Progressing toward urban topology and mobility trace for Vehicular Ad Hoc Network (VANET)

Lee, Chun Hoe; Lim, Kit Guan; Chua, Bih Lii; Chin, Renee Ka Yin; Teo, Kenneth Tze Kin

doi:10.1109/icos.2016.7882000

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…Then the agent should take a new observation s t+1 and output a new policy, and so on so forth. The final goal is to achieve the objective in (5).…”

Section: Problem Formulationmentioning

confidence: 99%

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Virtual Relay Selection in LTE-V: A Deep Reinforcement Learning Approach to Heterogeneous Data

Nguyen

Jiang

et al. 2020

IEEE Access

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The development of Long Term Evolution (LTE) enables wireless communication with high transmission rate, low latency, and wide coverage area. These outstanding features of LTE support the next generation of vehicle-to-everything (V2X) communication, which is named LTE-V. Among the various technologies in LTE-V, placing relay nodes on vehicles is a promising approach to save power and energy, and extend the transmission range. In this paper, we consider the virtual relay node selection problem. In the problem, a base station transmits data to a vehicle relay (also known as helper) who will further disseminate the received data to the vehicular subscribers nearby. The selection of the vehicle relay node is a challenging issue since the utility of the selection can only be known after this action has been made. Another challenge of this problem is that the traditional pure optimization is inapplicable due to the imperfect information available. Motivated by the recent success of Alpha Go Zero, we employ deep reinforcement learning (DRL) as a powerful tool for facing the above challenges and solving the problem without global information. We build a bridge between the traffic information and decision of relay node selection based on the reality that the utility of vehicle relay is highly correlated with the traffic density. In our work, deep convolutional neural networks are first applied to extract traffic patterns and then learn the traffic and network topology. Deep learning (DL) acts as a role to map features inside traffic and communication topology to the decisions. Then Q-Learning dynamically updates the utilities of decisions by trials in the environment. Finally, the overall rewards can be calculated to measure these decisions, and the Q function is also updated. Simulation results based on real traffic data validate that our proposed approach can achieve high utility performance.

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“…Then the agent should take a new observation s t+1 and output a new policy, and so on so forth. The final goal is to achieve the objective in (5).…”

Section: Problem Formulationmentioning

confidence: 99%

“…Vehicular ad hoc network (VANET) is a part of the novel approach for intelligent transportation system (ITS) with the aim to enhance driver's safety, regulating traffic and improving the whole driving experience [5]. In real-time traffic scenarios, VANET enables inter-vehicle, vehicle-toroadside, as well as inter-roadside communication.…”

Section: Introductionmentioning

confidence: 99%

Virtual Relay Selection in LTE-V: A Deep Reinforcement Learning Approach to Heterogeneous Data

Nguyen

Jiang

et al. 2020

IEEE Access

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“…Hasil penelitian menunjukkan kepadatan tinggi dari node bisa meningkatkan konektivitas jaringan dan pengiriman tetapi juga bisa mewujudkan tabrakan paket yang tinggi. Di samping itu, konektivitas node dengan dan tanpa sinyal lalu lintas memiliki perbedaan yang siginifikan [12].…”

Section: Pendahuluanunclassified

Analisis Performansi Protokol Routing Proaktif pada Jaringan Mobile Adhoc

Hidayah

Indrabayu

Areni

2018

jpe

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Intelligent Transportation Systems (ITS) menawarkan paradigma pemodelan baru yang mendukung komunikasi antar kendaraan secara real time menggunakan routing protocol yang disebut Vehicular Ad Hoc Network (VANET). Pada dasarnya kinerja routing protocol dipengaruhi oleh arus dan aturan lalu lintas yang bersifat dinamis sehingga perubahan tersebut akan menyebabkan perubahan pada kinerja routing protocol juga. Untuk itu, penelitian ini mengusulkan rancangan mobilitas realistis berdasarkan data makroskopis dan mikroskopis jalan perkotaan. Rancangan mobilitas dibagi menjadi 2 skenario berdasarkan kepadatan kendaraan, yaitu 125 dan 200 node. Penelitian ini bersifat simulasi dan dibagi menjadi 2 tahap. Tahap pertama yaitu simulasi mobilitas yang menunjukkan pergerakan kendaraan serta aturan lalu lintas yang disesuaikan dengan kondisi realistis. Tahap kedua adalah simulasi jaringan yang bertujuan untuk mengevaluasi kinerja routing protocol DSDV dan OLSR terhadap rancangan model mobilitas. Untuk menguji kinerja kedua routing protocol, maka digunakan 3 metrik pengujian yaitu Packet Delivery Ratio (PDR), Overhead Ratio (OR) dan End to End Delay (E2ED). Hasil simulasi menunjukkan OLSR unggul pada metrik PDR dan OR, yaitu masing-masing 88.62% dan 57.11%. Sedangkan E2ED terbaik ditunjukkan oleh DSDV dengan nilai 0.523 detik. Kinerja terbaik kedua routing protocol ditunjukkan pada skenario 125 node. Hal ini menunjukkan kedua routing protocol belum mampu mengatasi kondisi lalu lintas perkotaan yang sangat padat.

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“…Mobility simulators are mainly used to generate the movement of vehicles' pattern under a certain trace [8]. Some of the widely used simulators are Quadstone Paramics [14], Traffic Software Integrated System (TSIS-CORSIM) [15] and SUMO [16].…”

Section: Mobility Simulatorsmentioning

confidence: 99%

“…Some researchers tried to monitor traffic condition using trajectory clustering method to find out the factors causing heavy traffic congestion [2][3][4], while some of them trying to optimise the traffic light signal for optimum time allocation to ensure smooth traffic flow [5][6][7]. Apart from that, there are also researches known as vehicular ad hoc network (VANET) being carried out in making vehicles more intelligent [8]. Once intelligence has been given to vehicles, vehicles are able to react automatically under different circumstances to prevent unwanted accidents.…”

Section: Introductionmentioning

confidence: 99%

Sumo Enhancement for Vehicular Communication Development

Lim

Lee

Chin

et al. 2017

IJCT

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It is normal that every family is having at least one vehicle at their home as vehicles have become a daily needs for all of us. However, this also leads to the increased of road accidents where major causes are related to human errors which can be prevented. To tackle with this problem, vehicular ad hoc network (VANET) is introduced with the aim to make vehicles intelligent. In order to study the algorithm in VANET, a mobility simulator is needed for simulation purpose. In this case, SUMO is proved to be a good simulation tool in generating VANET environment while MATLAB is good for algorithm development. Yet, to develop a good simulation platform, modification on SUMO files are necessary. This paper discusses on the procedures in creating a lefthand traffic (LHT) simulation file that is suitable to be used in Malaysia. LHT simulation is not easy to achieve as modification on the road connection and traffic light files are required. This paper also showed the results of the simulation after SUMO files modification. Apart from that, this paper also showed the simulation of VANET environment using SUMO and MATLAB through a third party interfacing named TraCI4Matlab, which allows communication between MATLAB and SUMO simulator.

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Progressing toward urban topology and mobility trace for Vehicular Ad Hoc Network (VANET)

Cited by 13 publications

References 6 publications

Virtual Relay Selection in LTE-V: A Deep Reinforcement Learning Approach to Heterogeneous Data

Virtual Relay Selection in LTE-V: A Deep Reinforcement Learning Approach to Heterogeneous Data

Analisis Performansi Protokol Routing Proaktif pada Jaringan Mobile Adhoc

Sumo Enhancement for Vehicular Communication Development

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