Link Speed Research Articles

A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework

Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.

GranularNF

In this paper, we consider the challenges that arise from the need to scale virtualized network functions (VNFs) at 100 Gbps line speed and beyond. Traditional VNF designs are monolithic in state management and scheduling: internally maintaining all states and operations associated with them. Without proper design considerations, it suffers from limitations when scaling at 100 Gbps link speed and beyond: the inability of efficient utilization of the cache because of the contention due to the frequent control plane activities, computational/memory-intensive tasks taking up CPU times, shares states causing the synchronization among the cores. We address these limitations by arguing for the need to granularly decompose a VNF into data/control components that are co-located within a server but can be independently scaled among the cores. To realize the approach, we design a "serverless" programming framework with novel abstraction to optimize the data components that must process packets at the line speed, reduce the contention of the data states and enable run-time scheduling of different components for improved resource utilization. The abstractions, combined with the runtime system that we design, help NFV developers focus on the logic and correctness of VNF programming without worrying about how VNFs may be scaled in or out. We evaluate our platform by comparing it with monolithic approaches using different workloads and by analyzing its advantages of separation on scalability, performance determinism, and feature velocity.

Scheduling data gathering with background communications and a processing stage

A data gathering network is a distributed computer system where the workers have to transfer data to a node called the base station, which is responsible for their processing. Wireless sensor networks monitoring the environment or wired systems executing computations are examples of such networks. We analyze scheduling in data gathering networks with background communications. It is assumed that at most one worker can communicate with the base station at a time, and hence, the network works in a flow shop mode. The speed of each communication link may change in time because of various processes using the network. Communication and computation preemptions are possible. The objective is to minimize the time required to gather and process all data. We prove that this problem is strongly NP-hard and indicate several polynomially solvable cases. Heuristic algorithms are proposed and tested using computational experiments. On the basis of the obtained results, we identify the algorithms that perform best in specific types of networks.

Significance of Search Engine Optimization

As the Internet grows in popularity, hundreds of thousands of people use search engines such as Google and Yahoo to search for information. However, search engine users are most interested in some result pages. Therefore, selling a website with search engine results is an important task in website development. Search engine optimization (SEO) needs to complete this task. However, from time to time, the black hat SEO strategy behind search engines is used, and the page rank of search engine results is higher than it deserves. This research paper implies a new methodology for search engine optimization (SEO) without sandboxing via serps such as Google and Bing. Previously, rulebooks were based solely on the number of backlinks on a page. This technique employs a secure link building technique where link speed is key, without sacrificing website optimization. Today's algorithm updates are considered. This white paper describes the characteristics of search engine PageRank algorithms, SEO techniques, and strategies for ranking keywords.

Experiment and Analysis of Film-Soil Separation Motion Characteristics of a Chain Drive Residual Film Recovery Mechanism for the Tillage Layer

The working principle and key components of a chain drive residual film recovery machine were studied. A kinematic analysis of the chain drive soil separation mechanism was completed by applying ADAMS software to obtain the trajectory, displacement, velocity and acceleration of the center of mass of the chain plate link_1 in the x- and y-directions within the effective shaking displacement. A field test of the chain drive separation mechanism was carried out. When the shaking roller was in contact with the chain, the change in speed of the chain in the x- and y-directions was not significant, but the force in the y-direction was conducive to breaking the soil pieces and improving the rate of collecting the residual film. The change in the speed of link_1 in the y-direction speed with x-direction speed explained the action of the separation mechanism on conveying and throwing of the film-soil mixture in the horizontal and vertical directions, which revealed the mechanism of the chain drive film-soil separation equipment. The field test showed that the film collection rate of the chain drive separation mechanism was 68%, and the surface residue was 6%. The test results meet the relevant national and industry standards and design requirements. The results of this study provide guidance for the design and optimization of chain drive film-soil separation equipment.

