Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.

Graph Neural Network for Traffic Forecasting: A Survey.

Named data networking : a survey

We consider the problem of eff- ciently reconciling two similar sets held by different hosts while minimizing the communication complex- ity. This type of problem arises naturally from gossip protocols used for the distribution of information, but has other applications as well. We describe an ap- proach to such reconciliation based on the encoding of sets as polynomials. The resulting protocols ex- hibit tractable computational complexity and nearly optimal communication complexity. Moreover, these protocols can be adapted to work over a broadcast channel, allowing many clients to reconcile with one host based on a single broadcast.

Set Reconciliation with N early Optimal Communication Complexity

This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated.

Energy-Efficient Resource Allocation for 6G Backscatter-Enabled NOMA IoV Networks

The conceptualisation of the sixth generation of mobile wireless networks (6G) has already started with some potential disruptive technologies resonating as enablers for driving the emergence of a number of innovative applications. Particularly, 6G will be a prominent supporter for the evolution towards a truly Intelligent Transportation System and the realization of the Smart City concept by fulfilling the limitations of 5G, once vehicular networks are becoming highly dynamic and complex with stringent requirements on ultra-low latency, high reliability, and massive connections. More importantly, providing security and privacy to such critical systems should be a top priority as vulnerabilities can be catastrophic, thus there are huge concerns regarding data collected from sensors, people and their habits. In this paper, we provide a timely deliberation of the role that promissory 6G enabling technologies such as artificial intelligence, network softwarisation, network slicing, blockchain, edge computing, intelligent reflecting surfaces, backscatter communications, terahertz links, visible light communications, physical layer authentication, and cell-free massive multiple-input multiple-output (MIMO) will play on providing the expected level of security and privacy for the Internet of Vehicles.

/pdf/towards-6g-enabled-internet-of-vehicles-security-and-privacy-1iq7cbvb.pdf

Towards 6G-Enabled Internet of Vehicles: Security and Privacy

The rapid growth of the telecommunication landscape leads to a rapid rise of frauds in such networks. In this article, Wangiri fraud in which users are deceived by being charged for services without their knowledge during a call is tackled. In fact, Wangiri fraud has significant negative financial and reputation consequences for the mobile service providers and also has a bad psychological impact on the victims. In order to identify this fraudulent behavior, three Wangiri fraud patterns are defined by analyzing call records of over a year. Then, the security and performance of unsupervised and supervised machine learning (ML) methods in detecting one Wangiri pattern are evaluated using a large real-world Call Detail Records (CDRs) data set. In the context of Wangiri fraud detection, classification algorithms outperformed the others based on the chosen security and performance metrics. Finally, the performance evaluation of these algorithms is extended in detecting the other two real-world Wangiri fraud patterns. This article provides a detailed definition of the Wangiri fraud patterns and outlines the implementation and evaluation of ML algorithms in the context of detecting Wangiri fraud. The security analysis and experimental results demonstrate that depending on fraud patterns the best ML algorithm to detect Wangiri fraud may also vary.

Wangiri Fraud: Pattern Analysis and Machine-Learning-Based Detection

NFA: A neural factorization autoencoder based online telephony fraud detection

Plenty of technologies have been deployed to evolve and manage Transport Systems. But recently academia and industry efforts are concentrated on road safety via Intelligent Transportation Systems (ITS). Undeniably, as vehicular computing systems become interconnected, there will be a good target for malicious attacks by exploiting system vulnerabilities. In this context, we define the InterCor project. The purpose of this project is to improve and to enhance transportation safety and efficiency specially in case of interoperability between several C-ITS systems. We consider in this project that security and privacy are the central pieces of ITS system requirements. In fact, it is a versatile project that trying to resolve multiple challenges in term of security interoperability without divulging vehicle privacy. In this work we describe interoperability trust model used in InterCor project and tested security scenarios. We provide also some results of these tests.

Security Interoperability for Cooperative ITS: Architecture and Validation

The exponential growth of the Internet and the diversity of services delivered to the end users, such as Cloud computing, have catalyzed researches on new generation networks. Hence, several proposals have been made to change the network applications and Internet media; in particular network virtualization is in the center of research interest. It is widely considered given its ability of dynamic programming, energy saving and low cost. This promising paradigm boosts different researches in literature. Openflow, AKARI and 4 ward projects are concrete examples. They follow common approaches of virtualization but with different characteristics. In this paper, we present an analysis and comparison between these projects. We mainly focus on Openflow solution to validate on demand virtual network architecture based on the Mininet simulator. This work is part of the On-Demand research project sponsored by European Regional Development Fund.

Openflow and on demand networks

Access Control over large scale distributed system like Cloud computing are one of the most debated topics of computer security. Despite the common use and the popularity of the Cloud computing paradigm, significant risks and challenges are inherent to this new concept, especially when we talk about storage of sensitive data via insecure network. In this paper we look at the problem of protecting data from unauthorized access to the Cloud in the context of gSafe (government Safe) project. Indeed, gSafe project defines essential basic units for a probative storage Cloud. The cornerstone of the efficient cloud security architecture is a well-written access control policy. In today's information technology, many models of access control have been proposed like the Mandatory Access Control (MAC), Discretionary Access Control (DAC), Role-Based Access Control (RBAC) and the latest one Usage Control Authorization, oBligation and Condition (UCON ABC ). In this paper we use six entities proposed in the UCON ABC : Object, Subject, Right, Authorization, oBligation and Condition to model the access control management in the gSafe project. Then we present the XML scheme containing metadata for stored files and users' access authorizations. The proposed solution is validated and implemented over Hadoop distributed file system.

Access control in probative value Cloud

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mounira Msahli

Author Tools

Chat about Author