Internet of Vehicles (IoV): A Survey of Challenges and Solutions

Fadhil, Jawaher Abdulwahab; Sarhan, Qusay Idrees

doi:10.1109/acit50332.2020.9300095

Cited by 31 publications

(15 citation statements)

References 78 publications

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“…The proposed architecture was organized into seven layers concerning big data and multimedia data. Fadhil et al 9 investigated the previous studies by focusing on existing challenges in utilizing the IoV in urban cities. The authors introduced significant technical limitations of the IoV and presented the corresponding future solutions.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Connected vehicles in IoV are intelligent vehicles with a complicated software platform. Promoting vehicles from high dynamic nodes to intelligent vehicles with diverse communication capabilities convinced the leading companies to produce autonomous, intelligent, fully electric, or fuel‐efficient hybrid vehicles customized for the IoV ecosystem 9 …”

Section: Introductionmentioning

confidence: 99%

“…capabilities convinced the leading companies to produce autonomous, intelligent, fully electric, or fuel-efficient hybrid vehicles customized for the IoV ecosystem. 9 Vehicular network challenges include large-scale data management, unidirectional and multidimensional IVCs, road safety stability, real-time traffic monitoring, energy-consuming management, delay-sensitive applications, network heterogeneity, etc. 10,11 With the development of intelligent vehicles and the integration of sensors, real-time data management has become one of the essential concerns of the IoV system.…”

mentioning

confidence: 99%

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Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

Partovi

Zarei

Rahmani

2022

Int J Communication

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The Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real-time data-centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data-centric approaches. This paper systematically categorizes the 48 recent articles on data-driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data-centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data-driven approaches in IoV. K E Y W O R D S data trading system, data traffic management, data transmission, Internet of Vehicles, IoV | INTRODUCTIONVehicular ad hoc networks (VANETs) as highly dynamic but well-organized emerging networks play an effective role in accelerating the improvement of the intelligent transportation systems (ITS) application. 1 Today ITS applications in many nations are expanding, whereby the rapid growth of networked sensors and context-aware computing systems, along with emerging communication technologies, have made the recognition of smart cities possible shortly. 2 The Internet of Things (IoT) technology has faced an increase in the number of dynamic objects, including connected vehicles, which formed the Internet of Vehicles (IoV), and made our lives easier, safer, and smarter. [3][4][5] VANETs, as the central infrastructure of the IoV ecosystem, aim to help drivers with possible inter-vehicle communication (IVC) and realtime data propagation even in high mobility, helping safety driving and receiving alerts for road hazardous. 6,7 Plug-in electric vehicles (PEVs) and unmanned aerial vehicles (UAVs), as an emerging part of energy-aware VANETs, have a significant impact on the formation of the IoV. 8 Connected vehicles in IoV are intelligent vehicles with a complicated software platform. Promoting vehicles from high dynamic nodes to intelligent vehicles with diverse communication

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

Partovi

Zarei

Rahmani

2022

Int J Communication

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“…The concept of connected vehicles is known as vehicular communications or V2X. V2X includes vehicle-to-infrastructure/network (V2I/N) and vehicle-to-vehicle (V2V) [1,2].…”

Section: Introductionmentioning

confidence: 99%

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Hemavathi¹,

Akhila²,

Alotaibi

et al. 2022

Energies

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One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

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“…Even though the IoV will open many possibilities for a plethora of applications, there are still many challenges that are yet to be addressed [ 7 , 10 , 27 , 28 ]. Of the utmost importance is, for example, the management of the abundant information and resources available in these networks.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Approach for Cloud-Fog-Edge Computing SDN-VANETs Based on Fuzzy Logic: Effect of Different Parameters on Coordination and Management of Resources

Qafzezi

Bylykbashi

Ampririt

et al. 2022

Sensors

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The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources.

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Internet of Vehicles (IoV): A Survey of Challenges and Solutions

Cited by 31 publications

References 78 publications

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

An Intelligent Approach for Cloud-Fog-Edge Computing SDN-VANETs Based on Fuzzy Logic: Effect of Different Parameters on Coordination and Management of Resources

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