A feasibility study of using Manufacturing Message Specification report gateway model for IEC61850 inter-substation type-1 messaging over Wide Area Network

This paper presents a feasibility study of using TCP-based MMS report gateway model for transmitting type-1 messages over WAN. In IEC61850-90-5:2012, the Standard suggests to use UDP-based R-GOOSE for exchanging the type-1 messages, i.e. trip signals and other fast messages defined in the Standard, over WAN. However, due to the cyclic retransmission inherited from GOOSE, R-GOOSE messages are repeatedly sent to the network and thus pose unnecessary traffic to the WAN. With the proposed model, unnecessary re-transmissions can be eliminated. It can preserve bandwidth for more IEDs being imposed on the existing smart grid. To achieve this task, two IEC61850-based gateways for translating GOOSE Messages to and from the MMS report over WAN have been modeled and implemented. The authors then make use of an HLA-complied co-simulation platform to simulate a distance protection scenario through the gateways to investigate the communication latency with different links speed and utilization rate of WAN. Simulated results show that the proposed MMS report gateway model works well for different WAN link speed from DS3 to 1000BaseX. Simulation results further indicate that introducing QoS control to the WAN routers is an effective method to warrant the latency for satisfying the IEC61850 standard requirement. The result from this feasibility study reveals that the TCP-based MMS report gateway model indeed can be used as an extended feature for transmitting type-1 messages between substations over WAN. Furthermore, this method is suitable for connecting more IEC61850-based DER to the existing smart grid involving WAN.

Reengineering of the roller hardening machine of the sheet-rolling shop of JSC Ural Steel

In connection with the planned increase in the production of heat-treated rolled products of large thickness and bridge steel at JSC "Ural Steel", it became necessary to increase the productivity of thermal equipment. For this purpose, the roller hardening machine of the sheet rolling shop was re-engineered. Currently, the lifting and lowering of the lifting frame of the hardening machine is carried out by a spindle drive consisting of an electric motor, a bevel gearbox, spindle gears and cardan shafts. In the process of operation, the equipment of the electromechanical drive for lifting and lowering the pressure frame often fails due to the presence of a large number of rubbing elements, and significant labor costs are required to restore the working condition of the unit. To increase the reliability of the hardening machine, it is proposed to carry out its reengineering by replacing the electromechanical drive of lifting and lowering the pressure frame with a hydraulic drive. Replacing the electric drive with a hydraulic drive is advisable because the existing design already has hydraulic equipment, and at the same time a number of advantages are achieved: stepless regulation of the speed of the output link and ensuring small stable speeds; small size and weight; reversing the movement of the output link of the hydraulic transmission; high performance; Protection against overloads, due to the presence of safety valves, etc. The economic efficiency of design solutions is to reduce the cost of one ton of flat products due to a reduction in repair downtime (by 54 hours) and an increase in production (by 0.9%).

Evaluation of Bluetooth Detectors in Travel Time Estimation

With the current popularity of mobile devices with Bluetooth technology, numerous studies have developed methods to analyze the data from such devices to estimate a variety of traffic information, such as travel time, link speed, and origin–destination estimations. However, few studies have comprehensively determined the impact of the penetration rate on the estimated travel time derived from Bluetooth detectors. The objectives of this paper were threefold: (1) to develop a data-processing method to estimate the travel time based on Bluetooth transactional data; (2) to determine the impact of vehicle speeds on Bluetooth detection performance; and (3) to analyze how the Bluetooth penetration rate affected deviations in the estimated travel time. A 28 km toll section in Bangkok, Thailand, was chosen for the study. A number of Bluetooth detectors and microwave radar devices were installed to collect traffic data in October 2020. Five data-processing steps were developed to estimate the travel time. Based on the results, the penetration rate during the day (50 to 90 percent) was higher than during the night (20 to 50 percent). In addition, we found that speed had adverse effects on the MAC address detection capability of the Bluetooth detectors; for speeds greater than 80 km/h, the number of MAC addresses detected decreased. The minimum Bluetooth penetration rate should be at least 1 percent (or 37 vehicles/h) during peak periods and at least 5 percent (or 49 vehicles/h) during the off-peak period.

Link-level resilience analysis for real-world networks using crowd-sourced data

A number of recent disasters have challenged the functionality of transport networks. The significance of road transport infrastructure to the functioning means that systems need to be able to operate under undesirable conditions, and quickly return to acceptable levels of service. The objective of the study is to analyze real-world networks speed fluctuation and evaluate the quantitative relationship between resilience and graph-based metrics, and link attributes using crowd-sourced data. We measure resilience in terms of the rate (vehicle speed) at which the road network recovers from a disruptive event and define five metrics to quantify network resilience. We analyze more than 500 links affected by disruptions in multiple cities with more than millions of crowd-sourced data. Using changes in link speed before, during, and after the disruption, the resilience metrics are applied to three case studies that are categorized as no-notice disruption, notice disruption, and disruption caused by continuous events. The results indicate that link graph-based metrics and attributes have a high impact on network resilience. However, the relevance of different metrics and attributes to the link resilience is different. Population density, predictability of disasters, and human factors have a significant impact on the reduction and recovery phases.

