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نتیجه جستجو - Traffic flow

تعداد مقالات یافته شده: 59
ردیف عنوان نوع
1 The “Cyber Security via Determinism” Paradigm for a Quantum Safe Zero Trust Deterministic Internet of Things (IoT)
پارادایم «امنیت سایبری از طریق جبرگرایی» برای اینترنت اشیا قطعی (IoT) ایمن صفر کوانتومی-2022
The next-generation Internet of Things (IoT) will control the critical infrastructure of the 21st century, including the Smart Power Grid and Smart Cities. It will also support Deterministic Communications, where ‘deterministic traffic flows’ (D-flows) receive strict Quality-of-Service (QoS) guarantees. A ‘Cybersecurity via Determinism’ paradigm for the next-generation ‘Industrial and Tactile Deterministic IoT’ is presented. A forwarding sub-layer of simple and secure ‘deterministic packet switches’ (D-switches) is introduced into layer-3. This sub-layer supports many deterministic Software Defined Wide Area Networks (SD-WANs), along with 3 new tools for improving cyber security: Access Control, Rate Control, and Isolation Control. A Software Defined Networking (SDN) control-plane configures each D-switch (ie FPGA) with multiple deterministic schedules to support D-flows. The SDN control-plane can embed millions of isolated Deterministic Virtual Private Networks (DVPNs) into layer 3. This paradigm offers several benefits: 1) All congestion, interference, and Distributed Denial-of-Service (DDOS) attacks are removed; 2) Buffer sizes in D-switches are reduced by 1000C times; 3) End-to-end IoT delays can be reduced to ultra-low latencies, i.e., the speed-of-light in fiber; 4) The D-switches do not require Gigabytes of memory to store large IP routing tables; 5) Hardware support is provided in layer 3 for the US NIST Zero Trust Architecture; 6) Packets within a DVPN can be entirely encrypted using Quantum Safe encryption, which is impervious to attacks by Quantum Computers using existing quantum algorithms; 7) The probability of an undetected cyberattack targeting a DVPN can be made arbitrarily small by using long Quantum Safe encryption keys; and 8) Savings can reach $10s of Billions per year, through reduced capital, energy and operational costs.
INDEX TERMS: Cyber security | deterministic, the Internet of Things (IoT) | quantum computing, zero trust | encryption | privacy | Software Defined Networking (SDN) | industrial internet of things (IIoT) | tactile Internet of Things | FPGA | Industry 4.0 | deterministic Internet of Things.
مقاله انگلیسی
2 Wave breaking in a class of non-local conservation laws
شکستن موج در یک کلاس از قوانین حفاظت غیر محلی-2020
For models describing water waves, Constantin and Escher [3]’s works have long been considered as the cornerstone method for proving wave breaking phenomena. Their rigorous analytic proof shows that if the lowest slope of flows can be controlled by its highest slope initially, then the wave-breaking occur for the Whitham-type equation. Since this breakthrough, there have been numerous refined wave-breaking results established by generalizing the kernel which describes the dispersion relation of water waves. In this work, from a rich class of non-local conservation laws, a Riccati-type system that governs the flow’s gradient is extracted and investigated. The system’s leading coefficient functions are allowed to change their values and signs over time as opposed to the ones in many of other previous works are fixed constants. The blow-up analysis is illustrated via the Whitham-type equation with nonlinear drift.
