scholarly journals Goodput Modelling and Optimisation of Channel Assignment For Planning IEEE 802.11Wireless Backhaul Networks

2021 ◽  
Author(s):  
◽  
Ying Qu
Keyword(s):  
Ieee 802.11 ◽  
Channel Assignment ◽  
Network Performance ◽  
Service Providers ◽  
Fitness Function ◽  
Interference Model ◽  
Distribution Model ◽  
Border Effect ◽  
Multi Objective ◽  
Backhaul Networks

<p>IEEE 802.11Wireless backhaul networks (WBNs) provide scalable and cost-effective solutions for interconnecting small-cell networks and backbone networks or Internet. With newer and farther reaching applications being developed in IEEE 802.11 WBNs, such as smart grids and intelligent transportation systems, users expect high goodput and better fairness. However, some performance issues in IEEE 802.11 protocols such as border effect, exposed nodes and hidden nodes are exacerbated as network densification occurs, leading to goodput degradation and severe unfairness such as flow starvation (extreme low goodput). These issues may cause an IEEE 802.11 WBN to form a bottleneck and impact the overall network performance. Therefore, in-depth study is required in order to improve the IEEE 802.11 WBN planning to achieve better goodput and fairness.  This research aims to improve IEEE 802.11 WBN planning through goodput modelling and optimising channel assignment. A novel simple goodput distribution model is proposed to predict goodput and fairness in IEEE 802.11 WBNs. Simulation results show that the proposed goodput model accurately predicts goodput with consideration of carrier sensing effect and traffic demands. Based on this goodput model, a new interference model is proposed to more realistically reflect both local and global interference in IEEE 802.11 WBNs. With the proposed interference model, two anti-starvation channel assignments have been developed to prevent flow starvation. Simulation validations show that the new anti-starvation channel assignments effectively prevent flow starvation and improve network fairness in IEEE 802.11 WBNs.  This research also optimises channel assignment to achieve desired fairness and goodput. A multi-objective optimisation problem is formulated and a new fitness function is designed to evaluate a channel allocation with accurate prediction of goodput and fairness. Utilising the new fitness function, two multi-objective channel assignments have been developed to achieve both fairness and goodput. Compared with existing channel assignments through simulation, the proposed multi-objective channel assignments provide a set of feasible solutions that meet desired fairness and goodput.  This research helps network planners or service providers to improve the IEEE 802.11 WBN planning from predicting network performance to optimising goodput and fairness. The proposed goodput model, interference model, and fitness function are also useful for node placement, and optimising routing and scheduling in IEEE 802.11 WBNs.</p>

scholarly journals Goodput Modelling and Optimisation of Channel Assignment For Planning IEEE 802.11Wireless Backhaul Networks

2021 ◽  
Author(s):  
◽  
Ying Qu
Keyword(s):  
Ieee 802.11 ◽  
Channel Assignment ◽  
Network Performance ◽  
Service Providers ◽  
Fitness Function ◽  
Interference Model ◽  
Distribution Model ◽  
Border Effect ◽  
Multi Objective ◽  
Backhaul Networks

<p>IEEE 802.11Wireless backhaul networks (WBNs) provide scalable and cost-effective solutions for interconnecting small-cell networks and backbone networks or Internet. With newer and farther reaching applications being developed in IEEE 802.11 WBNs, such as smart grids and intelligent transportation systems, users expect high goodput and better fairness. However, some performance issues in IEEE 802.11 protocols such as border effect, exposed nodes and hidden nodes are exacerbated as network densification occurs, leading to goodput degradation and severe unfairness such as flow starvation (extreme low goodput). These issues may cause an IEEE 802.11 WBN to form a bottleneck and impact the overall network performance. Therefore, in-depth study is required in order to improve the IEEE 802.11 WBN planning to achieve better goodput and fairness.  This research aims to improve IEEE 802.11 WBN planning through goodput modelling and optimising channel assignment. A novel simple goodput distribution model is proposed to predict goodput and fairness in IEEE 802.11 WBNs. Simulation results show that the proposed goodput model accurately predicts goodput with consideration of carrier sensing effect and traffic demands. Based on this goodput model, a new interference model is proposed to more realistically reflect both local and global interference in IEEE 802.11 WBNs. With the proposed interference model, two anti-starvation channel assignments have been developed to prevent flow starvation. Simulation validations show that the new anti-starvation channel assignments effectively prevent flow starvation and improve network fairness in IEEE 802.11 WBNs.  This research also optimises channel assignment to achieve desired fairness and goodput. A multi-objective optimisation problem is formulated and a new fitness function is designed to evaluate a channel allocation with accurate prediction of goodput and fairness. Utilising the new fitness function, two multi-objective channel assignments have been developed to achieve both fairness and goodput. Compared with existing channel assignments through simulation, the proposed multi-objective channel assignments provide a set of feasible solutions that meet desired fairness and goodput.  This research helps network planners or service providers to improve the IEEE 802.11 WBN planning from predicting network performance to optimising goodput and fairness. The proposed goodput model, interference model, and fitness function are also useful for node placement, and optimising routing and scheduling in IEEE 802.11 WBNs.</p>