A Multi-Criteria Approach to Decision Making in Broadband Technology Selection

A new European Union regulatory framework for the telecom sector has been under a process of transposition to national laws by its member states that should have been completed by the end of 2020, notwithstanding some delays. A core purpose of the regulatory framework is to guarantee that most citizens will have access to very fast Internet connections, capable of 100 Mbps download link speed, regardless of where they live. According to this new framework, in areas where the market does not deliver, governments are to launch public tenders for the deployment, maintenance, and operations of network infrastructure as well as services, and public funds should be used to support the deployment of these broadband networks in less densely populated areas. Needless to say, public tenders of this nature are subject to different criteria when it comes to candidate evaluation. In this paper, we present a decision model for the selection of operators to deploy and maintain broadband networks in scarcely populated areas, taking into consideration infrastructure costs, the technical quality offered by the solutions, and the credibility of the candidates. We suggest an integrated multi-stakeholder multi-criteria approach and demonstrate how it can be used in this complex area and find that in the example provided, taking a relevant set of criteria into the analysis, optical fibre networks hold much higher chances to be used in these public tenders compared to networks based on the broadly favoured 5G technology.

QoE Aware Video Streaming and Optimal Relay for Emergency Message Dissemination in Graph Based VANET

Vehicular Ad hoc Network (VANET) is a dynamic network environment that supports the transmission of any type of data that varies with size and delay constraints. VANET also enables to aid safety message forwarding in case of emergency by means of data dissemination. The major problematic issues in routing and data dissemination are delay and broadcast storm. With the advancements in VANET technology not only normal traffic data transmission but also video streaming is transmitted while the delay constraint has to be very less for video streaming. This article addresses these two problems by designing a graph aware network management for emergency message dissemination by selecting an optimal relay using multi-objective shuffled shepherd optimization algorithm that computes degree, goodness, waiting time, transmission range and link utility. Graph is constructed with the link stability, signal strength, distance and speed between vehicles. The selected relay vehicle from the graph is responsible to disseminate the safety message, which reduces the broadcast storm. The use of reinforcement learning is able to select the most suitable low delay neighbors for video streaming. In routing, the normal traffic data transmission is performed which is tolerable to delay, so Type-2 fuzzy logic is used with dynamic belief entropy that defines routes with the computation of quality of forwarding, bandwidth, distance and hop counts. This proposed work is implemented on OMNeT++ simulator and the results show better performances than the existing work in terms of packet delivery ratio, end-to-end delay, duplicate packets, throughput, and frame loss.

Post-Processing of GPS Data for Bus Link Speed Determination based on GIS

Post-Processing of GPS Data for Bus Link Speed Determination based on GIS

The tolerance improvement method for adjustable speed drive based on fuzzy field-weakening vector control under voltage sag

Adjustable speed drive (ASD) has been widely used in modern industrial production process, however it is sensitive to voltage sags, resulting in the huge economic losses. Firstly, this paper researches on the response characteristics of DC link voltage, torque and speed of ASD under voltage sags and finds out the key parameters of control strategy which effect on the tolerance of ASD. Secondly, a fuzzy PI control method is proposed to adjust adaptively the control parameters of ASD in time to enhance the tolerance of ASD under voltage sags. Furthermore, this paper presents a field-weakening control method that reduces the reference flux value according to the residual voltage of voltage sag automatically, which ensures that ASD can operate within the acceptable range under the steady-state of voltage sag. Finally, this paper builds ASD simulation models based on MATLAB/Simulink and experimental platform in laboratory, to verify that the proposed fuzzy field-weakening vector control method. Both the simulation and experimental results show that ASD with the proposed method has better tolerance than with the traditional control method.© 2017 Elsevier Inc. All rights reserved.

On the Use of Carbon Cables from Plastic Solvent Combinations of Polystyrene and Toluene in Carbon Nanotube Synthesis.