Keywords:Nonlocal conservation law | Wave-breaking | Blow-up | Critical threshold | Traffic flow | Whitham equation
مقاله انگلیسی
3 Dynamic luminance tuning method for tunnel lighting based on data mining of real-time traffic flow
روش تنظیم پویا درخشندگی برای روشنایی تونل بر اساس داده کاوی جریان ترافیک در زمان واقعی-2020
Tunnel lighting constitutes one of the major expenses incurred in transportation lighting, and hence substantial research has been conducted to improve the efficiency of lighting and thus to minimize operating costs. This paper investigates an intelligent method for adjusting tunnel lighting with dynamic control based on data mining of traffic flow distribution, traffic composition, and vehicle speed distribution. Field monitoring data of traffic flow in five real expressway tunnels, which are in HeDa expressway, Jilin Province, China, was used in the analysis. The K-MEANS clustering algorithm was used to group (or cluster) the distribution of daily traffic volume into six-time periods, in which the traffic volume includes two peak periods (8:01–11:23 and 14:31–19:01). A dynamic luminance regulation method is proposed that distinguishes operational strategies under different time periods. Furthermore, the impact of tunnel length and traffic flow on the effect of energysaving and system sustainability of the proposed method was assessed. The results show that when using the proposed method, the energy-savings in tunnel lighting could be between about 50% and 60% for a daily traffic volume between 750 and 2500 vehicles. The results also show that the switching frequency of the lighting system is significantly reduced, which would significantly enhance the sustainability of the lighting system.
Keywords: Data mining | Energy management | Intelligent control | Tunnel lighting
مقاله انگلیسی
4 A flow-based approach for Trickbot banking trojan detection
یک رویکرد مبتنی بر جریان برای شناسایی تروجان بانکی Trickbot-2019
Nowadays, online banking is an attractive way of carrying out financial operations such as ecommerce, e-banking, and e-payments without much effort or the need of any physi- cal presence. This increasing popularity in online banking services and payment systems has created motivation for financial attackers to steal customer‘s credentials and money. Banking trojans have been a way of committing attacks on these financial institutions for more than a decade, and they have become one of the primary drivers of botnet traffic. How- ever, the stealthy nature of financial botnets requires new techniques and novel systems for detection and analysis in order to prevent losses and to ultimately take the botnets down. TrickBot, which specifically threatens businesses in the financial sector and their customers, has been behind man-in-the-browser attacks since 2016. Its main goal is to steal online banking information from victims when they visit their banking websites. In this study, we utilize machine learning techniques to detect TrickBot malware infections and to identify TrickBot related traffic flows without having to analyze network packet payloads, the IP addresses, port numbers and protocol information. Since command and control server IPs are updated almost daily, identification of TrickBot related traffic flows without looking at specific IP addresses is significant. We adopt behavior-based classification that uses artifacts created by the malware during the dynamic analysis of TrickBot malware samples. We compare the performance results of four different state-of-the-art machine learning algorithms, Random Forest, Sequential Minimal Optimization, Multilayer Perceptron, and Logistic Model to identify TrickBot related flows and detect a TrickBot infection. Then, we optimize the proposed classifier via exploring the best hyperparameter and feature set selection. Looking at network packet identifiers such as packet length, packet and flag counts, and inter-arrival times, the Random Forest classifier identifies TrickBot related flows with 99.9534% accuracy, 91.7% true positive rate.
Keywords:Trickbot | Banking trojan | Machine learning | Anomaly traffic detection | Dynamic analysis | Random Fores
مقاله انگلیسی
5 Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
مدل های یادگیری عمیق برای پیش بینی جریان ترافیک در وسایل نقلیه خودمختار: بررسی ، راه حل ها و چالش ها-2019
In the last few years, there has been an exponential increase in the usage of the autonomous vehicles across the globe. It is due to an exponential increase in the popularity and usage of the artificial intelligence techniques in various applications. Traffic flow predication is important for autonomous vehicles using which they decide their itinerary and take adaptive decisions (for example, turn let or right, move straight, lane change, stop, or accelerate) with respect to their surrounding objects. From the existing literature, it has been observed that research on autonomous vehicles has shifted from the traditional statistical models to adaptive machine learning techniques. However, existing machine learning models may not be directly applicable in this environment due to non-linear complex relationship between spatial and temporal data collected from the surroundings during the aforementioned adaptive decisions taken by the vehicles. So, with focus on these issues, in this article, we explore various deep learning models for traffic flow prediction in autonomous vehicles and compared these models with respect to their applicability in modern smart transportation systems. Various parameters are chosen to have a relative comparison among different deep learning models. Moreover, challenges and future research directions are also discussed in the article.
