Internet of Vehicles: Proposed Architecture, Network Models, Open Issues and Challenges

Singh, Alankar; Gaba, Lakshay; Sharma, Atul

doi:10.1109/aicai.2019.8701312

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Other works in the literature tried to provide an overview of the current research state, challenges, and potentials of VANETs, as well as the way forward to achieving the long, awaited ITS such as the work of [2], [3], and [8]. On the other hand, some researchers studied the feasibility of using alternative technologies for vehicular network models [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Vehicle Ad hoc Networks Under Wi-Fi-6 Technology

Hasan

Mahmood

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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Vehicle ad hoc networks are considered mobile networks where the nodes are mobile objects and can change their positions within an environment over time. These objects can be connected at any time according to a predefined strategy. Simulating this kind of network needs high attention to many details. Moreover, the literature lacks works that describe the requirements of simulating such networks. Therefore, this work tries to describe the requirements of simulating vehicle networks (VANETs). Moreover, the goal is to determine what is needed to simulate vehicle networks in terms of the distribution of vehicles, the movement patterns, and the routing protocols used. The simulation results show interesting facts about the VANET networks and the best strategies to minimize the consumption of network resources. Finally, this work considers two communication technologies among network nodes; Wi-Fi 5 and Wi-Fi 6.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Vehicle Ad hoc Networks Under Wi-Fi-6 Technology

Hasan

Mahmood

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…Kaiwartya et al [8] provided us with an IoV framework that focused on networking architecture mainly. Singh et al [9] presented an abstract network model for the IoV and related with different services relied on popular technologies. Ji et al [10] designed a novel network structure for the future IoV and gave a comprehensive review of the basic IoV information, which included several network architectures and representative applications of IoV.…”

Section: Related Workmentioning

confidence: 99%

Reliable Traffic Monitoring Mechanisms Based on Blockchain in Vehicular Networks

Guo,

Ding,

2020

Preprint

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The real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous incidents. In this paper, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget, but also maintains the truthfulness of auction process that avoids some vehicles to offer unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.

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“…Nevertheless, there are still several challenges that are mainly related to the availability of vehicles embedding the sensors due to the high vehicles mobility, frequent topology change, and driving behavior volatility [4]. Furthermore, vehicles in the IoV networks are characterized by their intermittent and short-lived availability in the target geographic zone of the requested service, which leads to an unsustainable service provision [5]. In fact, attaining perfect knowledge of the vehicular network for making definitive decisions on vehicle availability is practically unachievable.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Intelligent Sensor Virtualization and Provisioning in Internet of Vehicles (IoV)

Abbes,

Rekhis

2024

IEEE Access

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The Internet of Vehicles (IoV) is a powerful application of the Internet of Things (IoT) in the Intelligent Transportation System (ITS). It enables device connectivity, and interaction with the environment, and improves efficiency in utilizing sensor data. Leveraging the capabilities of vehicle sensors to provide virtual sensor service presents an opportunity to make the most of underutilized sensor resources and offer on-demand sensor services. However, challenges remain in ensuring spatiotemporal sensor availability due to the high mobility of vehicles and frequent changes in the IoV network topology. Furthermore, providing sensor services in the dynamic IoV-Cloud market, while balancing costeffectiveness for consumers and profitability for service providers, poses a significant challenge. To address these challenges, we develop an IoV-Cloud architecture tailored for vehicle virtual sensor provisioning, integrating interactive functional layers and components for a comprehensive solution. This architecture supports vehicle sensor virtualization and management, facilitating the intelligent provisioning of ondemand, elastic, and scalable vehicle virtual sensors from existing physical sensors within mobile vehicles. To address vehicle mobility and enhance sensor availability, our design permits virtual sensors to maintain data provision even when switched to different physical sensors. Further optimizing sensor utilization, our system allows multiple virtual vehicle sensors to share a single physical sensor by employing a configuration mapping mechanism. At the heart of our strategy is a reinforcement learning model that dynamically selects the most suitable vehicle physical sensor for each time slot throughout the service period, considering both physical availability and cost-effectiveness for the Sensor Cloud Service Provider (SCSP). We simulate the proposed intelligent sensor selection model using both Q-learning and SARSA algorithms, demonstrating its effectiveness in intelligent and dynamic sensor provisioning.

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Internet of Vehicles: Proposed Architecture, Network Models, Open Issues and Challenges

Cited by 12 publications

References 9 publications

Performance Evaluation of Vehicle Ad hoc Networks Under Wi-Fi-6 Technology

Performance Evaluation of Vehicle Ad hoc Networks Under Wi-Fi-6 Technology

Reliable Traffic Monitoring Mechanisms Based on Blockchain in Vehicular Networks

Reinforcement Learning for Intelligent Sensor Virtualization and Provisioning in Internet of Vehicles (IoV)

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