Deep Learning-aided Resource Orchestration for Vehicular Safety Communication

Khan, Mohammad Irfan; Aubet, François-Xavier; Pahl, Marc-Oliver; Härri, Jérôme

doi:10.1109/wd.2019.8734252

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…-Analyze the IEEE 802.11p MAC layer to gain insights into packet delivery ratio and backoff duration [13,33]. -Enhancing the protocol performance through improving random contention window selection [20,22], channel access mechanisms [38], throughput [25], collision probabilities [23], and congestion [16]. -Optimizing the channel access [27] [21].…”

Section: Related Researchmentioning

confidence: 99%

“…To evaluate their work, earlier efforts used simulators and numerical analysis. Simulations that were used include such ns-2 [7,25,35], ns-3 [1,10,20,21], iTETRIS [16], OPNET [23], OMNeT++ [27] along with SUMO, Veins and OpenStreetMap. Numerical analysis is basically performed based on MATLAB [13,38] and the probabilistic PRISM model [33].…”

Section: Related Researchmentioning

confidence: 99%

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F-802.11P: A Fuzzy Enhancement for IEEE 802.11P in Vehicle-to-Everything Communications

Sayedahmed¹,

M.²,

Hefny³

2022

IJCNC

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Vehicle-to-Everything communications (V2X) are becoming increasingly popular as a solution for safer roads and better traffic management. One of the essential protocols in V2X is the Dedicated Short Range Communication (DSRC) protocol suite. DSRC includes the IEEE 802.11p protocol that operates at the medium access control (MAC) and physical (PHY) layers. Upon collision, the IEEE 802.11p MAC layer applies a carrier sense multiple access/collision avoidance (CSMA/CA) mechanism that randomly selects a backoff time to re-check the channel activity and then retransmit. However, the random selection of the backoff time may lead to further packet collisions that decrease the utilization of the communication channel, which suffers from a limited bandwidth in the first place. This paper proposes a fuzzy model based on rational decision-making, which we call F-802.11p, to improve the IEEE 802.11p protocol backoff time selection by limiting the IEEE 802.11p beacon messages to better use of the available bandwidth. A simulation study presents the evaluation of our work compared to IEEE 802.11p. We deployed the simulation software in two scenarios: the Veins Framework map and the map of New Administrative Cairo in Egypt. We base our comparison on slots backoff, times into backoff, PHY busy time, MAC busy time, total lost packets, and generated/received beacon messages. Simulation results show that both protocols have comparable results in slots backoff, times into back off, and the generated beacon messages. At the same time, our F-802.11p significantly outperforms the IEEE 802.11p in PHY busy time, MAC busy time, total lost packets, and the received beacon messages in both scenarios.

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

F-802.11P: A Fuzzy Enhancement for IEEE 802.11P in Vehicle-to-Everything Communications

Sayedahmed¹,

M.²,

Hefny³

2022

IJCNC

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“…Though vehicular communications systems are designed to monitor the channel activity and adapt the transmission periods and power, collisions still happens. The prediction of the channel activity based on the transmissions pattern is proposed in [12] to address this problem. Alternatively, two methods for the estimation of packet errors in VANETs, based on the packet-level Maket and Gaussian mixture models, are presented in [13].…”

Section: A Hdpl Challengesmentioning

confidence: 99%

Packet Inter-Reception Time Conditional Density Estimation Based on Surrounding Traffic Distribution

Jornod

Assaad²,

Kürner

2020

IEEE Open J. Intell. Transp. Syst.

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Cooperation is an enabler for autonomous vehicles. A promising application of cooperative driving is high-density platooning, where trucks drive with low inter-vehicle distances. It aims at increasing the road and fuel efficiency whilst guaranteeing safety. The safe and efficient coordination of the control requires the regular and reliable exchange of V2V messages. The performance of the vehicular application has been shown to be strongly affected by the variation of the performances of the communications system. To be able to adapt their functional settings to these variations, vehicles need the ability to predict it. We present a prediction model for the packet inter-reception time platoon messages in an IEEE 802.11p network. This performance indicator is the subject of extensive research as it captures the irregularity of input for the control loop. The prediction model uses conditional density estimation based on the exponential distribution. We fit this model using a multi-layer perceptron regressor based on features representing the surrounding communication environment. The presented results are based on data collected during a full scale platooning simulations using ns-3 and SUMO. We compare different environment abstraction models and show the potential of on-line learning. INDEX TERMS Vehicular ad hoc networks, intelligent vehicles, prediction algorithms, cooperative systems. GUILLAUME JORNOD received the B.Sc. and M.Sc. degrees in environmental science and engineering (ing. env. dipl. EPF) from the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, in 2012 and 2014, respectively, and the M.Sc. degree in management from University College London, U.K., in 2015. He is currently pursuing the Ph.D. degree in the field of vehicle-to-vehicle communications in conjunction with Technische Universität Braunschweig, under the supervision of Prof. Dr.-Ing. T. Kürner.

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“…Intelligence is the further abstraction and concentration of information [6]. Although much progress has been made in developing artificial intelligence (AI) [7]- [9], it is far from human learning, which requires much fewer data sets and is more flexible in adapting to new environments. According to the Big History Project [10], a decisive feature of human learning is collective learning.…”

Section: Introductionmentioning

confidence: 99%

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Tang,

Yu,

Xie

et al. 2022

Preprint

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The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence.

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Deep Learning-aided Resource Orchestration for Vehicular Safety Communication

Cited by 13 publications

References 20 publications

F-802.11P: A Fuzzy Enhancement for IEEE 802.11P in Vehicle-to-Everything Communications

F-802.11P: A Fuzzy Enhancement for IEEE 802.11P in Vehicle-to-Everything Communications

Packet Inter-Reception Time Conditional Density Estimation Based on Surrounding Traffic Distribution

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

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