On the Statistical Delay Performance of Large-Scale IoT Networks

Mei, Muyu; Yao, Mingwu; Yang, Qinghai; Qin, Meng; Jing, Zewei; Kwak, Kyung Sup; Rao, Ramesh R.

doi:10.1109/tvt.2022.3175019

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…They only evaluate the average performance of the network, not the instantaneous performance as a function of time. Recent research on large-scale IoT networks using stochastic geometry techniques has been published in [14]. Based on a spatial distribution model of homogeneous IoT devices, the authors calculate the delay bound of transmissions in some exceptional situations.…”

Section: Literature Reviewmentioning

confidence: 99%

PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment

Firouzabadi,

Mellah,

Manzanilla-Salazar

et al. 2023

IEEE Access

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Given the proliferation of sensors and actuators, evaluating the network performance of current and forthcoming city-wide Internet of Things (IoT) applications is a challenging task. To overcome this challenge, we created a large-scale simulator called PIoT over the years that can assess the performance of multiple millions of mobile/static IoT devices using a 4G/5G cellular infrastructure. In PIoT, different Key Performance Indicators (KPIs) are defined and collected to produce data to evaluate applications and network performance. PIoT is an on-going academic simulator project, and its most recent version is accessible to the public through the user interface found on http://www.piotsimulation.com without any installation requirements. It uses a realistic database that contains the real locations and features of Base Stations (BSs) and the real locations of IoT user equipment devices. The interface can be used by operators and researchers to understand network behavior when deploying new applications or to gather data to feed artificial intelligence and machine learning algorithms. The objective of this paper is to present a detailed description of the PIoT modeling architecture, as well as some use cases to help potential users understand the type of capabilities that are available when using the simulator. Limitations and comparisons with other popular engines are also included in the paper.

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Section: Literature Reviewmentioning

confidence: 99%

PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment

Firouzabadi,

Mellah,

Manzanilla-Salazar

et al. 2023

IEEE Access

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“…1 Given a stochastic process U (s, t), 0 ≤ s ≤ t, the MGF of U (s, t) is MU (θ, s, t) E e θU (s,t) , while the inverse-MGF of U (s, t) is MU (θ, s, t) E e −θU (s,t) . In SNC, the larger θm (e.g., θm → ∞) indicates more stringent statistical QoS requirements, while the smaller θm (e.g., θm → 0) implies looser statistical QoS requirements [12], [23]- [26], [34]- [39].…”

Section: A the Promotion Of Mgf-sncmentioning

confidence: 99%

“…The concept of statistical QoS guarantees has been extensively studied for time-sensitive networks [12], [35]- [39]. However, the closed-form expression of the statistical delay violation probability (SDVP) is typically unavailable to derive.…”

Section: A the Promotion Of Mgf-sncmentioning

confidence: 99%

Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective

Chen¹,

Lu²,

Qin³

et al. 2023

Preprint

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In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and lowlatency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem that is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE) have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems and prove that the EP-EC maximization problem is equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent innersearch collaborative algorithm. Extensive simulations demonstrate that the proposed framework in

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“…Additionally, QT models only allow the computation of expected values (e.g., expected queue delay or expected delay), not providing any insight into the distribution of the delay. Network Calculus-based (NC) models were also proposed in the literature [9][10][11]. NC models allow the computation of a delay bound of a flow traversing the network [12].…”

Section: Introductionmentioning

confidence: 99%

Estimation of 5G Core and RAN End-to-End Delay through Gaussian Mixture Models

Fadhil

Oliveira

2022

Computers

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Network analytics provide a comprehensive picture of the network’s Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the Core and the Radio Access Network (RAN) E2E delay of 5G networks with the Standalone (SA) and Non-Standalone (NSA) topologies when a single known Probability Density Function (PDF) is not suitable to model its distribution. To this end, multiple PDFs, denominated as components, are combined in a Gaussian Mixture Model (GMM) to represent the distribution of the E2E delay. The accuracy and computation time of the GMM is evaluated for a different number of components and a number of samples. The results presented in the paper are based on a dataset of E2E delay values sampled from both SA and NSA 5G networks. Finally, we show that the GMM can be adopted to estimate a high diversity of E2E delay patterns found in 5G networks and its computation time can be adequate for a large range of applications.

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On the Statistical Delay Performance of Large-Scale IoT Networks

Cited by 5 publications

References 39 publications

PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment

PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment

Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective

Estimation of 5G Core and RAN End-to-End Delay through Gaussian Mixture Models

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