Traffic Management Services Research Articles

Cooperative approach for data-centric and node-centric misbehavior detection in VANET

Vehicular Ad Hoc Network (VANET) has risen as a paramount technology for efficiently providing traffic management, safety and infotainment services to road users. Vehicles are allowed to use pseudo identities during vehicular network access to preserve their privacy. This property makes VANET vulnerable to Sybil attack, performed by exploiting the set of pseudo identities to send messages. Detecting a Sybil attack solely by verifying the accuracy of messages received is challenging, as the messages sent through Sybil identities can appear plausible. Current data-centric and certain machine learning-based approaches only identify Sybil attacks within a local context. It is necessary to find the connection between the Sybil nodes both locally and at the Road Side Unit (RSU) level to effectively mitigate this attack. Hence, we introduce a novel cooperative and hybrid misbehavior detection framework for Sybil attack detection in VANET. It does not only detect Sybil identities but also establishes connections between them by analyzing their speed time series with the Dynamic Time Warping (DTW) technique. Furthermore, it confirms the association between Sybil nodes through node-centric detection using Dempster Shafer Theory (DST) at RSU. This advanced detection can help the Linkage Authority (LA) to find and revoke the actual node responsible for carrying out Sybil attack globally. This is the first framework in its category which can provide accurate detection at both local and RSU level in different scenarios. We acquired a higher detection rate by assessing performance with an existing dataset and a generated real-time Sybil attack dataset.

RE-Trace : Re-identification of Modified GPS Trajectories

GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace —a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Characterizing Performance Tradeoffs from Air Traffic Management Services for Advanced Air Mobility

The emergence of Advanced Air Mobility (AAM) has called for novel Air Traffic Management (ATM) solutions to enable the projected high number of operations, particularly in already dense and complex metroplex airspace. Understanding the multidimensional performance impacts of developing air traffic rules, procedures, and services is key to guiding this new traffic management system design. This paper presents a simulation framework to provide a quantitative assessment of the operational performance of ATM services in support of AAM. The framework embeds several modules for high-fidelity modeling of urban air traffic flows based on actual data, ATM service provision, stochastic simulation, and performance quantification. Two key services are analyzed: airspace geofencing and strategic deconfliction. We evaluate the use of these services in a scenario of air mobility operations associated with daily urban commuting in the Sao Paulo metropolitan region. Monte Carlo experiments are performed to simulate the execution of aircraft trajectories managed under different ATM service configurations, allowing for the characterization of operational performance tradeoffs, particularly in terms of safety and efficiency. The work contributes to quantifying the value of novel ATM services for integrating AAM operations in dense metroplex airspace, supporting their design and implementation.

Market Structures for Service Providers in Advanced Air Mobility

Proposed concepts of operations for advanced air mobility rely on private service providers being responsible for providing air traffic management services to uncrewed aircraft such as drones and autonomous air taxis. While such proposals are unprecedented in the aviation context, one can draw parallels to the Internet and the role played by Internet service providers in managing web traffic. A study of the evolution of the Internet illustrates that, without clear rules for cooperation around a nascent market, private profit motives incentivize against service provider cooperation, especially for traffic flows that traverse multiple regions managed by different service providers. To address this problem, we propose a profit-sharing mechanism based on the Shapley value that incentivizes service providers to cooperate. We show that this mechanism i) ensures that service providers route flights along globally optimal routes, and ii) encourages service providers to work together in providing more efficient routes. We study the allocation of sectors to service providers and show that different allocations can cause large differences in profit earned. Finally, we discuss some of the remaining challenges with having a federated network of private service providers supporting traffic management for advanced air mobility operations.

Preliminary investigation and proposal of periodic orbits and their utilization for logistics in the cislunar regime

