Deep Collaborative Intelligence-Driven Traffic Forecasting in Green Internet of Vehicles

Guo, Zhiwei; Yu, Keping; Igorevich, Kostromitin Konstantin; Mumtaz, Shahid; Wei, Wei; Shi, Peng; Rodrigues, Joel J. P. C.

doi:10.1109/tgcn.2022.3193849

Cited by 45 publications

(21 citation statements)

References 35 publications

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“…The framework aimed to optimize the communication and trajectory of UAVs to enhance connectivity and data exchange among vehicles in IoV environments, leveraging the capabilities of UAVs as aerial base stations. Guo et al (2023) presented a deep collaborative intelligence-driven traffic forecasting approach for the green IoV. The method aimed to predict traffic patterns and congestion in IoV environments using collaborative intelligence techniques, combining DL and intelligent data analysis to support environmentally friendly transportation planning.…”

Section: Big Data Challenges and Opportunitiesmentioning

confidence: 99%

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: Big Data Challenges and Opportunitiesmentioning

confidence: 99%

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

Hemmati,

Zarei,

Rahmani

2024

IJPCC

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“…The goal is to achieve the interconnection of data and applications, and to further achieve ecological synergy of the whole value chain [16]. Generalized to tobacco industries, they need to switch into the mode of horizontal, vertical and end-to-end intelligent collaboration [17]. One is to realize the vertical information collaboration of management flow, production flow and logistics within the enterprise.…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Liu

Wang

et al. 2023

IEEE Access

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The whole process of tobacco production is composed of many components, in which their operation and administration are currently independent. It is required to deploy smart manufacturing workflow for the whole production process, in order to realize centralized effective global scheduling. This requires an advanced administration control platform that has strong abilities of multisource data integration and automatic decision support. To bridge such research gap, this paper designs an optimal scheduling control system for smart manufacturing processes of tobacco industry. First of all, this work discusses major characteristics of future-generation production control patterns in intelligent tobacco factories (ITF). Then, a five-layer architecture for optimal scheduling control of ITF is proposed, which contains Internetof-Things layer, centralized control layer, model layer, platform layer and operation layer. In addition, a production scheduling optimization strategy is also developed for the proposed system to serve as the software algorithm that drives the running of whole smart manufacturing processes. Finally, this paper presents a comparative analysis of the proposed system's transformation in a cigarette factory. Naturally, the effectiveness of the proposed production optimization scheduling strategy is verified through simulation.

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“…In classroom teaching, there is a need for classroom assessment [9]. In particular, the current development of terminals such as smart phones have significantly reduced the quality of many classroom teaching and interactions [10]. However, in the prior art, although there are enough solutions to analyze classroom assessment through classroom videos, there is no relevant novelty on how to use foreground and background pixel pairs and grayscale information to extract video foreground objects and further analyze classroom assessment [11].…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Educational Evaluation System Using Deep Learning

Yan

Lu²

2023

IEEE Access

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Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

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Deep Collaborative Intelligence-Driven Traffic Forecasting in Green Internet of Vehicles

Cited by 45 publications

References 35 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

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Design of an Intelligent Educational Evaluation System Using Deep Learning

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