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Resource-efficient Transmission of Vehicular Sensor Data Using Context-aware Communication [article]

Benjamin Sliwa and Thomas Liebig and Robert Falkenberg and Johannes Pillmann and Christian Wietfeld
2018 arXiv   pre-print
vehicular scenarios based on measurement data obtained from field experiments.  ...  Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes.  ...  ACKNOWLEDGMENT Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained  ... 
arXiv:1804.05533v1 fatcat:zkj3c5xikbhb5klighkzskvepu

Role of Machine Learning in WSN and VANETs

Maryam Gillani, Hafiz Adnan Niaz, Muhammad Tayyab
2021 International Journal of Electrical and Computer Engineering Research  
For such dynamicity, Machine learning (ML) approaches are considered favourable.  ...  In this paper, a quick summary of primary ML concepts are discussed along with several algorithms based on ML for WSN and VANETs.  ...  Machine learning is effective in enhancing the flow in predicting traffic performance and achieve a real-time response.  ... 
doi:10.53375/ijecer.2021.24 fatcat:72mtbdb3uvaepj6efg7lfz6x6q

Artificial Intelligence for Vehicle-to-Everything: A Survey

Wang Tong, Azhar Hussain, Wang Xi Bo, Sabita Maharjan
2019 IEEE Access  
INDEX TERMS Artificial intelligence, machine learning, VANETs, V2X, predictions, platoon, VEC.  ...  Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions  ...  It has been used for two correlated tasks: 1) prediction of LTE communication connectivity, and 2) the prediction of vehicular traffic.  ... 
doi:10.1109/access.2019.2891073 fatcat:kpojxobitvd7fdinkvqnchvtdm

Machine Learning for Vehicular Networks [article]

Hao Ye, Le Liang, Geoffrey Ye Li, JoonBeom Kim, Lu Lu, May Wu
2018 arXiv   pre-print
In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area.  ...  After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks.  ...  In [6] , a probabilistic graphical model, Poisson regression trees (PRT), has been used for two correlated tasks, the LTE communication connectivities prediction and the vehicular traffic prediction.  ... 
arXiv:1712.07143v2 fatcat:wf5eupa4o5bwlekunltohu2hli

Effective Prediction of V2I Link Lifetime and Vehicle's Next Cell for Software Defined Vehicular Networks: A Machine Learning Approach

Soufian Toufga, Slim Abdellatif, Philippe Owezarski, Thierry Villemur, Doria Relizani
2019 2019 IEEE Vehicular Networking Conference (VNC)  
In this paper, we propose a machine learning based approach for Software Defined Vehicular Networks that allows a cell to estimate the attachment duration of each newly associated vehicle at the association  ...  Our proposed models have been evaluated on a large dataset, which we have generated based on a real mobility trace from the city of Luxembourg, and the evaluation shows promising results in terms of prediction  ...  Our proposed next cell prediction scheme is also based on a supervised machine learning scheme.  ... 
doi:10.1109/vnc48660.2019.9062803 dblp:conf/vnc/ToufgaAOVR19 fatcat:sfjtmxovy5ferfku5ggxff672e

Distributed Vehicular Computing at the Dawn of 5G: a Survey [article]

Ahmad Alhilal, Tristan Braud, Pan Hui
2020 arXiv   pre-print
technologies and their integration on top of the vehicular computing architectures.  ...  After reviewing the main vehicular applications requirements and challenges, we follow a bottom-up approach, starting with the promising technologies for vehicular communications, all the way up to Artificial  ...  Deep-learning is another approach to predict traffic flow. The autoEncoder model is used as a supportive technique to predict the complex linear traffic flow.  ... 
arXiv:2001.07077v1 fatcat:32pl575ekbg65edqrxluoynbk4

A Survey on Resource Allocation in Vehicular Networks [article]

Md. Noor-A-Rahim, Zilong Liu, Haeyoung Lee, G. G. Md. Nawaz Ali, Dirk Pesch, Pei Xiao
2020 arXiv   pre-print
Dedicated Short Range Communications (DSRC) and cellular based vehicular networks.  ...  , traffic efficiency, comfort driving, infotainment, etc.  ...  MACHINE LEARNING BASED RA FOR VEHICULAR COMMUNICATIONS In vehicular networks, whilst vehicles are expected to employ various facilities such as advanced on-board sensors including radar and cameras and  ... 
arXiv:1909.13587v2 fatcat:qs5bis3usnfo5mrsyek2bgim3u