The world is in the midst of what is widely considered the second Space Age due to the vastly growing commercial space sector and progress with international initiatives such as the Artemis program, led by the National Aeronautics and Space Administration (NASA). Cislunar space is becoming a region of growing interest as nations, international organizations, and private corporations look to expand their presence and outreach. The planned development of space infrastructure, such as lunar habitats, mining operations, and other installations, is driving the need for studies regarding common orbital pathways to ensure logistical support for future cislunar missions. Along with common orbital pathways, comprehensive and internationally acknowledged space policy and doctrine is required to ensure the safety, stability, and peace of this new frontier of human endeavor. This study serves to address one of the mission sets that will come to shape the emerging economy of the wider Earth-Moon system, space logistics. Specifically, this study will discuss potential orbits for a cislunar space situational awareness network that would support a space traffic management service, along with three cislunar logistics missions: personnel and cargo transport, space tourism, and search and rescue. Characteristics considered for orbits include orbit stability, period, and the proximity to key nodes in cislunar space such as the Moon and Lagrange points. Using Circular Restricted Three-Body Problem (CR3BP) dynamics, fourteen sample cislunar periodic orbits are proposed to accomplish the cislunar missions discussed. This study adds to the limited work in the field of cislunar orbital design to generate novel orbits and apply them to logistical use cases. All missions, except tourism, feature a “touring” class of cislunar periodic orbits due to their unique geometries enabling them to traverse a wide range of cislunar space. Preliminary analysis indicates that all of the proposed orbits are classified as marginally unstable, and suggests that minimal propellant is required to maintain a space vehicle's intended cislunar trajectory. Finally, existing law, policy, and governance surrounding cislunar space logistics and operations is discussed specifically concerning space traffic management, transportation, and humans touring or being rescued from space. Special attention is given to both the ambiguity of the law and the limitations of current norms of behavior, patent and property concerns, and tense geopolitical situations that can further complicate peace. Overall, this study highlights the underdevelopment of current space law and policy with the ambiguity of present-day norms discussed.

GeoPM-DMEIRL: A deep inverse reinforcement learning security trajectory generation framework with serverless computing

Vehicle trajectory data is essential for traffic management and location-based services. However, the release of trajectories raises serious privacy concerns because they contain sensitive information, such as home addresses and workplaces, etc, making it indispensable to consider privacy protection when releasing trajectory data. It is urgent to reconcile data utility with privacy in trajectory generation calls for advanced methods that adhere to real-world traffic rules. Leveraging serverless computing for deep learning provides an efficient solution, bypassing infrastructure complexities to handle the scale trajectory data. In this paper, a Geo-Piecewise Mechanism Deep Maximum Entropy Inverse Reinforcement Learning (GeoPM-DMEIRL) is proposed for secure trajectory generation, which consists of three key components, namely GeoPM, A2C reinforcement learning and DMEIRL. Firstly, GeoPM is a novel local differential privacy mechanism that incorporates Geo-aware gridding techniques. The Piecewise Mechanism is used to perturb the user trajectory while ensuring that the perturbed trajectory conforms to real-world traffic rules. Secondly, an A2C reinforcement learning network is refined to train the optimal trajectory generation strategy. Thirdly, Deep Maximum Entropy Inverse Reinforcement Learning is improved to train the weights of the A2C reward function. Finally, real-world data are collected in the experiments and the serverless computing platform AWS Lambda is used for training the reinforcement learning models. Experimental results show that our proposed GeoPM-DMEIRL framework can effectively resist user re-identification attacks, which can improve the utility by an average of 54.605% and enhance the privacy by an average of 30.678%. Meanwhile, GeoPM-DMEIRL is able to maintain the utility of the data while protecting the privacy of the user’s trajectories.

PUTS: Privacy-Preserving and Utility-Enhancing Framework for Trajectory Synthesization

Vehicle trajectory data is essential for traffic management and location-based services. However, publishing real-life trajectory data has been challenging because vehicle trajectories contain users' sensitive information. Differential privacy addresses such problems by publishing a synthetic version of the input dataset, but existing works always assume the real-world data is absolutely accurate. This assumption no longer holds in trajectory data because it typically contains errors due to inaccurate positioning services, which leads to poor performance of data synthesized by such trajectories. Even worse, existing works may generate unrealistic trajectories due to their coarse data synthesis methods, resulting in low practical utility or even inability to handle complex tasks. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy-preserving and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</u> tility-enhancing framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> rajectory <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ynthesization ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> ). Our framework mitigates the impact of data errors in trajectories on differential privacy mechanisms, by exploiting map-matching techniques and real-world road network structure. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> , a two-layer approach from path to trajectory synthesis is proposed to not only guarantee the reality of synthetic trajectories, but also scale up <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> in real-world applications. Extensive experiments on real-world datasets show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PUTS</i> significantly outperforms existing methods in terms of utility in a range of real-world applications.

