An End-to-End Load Balancer Based on Deep Learning for Vehicular Network Traffic Control

Li, Jinglin; Luo, Guiyang; Cheng, Nan; Yuan, Quan; Wu, Zhi Fen; Gao, Shangce; Liu, Zhihan

doi:10.1109/jiot.2018.2866435

Cited by 71 publications

(22 citation statements)

References 34 publications

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“…However, this has not been fully investigated. Among the few related contributions, Li et al [5] introduced a convolutional neural network (CNN) to predict the road traffic situation and then a proactive load balancing approach was proposed enabling cooperation among mobile edge servers. Liu et al [6] investigated the feature of a Rayleigh fading channel and proposed to train a long short-term memory (LSTM) model to predict the future channel parameters.…”

Section: Motivated By the Above Issues This Letter Studies On The VImentioning

confidence: 99%

“…Therefore, to identify the pedestrian with maximum inference accuracy within a certain delay, the vehicle needs to perform pre-braking with the probability η m and offload their DLTs with the optimal offloading probability ̺ * m derived in (11). Calculate optimal offloading probability (̺ * m ); 5 Calculate the optimal inference error rate threshold (ǫ th * m ); 6 Evaluate the inference error rate, i.e., ǫ L m = g(Q, D V m ); 7 if ǫ L m ≥ ǫ th * m then 8 Offload the DLT to the MES; Observation 4. There exists a trade-off between the inference error rate and inference delay, as indicated by (6) and (7).…”

Section: Optimized Offloading Framework Designmentioning

confidence: 99%

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Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework

Yang

Xiao

et al. 2020

IEEE Wireless Commun. Lett.

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This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the delay and the inference accuracy are incurred by wireless communications and computing, an optimization problem is formulated to maximize the inference accuracy subject to the offloading probability, the pre-braking probability, and data quality. Simulations demonstrate the superiority of the proposed offloading framework.

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Section: Motivated By the Above Issues This Letter Studies On The VImentioning

confidence: 99%

Section: Optimized Offloading Framework Designmentioning

confidence: 99%

Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework

Yang

Xiao

et al. 2020

IEEE Wireless Commun. Lett.

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“…Lv et al proposed a modified cyber-physical system for automated electric vehicles based on unsupervised machine learning algorithms [35]. Li et al proposed a proactively load balancing approach for vehicular network traffic control based on V2I communication, convolutional neural networks and deep learning to enable efficient cooperation among mobile edge servers [36]. (iii) Big data technologies.…”

Section: Data Analysis For Automaticmentioning

confidence: 99%

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Gui

2020

Journal of Advanced Transportation

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Aggravating energy shortages and increasing labor costs have become global problems and have garnered special importance in recent years in the transportation sector, especially in taxi markets. Automatic vehicles have a bright future, however, there is an equal amount of skepticism and concern about safety for all the optimism. To unlock the potential of automatic vehicles in intelligent transportation systems, a lot more data and testing are required to promote safety level as far as possible and achieve the organizational aim of reducing accidents to zero tolerance. And it is vital to provide accurate models for vehicle movement analyses. In this study, Modified Digital Elevation (MDE) model and Spherical Bilinear Interpolation (SBI) model were proposed for vehicle movement analyses considering altitude. And the experimental data of 9,990 GPS-enabled taxis in Sanya, Zhengzhou, and Liaoyang were adopted to support comparisons. Measurement results showed that MDE model had over 99% less disparity with direct solution than original model and SBI model could further improve the effects. It indicated that the application of MDE model and SBI model could improve both accuracy and efficiency of vehicle movement analyses and it had a bright future in the field of automatic vehicles. Future directions could be improving models and expanding data.

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“…Furthermore, crop growth models can be also built by these techniques [1][2][3][4][5][6][7][8]. For instance, supervised learning techniques (e.g., neural network (NN) [9][10][11][12][13], convolutional neural network (CNN) [14][15][16][17][18], recurrent neural network (RNN) [19][20][21][22][23], and ensemble neural networks (ENN) [24][25][26][27][28]) can be used to forecast weather information and crop growth to improve crop quantities and reduce disaster damage. Furthermore, unsupervised learning techniques (e.g., auto-encoder (AE) [29][30][31][32][33], de-noise auto-encoder (DAE) [34], restricted Boltzmann machine (RBM) [35,36], deep belief network (DBN) [37,38], and deep Boltzmann machine (DBM) [39,40]) can be used to represent data and reduce dimensions for regulation and overfitting prevention.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques for Agronomy Applications

2019

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This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.

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An End-to-End Load Balancer Based on Deep Learning for Vehicular Network Traffic Control

Cited by 71 publications

References 34 publications

Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework

Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Deep Learning Techniques for Agronomy Applications

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