Optimal management of parking lots as a big data for electric vehicles using internet of things and Long–Short term Memory

Ding, Xuefeng; Gan, Qihong; Shaker, Mir Pasha

doi:10.1016/j.energy.2023.126613

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…The analysis of ML and AI category articles shows that these research efforts often seek to provide innovative solutions to deal with various challenges. Using MATLAB (Salim et al, 2023;Chen et al, 2022), SUMO (Nallaperuma et al, 2019), IEEE 33-bus (Ding et al, 2023) and Python (Ullah et al, 2022) simulations, the studies evaluate strong points and validate major contributions including optimized communication efficiency (Salim et al, 2023), reduced overhead (Salim et al, 2023), efficient model training (Akbar et al, 2023), privacy protection (Zhou et al, 2021) and real-time decision-making (Nallaperuma et al, 2019). However, common weaknesses include algorithm complexity (Salim et al, 2023), absence of real-world adaptation (Ding et al, 2023), data privacy concerns related to SDN infrastructure (Akbar et al, 2023), model drift (Zhou et al, 2021) and deployment challenges Big data challenges and opportunities (Ning et al, 2021b).…”

Section: Methodsmentioning

confidence: 99%

“…The proposed architecture aimed to enhance the security of IoV environments by accurately and efficiently detecting intrusions and cyber-attacks using a combination of DL techniques. Ding et al (2023) focused on the impact of electric vehicles on the smart grid. They considered the growing influence of electric vehicles in the transportation industry and their need for efficient battery charging.…”

Section: Methodsmentioning

confidence: 99%

“…Ding et al (2023) focused on the impact of electric vehicles on the smart grid. They considered the growing influence of electric vehicles in the transportation industry and their need for efficient battery charging.…”

Section: Methodsmentioning

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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