For every three people on the planet, there are approximately two Tonnes (Te) of plastic waste. We show that carbon recovery from polystyrene (PS) plastic is enhanced by the coaddition of solvents to grow carbon nanotubes (CNTs) by liquid injection chemical vapour deposition. Polystyrene was loaded up to 4 wt% in toluene and heated to 780 °C in the presence of a ferrocene catalyst and a hydrogen/argon carrier gas at a 1:19 ratio. High resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and Raman spectroscopy were used to identify multiwalled carbon nanotubes (MWCNTs). The PS addition in the range from 0 to 4 wt% showed improved quality and CNT homogeneity; Raman “Graphitic/Defective” (G/D) values increased from 1.9 to 2.3; mean CNT diameters increased from 43.0 to 49.2 nm; and maximum CNT yield increased from 11.37% to 14.31%. Since both the CNT diameters and the percentage yield increased following the addition of polystyrene, we conclude that carbon from PS contributes to the carbon within the MWCNTs. The electrical contact resistance of acid-washed Bucky papers produced from each loading ranged from 2.2 to 4.4 Ohm, with no direct correlation to PS loading. Due to this narrow range, materials with different loadings were mixed to create the six wires of an Ethernet cable and tested using iPerf3; the cable achieved up- and down- link speeds of ~99.5 Mbps, i.e., comparable to Cu wire with the same dimensions (~99.5 Mbps). The lifecycle assessment (LCA) of CNT wire production was compared to copper wire production for a use case in a Boeing 747-400 over the lifespan of the aircraft. Due to their lightweight nature, the CNT wires decreased the CO2 footprint by 21 kTonnes (kTe) over the aircraft’s lifespan.

Forecasting Quality of Service for Next-Generation Data-Driven WiFi6 Campus Networks

Forecasting the users’ movements and behaviors is extremely valuable for early warning systems to provide high-quality service in wireless and cellular networks. However, forecasting the service of the specific network devices with the additional knowledge of user behaviors is underexplored. This study proposes a WiFi6-specific QoS forecasting engine, which uses a spatio-temporal graph approach to predict QoS parameters, e.g., throughput, in terms of user position in WiFi6 networks. Since WiFi6 networks are planning to meet various traffic types with dense users, it is crucial to analyze it in both spatial and temporal manner by preserving graph-structured data. In this study, we modeled the problem with a novel deep learning approach, Graph Convolution Networks (GCNs), by adapting the Omni-Scale 1D CNN for temporal analysis. Then, we analyze the forecasting performance with two datasets in terms of variety error metrics over loss rate, link speed, throughput, and round trip time (RTT). Also, we give the baselines, ARIMA, FARIMA, SVR, and RNN to compare the proposed solution in terms of accuracy. Finally, we present the simulation results to compare the proposed QoS forecasting approach with user mobility forecasting. All experiments show that proposed WiFi6-specific QoS forecasting gives superior results for multi-horizon QoS prediction with respect to user positions considering heterogeneous traffic types.

Using machine learning to find the hidden relationship between RTT and TCP throughput in WiFi

Is it possible to find hidden relationships among variables in WiFi network using machine learning (ML)? Can we use ML to find a variable that significantly affects the TCP throughput in WiFi? In this work, we employ a publicly available WiFi dataset to investigate these questions. We use ML techniques, including principal component analysis (PCA), linear regression (LR), and random forest (RF), to study the effect of link speed, received signal strength, round-trip time (RTT), and number of available access points on TCP throughput in WiFi. More specifically, we are interested in employing ML to find the variable that most accurately predicts and thereby most significantly affects the throughput. Simple correlation analysis indicates that a combination of multiple variables is more likely to act as a reasonable predictor of the throughput, whereas a single variable, such as RTT, alone is not likely to predict the throughput with reasonable accuracy. From PCA, the first principal component (PC1) is seen as highly correlated to RTT. During predictive analysis, it is observed that the LR model is unable to find any hidden relationship between throughput and other variables. However, the RF model discovers that RTT explains the variation in throughput more closely and as such it predicts the throughput more accurately compared to other variables. PC1 captures nearly all of the variation in throughput with the RF model and predicts throughput with very high accuracy, which indirectly confirms RTT as the variable that most significantly affects the TCP throughput in WiFi. Consequently, we discover a very close relationship between RTT and TCP throughput using appropriate ML techniques, and these results can be helpful in developing a better understanding of the relationship between latency and throughput for designing future low-latency networks.