Vehicular Network-Aware Route Selection Considering Communication Requirements of Users for ITS

Michal Vondra, Zdenek Becvar, Pavel Mach
2018 IEEE Systems Journal  
While the maximum tolerated traveling time is defined by the vehicular users, an estimation of available throughput is based on a vehicular movement prediction.  ...  Increasing demands of mobile users on communication and new types of devices, such as sensors, machines, and vehicles, impose high load on cellular networks.  ...  The estimation is based on the movement prediction of all vehicular users and on an estimation of the requirements of vehicular users on the network throughput.  ... 
doi:10.1109/jsyst.2016.2623762 fatcat:fpqg73lcufg4db7luwvmc436mi

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Benjamin Sliwa, Robert Falkenberg, Thomas Liebig, Nico Piatkowski, Christian Wietfeld
2019 IEEE transactions on intelligent transportation systems (Print)  
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast.  ...  In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission.  ...  As a consequence, this paper applies a novel machine learning-based approach for power estimation [16] , which is based on the available LTE downlink indicators.  ... 
doi:10.1109/tits.2019.2930109 fatcat:xeyx7ixhofazbnxipyp4p6ezgy

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Ida Nurcahyani, Jeong Woo Lee
2021 Sensors  
This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism.  ...  Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms.  ...  DL or DNN is a subdomain of machine learning which can recognize hidden patterns in the dataset and predict an output Y based on a given input X.  ... 
doi:10.3390/s21196542 pmid:34640858 fatcat:6kzl53zterdcpexpvkinkvqqwe

Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks [article]

Benjamin Sliwa, Christian Wietfeld
2019 arXiv   pre-print
on multiple machine learning models and data aggregation strategies.  ...  In this paper, we present the results of a comprehensive real-world measurement campaign in public cellular networks in different scenarios and analyze the performance of online data rate prediction based  ...  ACKNOWLEDGMENT Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained  ... 
arXiv:1904.10177v2 fatcat:6vrjofk6lfarjlrgdoicb2wady

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic [article]

Benjamin Sliwa and Thomas Liebig and Tim Vranken and Michael Schreckenberg and Christian Wietfeld
2019 arXiv   pre-print
Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing  ...  In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science  ...  ACKNOWLEDGMENT Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained  ... 
arXiv:1901.03025v1 fatcat:ipmeamwcu5edtesnahqa4hdmoe

Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning

Robert Falkenberg, Karsten Heimann, Christian Wietfeld
2017 GLOBECOM 2017 - 2017 IEEE Global Communications Conference  
Although the device can identify its current radio conditions based on the received signal strength and quality, it has no information about the cell's traffic load caused by other users.  ...  Compared to an earlier work, our approach reduces the average prediction error below one third. Applied in public networks, the predicted data rate differs by less than 1.5 Mbit/s in 93% of cases.  ...  Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning Robert Falkenberg, Karsten Heimann and Christian Wietfeld  ... 
doi:10.1109/glocom.2017.8254567 dblp:conf/globecom/FalkenbergHW17 fatcat:c6n7ctnkffboxfsknalf4nbyym

Data-driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems

Benjamin Sliwa, Christian Wietfeld
2019 IEEE Access  
learning approach.  ...  carried out based on network simulations.  ...  ACKNOWLEDGMENT Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained  ... 
doi:10.1109/access.2019.2956211 fatcat:3wxmwvmxhbauzbrovplxgxdxuq

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Le Liang, Hao Ye, Geoffrey Ye Li
2019 IEEE Internet of Things Journal  
After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance  ...  As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and  ...  In this section, we discuss how to exploit machine learning to efficiently learn and robustly predict such dynamics based on data from a variety of sources.  ... 
doi:10.1109/jiot.2018.2872122 fatcat:n25uma5isfduvk3hh5mvnai4fy
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