Bursty data service latency analysis under fractional calculus fluid model of Multi-hop Wireless Networks

2021 ◽  
Author(s):  
Lei Chen ◽  
Chuangeng Tian ◽  
Ping Cui ◽  
Kailiang Zhang ◽  
Yuan An
Keyword(s):  
Wireless Networks ◽  
Fractional Calculus ◽  
Fluid Model ◽  
Data Service ◽  
Latency Analysis ◽  
Service Latency

scholarly journals Latency Analysis of Wireless Networks for Proximity Services in Smart Home and Building Automation: The Case of Thread

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 4856-4867 ◽  
Author(s):  
Dapeng Lan ◽  
Zhibo Pang ◽  
Carlo Fischione ◽  
Yu Liu ◽  
Amir Taherkordi ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Smart Home ◽  
Building Automation ◽  
Latency Analysis ◽  
Proximity Services

scholarly journals The Influence of Magnetohydrodynamic Flow and Slip Condition on Generalized Burgers’ Fluid with Fractional Derivative

2020 ◽  
Vol 17 (1) ◽  
pp. 0150
Author(s):  
Nassief Et al.
Keyword(s):  
Shear Stress ◽  
Fractional Calculus ◽  
Fractional Derivative ◽  
Velocity Distribution ◽  
Constant Pressure ◽  
Fluid Model ◽  
Magnetohydrodynamic Flow ◽  
Slip Condition ◽  
Constitutive Relationship ◽  
Burgers Fluid

This paper investigates the effect of magnetohydrodynamic (MHD) of an incompressible generalized burgers’ fluid including a gradient constant pressure and an exponentially accelerate plate where no slip hypothesis between the burgers’ fluid and an exponential plate is no longer valid. The constitutive relationship can establish of the fluid model process by fractional calculus, by using Laplace and Finite Fourier sine transforms. We obtain a solution for shear stress and velocity distribution. Furthermore, 3D figures are drawn to exhibit the effect of magneto hydrodynamic and different parameters for the velocity distribution.

Buffer Control Techniques for QoS Provisioning in Wireless Networks

2010 ◽  
pp. 157-182
Author(s):  
Michael M. Markou ◽  
Christos G. Panayiotou
Keyword(s):  
Wireless Networks ◽  
Fluid Model ◽  
Discrete Event ◽  
Sample Path ◽  
Performance Measure ◽  
Control Techniques ◽  
Buffer Control ◽  
Using Data ◽  
Stochastic Fluid ◽  
Network Buffer

This chapter introduces the network buffer control techniques as a mean to provide QoS. This problem has been extensively studied in the context of wirelined networks; however, the proliferation of wireless networks and the introduction of multimedia applications has significantly changed the characteristics of the traffic mix that flows on the network. The objective of this chapter is to create a new methodology for automatically adapting the various buffer thresholds such that the network exhibits optimal or near optimal performance even as network conditions change. The behavior of the network (generally a discrete event system—DES) is approximated by that of a stochastic fluid model (SFM); then using infinitesimal perturbation analysis (IPA) we obtain sensitivity estimators of the performance measure(s) of interest with respect to the control parameter. These estimators are easy to compute using data observed from the DES’s sample path. Finally, the computed estimators are used in stochastic approximation algorithms to adjust the thresholds.

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