A Study on Enhancing Channel Estimation in Vehicular Communications: Metamaterial-Enhanced Microstrip Antennas

Vehicular communication systems play a pivotal role in modern transportation, offering solutions for traffic management, road safety, and infotainment services. Effective optimization of channel estimation in these systems is imperative to overcome inherent challenges. This paper has explored the issues that hinder reliable and efficient channel estimation in vehicular communications. High mobility and dynamic environments, multipath fading, heterogeneous networks, scalability, and security and privacy concerns were identified as key challenges. Addressing these challenges is crucial for ensuring the robustness and effectiveness of vehicular communication systems. By advancing channel estimation techniques, we can enhance data throughput, reduce latency, and improve overall network performance, ultimately contributing to safer and more efficient transportation systems.

Synthesizing Realistic Trajectory Data With Differential Privacy

Vehicle trajectory data is critical for traffic management and location-based services. However, the released trajectories raise serious privacy concerns because they contain sensitive information such as homes and workplaces. Based on differential privacy, this problem can be addressed by generating synthetic trajectories from the original sensitive data while guaranteeing personal privacy. Unfortunately, existing methods focus on synthesizing trajectory datasets that preserve summary-level statistics (e.g., the overall distribution of user movements), making these synthetic trajectories lose individual-level mobility patterns. As shown in our experiment, this results in the low performance of their synthetic datasets in real-world applications. To address these limitations, we propose a novel solution for Synthesizing Private and Realistic Trajectories, namely SPRT, whose key idea is to integrate the public geography structures of the target area into the process of private trajectory synthesis. This enables us to capture more accurate mobility patterns to synthesize realistic trajectories, which can preserve both summary-level statistics and individual-level mobility behaviors. Consequently, the synthetic trajectories generated by SPRT are more similar to real trajectories and therefore more practical. We evaluate the performance of SPRT in real-world applications by applying its synthetic data to a series of trajectory analytic tasks. The results demonstrate that our solution improves data utility by at least 37% over state-of-the-art approaches.

РЕАЛІЗАЦІЯ ПРОЄКТІВ МІСЬКОЇ МОБІЛЬНОСТІ «РОЗУМНИХ» МІСТ: ПІДХОДИ ДО ОБГРУНТУВАННЯ ТА ОЦІНЮВАННЯ

The article discusses the importance of implementing smart urban mobility projects in the context of smart city strategies. The object of the study is the global experience of implementing smart urban mobility programs. The subject of the study is the theoretical and practical aspects of justifying and defining indicators for the implementation of urban mobility projects. Research methods: generalization – defining the tasks and indicators of urban mobility projects in terms of stakeholders and urban mobility solutions for Ukrainian cities; analysis and synthesis – studying the world experience of implementing urban mobility programs and developing proposals based on it; system analysis – justifying the place and importance of urban mobility development in smart city strategies. The article proposes the principle of stakeholders in defining the objectives of urban mobility projects and indicators to assess the success of their implementation. The stakeholder principle takes into account the fact that the problems to be solved can be very different for each of the stakeholders. The main stakeholders are the city's residents, the city's authorities, and the business community involved in transportation and transportation infrastructure. A system of indicators for the implementation of smart urban mobility is proposed. The author summarizes the global experience of implementing urban mobility projects and individual start-ups, in particular in cities such as Venice, Barcelona, Poznan, Lublin, Hamburg and Prague. The author proposes project solutions for smart urban mobility in Ukrainian cities based on the generalization of results from the implementation of smart mobility programs and projects in European cities. These solutions include mobility as a service, intelligent urban traffic management, cloud solutions for smart parking, urban micromobility management, and digital passenger information systems. The proposed solutions aim to create shared mobility services, a unified payment system for public transportation, intelligent traffic management services, optimized use of parking spaces, electronic notification of parking availability, a system of bicycles and electric scooters, and a system of digital signage and mobile applications for passengers.

