A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

Panigrahy, Saroj Kumar; Emany, Harika

doi:10.3390/s23010555

Cited by 32 publications

(10 citation statements)

References 145 publications

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“…This paper aims to present a comprehensive framework for integrating ITS with smart city infrastructure, addressing these research gaps and offering novel insights. By examining specific approaches and strategies for traffic management, public transportation systems, and pedestrian and cyclist safety, this framework strives to provide a holistic integration solution (Paiva et al, 2021;Panigrahy and Emany, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

A framework for integrating intelligent transportation systems with smart city infrastructure

Alanazi,

Alenezi

2024

J. Infras. Policy. Dev.

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As cities continue to face the increasing demands of urban transportation and the need for sustainable mobility solutions, the integration of intelligent transportation systems (ITS) with smart city infrastructure emerges as a promising approach. This paper presents a novel framework for integrating ITS with smart city infrastructure, aiming to address the challenges of urban transportation and promote sustainable mobility. The framework is developed through a comprehensive literature review, case studies, and stakeholder interviews, providing significant insights into the integration process. Our research outlines the key components of smart city infrastructure that can be integrated with ITS, highlights the benefits of integration, and identifies the challenges and barriers that need to be addressed. Additionally, we propose and apply evaluation methods to assess the effectiveness of ITS integration with smart city infrastructure. The results demonstrate the novelty and significance of this framework, as it significantly reduces traffic congestion, improves air quality, and enhances citizen satisfaction. This paper contributes to the existing literature by providing a comprehensive approach to integrating ITS with smart city infrastructure, offering a transformative solution for urban transportation challenges.

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Section: Literature Reviewmentioning

confidence: 99%

A framework for integrating intelligent transportation systems with smart city infrastructure

Alanazi,

Alenezi

2024

J. Infras. Policy. Dev.

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“…The intelligent networked vehicle system is an innovative vehicle system that combines advanced computer technology and vehicle communication technology [1][2][3][4][5]. Its design goal is to provide everyone with a safer, more efficient and intelligent travel experience through real-time communication with surrounding vehicles, infrastructure and cloud servers.…”

Section: Introductionmentioning

confidence: 99%

A refinement and abstraction method of the SPZN formal model for intelligent networked vehicles systems

2024

KSII TIIS

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Security and reliability are the utmost importance facts in intelligent networked vehicles. Stochastic Petri Net and Z (SPZN) as an excellent formal verification tool for modeling concurrent systems, can effectively handles concurrent operations within a system, establishes relationships among components, and conducts verification and reasoning to ensure the system's safety and reliability in practical applications. However, the application of a system with numerous nodes to Petri Net often leads to the issue of state explosion. To tackle these challenges, a refinement and abstraction method based on SPZN is proposed in this paper. This approach can not only refine and abstract the Stochastic Petri Net but also establish a corresponding relationship with the Z language. In determining the implementation rate of transitions in Stochastic Petri Net, we employ the interval average and weighted average method, which significantly reduces the time and space complexity compared to alternative techniques and is suitable for expert systems at various levels. This reduction facilitates subsequent comprehensive system analysis and module analysis. Furthermore, by analyzing the properties of Markov Chain isomorphism in the case study, recommendations for minimizing system risks in the application of intelligent parking within the intelligent networked vehicle system can be put forward.

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“…Vehicular Ad hoc Networks (VANETs), evolved from Mobile Ad hoc Networks (MANETS), play a significant role in mitigating problems associated with road traffic in urban areas. The integration of big data, cloud/edge computing and the IoT is motivating the progression of VANETs into the conception of the Internet of Vehicles (IoV) [ 9 , 10 ]. Intelligent vehicles linked together exploit vehicle-to-everything (V2X) communications to ensure road safety and efficient traffic flows, thus assisting cutting-edge transport and road mobility [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Siddiqui

Mahmood

Sheng

et al. 2023

Sensors

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The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

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A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

Cited by 32 publications

References 145 publications

A framework for integrating intelligent transportation systems with smart city infrastructure

A framework for integrating intelligent transportation systems with smart city infrastructure

A refinement and abstraction method of the SPZN formal model for intelligent networked vehicles systems

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

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