of future work.…”

Section: I S Y S T E M M O D E Lmentioning

confidence: 99%

Computation Offloading Scheduling for Periodic Tasks in Mobile Edge Computing

Jošilo

Dán

2020

IEEE/ACM Trans. Networking

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Motivated by various delay sensitive applications, we address the problem of coordinating the offloading decisions of wireless devices that periodically generate computationally intensive tasks. We consider autonomous devices that aim at minimizing their own cost by choosing when to perform their tasks and whether or not to offload their tasks to an edge cloud through one of the multiple wireless links. We develop a game theoretical model of the problem, prove the existence of pure strategy Nash equilibria and propose a polynomial complexity algorithm for computing an equilibrium. Furthermore, we characterize the structure of the equilibria, and by providing an upper bound on the price of anarchy of the game we establish an asymptotically tight bound on the approximation ratio of the proposed algorithm. Our simulation results show that the proposed algorithm achieves significant performance gain compared to uncoordinated computation offloading at a computational complexity that is on average linear in the number of devices.

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“…The BBU pool is connected to the target RRHs by fronthaul links. For a manufacturing process, the communication distance is normally less than 100m [10], thus we assume that all the units can be covered by the radio range of one RRH in our target scenario.…”

Section: A C-ran Systemmentioning

confidence: 99%

Massive MIMO Pilot Scheduling over Cloud RAN for Industry 4.0

Peng

Tärneberg

Fitzgerald

et al. 2020

2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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Cloud-RAN (C-RAN) is a promising paradigm for the next generation radio access network infrastructure, which offers centralized and coordinated base-band signal processing in a BBU pool. This requires extremely low latency fronthaul links to achieve real-time signal processing. In this paper, we investigate massive MIMO pilot scheduling in a C-RAN infrastructure under a factory automation scenario. We use simulations to provide insights on the feasibility of C-RAN deployment for industrial communication, which has stringent criteria to meet Industry 4.0 standards. Our experiment results show that, concerning a pilot scheduling problem, the C-RAN system is capable of meeting the industrial criteria when there is fronthaul latency in the order of milliseconds.

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Traffic modeling for aggregated periodic IoT data

Cited by 35 publications

References 24 publications

Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

Computation Offloading Scheduling for Periodic Tasks in Mobile Edge Computing

Massive MIMO Pilot Scheduling over Cloud RAN for Industry 4.0

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