Extended data plane architecture for in-network security services in software-defined networks

Software-Defined Networking (SDN)-based Network Function Virtualization (NFV) technologies improve the dependability and resilience of networks by enabling administrators to spawn and scale-up traffic management and security services in response to dynamic network conditions. However, in practice, they often suffer from poor performance and require complex configurations because network packets must be ‘detoured’ to each virtualized security service, which expends bandwidth and increases network propagation delay. To address these challenges, we propose a new SDN-based data plane architecture, called DPX (Data Plane eXtension), that natively supports in-network security services. The DPX action model reduces redundant processing caused by frequent packet parsing and provides administrators with a simplified (and less error-prone) method for configuring security services into the network. DPX also increases the efficiency of enforcing complex security policies by introducing a novel technique called action clustering, which aggregates security actions from multiple flows into a small number of synthetic rules. Also, the application of action clustering (i.e., advanced and global) provides more diverse policies and network-wide detection. We present an implementation of DPX in hardware using NetFPGA-SUME and in software using Open vSwitch. We evaluate the performance of the DPX prototype and the efficacy of its flow-table simplifications against a range of complex network policies exposed to line rates of 10 Gbps.

A data-driven learning method for online prediction of drone battery discharge

This paper describes an adaptive method to predict the battery discharge of a multirotor drone over a generic path. A proper assessment of battery state of discharge trend is critical to ensure a safe operation of battery-powered aerial vehicles in critical environments, such as the urban ones. Several standard paths were executed by a commercial, battery-powered drone to acquire the data needed to train a Deep Learning based method; telemetry files and ground-collected data were processed to train the proposed method according to a trajectory segmentation strategy. Two learning configurations were trained to predict the time-of-flight and the integral of the battery current needed to fly the standard path segments. The current integral for each standard path segment is exploited to estimate the corresponding reduction of the battery state of charge. Based on path segmentation into predefined standard sections, the presented solution allows to predict time of flight and battery consumption along any generic path. This can be exploited to plan a complete path at strategic stage, as well as to estimate the remaining available power resource at any intermediate point along a generic trajectory both at strategic stage and during mission execution to be exploited by the drone operator or by a traffic management service. To validate the technique, a scaled package delivery mission is presented as an example of contingency management application. The maximum distance that the drone can reach from selected points along the mission path was computed according to the remaining battery level. Considering a random distribution of safe-landing areas around the planned path, the computed maximum distance was used to determine which safe-landing areas can be reached by the drone from a generic point of the path after an unexpected event.

Satellite maneuver detection and estimation with optical survey observations

Maneuver detection and estimation is deemed crucial for maintaining catalogs of Resident Space Objects (RSOs) as it helps to avoid sets of duplicated objects and track correlation issues. In fact, maneuvers, along with launches and break-up events, are the main source of potential new object detections during RSOs cataloging activities. For the continuous and reliable provision of Space Situational Awareness (SSA) and Space Traffic Management (STM) services, a challenging trade-off between detection time and characterization accuracy of maneuvers needs to be performed. In this paper, two novel and operationally feasible methodologies are proposed for maneuver detection and estimation. The first, a track-to-orbit methodology, uses a pre-maneuver orbit to linearize the dynamics and estimate the single burn that minimizes the residuals of the post-maneuver tracks. The second, an orbit-to-orbit methodology, estimates the double burn that solves a minimization problem between the pre-maneuver and post-maneuver orbits. Both methods, based on an optimal control approach, are not only proposed to tackle the maneuver estimation problem but also to be integrated on operational and robust association frameworks. Results are presented for optical scenarios with both simulated and real data, providing insightful conclusions on the capabilities, performance and limitations of the proposed methods. Particular emphasis is given to the importance of the track association, since a single track is usually not enough to perform a reliable estimation of the maneuver. Besides, the capability of the methods to provide a solution to the association problem, even when not perfectly characterizing the true maneuver, is discussed.

Analysis of the capabilities of MPLS technology for managing traffic in communication networks

To support a growing number of users and multiple classes of applications with different performance requirements and characteristics, service providers have been forced to adapt to new technologies. To improve traffic management and Internet service quality, the Internet Engineering Task Force (IETF) proposed MPLS technology to support several classes of latency-critical applications. Traditional IP networks use a hop-by-pop principle for transmitting traffic. This leads to aggregation of heterogeneous traffic on links in different parts of the network, which causes considerable possible growth of congestion and leaves the network with both unbalanced use of resources and link failure in congested parts. This raises the need for traffic engineering to ensure bandwidth guarantees and efficient use of network resources. To overcome these problems, the IETF has proposed a new data transmission mechanism, which is MPLS (Multi protocol label switching), in accordance with the current requirements. The application of MPLS (Multi protocol label switching) technology in modern communication networks is defined by the author as a research task. The report discusses the prospects of MPLS as a universal technology that supports several protocols. The features of construction of virtual private networks (VPN) on MPLS are considered, and how traffic engineering in MPLS takes into account the use of resources, which makes the development of routes based on separate streams or different streams between the same endpoints more effective.