Keywords: Cognitive Internet of Things | Traffic flow prediction | Machine learning | Deep learning | Autonomous vehicles
مقاله انگلیسی
6 Quality of service in delay tolerant networks: A survey
کیفیت خدمات در شبکه های مقاوم تاخیری: یک بررسی-2018
Delay tolerant networks (DTNs) are characterized by the absence of the end-to-end path due to intermittent connectivity among the nodes. Such networks are potentially applicable in the challenging scenarios, e.g. interplanetary communication, post-disaster environment, where traditional communication infrastructure is partially or fully absent. Each application requires some quality of service (QoS) guarantees for the traffic flow. QoS support cannot be provided to a network without QoS provisioning. However, QoS provisioning in a DTN is more difficult task than traditional networks, because of its inherent characteristics. There exist various issues which affect QoS in DTNs. In this paper, we explore the issues that influence QoS in DTNs. Subsequently, we analyze the effects of the issues on the QoS in terms of delivery ratio, packet drop etc. We also review various QoS management solutions in DTNs. The schemes on the QoS issues are classified based on their underlying approaches and key features. The paper is concluded with a brief discussion on some of the open research issues regarding QoS in DTNs.
keywords: Delay tolerant networks| Quality of service| QoS issues| QoS management solutions
مقاله انگلیسی
7 Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective
تصمیم گیری بهینه برای پردازش داده های بزرگ در محیط لبه-ابر: چشم انداز SDN-2018
With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services such as e-commerce, e-health, e-banking, etc., to the end users. These services are hosted on massive geodistributed data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multiedge-cloud environment involves huge data migrations across the underlying network infrastructure, which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedgecloud environment using software-defined networks (SDN). To handle the inter-DC migrations efficiently, an SDN-based control scheme is presented, which provides energy-aware network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide costeffective inter-DC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.
Index Terms: Cloud data centers, edge computing, energy efficiency, software-defined networks (SDNs), Stackel berg game
مقاله انگلیسی
8 Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities: Part 3
مسائل و چالش های جدید در داده های بزرگ و اجرای آن به سوی شهرهای هوشمند آینده: قسمت 3-2018
The articles in this special section examine emerging trends in Big Data for future smart cities. One of the greatest advantages in smart city is big-data- based decision making, which needs to capture, store, process, and analyze a large amount of data generated by several sources to transform the data into useful knowledge for making proper decisions. For example, with the help of big data and its Implementation, citizens could rapidly find available parking slots in large urban areas; big data can contribute in the city’s efforts to reduce pollution through the deployment of street sensors. These sensors can measure traffic flows at different times as well as total emissions. The government can implement actions to divert traffic to less congested areas in a move to reduce carbon emissions in a particular area.
Keywords: Special issues and sections, Smart cities, Big Data, Wireless sensor networks, Cloud computing, Privacy
مقاله انگلیسی
9 Big Data Analytics in Intelligent Transportation Systems: A Survey
تحلیل داده های بزرگ در سیستم های حمل و نقل هوشمند: نظرسنجی-2018
Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
Index Terms: Big data analytics, intelligent transportation systems (ITS), machine learning, transportation
مقاله انگلیسی
10 Characteristics of Traffic Flow Management in Multimodal Transport Hub (by the Example of the Seaport)
خصوصیات مدیریت جریان ترافیکی در مرکز حمل و نقل چندمنظوره (با مثال از بندر)-2017
In order to ensure efficient functioning of multimodal transport hub it is necessary to provide proper traffic management both inside and outside of it. This article presents assessment of freight traffic flows management impact on the state of traffic flows in adjoining street-road network. This is one of actual tasks aiming to ensure traffic safety in proximity to large terminal complexes. The following results were obtained in the course of work: – limiting values of street-road network stable functioning parameters for infinite horizon were defined; – periods of hub steady functioning in condition of negative stability were defined with the use of traffic micro simulation programs – a non-conventional approach in solving the problems of assessment of functioning of terminal complexes and adjoining street road networks. – conditions of implementation of adaptive simulation model enabling execution of transport flows within the terminal were defined.
Keywords: Simulation | stability | functioning | street-road network | traffic flow | transport hub | service point
مقاله انگلیسی
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