Routing using reinforcement learning in vehicular ad hoc networks

Abstract

In vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry‐and‐forward scenarios; however, these techniques fail in obtaining accurate traffic density in real‐time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on‐demand routing information. The simulation results show that the DRL‐based routing is effective in routing the data packets between the source and destination vehicles than other existing method.

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

“…There are many variants of hybrid routing protocols detailed in [85] like: Zone Routing Protocol (ZRP) [86], Distributed Dynamic Routing (DDR) [87], Distributed Spanning Tree (DST) [87]. In addition, there are some Hybrid protocols based on AI and RL, such as RL-Based Hybrid Routing protocol (RHR) [88], Q-Learning and GRID-based routing Protocol for vehicular Adhoc networks (QGRID) [89], RL assisted Zone based VANET Routing Protocol (RLZRP) [90], Adaptive Data Collection Protocol using RL (ADCPRL) [91], and VANET Routing using Deep RL Technique (VRDRT) [92].…”

QoE Aware Video Streaming and Optimal Relay for Emergency Message Dissemination in Graph Based VANET

“…The authors M. Saravanan, P. Ganeshkumar have proposed deep reinforcement learning method for the purpose of selecting a route for transmission [27]. In this work two phases were followed they are route establishment and optimal route selection.…”

Primena Učenja Potkrepljivanjem U Protokolima Rutiranja Za Dinamičke Bežične Ad Hoc Mreže

“…[10] protok gubitak paketa, protok TnesorFlow, Phyton [11] da li je čvor prvi sused, broj skokova, isplativost akcije, kvalitet veze stepen uspešno isporučenih paketa, broj kolizija MAC okvira, kašnjenje, protok NS-2 [12] da li je poruka isporučena grid-u sa odredišnim čvorom stepen uspešno isporučenih paketa, broj skokova, kašnjenje, broj prosleđivanja, protok nije naglašeno [13] tip i destinacija kontrolnih paketa stepen uspešno isporučenih paketa, vreme od slanja zahteva do prijema odgovora, overhed NS-3 [14] reputacija i isplativost akcije odgovarajućeg čvora stepen uspešno isporučenih paketa, reputacija, korisnost nije naglašeno [15] direktna veza sa odredištem, odnosno broj skokova i proteklo vreme od poslednje konekcije stepen uspešno isporučenih paketa, kašnjenje ONE [16] broj skokova, pouzdanost veze, propusni opseg stepen uspešno isporučenih paketa, kašnjenje, prosečna dužina putanje, overhed NS-2 [17] razni neželjeni efekti / nisu vršene simulacije [18] opterećenje relejnog čvora i zagušenje zemaljske mreže stepen uspešno isporučenih paketa, iskorišćenost mreže, kašnjenje OPNET [19] da li je kontrolni paket stigao od čvora pošiljaoca stepen uspešno isporučenih paketa, kašnjenje, broj skokova, overhed QualNet [20] broj skokova, kvalitet veze protok, broj gejtvej čvorova nije naglašeno [21] uspešnost slanja paketa stepen uspešno isporučenih paketa, kašnjenje, overhed NS-2 [22] kvalitet veze, vreme isteka trajanja veze, kašnjenje stepen uspešno isporučenih paketa, kašnjenje QualNet [23] gubitak energije, brzina prenosa potrošnja energije, gubitak paketa, ukupno vreme prenosa, verovatnoća prekida komunikacije nije naglašeno Evaluacija performansi predloženih protokola vršena je u različitim simulacionim okruženjima, a neka od najčešće korišćenih su NS-3, NS-2, OPNET, Python, QualNet, MATLAB, itd. U simulacijama su posmatrane različite mrežne performanse, zavisno od toga šta je bio cilj optimizacije.…”