Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions

Chiroma, Haruna; Abdulhamid, Shafií Muhammad; Hashem, Mohamed; Adewole, Kayode S.; Ezugwu, Absalom E.; Abubakar, Sule; Shuib, Liyana

doi:10.1155/2021/9022558

Cited by 12 publications

(21 citation statements)

References 83 publications

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“…While the study provided useful insights into IoV data management, it lacked a comprehensive taxonomy and a more structured presentation. Chiroma et al (2021) focused on the incorporation of DL and big data analytics within the IoV. They presented a taxonomy of various DL approaches used to process and analyze the massive volumes of data produced by linked vehicles.…”

Section: Relevant Workmentioning

confidence: 99%

“…However, the absence of a taxonomy in their work raises considerations regarding the organization and clarity of their insights. Chiroma et al (2021) navigated the integration of deep learning (DL) and big data analytics in IoV, providing a taxonomy of DL approaches while acknowledging challenges. While their work is insightful, scrutiny reveals areas for potential refinement.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this systematic literature review (SLR) aims to examine the research field of big data in IoV comprehensively. By analyzing scientific articles written in recent years, the proposed SLR aims to provide a comprehension of the key developments, challenges and future research directions of big data in IoV (Chiroma et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

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Big data challenges and opportunities in Internet of Vehicles: a systematic review

Hemmati,

Zarei,

Rahmani

2024

IJPCC

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Purpose Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research. Design/methodology/approach This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis. Findings This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues. Research limitations/implications This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted. Originality/value This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

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Section: Relevant Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big data challenges and opportunities in Internet of Vehicles: a systematic review

Hemmati,

Zarei,

Rahmani

2024

IJPCC

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“…Performance parameters like latency, packet loss, and throughput were considered as indicators in the experiment. In [32], the authors have addressed management of big data.…”

Section: Related Workmentioning

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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“…The local storage of the vehicle is used to store such input data and analyze results of the environment. Such information about the location, speed, traffic, road condition, local weather, and other required data is shared between the network participants [3].…”

Section: Introductionmentioning

confidence: 99%

Active Authentication Protocol for IoV Environment with Distributed Servers

Manikandan¹,

Rahaman²,

Song³

2022

Computers, Materials &Amp; Continua

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The Internet of Vehicles (IoV) has evolved as an advancement over the conventional Vehicular Ad-hoc Networks (VANETs) in pursuing a more optimal intelligent transportation system that can provide various intelligent solutions and enable a variety of applications for vehicular traffic. Massive volumes of data are produced and communicated wirelessly among the different relayed entities in these vehicular networks, which might entice adversaries and endanger the system with a wide range of security attacks. To ensure the security of such a sensitive network, we proposed a distributed authentication mechanism for IoV based on blockchain technology as a distributed ledger with an ouroboros algorithm. Using timestamp and challenge-response mechanisms, the proposed authentication model can withstand several security attacks such as Man-in-Middle (MiM) attacks, Distributed Denial of Service (DDoS) attacks, server spoofing attacks and more. The proposed method also provides a solution for single-point failure, forward secrecy, revocability, etc. We exhibit the security of our proposed model by using formal (mathematical) analysis and informal analysis. We used Random Oracle Model to perform the mathematical analysis. In addition, we compared the communication cost, computation cost, and security of the proposed model with the related existing studies. We have verified the security of the model by using AVISPA tool simulation. The security analysis and computation analysis show that the proposed protocol is viable.

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Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions

Cited by 12 publications

References 83 publications

Big data challenges and opportunities in Internet of Vehicles: a systematic review

Big data challenges and opportunities in Internet of Vehicles: a systematic review

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

Active Authentication Protocol for IoV Environment with Distributed Servers

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