Server-Side Segment Selection for Low-Latency Streaming—S4S

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Over-the-top (OTT) streaming growth is driven by the increasing number of wireless connected devices such as tablets and phones. An enabler for using these devices is adaptive bitrate (ABR) streaming that allows devices to select at each moment the stream quality that best fits the available bandwidth. The overall experience is highly dependent on the bandwidth estimation. The current approach to bandwidth estimation in HTTP-based ABR players is challenged by the evolution toward low-latency protocols (DASH CTE—Dynamic Adaptive Streaming over HTTP—Chunked Transfer Encoding) or (HLS LL—HTTP Live Streaming—Low-Latency), which result in micro-burst of traffic. Indeed, estimation at the client side, at the HTTP level, relies on the assumption that a relatively large segment of data is available and can be sent at the link speed, which is not the case anymore with low-latency protocols. To address this issue, we propose a novel scheme Server-Side Segment Selection for streaming (S4S), which allows players to interact with an S4S-enabled server to improve the overall experience. First, bandwidth estimation is done at the server side, using transport’s congestion control sender-side stats, leading to more precise estimates even in the presence of small burst, especially when using modern congestion control algorithms such as Bottleneck Bandwidth and Round-trip propagation time (BBR) or Performance-orientated Congestion Control (PCC). Second, we define a protocol for allowing the network to control the bandwidth versus quality tradeoff</i> .

Coordination of Traffic Signals on Arterial Streets in Duhok City

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Duhok City lies in the north of Iraq and it has mountainous terrain. The poor road infrastructure of the city and also the daily increasing number of vehicles had left no choice other than applying traffic control systems to solve congestion problems in the city streets. Traffic control signal coordination which is one of efficient method of traffic control system that can improve the traffic operation and safety for series of signalized intersections along the arterials in the city. In this study the benefit of actuating traffic signal coordination was evaluated, and comparison in control delay against the same signalized intersection in case of being isolated was made. To achieve this four successive signalized intersections on Barzani street and three successive signalized intersections on Zakho street which are two major arterials in Duhok City were chosen. Traffic data (traffic volumes, vehicle link and approach speeds, and passenger car equivalent) during peak hours were collected. Also, geometric data and signalization data were collected using different methods, such as video recording technique, moving vehicle method and manual methods. To check the coordination attainability, the coupling index and key intersection was determined, then time-space diagrams were constructed representing one-way coordination for the intersections on Barzani and Zakho Streets, and others represented two-way coordination for the intersections on Zakho Street with acceptable progression bandwidth efficiency. The results of this study showed that a progression bandwidth of 54 seconds for east direction coordination and 17 seconds for west direction coordination on Barzani Street under suggested controlled speed of 60 kph pleasant with the present data. For Zakho Street, the progression bandwidth is 19 seconds for east direction coordination and 18 seconds for west direction coordination under suggested controlled speed of 40 kph. The results show that traffic signal coordination had led to high reduction in intersection control delays. On Barzani arterial street the reduction was between 16.2% and 51.8%, while for Zakho arterial street the reduction ranges between 16.93% and 26.11%.

Evaluation of urban bus service reliability on variable time horizons using a hybrid deep learning method

Unreliable transit services can negatively impact transit ridership and discourage passengers from regularly choosing public transport. As the most important content of bus service reliability, accurate bus arrival prediction can improve travel efficiency for enabling a reliable and convenient transportation system. Accordingly, this paper proposes a novel deep learning method, i.e. variational mode decomposition long short-term memory (VMD-LSTM), for bus travel speed prediction in urban traffic networks using a forecast of bus arrival information on variable time horizons. The method uses the temporal and spatial patterns of the average bus speed series. The results show that the VMD-LSTM model outperforms other models on forecasting bus link speed series in future time intervals, whereas the artificial neural network model achieves the worst prediction. In conclusion, the VMD-LSTM method can detect irregular peaks of transit samples from a series of temporal or spatial variations and performs better on major and auxiliary corridors.

Recurrent Neural Network for Estimating Speed Using Probe Vehicle Data in an Urban Area

Urban traffic networks comprise a combination of various links. These networks are complicated as they have numerous intersections, meaning that using an analytical approach or parametric models to estimate driving speeds on arterial or highway roads results in low accuracy. In this study, a model is developed to estimate the link speed using speed data collected by a probe vehicle driven across different urban traffic links, which have interrupted flows. We discover multimodal distributions of travel speeds in each link’s probe vehicle data and use them to separate the vehicle groups and calculate the mean speed of links. This strategy makes it possible to obtain more detailed data, which are used to determine the traffic state and increase the accuracy of the model. The developed nonlinear model, suitable for low correlations of consecutive links’ speed data, is built on a recurrent neural network. Moreover, this study merges three machine-learning techniques to apply low correlations between link properties and speed states. The model developed lowered the mean absolute error by 35.9% on average when compared with the long short-term memory with raw data: 46.8% for the slow state, 55.7% for the state change, and 48.0% for the sudden change over 10 km/h.