Application of Graph Learning With Multivariate Relational Representation Matrix in Vehicular Social Networks

Wan, Liangtian; Li, Xiaona; Xu, Jinan; Sun, Lu; Wang, Xianpeng; Liu, Kaihui

doi:10.1109/tits.2022.3224326

Cited by 7 publications

(6 citation statements)

References 42 publications

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“…The similarity is a social feature that characterizes a group of devices which share the same common characteristics such as the interests, geographical locations, needs [47] and preferences [44]. The similarity metrics can be [48]: the community-of-interest similarity which is calculated based on the interest similarity.…”

Section: ) Similaritymentioning

confidence: 99%

Blockchain-Based Authorization Mechanism for Educational Social Internet of Things

Dallel,

Ayed,

Tahar

2024

IEEE Access

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The Social Internet of Things (SIoT) paradigm has been integrated in the education domain to enable educational IoT devices to establish social relationships and exchange academic services. Nonetheless, the social relationships are not adapted to the educational context where devices must be socially linked based on their academic roles and activities. Furthermore, the exchange of services raises the requirement to implement an access control mechanism. In SIoT, social constraints such as the social relationship type and contact frequency are critical requirements to make an access decision. However, these constraints cannot be specified using the eXtensible Access Control Markup Language (XACML) standard as device attributes nor as contextual conditions. In this paper, we propose an Educational Social Internet of Things (EducationalSIoT) platform implemented as an application-specific blockchain where we define new social relationships for educational devices. To control the access to the academic services, we suggest extending the XACML policy model by considering the social requirements, and accordingly, we adjust the policy evaluation process and suggest priority-based combining algorithms. Additionally, our platform ensures the delegation of access permission by defining delegation policies and controlling the delegation operation with consideration of the social features. The simulation results show that by integrating social features, an access request is evaluated in 0.22ms and a delegation request is evaluated in 0.32ms. Finally, we guarantee that our platform is protected against the man-in-the-middle and replay attacks.

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Section: ) Similaritymentioning

confidence: 99%

Blockchain-Based Authorization Mechanism for Educational Social Internet of Things

Dallel,

Ayed,

Tahar

2024

IEEE Access

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“…• LVW-OBPNN model: Each base classifier adopts the LVW algorithm for feature selection. The error rate of the base classifier serves as the sole evaluation criterion for the feature subset, which is equivalent to using the accuracy [38] of the base classifier as the evaluation criterion for the feature subset. Each base classifier selects a different feature subset.…”

Section: Contrast Modelmentioning

confidence: 99%

“…The Proposed LVW-MECO Algorithm 4.3.1. M-LVW Algorithm LVW algorithm [38] uses the error rate of classifiers as the evaluation criterion for feature subset selection. It is unable to adapt to different practical needs by setting different performance evaluation metrics for classifiers and optimizing the feature subset selection based on those metrics.…”

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confidence: 99%

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data

Wang,

Xiong,

Chen

et al. 2023

Sensors

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The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.

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“…The increasing number of wireless devices, together with the requirements for effectiveness and reliability of information transmission, have led to the development of various new communication technologies for addressing unprecedented challenges [1][2][3][4]. Among them, the multiple-input multiple-output (MIMO) technology with sensing capability has been a research hotspot [5].…”

Section: Introductionmentioning

confidence: 99%

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Zhang,

Shi,

Chen

et al. 2024

Remote Sensing

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Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).

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Application of Graph Learning With Multivariate Relational Representation Matrix in Vehicular Social Networks

Cited by 7 publications

References 42 publications

Blockchain-Based Authorization Mechanism for Educational Social Internet of Things

Blockchain-Based Authorization Mechanism for Educational Social Internet of Things

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

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