Investigating the Suitability of Different Routing Strategies In Specific VANET Application Class

Vehicular Adhoc Network (VANET), is an emerging technology that holds the opportunity to create potential applications that directly impact peoples' lives, traffic management, and infotainment services. Understanding VANET applications and the available routing protocols can help to infer the most suitable protocols that satisfy VANET application requirements. This paper develops a systematic classification methodology to classify VANET applications from a routing perspective, each application class has different network requirements which are laid down by VANET Projects conducted in different countries. Some of these requirements are related to the routing aspects and need to be satisfied by the selected routing strategies (proactive and reactive). The paper identifies routing strategies performance metrics related to each application class requirement, to efficiently guide the development of these routing strategies towards guaranteeing satisfactory performance for the applications under a wide variety of realistic VANET scenarios. It is also worth mentioning that minimum delay is a requirement needed by time and event-driven application classes. However, high reliability is a requirement needed by on-demand applications. The paper aims to provide a comparative study on the performance of routing strategies in different VANET application classes, to identify which routing strategies have better performance in specific VANET applications class. End-to-end delay is employed as a performance metric to evaluate the short delay requirement, while, the Routing Overhead (RO) is used to assess the reliability requirement. Simulation results showed that proactive routing protocol has a lower delay, which means that it is suitable for delay-sensitive applications such as time-driven and event-driven applications. The result also showed that the reactive routing protocol outperforms the proactive routing protocol in terms of RO, which means that reactive routing protocols can be nominated as proper routing strategies to satisfy the reliability requirement of the On-demand driven applications.

Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.

Availability model for edge-fog-cloud continuum: an evaluation of an end-to-end infrastructure of intelligent traffic management service

Availability model for edge-fog-cloud continuum: an evaluation of an end-to-end infrastructure of intelligent traffic management service

A Survey on Operation Concept, Advancements, and Challenging Issues of Urban Air Traffic Management

The recent growth and adoption of unmanned aerial vehicles (UAVs) is due to their low development cost, high aerial mobility, advanced battery technology, rotors, gyroscopes, GPS, cameras, sensors, and wide range of applications. The UAV offers new possibilities for business in civil and noncivil applications such as parcel delivery, aerial mapping, agriculture, wildlife conservation, surveillance, and search and rescue. However, considering the exponential growth of various types of aerial vehicles, it is clear that advanced air traffic management services are urgently needed to control and handle the ever-increasing air traffics. In this paper, we first present the two major low-altitude UAV traffic management systems in the United States and Europe and compare them thoroughly. We present the global vision and advancement of UAV traffic management system. To understand the complexity of future air transportation system, we discuss the necessary elements required for an urban air traffic management system. We provide key strategic elements for an advanced urban traffic management through connectivity, open source, safety enhancement, and automation to maximize its impacts on the innovative future air traffic and mobility system. In the end, we discuss the challenging security issues in urban traffic management system.

Internet of Vehicles: Architecture, services, and applications

SummaryThe connection between objects and information exchange has been possible in recent years, with the advent of the Internet of Things (IoT) in different industries. We can meet different requirements in each industry utilizing this feature. Intelligent transportation uses the Internet of Vehicles (IoV) as a solution for communication among vehicles. It improves traffic management applications and services to guarantee safety on roads. We categorize services, applications, and architectures and propose a taxonomy for IoV. Then, we study open issues and challenges for future works. We highlighted applications and services due to drivers' requirements and nonfunctional requirements, considering the qualitative characteristic. This paper summarizes the current state of the IoV in architectures, services, and applications. It can be a start view to provide the solutions for challenges in traffic management in cities. The present study is beneficial for smart city developments and management. According to this paper's result, the services and applications evaluate performance with 34% frequency, safety and data accuracy, and security with a 13% frequency in selected papers. These measurements are essential due to the IoV characteristics such as real‐time operation, accident avoidance in applications, and complicated user data management.