MODEL DISTRIBUSI PERJALANAN PENDUDUK UNTUK PENENTUAN KORIDOR ‘LRT’ DI PULAU BATAM

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Djoko Prijo Utomo
Keyword(s):  
Network Performance ◽  
Travel Demand ◽  
Mass Transit ◽  
Distribution Model ◽  
Economic Activities ◽  
Transit System ◽  
Trip Distribution ◽  
Home Interview ◽  
Interview Survey ◽  
Private Car

In consequence of the increasing of regional economic activities in Pulau Batam, a reliable transportation system is required. Decreasing road network performance as a result of increasing traffic volume needs a strategic planning to anticipate the worsening condition in the future. One of the solutions is by providing mass transit system which is expected to attract private car users. Therefore, determination of potential corridor of mass transit system need to be identified so that the system provide better accessibility. Trip pattern in Pulau Batam must be known by developing trip distribution model. The trip distribution model is calibrated using origin-destination (O-D) data that is based on home interview survey. The validated model will be used to forecast and simulate travel demand onto transport network. Result of model calibration process shows mean trip length difference between model and survey is equal 0.141 %. From simulation of trip assignment is obtained that potential corridor for mass transit system using LRT is Batu Ampar – Batu Aji via Muka Kuning. Passenger forecast in the year 2030 is 193,990 passenger/day (2 directions).

Investigating the Analysis of Power Saving Mode in IEEE 802.11 for Wi-Fi Communication

2018 ◽  
Vol 6 (3) ◽  
pp. 13-19
Author(s):  
Isam Aameer Ibrahim ◽  
Haider TH Salim ◽  
Hasan F. Khazaal
Keyword(s):  
Ieee 802.11 ◽  
Network Performance ◽  
Power Saving ◽  
Packet Delay ◽  
Access Point ◽  
Battery Life ◽  
Global Issues ◽  
Time Ratio ◽  
Power Saving Mode ◽  
Saving Mode

One of the major global issues today is energy consumption. Consequently, power management was introduced in various communication technologies. For IEEE 802.11wireless communication, there is a Power Saving Mode scheme (PSM) for increase the battery life of cell phone. In this PSM, there are two key parameters: beacon period interval (BI) and listen interval(LI). In most work these values are chosen arbitrary. Here, a scheme to determine the optimal BI and LI for accomplishing the most astounding conceivable vitality proficiency is introduced. This is implemented with the application of a numerical sample to the standard IEEE 802.11 PSM and Access Point-PSM (AP-PSM) schemes. To ensure the quality of network performance analysis on the normal and change of parcel delays is doing. The well-known queuing (M/G/I) model with bulk services are utilized. After the implementation of the proposed analysis, “maximum rest plan time ratio optimal Sleep Scheme (OSS)” which is when participate stations stay in the doze mode it can be determined. In this research shows that the optimal BI and LI produce optimal OSS time ratio scheme also achieved optimal average and variance of packet delay.

A non-overshooting controller for vehicle path following

2021 ◽  
pp. 014233122199438
Author(s):  
Tong Xu ◽  
Dong Wang ◽  
Weigong Zhang
Keyword(s):  
Field Experiments ◽  
Fitness Function ◽  
Path Following ◽  
Settling Time ◽  
Distribution Model ◽  
Positioning Errors ◽  
Guidance Law ◽  
Initial Control ◽  
Vehicle Path ◽  
Pavement Construction

Unmanned pavement construction is of great significance in China, and one of the most important issues is how to follow the designed path near the boundary of the pavement construction area to avoid curbs or railings. In this paper, we raise a simple yet effective controller, named the proportional-integral-radius and improved particle swarm optimization (PIR-IPSO) controller, for fast non-overshooting path-following control of an unmanned articulated vehicle (UAV). Firstly, UAV kinematics model is introduced and segmented UAV steering dynamics model is built through field experiments; then, the raw data collected by differential global positioning system (DGPS) is used to build the measurement error distribution model that simulates positioning errors. Next, line of sight (LOS) guidance law is introduced and the LOS initial parameter is assigned based on human driving behavior. Besides, the initial control parameters tuned by the Ziegler-Nichols (ZN) method are used as the initial iterative parameters of the PSO controller. An improved PSO fitness function is also designed to achieve fast non-overshoot control performance. Experiments show that compared with the PSO, ZN and ZN-PSO controller, the PIR-PSO-based controller has significantly less settling time and almost no overshoot in various UAV initial states. Furthermore, compared with other controllers, the proposed PIR-IPSO-based controller achieves precise non-overshoot control, relatively less settling time and centimeter-level positioning error in various initial deviations.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

scholarly journals Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 621
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Network Performance ◽  
Service Providers ◽  
Perceived Quality ◽  
Internet Service Providers ◽  
Performance Parameters ◽  
Internet Service ◽  
Access Points

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

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